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The Vermont Department of Health and the U.S. Geological Survey analyzed the concentrations of chloride in groundwater samples collected from 4,319 domestic wells across Vermont between 2011 and 2018. Ninety of these wells were sampled twice and the remaining 4,229 were sampled once. This sample size represents approximately 4 percent of all wells in the State of Vermont. More than half of the wells sampled statewide had groundwater chloride concentrations less than 5 milligrams per liter, whereas more than 1 percent had groundwater concentrations greater than 250 milligrams per liter. Statistical analysis of this dataset revealed distinct patterns in the distribution of chloride in domestic wells. Wells closer (less than 100 meters) to a paved road had significantly higher concentrations of chloride than wells farther ( more than 100 meters) away. Also, wells in urban and in high population density areas, particularly Chittenden and Grand Isle Counties, had significantly higher concentrations of chloride and exhibited greater change in concentrations of chloride over time than wells in less populated areas. This evaluation addresses the distribution of chloride concentrations across the State, which may have adverse health impacts from water infrastructure corrosion and implications for deicing salt application at the State and local levels.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191148","collaboration":"Prepared in cooperation with the Vermont Department of Health","usgsCitation":"Levitt, J.P., and Larsen, S.L., 2020, Groundwater chloride concentrations in domestic wells and proximity to roadways in Vermont, 2011–2018: U.S. Geological Survey Open-File Report 2019–1148, 12 p., https://doi.org/10.3133/ofr20191148.","productDescription":"Report: 12 p.; Data Release","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-104230","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":373835,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1148/ofr20191148.pdf","text":"Report","size":"77.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1148"},{"id":373355,"rank":3,"type":{"id":30,"text":"Data 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\"}}]}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2020-04-08","noUsgsAuthors":false,"publicationDate":"2020-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Levitt, Joseph P. 0000-0002-2058-9516 jlevitt@usgs.gov","orcid":"https://orcid.org/0000-0002-2058-9516","contributorId":198353,"corporation":false,"usgs":false,"family":"Levitt","given":"Joseph","email":"jlevitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781681,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larsen, Sille L. 0000-0001-9356-4726","orcid":"https://orcid.org/0000-0001-9356-4726","contributorId":218798,"corporation":false,"usgs":false,"family":"Larsen","given":"Sille","email":"","middleInitial":"L.","affiliations":[{"id":39914,"text":"Vermont Department of Health","active":true,"usgs":false}],"preferred":false,"id":781682,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209419,"text":"70209419 - 2020 - The relation of geogenic contaminants to groundwater age, aquifer hydrologic position, water type, and redox conditions in Atlantic and Gulf Coastal Plain aquifers, eastern and south-central USA","interactions":[],"lastModifiedDate":"2020-04-08T14:08:17.456546","indexId":"70209419","displayToPublicDate":"2020-04-08T09:03:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"The relation of geogenic contaminants to groundwater age, aquifer hydrologic position, water type, and redox conditions in Atlantic and Gulf Coastal Plain aquifers, eastern and south-central USA","docAbstract":"Groundwater age distributions developed from carbon-14 (14C), tritium (3H), and helium-4 (4He) concentrations, along with aquifer hydrologic position, water type, and redox conditions, were compared to geogenic contaminants of concern (GCOC) from 252 public-supply wells in six Atlantic and Gulf Coastal Plain unconsolidated-sediment aquifers. Concentrations of one or more GCOCs in 168 (67%) wells exceeded MCLs (maximum contaminant levels), SMCLs (secondary MCLs), or HBSLs (health-based screening levels). Human-health benchmark thresholds (MCLs or HBSLs) were exceeded in 31 (12%) wells, and included 0.8% for fluoride (F), 2.4% for arsenic (As), 4% for lead-210 (210Pb), and 4.8% for polonium-210 (210Po). Values of pH increase with age and were outside the SMCL in 31% of wells (23% < 6.5 and 7.5% > 8.5, SMCL). Among GCOCs with concentrations that increased significantly with groundwater age, the frequency of sentry threshold exceedances (i.e., one-half of MCL, SMCL, or HBSL) included 40% for dissolved solids (DS), 12% for chloride (Cl), 3.6% for F, 4.4% for As, and 9.1% for 210Po. Iron (Fe) concentrations did not correlate with groundwater age but exceeded sentry thresholds in 29% of wells. Groundwater age, water types, redox, pH, and GCOCs varied because of unique hydrogeologic features of the aquifers (recharge locations and geometry). As expected, primarily confined aquifers had young, oxic, low to near-neutral pH water near the outcrop (recharge area), and older, reduced, high pH water deeper and farther along flow paths. However, unique aquifer hydrogeologic conditions, such as multiple-recharge zones produced anomalous patterns of young and old groundwater at varying depths and locations along flow paths. Evidence for this variability is seen in disequilibrium patterns in the progression of the chemical evolution of groundwater with hydrologic position. When hydrogeologic differences are considered, groundwater age combined with hydrologic-position data, can provide a strong basis for inferring potential occurrence of GCOCs.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.137835","collaboration":"","usgsCitation":"Degnan, J.R., Lindsey, B.D., Levitt, J.P., and Szabo, Z., 2020, The relation of geogenic contaminants to groundwater age, aquifer hydrologic position, water type, and redox conditions in Atlantic and Gulf Coastal Plain aquifers, eastern and south-central USA: Science of the Total Environment, v. 723, 137835, 15 p., https://doi.org/10.1016/j.scitotenv.2020.137835.","productDescription":"137835, 15 p.","ipdsId":"IP-099819","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":457138,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.137835","text":"Publisher Index Page"},{"id":373839,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Gulf Coastal Plain aquifers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.49218749999999,\n 27.566721430409707\n ],\n [\n -99.0966796875,\n 26.509904531413927\n ],\n [\n -97.470703125,\n 25.918526162075153\n ],\n [\n -97.03125,\n 26.03704188651584\n ],\n [\n -96.767578125,\n 27.605670826465445\n ],\n [\n -94.6142578125,\n 29.036960648558267\n ],\n [\n -92.94433593749999,\n 29.80251790576445\n ],\n [\n -91.14257812499999,\n 29.152161283318915\n ],\n [\n -88.9453125,\n 29.11377539511439\n ],\n [\n -89.384765625,\n 30.183121842195515\n ],\n [\n -87.6708984375,\n 30.221101852485987\n ],\n [\n -85.1220703125,\n 29.53522956294847\n ],\n [\n -84.24316406249999,\n 30.14512718337613\n ],\n [\n -82.7490234375,\n 28.613459424004414\n ],\n [\n -82.705078125,\n 28.188243641850313\n ],\n [\n -82.1337890625,\n 26.588527147308614\n ],\n [\n -80.9912109375,\n 25.16517336866393\n ],\n [\n -80.2001953125,\n 25.403584973186703\n ],\n [\n -80.0244140625,\n 27.137368359795584\n ],\n [\n -81.2548828125,\n 30.183121842195515\n ],\n [\n -81.1669921875,\n 31.316101383495624\n ],\n [\n -78.79394531249999,\n 33.17434155100208\n ],\n [\n -75.498046875,\n 35.782170703266075\n ],\n [\n -75.7177734375,\n 37.37015718405753\n ],\n [\n -74.794921875,\n 38.8225909761771\n ],\n [\n -74.0478515625,\n 40.54720023441049\n ],\n [\n -74.8828125,\n 40.84706035607122\n ],\n [\n -77.47558593749999,\n 39.36827914916014\n ],\n [\n -79.62890625,\n 36.13787471840729\n ],\n [\n -81.5625,\n 34.161818161230386\n ],\n [\n -84.90234375,\n 32.21280106801518\n ],\n [\n -85.69335937499999,\n 32.02670629333614\n ],\n [\n -87.7587890625,\n 32.175612478499325\n ],\n [\n -88.41796875,\n 33.137551192346145\n ],\n [\n -88.4619140625,\n 35.17380831799959\n ],\n [\n -88.857421875,\n 36.27970720524017\n ],\n [\n -89.3408203125,\n 36.87962060502676\n ],\n [\n -90.04394531249999,\n 35.24561909420681\n ],\n [\n -91.0546875,\n 33.46810795527896\n ],\n [\n -90.9228515625,\n 32.32427558887655\n ],\n [\n -93.1201171875,\n 31.466153715024294\n ],\n [\n -97.646484375,\n 30.259067203213018\n ],\n [\n -99.49218749999999,\n 27.566721430409707\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"723","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Degnan, James R. 0000-0002-5665-9010 jrdegnan@usgs.gov","orcid":"https://orcid.org/0000-0002-5665-9010","contributorId":498,"corporation":false,"usgs":true,"family":"Degnan","given":"James","email":"jrdegnan@usgs.gov","middleInitial":"R.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786439,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":175346,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce","email":"blindsey@usgs.gov","middleInitial":"D.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":786440,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Levitt, Joseph Patrick 0000-0002-2058-9516","orcid":"https://orcid.org/0000-0002-2058-9516","contributorId":223857,"corporation":false,"usgs":true,"family":"Levitt","given":"Joseph","email":"","middleInitial":"Patrick","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786441,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Szabo, Zoltan 0000-0002-0760-9607","orcid":"https://orcid.org/0000-0002-0760-9607","contributorId":203408,"corporation":false,"usgs":true,"family":"Szabo","given":"Zoltan","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":786442,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70213220,"text":"70213220 - 2020 - Vegetation‐groundwater dynamics at a former uranium mill site following invasion of a biocontrol agent: A time series analysis of Landsat normalized difference vegetation index data","interactions":[],"lastModifiedDate":"2020-09-15T13:10:28.365993","indexId":"70213220","displayToPublicDate":"2020-04-08T08:00:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Vegetation‐groundwater dynamics at a former uranium mill site following invasion of a biocontrol agent: A time series analysis of Landsat normalized difference vegetation index data","docAbstract":"

Because groundwater recharge in dry regions is generally low, arid and semiarid environments have been considered well‐suited for long‐term isolation of hazardous materials (e.g., radioactive waste). In these dry regions, water lost (transpired) by plants and evaporated from the soil surface, collectively termed evapotranspiration (ET), is usually the primary discharge component in the water balance. Therefore, vegetation can potentially affect groundwater flow and contaminant transport at waste disposal sites. We studied vegetation health and ET dynamics at a Uranium Mill Tailings Radiation Control Act (UMTRCA) disposal site in Shiprock, New Mexico, where a floodplain alluvial aquifer was contaminated by mill effluent. Vegetation on the floodplain was predominantly deep‐rooted, non‐native tamarisk shrubs (Tamarix sp.). After the introduction of the tamarisk beetle (Diorhabda sp.) as a biocontrol agent, the health of the invasive tamarisk on the Shiprock floodplain declined. We used Landsat normalized difference vegetation index (NDVI) data to measure greenness and a remote sensing algorithm to estimate landscape‐scale ET along the floodplain of the UMTRCA site in Shiprock prior to (2000–2009) and after (2010–2018) beetle establishment. Using groundwater level data collected from 2011 to 2014, we also assessed the role of ET in explaining seasonal variations in depth to water of the floodplain. Growing season scaled NDVI decreased 30% (p < .001), while ET decreased 26% from the pre‐ to post‐beetle period and seasonal ET estimates were significantly correlated with groundwater levels from 2011 to 2014 (r2 = .71; p = .009). Tamarisk greenness (a proxy for health) was significantly affected by Diorhabda but has partially recovered since 2012. Despite this, increased ET demand in the summer/fall period might reduce contaminant transport to the San Juan River during this period.

","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13772","usgsCitation":"Jarchow, C.J., Waugh, W.J., Didan, K., Barreto-Munoz, A., Herrmann, S.M., and Nagler, P.L., 2020, Vegetation‐groundwater dynamics at a former uranium mill site following invasion of a biocontrol agent: A time series analysis of Landsat normalized difference vegetation index data: Hydrological Processes, v. 34, no. 12, p. 2739-2749, https://doi.org/10.1002/hyp.13772.","productDescription":"11 p.","startPage":"2739","endPage":"2749","ipdsId":"IP-112673","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":489705,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.13772","text":"Publisher Index Page"},{"id":378391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Shiprock","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.71383666992188,\n 36.766667073939736\n ],\n [\n -108.66302490234375,\n 36.766667073939736\n ],\n [\n -108.66302490234375,\n 36.806261006694555\n ],\n [\n -108.71383666992188,\n 36.806261006694555\n ],\n [\n -108.71383666992188,\n 36.766667073939736\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarchow, Christopher J. 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":5813,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":798690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waugh, William J.","contributorId":196107,"corporation":false,"usgs":false,"family":"Waugh","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":798691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Didan, Kamel","contributorId":130999,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":798692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barreto-Munoz, Armando","contributorId":131000,"corporation":false,"usgs":false,"family":"Barreto-Munoz","given":"Armando","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":798693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herrmann, Stefanie M. 0000-0002-4069-2019","orcid":"https://orcid.org/0000-0002-4069-2019","contributorId":20234,"corporation":false,"usgs":true,"family":"Herrmann","given":"Stefanie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":798694,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798634,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70210529,"text":"70210529 - 2020 - Comparison of fecal glucocorticoid metabolite concentrations in hand‐ versus parent‐reared whooping cranes (Grus americana)","interactions":[],"lastModifiedDate":"2020-08-04T14:12:43.139962","indexId":"70210529","displayToPublicDate":"2020-04-08T07:37:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3807,"text":"Zoo Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Comparison of fecal glucocorticoid metabolite concentrations in hand‐ versus parent‐reared whooping cranes (Grus americana)","title":"Comparison of fecal glucocorticoid metabolite concentrations in hand‐ versus parent‐reared whooping cranes (Grus americana)","docAbstract":"

Endangered whooping cranes (Grus americana ) have been produced in captivity for reintroduction programs since the 1980s, using techniques such as artificial insemination, multiple clutching, and captive‐rearing to speed recovery efforts. Chicks are often hand‐reared (HR) by caretakers in crane costumes, socialized into groups and released together, unlike parent‐reared (PR) cranes that are raised individually by a male/female crane pair and released singly. HR cranes historically exhibit greater morbidity rates during development than PR cranes, involving musculoskeletal and respiratory system disease, among others. We hypothesized that HR crane chicks exhibit a higher baseline fecal glucocorticoid metabolite (FGM) concentrations during the development compared with PR chicks. Fecal samples were collected between 15 and 70 days of age from HR (n  = 15) and PR (n  = 8) chicks to test for differences in FGM concentrations using a radioimmunoassay technique following ethanol extraction for steroids. Linear mixed model analysis suggests increasing age of the chick was associated with an increase in FGM (p  < .001). Analysis also supported the interaction between rearing strategy and sex of the crane chick (p  < .01). Female PR chicks had greater FGM concentrations than all other groups (PR male, p  < .01; HR female, p  < .001; and HR male, p  < .001). This result suggests that there may be an effect of rearing strategy on stress physiology of whooping crane chicks, especially among females. Further research is needed to investigate whether the FGM concentrations are reflective of true differences in stress physiology of young cranes and whether this may impact health and conservation success.zo

","language":"English","publisher":"Wiley","doi":"10.1002/zoo.21541","usgsCitation":"Brown, M.E., Torkelson, M.R., Olsen, G.H., Krisp, A., and Hartup, B.K., 2020, Comparison of fecal glucocorticoid metabolite concentrations in hand‐ versus parent‐reared whooping cranes (Grus americana): Zoo Biology, v. 39, no. 4, p. 276-280, https://doi.org/10.1002/zoo.21541.","productDescription":"5 p.","startPage":"276","endPage":"280","ipdsId":"IP-107558","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":375456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Brown, Megan E.","contributorId":146367,"corporation":false,"usgs":false,"family":"Brown","given":"Megan","email":"","middleInitial":"E.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":790535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torkelson, Miranda R.","contributorId":194524,"corporation":false,"usgs":false,"family":"Torkelson","given":"Miranda","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":790536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olsen, Glenn H. 0000-0002-7188-6203 golsen@usgs.gov","orcid":"https://orcid.org/0000-0002-7188-6203","contributorId":40918,"corporation":false,"usgs":true,"family":"Olsen","given":"Glenn","email":"golsen@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":790537,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krisp, Ashley","contributorId":225147,"corporation":false,"usgs":false,"family":"Krisp","given":"Ashley","email":"","affiliations":[{"id":41049,"text":"School of Veterinary Medicine, University of Wisconsin, Madison, WI 53706, USA","active":true,"usgs":false}],"preferred":false,"id":790538,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hartup, Barry K.","contributorId":209630,"corporation":false,"usgs":false,"family":"Hartup","given":"Barry","email":"","middleInitial":"K.","affiliations":[{"id":16606,"text":"International Crane Foundation","active":true,"usgs":false}],"preferred":false,"id":790539,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209463,"text":"70209463 - 2020 - Seismic and geodetic progression of the 2018 summit caldera collapse of Kīlauea Volcano","interactions":[],"lastModifiedDate":"2020-04-09T12:39:04.12856","indexId":"70209463","displayToPublicDate":"2020-04-08T07:36:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Seismic and geodetic progression of the 2018 summit caldera collapse of Kīlauea Volcano","docAbstract":"The 2018 eruption of Kīlauea volcano, Hawaiʻi, resulted in a major collapse of the summit caldera along with an effusive eruption in the lower East Rift Zone. The caldera collapse comprised 62 highly similar collapse cycles of strong ground deformation and earthquake swarms that ended with a magnitude 5 collapse event and one partial cycle that did not end with a collapse event. We analyzed geodetic and seismic data to better understand how the caldera collapse progressed over 3 months of activity, focusing on the cyclical activity. We identified 3 main phases of collapse: initial ring-fault activation and small explosions (Phase 1), an eastward shift in activity and freeing of the central piston (Phase 2), and a recoupling of the piston to the reservoir followed by relatively steady behavior until the eruption’s end (Phase 3). Additionally, we observed geodetic evidence of tangential motion from the localization of the main ring fault (Phase 2) and the formation of a major peripheral ring fault on the eastern side of the collapse caldera during Phase 3. Both geodetic and seismic parameters suggest that the collapse may have had an eastward-component of motion after the ring fault system had formed. The cyclical seismic and geodetic parameters show no obvious signs that the collapse was coming to an end, with the only notable change being a significant increase in the ratio of cyclical displacement to co-collapse displacement observed during the last complete cycle on GNSS stations outside the caldera region.","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2020.116250","collaboration":"","usgsCitation":"Tepp, G., Hotovec-Ellis, A.J., Shiro, B., Johanson, I.A., Thelen, W., and Haney, M.M., 2020, Seismic and geodetic progression of the 2018 summit caldera collapse of Kīlauea Volcano: Earth and Planetary Science Letters, v. 540, 116250, https://doi.org/10.1016/j.epsl.2020.116250.","productDescription":"116250","ipdsId":"IP-114024","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":457144,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2020.116250","text":"Publisher Index Page"},{"id":373856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.31097412109372,\n 19.391477141932153\n ],\n [\n -155.2313232421875,\n 19.391477141932153\n ],\n [\n -155.2313232421875,\n 19.43454305903574\n ],\n [\n -155.31097412109372,\n 19.43454305903574\n ],\n [\n -155.31097412109372,\n 19.391477141932153\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"540","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tepp, Gabrielle 0000-0001-5388-5138","orcid":"https://orcid.org/0000-0001-5388-5138","contributorId":206305,"corporation":false,"usgs":true,"family":"Tepp","given":"Gabrielle","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":786571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hotovec-Ellis, Alicia J. 0000-0003-1917-0205","orcid":"https://orcid.org/0000-0003-1917-0205","contributorId":211785,"corporation":false,"usgs":true,"family":"Hotovec-Ellis","given":"Alicia","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":786572,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shiro, Brian 0000-0001-8756-288X","orcid":"https://orcid.org/0000-0001-8756-288X","contributorId":204040,"corporation":false,"usgs":true,"family":"Shiro","given":"Brian","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":786573,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johanson, Ingrid A. 0000-0002-6049-2225","orcid":"https://orcid.org/0000-0002-6049-2225","contributorId":215613,"corporation":false,"usgs":true,"family":"Johanson","given":"Ingrid","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":786574,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thelen, Weston 0000-0003-2534-5577","orcid":"https://orcid.org/0000-0003-2534-5577","contributorId":215530,"corporation":false,"usgs":true,"family":"Thelen","given":"Weston","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":786575,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":786576,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209557,"text":"70209557 - 2020 - Testing ecosystem accounting in the United States: A case study for the Southeast","interactions":[],"lastModifiedDate":"2020-09-01T19:55:33.306925","indexId":"70209557","displayToPublicDate":"2020-04-08T07:05:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1477,"text":"Ecosystem Services","active":true,"publicationSubtype":{"id":10}},"title":"Testing ecosystem accounting in the United States: A case study for the Southeast","docAbstract":"Ecosystem accounts, as formalized by the System of Environmental-Economic Accounting Experimental Ecosystem Accounts (SEEA EEA), have been compiled in a number of countries, yet there have been few attempts to develop them for the U.S. We explore the potential for U.S. ecosystem accounting by compiling ecosystem extent, condition, and ecosystem services supply and use accounts for a ten-state region in the Southeast. The pilot accounts address air quality, water quality, biodiversity, carbon storage, recreation, and pollination for selected years from 2001 to 2015. Results illustrate how information from ecosystem accounts can contribute to policy and decision-making. Using an example from Atlanta, we also show how ecosystem accounts can be considered alongside other SEEA accounts to give a more complete picture of a local area’s environmental-economic trends. The process by which we determined where to place metrics within the accounting framework, which was strongly informed by the National Ecosystem Services Classification System (NESCS), can provide guidance for future ecosystem accounts in the U.S. and other countries. Finally, we identify knowledge gaps that limit the inclusion of certain ecosystem services in the accounts and suggest future research that can close these gaps and improve future U.S. ecosystem accounts.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2020.101099","usgsCitation":"Warnell, K., Russell, M.J., Rhodes, C., Bagstad, K.J., Olander, L.P., Nowak, D., Poudel, R., Glynn, P.D., Hass, J.L., Hiribayashi, S., Ingram, J.C., Matuszak, J., Oleson, K.L., Posner, S.M., and Villa, F., 2020, Testing ecosystem accounting in the United States: A case study for the Southeast: Ecosystem Services, v. 43, 101099, 18 p., https://doi.org/10.1016/j.ecoser.2020.101099.","productDescription":"101099, 18 p.","ipdsId":"IP-108492","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":457146,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoser.2020.101099","text":"Publisher Index Page"},{"id":437031,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VET1YX","text":"USGS data release","linkHelpText":"Data Release for Testing ecosystem accounting in the United States: A case study for the Southeast - 2022 Updates (version 2.0, February 2023)"},{"id":437030,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MF0K9X","text":"USGS data release","linkHelpText":"Data Release for Testing ecosystem accounting in the United States: A case study for the Southeast"},{"id":373944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Arkansas, Louisiana, Tennessee, Mississippi, Alabama, North Carolina, South Carolina, Georgia, 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\"}}]}","volume":"43","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Warnell, Katie","contributorId":224044,"corporation":false,"usgs":false,"family":"Warnell","given":"Katie","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":786847,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Marc J.","contributorId":191375,"corporation":false,"usgs":false,"family":"Russell","given":"Marc","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":786848,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rhodes, Charles","contributorId":211873,"corporation":false,"usgs":false,"family":"Rhodes","given":"Charles","affiliations":[{"id":38338,"text":"U.S. EPA-ORISE Fellow","active":true,"usgs":false}],"preferred":false,"id":786849,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 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J.","affiliations":[],"preferred":false,"id":786852,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Poudel, Rajendra","contributorId":190430,"corporation":false,"usgs":false,"family":"Poudel","given":"Rajendra","email":"","affiliations":[],"preferred":false,"id":786853,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Glynn, Pierre D. 0000-0001-8804-7003 pglynn@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7003","contributorId":2141,"corporation":false,"usgs":true,"family":"Glynn","given":"Pierre","email":"pglynn@usgs.gov","middleInitial":"D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":786854,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hass, Julie L.","contributorId":211867,"corporation":false,"usgs":false,"family":"Hass","given":"Julie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":786855,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hiribayashi, Satoshi 0000-0003-4259-2748","orcid":"https://orcid.org/0000-0003-4259-2748","contributorId":224046,"corporation":false,"usgs":false,"family":"Hiribayashi","given":"Satoshi","email":"","affiliations":[{"id":40823,"text":"Davey Institute","active":true,"usgs":false}],"preferred":false,"id":786856,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ingram, Jane Carter","contributorId":211862,"corporation":false,"usgs":false,"family":"Ingram","given":"Jane","email":"","middleInitial":"Carter","affiliations":[{"id":38334,"text":"Ernst and Young","active":true,"usgs":false}],"preferred":false,"id":786857,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Matuszak, John","contributorId":211869,"corporation":false,"usgs":false,"family":"Matuszak","given":"John","email":"","affiliations":[{"id":38336,"text":"U.S. Department of State","active":true,"usgs":false}],"preferred":false,"id":786858,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Oleson, Kirsten L. L. 0000-0002-7992-5051","orcid":"https://orcid.org/0000-0002-7992-5051","contributorId":211871,"corporation":false,"usgs":false,"family":"Oleson","given":"Kirsten","email":"","middleInitial":"L. L.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":786859,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Posner, Stephen M.","contributorId":211872,"corporation":false,"usgs":false,"family":"Posner","given":"Stephen","email":"","middleInitial":"M.","affiliations":[{"id":38335,"text":"COMPASS","active":true,"usgs":false}],"preferred":false,"id":786860,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Villa, Ferdinando 0000-0002-5114-3007","orcid":"https://orcid.org/0000-0002-5114-3007","contributorId":208486,"corporation":false,"usgs":false,"family":"Villa","given":"Ferdinando","email":"","affiliations":[{"id":32916,"text":"Basque Centre for Climate Change","active":true,"usgs":false}],"preferred":false,"id":786861,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70209466,"text":"70209466 - 2020 - Describing historical habitat use of a native fish-Cisco (Coregonus artedi)-In Lake Michigan between 1930 and 1932","interactions":[],"lastModifiedDate":"2020-07-09T14:47:53.051322","indexId":"70209466","displayToPublicDate":"2020-04-08T07:04:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Describing historical habitat use of a native fish-Cisco (Coregonus artedi)-In Lake Michigan between 1930 and 1932","title":"Describing historical habitat use of a native fish-Cisco (Coregonus artedi)-In Lake Michigan between 1930 and 1932","docAbstract":"

With the global-scale loss of biodiversity, current restoration programs have been often required as part of conservation plans for species richness and ecosystem integrity. The restoration of pelagic-oriented cisco (Coregonus artedi) has been an interest of Lake Michigan managers because it may increase the diversity and resilience of the fish assemblages and conserve the integrity of the ecosystems in a changing environment. To inform restoration, we described historical habitat use of cisco by analyzing a unique fishery-independent dataset collected in 1930–1932 by the U.S. Bureau of Fisheries’ first research vessel Fulmar and a commercial catch dataset reported by the State of Michigan in the same period, both based on gear fished on the bottom. Our results confirmed that the two major embayments, Green Bay and Grand Traverse Bay, were important habitats for cisco and suggest that cisco could complete the entire lifecycle within either of the Bays as there was no lack of summer feeding and fall spawning habitats. Seasonally, our results showed that cisco stayed in nearshore waters in spring, migrated to offshore waters in summer, and then migrated back to nearshore waters in fall for spawning. The results also suggest that in summer, most ciscoes were in waters with bottom depths of 20–70 m, but the highest cisco density occurred in waters with a bottom depth around 40 m. We highlight the importance of embayment habitats to cisco restoration and the seasonal migration pattern of cisco identified in this study, which suggests that a restored cisco population can diversify the food web by occupying different habitats from the exotic fishes that now dominate the pelagic waters of Lake Michigan.

","language":"English","publisher":"PLoS ONE","doi":"10.1371/journal.pone.0231420","usgsCitation":"Kao, Y., Bunnell, D., Eshenroder, R.L., and Murray, D.N., 2020, Describing historical habitat use of a native fish-Cisco (Coregonus artedi)-In Lake Michigan between 1930 and 1932: PLoS ONE, v. 15, no. 4, e0231420, 21 p., https://doi.org/10.1371/journal.pone.0231420.","productDescription":"e0231420, 21 p.","ipdsId":"IP-112754","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":457149,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0231420","text":"Publisher Index Page"},{"id":437032,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JMLWER","text":"USGS data release","linkHelpText":"1930-1932 Gill net data from Lake Michigan"},{"id":373854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.1875,\n 41.541477666790286\n ],\n [\n -86.46240234375,\n 41.83682786072714\n ],\n [\n -85.97900390625,\n 42.79540065303723\n ],\n [\n -86.4404296875,\n 43.67581809328341\n ],\n [\n -85.869140625,\n 44.74673324024678\n ],\n [\n -85.4736328125,\n 44.63739123445585\n ],\n [\n -84.52880859375,\n 45.82879925192134\n ],\n [\n -85.18798828125,\n 46.27103747280261\n ],\n [\n -86.0888671875,\n 46.13417004624326\n ],\n [\n -87.38525390624999,\n 45.78284835197676\n ],\n [\n -87.978515625,\n 45.01141864227728\n ],\n [\n -88.1982421875,\n 44.449467536006935\n ],\n [\n -88.06640625,\n 44.402391829093915\n ],\n [\n -87.451171875,\n 44.933696389694674\n ],\n [\n -87.099609375,\n 45.182036837015886\n ],\n [\n -87.4951171875,\n 44.512176171071054\n ],\n [\n -87.7587890625,\n 43.8028187190472\n ],\n [\n -88.00048828124999,\n 42.94033923363181\n ],\n [\n -87.890625,\n 42.407234661551875\n ],\n [\n -87.71484375,\n 41.902277040963696\n ],\n [\n -87.1875,\n 41.541477666790286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kao, Yu-Chun","contributorId":35626,"corporation":false,"usgs":false,"family":"Kao","given":"Yu-Chun","affiliations":[{"id":6649,"text":"University of Michigan, School of Natural Resources and Environment","active":true,"usgs":false}],"preferred":false,"id":786603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunnell, David 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":217344,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":786604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eshenroder, Randy L.","contributorId":177867,"corporation":false,"usgs":false,"family":"Eshenroder","given":"Randy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":786605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murray, Devin N. 0000-0003-1429-6669","orcid":"https://orcid.org/0000-0003-1429-6669","contributorId":223909,"corporation":false,"usgs":false,"family":"Murray","given":"Devin","email":"","middleInitial":"N.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":786606,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208493,"text":"ofr20201015 - 2020 - Defining technology operational readiness for the 3D Elevation Program—A plan for investment, incubation, and adoption","interactions":[],"lastModifiedDate":"2020-04-15T16:15:08.574329","indexId":"ofr20201015","displayToPublicDate":"2020-04-07T17:50:00","publicationYear":"2020","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":"2020-1015","displayTitle":"Defining Technology Operational Readiness for the 3D Elevation Program—A Plan for Investment, Incubation, and Adoption","title":"Defining technology operational readiness for the 3D Elevation Program—A plan for investment, incubation, and adoption","docAbstract":"

The 3D Elevation Program (3DEP) is an acquisition strategy that uses data from commercial remote sensing technologies to create three-dimensional maps of the United States and U.S. territories. Currently, light detection and ranging and interferometric synthetic aperture radar are the two commercial technologies being used to provide three-dimensional information to meet the program’s operational requirements. This is because there is not a well-established process for vendors of new and novel instruments to know when and how 3DEP will accept their technologies into the 3DEP portfolio. The purpose of this plan is to provide a strategy and rules for communication between 3DEP and commercial partners interested in proposing their modalities for use in the program. To accomplish this, 3DEP will also consider how it invests in new technologies and how it disseminates data to and categorizes data for the broader community and the public.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20201015","collaboration":"","usgsCitation":"Stoker, J.M., 2020, Defining technology operational readiness for the 3D Elevation Program—A plan for investment, incubation, and adoption: U.S. Geological Survey Open-File Report 2020–1015, 7 p., \nhttps://doi.org/ 10.3133/ ofr20201015.","productDescription":"iv, 7 p.","onlineOnly":"Y","ipdsId":"IP-110726","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":373798,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1015/ofr20201015.pdf","text":"Report","size":"932 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1015"},{"id":373797,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1015/coverthb2.jpg"}],"contact":"

Director, National Geospatial Program
U.S. Geological Survey
MS-511
12201 Sunrise Valley Drive
Reston, VA 20192

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-04-07","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":782145,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70206191,"text":"sir20195120 - 2020 - Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and northern Chihuahua, Mexico","interactions":[{"subject":{"id":70197406,"text":"ofr20181091 - 2018 - Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico","indexId":"ofr20181091","publicationYear":"2018","noYear":false,"title":"Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico"},"predicate":"SUPERSEDED_BY","object":{"id":70206191,"text":"sir20195120 - 2020 - Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and northern Chihuahua, Mexico","indexId":"sir20195120","publicationYear":"2020","noYear":false,"title":"Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and northern Chihuahua, Mexico"},"id":1}],"lastModifiedDate":"2022-04-25T19:02:23.235988","indexId":"sir20195120","displayToPublicDate":"2020-04-07T14:58:16","publicationYear":"2020","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":"2019-5120","displayTitle":"Rio Grande Transboundary Integrated Hydrologic Model and Water-Availability Analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico","title":"Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and northern Chihuahua, Mexico","docAbstract":"

Changes in population, agricultural development and practices (including shifts to more water-intensive crops), and climate variability are increasing demands on available water resources, particularly groundwater, in one of the most productive agricultural regions in the Southwest—the Rincon and Mesilla Valley parts of Rio Grande Valley, Doña Ana and Sierra Counties, New Mexico, and El Paso County, Texas. The goal of this study was to produce an integrated hydrological simulation model to help evaluate water-management strategies, including conjunctive use of surface water and groundwater for historical conditions, and to support long-term planning for the Rio Grande Project. This report describes model construction and applications by the U.S. Geological Survey, working in cooperation and collaboration with the Bureau of Reclamation.

This model, the Rio Grande Transboundary Integrated Hydrologic Model, simulates the most important natural and human components of the hydrologic system, including selected components related to variations in climate, thereby providing a reliable assessment of surface-water and groundwater conditions and processes that can inform water users and help improve planning for future conditions and sustained operations of the Rio Grande Project (RGP) by the Bureau of Reclamation. Model development included a revision of the conceptual model of the flow system, construction of a Transboundary Rio Grande Watershed Model (TRGWM) water-balance model using the Basin Characterization Model, and construction of an integrated hydrologic flow model with MODFLOW-One-Water Hydrologic Flow Model version 2 (referred to as MF-OWHM2). The hydrologic models were developed for and calibrated to historical conditions of water and land use, and parameters were adjusted so that simulated values closely matched available measurements (calibration). The calibrated model was then used to assess the use and movement of water in the Rincon Valley, Mesilla Basin, and northern part of the Conejos-Médanos Basin, with the entire region referred to as the “Transboundary Rio Grande” or TRG. These tools provide a means to understand hydrologic system response to the evolution of water use in the region, its availability, and potential operational constraints of the RGP.

The conceptual model identified surface-water and groundwater inflows and outflows that included the movement and use of water both in natural and in anthropogenic systems. The groundwater-flow system is characterized by a layered geologic sedimentary sequence combined with the effects of groundwater pumping, operation of the RGP, natural runoff and recharge, and the application of irrigation water at the land surface that is captured and reused in an extensive network of canals and drains as part of the conjunctive use of water in the region.

Historical groundwater-level fluctuations followed a cyclic pattern that were aligned with climate cycles, which collectively resulted in alternating periods of wet or dry years. Periods of drought that persisted for one or more years are associated with low surface-water availability that resulted in higher rates of groundwater-level decline. Rates of groundwater-level decline also increased during periods of agricultural intensification, which necessitated increasing use of groundwater as a source of irrigation water. Agriculture in the area was initially dominated by alfalfa and cotton, but since 1970 more water-intensive pecan orchards and vegetable production have become more common. Groundwater levels substantially declined in subregions where drier climate combined with increased demand, resulting in periods of reduced streamflows.

Most of the groundwater was recharged in the Rio Grande Valley floor, and most of the pumpage and aquifer storage depletion was in Mesilla Basin agricultural subregions. A cyclic imbalance between inflows and outflows resulted in the modeled cyclic depletion (groundwater withdrawals in excess of natural recharge) of the groundwater basin during the 75-year simulation period of 1940–2014. Changes in groundwater storage can vary considerably from year to year, depending on land use, pumpage, and climate conditions. Climatic drivers of wet and dry years can greatly affect all inflows, outflows, and water use. Although streamflow and, to a minor extent, precipitation during inter-decadal wet-year periods replenished the groundwater historically, contemporary water use and storage depletion could have reduced the effects of these major recharge events. The average net groundwater flow-rate deficit for 1953–2014 was estimated to be about 1,090 acre-feet per year.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195120","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hanson, R.T., Ritchie, A.B., Boyce, S.E., Galanter, A.E., Ferguson, I.A., Flint, L.E., Flint, A., and Henson, W.R., 2020, Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and northern Chihuahua, Mexico: U.S. Geological Survey Scientific Investigations Report 2019–5120, 186 p., https://doi.org/10.3133/sir20195120.","productDescription":"Report: x, 186 p.; Application Site; Data Release","numberOfPages":"186","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102507","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":399603,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109906.htm"},{"id":373766,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J9NYND","linkHelpText":"Digital hydrologic and geospatial data for the Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico"},{"id":373765,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://ca.water.usgs.gov/sustainable-groundwater-management/gwm/archive1/SIR2019-5120_RGTIHM_Rio_Grande.7z"},{"id":373695,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5120/sir20195120.pdf","text":"Report","size":"25 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":373694,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5120/coverthb.jpg"}],"country":"Mexico, United States","state":"Chihuahua, New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.2942,\n 31.5833\n ],\n [\n -106.3333,\n 31.5833\n ],\n [\n -106.3333,\n 33\n ],\n [\n -107.2942,\n 33\n ],\n [\n -107.2942,\n 31.5833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819

","tableOfContents":"

","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2020-04-07","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Hanson, Randall T. 0000-0002-9819-7141 rthanson@usgs.gov","orcid":"https://orcid.org/0000-0002-9819-7141","contributorId":801,"corporation":false,"usgs":true,"family":"Hanson","given":"Randall","email":"rthanson@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773800,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ritchie, Andre B. 0000-0003-1289-653X","orcid":"https://orcid.org/0000-0003-1289-653X","contributorId":214611,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andre","email":"","middleInitial":"B.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773801,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773802,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Galanter, Amy E. 0000-0002-2960-0136","orcid":"https://orcid.org/0000-0002-2960-0136","contributorId":205393,"corporation":false,"usgs":true,"family":"Galanter","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773803,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ferguson, Ian A. iferguson@usbr.gov","contributorId":205350,"corporation":false,"usgs":false,"family":"Ferguson","given":"Ian","email":"iferguson@usbr.gov","middleInitial":"A.","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":773804,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773805,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":786146,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henson, Wesley R. 0000-0003-4962-5565 whenson@usgs.gov","orcid":"https://orcid.org/0000-0003-4962-5565","contributorId":384,"corporation":false,"usgs":true,"family":"Henson","given":"Wesley","email":"whenson@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773806,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70205106,"text":"sir20185158 - 2020 - Hydrogeologic framework and simulation of predevelopment groundwater flow, eastern Abu Dhabi Emirate, United Arab Emirates","interactions":[],"lastModifiedDate":"2020-04-08T11:09:10.81413","indexId":"sir20185158","displayToPublicDate":"2020-04-07T14:15:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5158","displayTitle":"Hydrogeologic Framework and Simulation of Predevelopment Groundwater Flow, Eastern Abu Dhabi Emirate, United Arab Emirates","title":"Hydrogeologic framework and simulation of predevelopment groundwater flow, eastern Abu Dhabi Emirate, United Arab Emirates","docAbstract":"

Groundwater in eastern Abu Dhabi in the United Arab Emirates is an important resource that is widely used for irrigation and domestic supplies in rural areas. The U.S. Geological Survey and the Environment Agency—Abu Dhabi cooperated on an investigation to integrate existing hydrogeologic information and to answer questions about regional groundwater resources in Abu Dhabi by developing a numerical groundwater flow model based on MODFLOW–2005 software. The groundwater flow model developed in this investigation provides an improved understanding of groundwater conditions in the eastern region of the Emirate of Abu Dhabi. The flow model simulates steady-state predevelopment conditions from before the rapid growth of modern pumping in the 1980s and was calibrated with 1,342 groundwater-level observations by use of automated and manual calibration techniques. The calibrated model provides good accuracy, with a mean error of 0.50 meters and a standard error of 5.92 meters for simulated groundwater levels. The results of the regional water budget simulation show that gap recharge, which is groundwater inflow through mountain-front gap alluvium, is the greatest source of water to the aquifer. In the base simulation scenario, gap recharge represents 80 percent of total inflow (119,470 of 149,403 cubic meters per day) and the greatest outflow from the aquifer is from evapotranspiration (93 percent of total outflow). Model scenario and sensitivity results reveal a need for data that more thoroughly and more accurately describe aquifer hydraulic conductivity, inflow to the aquifer from the Oman Mountains, and recharge from precipitation on the piedmont. Additional long-term aquifer pumping test observations would improve understanding of aquifer hydraulic conductivity, which would also improve model accuracy. Future studies can modify the model to understand the effect of land-use change and water use on groundwater supplies and simulate more complex groundwater flow conditions in a predictive mode.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185158","collaboration":"Prepared in cooperation with the Environment Agency—Abu Dhabi","usgsCitation":"Eggleston, J.R., Mack, T.J., Imes, J.L., Kress, W., Woodward, D.W., and Bright, D.J., 2020, Hydrogeologic framework and simulation of predevelopment groundwater flow, eastern Abu Dhabi Emirate, United Arab Emirates: U.S. Geological Survey Scientific Investigations Report 2018–5158, 48 p., https://doi.org/10.3133/sir20185158.","productDescription":"Report: viii, 48 p.; Data Release","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088658","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":373295,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5158/coverthb.jpg"},{"id":373296,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5158/sir20185158.pdf","text":"Report","size":"6.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5158"},{"id":373297,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZWZISB","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-2005 Groundwater Flow Model to Simulate Predevelopment Groundwater Flow in the Eastern Abu Dhabi Emirate, United Arab Emirates"}],"country":"United Arab Emirates","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[51.57952,24.2455],[51.75744,24.29407],[51.79439,24.01983],[52.57708,24.17744],[53.40401,24.15132],[54.008,24.12176],[54.69302,24.79789],[55.43902,25.43915],[56.07082,26.05546],[56.26104,25.71461],[56.39685,24.92473],[55.88623,24.92083],[55.80412,24.2696],[55.98121,24.13054],[55.52863,23.9336],[55.52584,23.52487],[55.23449,23.11099],[55.20834,22.70833],[55.0068,22.49695],[52.00073,23.00115],[51.61771,24.01422],[51.57952,24.2455]]]},\"properties\":{\"name\":\"United Arab Emirates\"}}]}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2020-04-07","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Eggleston, Jack R. 0000-0001-6633-3041","orcid":"https://orcid.org/0000-0001-6633-3041","contributorId":204628,"corporation":false,"usgs":true,"family":"Eggleston","given":"Jack R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mack, Thomas J. 0000-0002-0496-3918","orcid":"https://orcid.org/0000-0002-0496-3918","contributorId":218727,"corporation":false,"usgs":true,"family":"Mack","given":"Thomas J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Imes, Jeffrey L. 0000-0001-5220-5866 jimes@usgs.gov","orcid":"https://orcid.org/0000-0001-5220-5866","contributorId":218728,"corporation":false,"usgs":true,"family":"Imes","given":"Jeffrey","email":"jimes@usgs.gov","middleInitial":"L.","affiliations":[{"id":349,"text":"International Water Resources Branch","active":true,"usgs":true}],"preferred":true,"id":770049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kress, Wade 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":203539,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770050,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Woodward, Dennis W. 0000-0001-6608-7020 woody@usgs.gov","orcid":"https://orcid.org/0000-0001-6608-7020","contributorId":218729,"corporation":false,"usgs":true,"family":"Woodward","given":"Dennis","email":"woody@usgs.gov","middleInitial":"W.","affiliations":[{"id":349,"text":"International Water Resources Branch","active":true,"usgs":true}],"preferred":true,"id":770051,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bright, Daniel J. 0000-0001-5530-4501 djbright@usgs.gov","orcid":"https://orcid.org/0000-0001-5530-4501","contributorId":218145,"corporation":false,"usgs":false,"family":"Bright","given":"Daniel","email":"djbright@usgs.gov","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":349,"text":"International Water Resources Branch","active":true,"usgs":true}],"preferred":true,"id":770052,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216817,"text":"70216817 - 2020 - Comparison of settlement-era vegetation reconstructions for STEPPS and REVEALS pollen–vegetation models in the northeastern United States","interactions":[],"lastModifiedDate":"2020-12-09T12:55:37.432205","indexId":"70216817","displayToPublicDate":"2020-04-07T14:01:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of settlement-era vegetation reconstructions for STEPPS and REVEALS pollen–vegetation models in the northeastern United States","docAbstract":"

Reconstructions of prehistoric vegetation composition help establish natural baselines, variability, and trajectories of forest dynamics before and during the emergence of intensive anthropogenic land use. Pollen–vegetation models (PVMs) enable such reconstructions from fossil pollen assemblages using process-based representations of taxon-specific pollen production and dispersal. However, several PVMs and variants now exist, and the sensitivity of vegetation inferences to PVM selection, variant, and calibration domain is poorly understood. Here, we compare the reconstructions, parameter estimates, and structure of a Bayesian hierarchical PVM, STEPPS, both to observations and to REVEALS, a widely used PVM, for the pre–Euro-American settlement-era vegetation in the northeastern United States (NEUS). We also compare NEUS-based STEPPS parameter estimates to those for the upper midwestern United States (UMW). Both PVMs predict the observed macroscale patterns of vegetation composition in the NEUS; however, reconstructions of minor taxa are less accurate and predictions for some taxa differ between PVMs. These differences can be attributed to intermodel differences in structure and parameter estimates. Estimates of pollen productivity from STEPPS broadly agree with estimates produced for use in REVEALS, while comparison between pollen dispersal parameter estimates shows no significant relationship. STEPPS parameter estimates are similar between the UMW and NEUS, suggesting that STEPPS parameter estimates are transferable between floristically similar regions and scales.

","language":"English","publisher":"Cambridge University Press","doi":"10.1017/qua.2019.81","usgsCitation":"Trachsel, M., Dawson, A., Paciorek, C.J., Williams, J.W., McLachlan, J.S., Cogbill, C.V., Foster, D.R., Goring, S.J., Jackson, S., Oswald, W.W., and Shuman, B.N., 2020, Comparison of settlement-era vegetation reconstructions for STEPPS and REVEALS pollen–vegetation models in the northeastern United States: Quaternary Research, v. 95, p. 23-42, https://doi.org/10.1017/qua.2019.81.","productDescription":"20 p.","startPage":"23","endPage":"42","ipdsId":"IP-111664","costCenters":[{"id":41166,"text":"Southwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":497090,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":381131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.884765625,\n 44.77793589631623\n ],\n [\n -67.25830078125,\n 45.19752230305682\n ],\n [\n -67.43408203124999,\n 45.182036837015886\n ],\n [\n -67.43408203124999,\n 45.62940492064501\n ],\n [\n -67.74169921875,\n 45.72152152227954\n ],\n [\n -67.82958984375,\n 47.07012182383309\n ],\n [\n -68.31298828125,\n 47.42808726171425\n ],\n [\n -68.994140625,\n 47.234489635299184\n ],\n [\n -69.12597656249999,\n 47.47266286861342\n ],\n [\n -70.1806640625,\n 46.45299704748289\n ],\n [\n -70.81787109374999,\n 45.336701909968134\n ],\n [\n -71.05957031249999,\n 45.321254361171476\n ],\n [\n -71.30126953124999,\n 45.30580259943578\n ],\n [\n -71.4990234375,\n 44.96479793033101\n ],\n [\n -74.8828125,\n 44.99588261816546\n ],\n [\n 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]\n ]\n }\n }\n ]\n}","volume":"95","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Trachsel, Mathias","contributorId":245526,"corporation":false,"usgs":false,"family":"Trachsel","given":"Mathias","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":806372,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dawson, Andria","contributorId":245527,"corporation":false,"usgs":false,"family":"Dawson","given":"Andria","affiliations":[{"id":40107,"text":"Mount Royal University","active":true,"usgs":false}],"preferred":false,"id":806373,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paciorek, Christopher J.","contributorId":245528,"corporation":false,"usgs":false,"family":"Paciorek","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":806374,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, John W.","contributorId":245534,"corporation":false,"usgs":false,"family":"Williams","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":806381,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLachlan, Jason S.","contributorId":245535,"corporation":false,"usgs":false,"family":"McLachlan","given":"Jason","email":"","middleInitial":"S.","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":806382,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cogbill, Charles V.","contributorId":245529,"corporation":false,"usgs":false,"family":"Cogbill","given":"Charles","email":"","middleInitial":"V.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":806375,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Foster, David R.","contributorId":245530,"corporation":false,"usgs":false,"family":"Foster","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":806376,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goring, Simon J.","contributorId":245531,"corporation":false,"usgs":false,"family":"Goring","given":"Simon","email":"","middleInitial":"J.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":806377,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jackson, Stephen 0000-0002-1487-4652","orcid":"https://orcid.org/0000-0002-1487-4652","contributorId":219995,"corporation":false,"usgs":true,"family":"Jackson","given":"Stephen","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":806378,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Oswald, W. Wyatt","contributorId":245532,"corporation":false,"usgs":false,"family":"Oswald","given":"W.","email":"","middleInitial":"Wyatt","affiliations":[{"id":33637,"text":"Emerson College","active":true,"usgs":false}],"preferred":false,"id":806379,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shuman, Bryan N.","contributorId":245533,"corporation":false,"usgs":false,"family":"Shuman","given":"Bryan","email":"","middleInitial":"N.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":806380,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70208710,"text":"fs20203016 - 2020 - Assessing geohazards to the Denali National Park road with geologic mapping","interactions":[],"lastModifiedDate":"2022-04-20T18:43:23.96299","indexId":"fs20203016","displayToPublicDate":"2020-04-07T11:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3016","displayTitle":"Assessing Geohazards to the Denali National Park Road with Geologic Mapping","title":"Assessing geohazards to the Denali National Park road with geologic mapping","docAbstract":"

Denali National Park (DENA) is home to iconic and breathtaking landscapes surrounding the tallest mountain range in North America, the Alaska Range. The park, which covers 6 million acres, is a major draw for tourism and recreation, making it an important economic engine for central Alaska. However, the geologic forces that created the beautiful, steep landscape of DENA also make it prone to geologic hazards (geohazards) like landslides, debris flows, and earthquakes. DENA has only one major road, called the Park Road, that serves nearly all of its infrastructure. The success of DENA as a visitor destination, an economic engine, and a safe environment for visitors, residents, and staff relies on the resilience of this road, making it a major transportation lifeline for the region.

Since 2017, the National Park Service and the U.S. Geological Survey National Cooperative Geologic Mapping Program have partnered to produce a new high-resolution geologic map of the Park Road corridor to identify and address ongoing geohazards affecting DENA infrastructure. In the area of Polychrome Overlook, this map is being used to guide a new route for the Park Road around an area of landslide-prone slopes, where ongoing slumping is costing National Park Service millions of dollars in annual road maintenance costs. Beyond this area, the map serves as a park resource to assess geohazard risk in future infrastructure and management decisions. Geologic mapping is also fueling new research in understanding the geologic and tectonic history of DENA, while training a new generation of geologic mappers through the USGS EDMAP program.


","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203016","collaboration":"Prepared in collaboration with the National Park Service","usgsCitation":"Hudson, A.M., Ruleman, C.A., Capps, D.M., 2020, Assessing geohazards to the Denali National Park Road with geologic mapping: U.S. Geological Survey Fact Sheet 2020–3016, 2 p., https://doi.org/10.3133/fs20203016.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-113547","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":399201,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109903.htm"},{"id":373732,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3016/coverthb.jpg"},{"id":373733,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3016/fs20203016.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3016"}],"country":"United States","state":"Alaksa","otherGeospatial":"Denali National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.8431,\n 63.5278\n ],\n [\n -149.3,\n 63.5278\n ],\n [\n -149.3,\n 63.75\n ],\n [\n -149.8431,\n 63.75\n ],\n [\n -149.8431,\n 63.5278\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-04-07","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Hudson, Adam M. 0000-0002-3387-9838 ahudson@usgs.gov","orcid":"https://orcid.org/0000-0002-3387-9838","contributorId":195419,"corporation":false,"usgs":true,"family":"Hudson","given":"Adam","email":"ahudson@usgs.gov","middleInitial":"M.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":783114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruleman, Chester A. 0000-0002-1503-4591 cruleman@usgs.gov","orcid":"https://orcid.org/0000-0002-1503-4591","contributorId":1264,"corporation":false,"usgs":true,"family":"Ruleman","given":"Chester","email":"cruleman@usgs.gov","middleInitial":"A.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":783115,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Capps, Denny M","contributorId":222771,"corporation":false,"usgs":false,"family":"Capps","given":"Denny","email":"","middleInitial":"M","affiliations":[{"id":40598,"text":"Denali National Park, National Park Service","active":true,"usgs":false}],"preferred":false,"id":783116,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209098,"text":"ofr20201028 - 2020 - Ground-motion predictions for California — Comparisons of three prediction equations","interactions":[],"lastModifiedDate":"2022-04-21T21:00:24.232588","indexId":"ofr20201028","displayToPublicDate":"2020-04-07T10:41:29","publicationYear":"2020","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":"2020-1028","displayTitle":"Ground-Motion Predictions for California — Comparisons of Three Prediction Equations","title":"Ground-motion predictions for California — Comparisons of three prediction equations","docAbstract":"

We systematically evaluate datasets, functional forms, independent parameters of estimation, and resulting ground-motion predictions (as median and aleatory variability) of the Graizer and Kalkan (2015, 2016) (GK15) ground-motion prediction equation (GMPE) with the next generation of attenuation project (NGA-West2) models of Abrahamson and others (2014) (ASK14) and Boore and others (2014) (BSSA14) for application to earthquakes in California. This evaluation is performed in three stages: (1) by comparing attenuation, magnitude scaling, style-of-faulting effects, site response, response-spectral shape and amplitude, and standard deviations; (2) by comparing median predictions, standard deviations, and analyses of residuals with respect to near-field (within 20 kilometers [km] of the fault) and intermediate-field (50 to 70 km from the fault) records from major earthquakes in California, and (3) by comparing total, intra-event, and inter-event residual distributions among the GMPEs with respect to a near-source (within 80 km of the fault) subset of the NGA-West2 database covering 975 ground motions from 73 events in California ranging from moment magnitude 5 to 7.36. The results reveal that the scaling features of the GK15 GMPE and the ASK14 and BSSA14 GMPEs are, in general, similar in terms of distance attenuation but differ in terms of scaling with magnitude, style of faulting, and site effects. The original standard deviations of GMPEs are also different. For the near-source California subset, the three GMPEs result in standard deviations that are similar to each other. The mixed-effect residuals analysis shows that the GK15 GMPE has no perceptible trend with respect to the independent predictors.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201028","collaboration":"Prepared in cooperation with the U.S. Nuclear Regulatory Commission","usgsCitation":"Kalkan, E., and Graizer, V., 2020, Ground-motion predictions for California — Comparisons of three prediction equations: U.S. Geological Survey Open-File Report 2020–1028, 28 p., https://doi.org/10.3133/ofr20201028.","productDescription":"vii, 28 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-087031","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"links":[{"id":373754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1028/coverthb.jpg"},{"id":373755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1028/ofr20201028.pdf","text":"Report"},{"id":399470,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109905.htm"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.4091796875,\n 42.16340342422401\n ],\n [\n -124.91455078125,\n 41.02964338716638\n ],\n [\n -124.78271484375,\n 39.690280594818034\n ],\n [\n -123.96972656249999,\n 38.839707613545144\n ],\n [\n -122.51953124999999,\n 36.96744946416934\n ],\n [\n -121.70654296874999,\n 35.11990857099681\n ],\n [\n -118.564453125,\n 33.55970664841198\n ],\n [\n -117.22412109375,\n 32.63937487360669\n ],\n [\n -114.63134765625001,\n 32.7503226078097\n ],\n [\n -114.14794921875,\n 34.21634468843463\n ],\n [\n -114.41162109375,\n 34.77771580360469\n ],\n [\n -119.90478515625,\n 39.18117526158749\n ],\n [\n -119.86083984375,\n 42.01665183556825\n ],\n [\n -124.4091796875,\n 42.16340342422401\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Earthquake Science Center
U.S. Geological Survey
350 N. Akron Road
Moffett Field, CA 94035

","tableOfContents":"

","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2020-04-07","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":784922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graizer, Vladimir","contributorId":223388,"corporation":false,"usgs":false,"family":"Graizer","given":"Vladimir","email":"","affiliations":[{"id":40706,"text":"U.S. NRC","active":true,"usgs":false}],"preferred":false,"id":784923,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70222341,"text":"70222341 - 2020 - North American Commission on Stratigraphic Nomenclature Note 70: Records of the Stratigraphic Commission 2014-2017","interactions":[],"lastModifiedDate":"2021-09-09T14:56:21.828701","indexId":"70222341","displayToPublicDate":"2020-04-07T09:52:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3481,"text":"Stratigraphy","active":true,"publicationSubtype":{"id":10}},"title":"North American Commission on Stratigraphic Nomenclature Note 70: Records of the Stratigraphic Commission 2014-2017","docAbstract":"

NACSN (North American Commission on Stratigraphic Nomenclature) Note 70 is a summary of the activities of the Commission from October 2014-October 2017. This note is condensed from the minutes of the 69th through the 72nd meetings of the NACSN held in conjunction with the Annual Meetings of the Geological Society of America. The 69th meeting of the NACSN was held October 20, 2014, in Vancouver, British Columbia; the 70th meeting on November 2, 2015, in Baltimore, Maryland; the 71st meeting on September 26, 2016, in Denver, Colorado; and the 72nd meeting on October 23, 2017, in Seattle, Washington. Members of the NACSN who served as officers during the period 2014-2017 serve as co-authors of this note and are listed with their roles in Appendix A. The mission of the NACSN is to develop statements of stratigraphic principles, recommend procedures applicable to the classification of nomenclature of stratigraphic units, review problems in classifying and naming stratigraphic and related units, and formulate expressions of judgement on these matters. Commissioners of the NACSN represent various geoscience professional organizations from Canada, Mexico, and the United States who have an interest in maintaining a stable stratigraphic nomenclature. Commissioners and their constituencies for 2014-2017 are listed in Appendix B.

","language":"English","publisher":"Micropaleontology Press","doi":"10.29041/strat.17.1.59-62","usgsCitation":"Fluegeman, R.H., Brett, C.E., Brunton, F., Edwards, L.E., and Harper, H., 2020, North American Commission on Stratigraphic Nomenclature Note 70: Records of the Stratigraphic Commission 2014-2017: Stratigraphy, v. 17, no. 1, p. 57-62, https://doi.org/10.29041/strat.17.1.59-62.","productDescription":"6 p.","startPage":"57","endPage":"62","ipdsId":"IP-114840","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":389003,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387370,"type":{"id":15,"text":"Index Page"},"url":"https://www.micropress.org/microaccess/stratigraphy/issue-358/article-2176"}],"volume":"17","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fluegeman, Richard H.","contributorId":139942,"corporation":false,"usgs":false,"family":"Fluegeman","given":"Richard","email":"","middleInitial":"H.","affiliations":[{"id":13322,"text":"Ball State University","active":true,"usgs":false}],"preferred":false,"id":819679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brett, Carlton E.","contributorId":214141,"corporation":false,"usgs":false,"family":"Brett","given":"Carlton","email":"","middleInitial":"E.","affiliations":[{"id":7159,"text":"University of Cincinnati","active":true,"usgs":false}],"preferred":false,"id":819680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brunton, Frank","contributorId":261296,"corporation":false,"usgs":false,"family":"Brunton","given":"Frank","affiliations":[{"id":13320,"text":"Ontario Geological Survey","active":true,"usgs":false}],"preferred":false,"id":819681,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edwards, Lucy E. 0000-0003-4075-3317 leedward@usgs.gov","orcid":"https://orcid.org/0000-0003-4075-3317","contributorId":2647,"corporation":false,"usgs":true,"family":"Edwards","given":"Lucy","email":"leedward@usgs.gov","middleInitial":"E.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":819682,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harper, Howard","contributorId":139944,"corporation":false,"usgs":false,"family":"Harper","given":"Howard","email":"","affiliations":[{"id":13323,"text":"Society for Sedimentary Geology","active":true,"usgs":false}],"preferred":false,"id":819683,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209678,"text":"70209678 - 2020 - Depth-dependent soil mixing persists across climate zones","interactions":[],"lastModifiedDate":"2020-05-05T17:24:18.832543","indexId":"70209678","displayToPublicDate":"2020-04-07T09:51:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2982,"text":"PNAS","active":true,"publicationSubtype":{"id":10}},"title":"Depth-dependent soil mixing persists across climate zones","docAbstract":"

Soil mixing over long (>102 y) timescales enhances nutrient fluxes that support soil ecology, contributes to dispersion of sediment and contaminated material, and modulates fluxes of carbon through Earth’s largest terrestrial carbon reservoir. Despite its foundational importance, we lack robust understanding of the rates and patterns of soil mixing, largely due to a lack of long-timescale data. Here we demonstrate that luminescence, a light-sensitive property of minerals used for geologic dating, can be used as a long-timescale sediment tracer in soils to reveal the structure of soil mixing. We develop a probabilistic model of transport and mixing of tracer particles and associated luminescence in soils and compare with a global compilation of luminescence versus depth in various locations. The model–data comparison reveals that soil mixing rate varies over the soil depth, with this depth dependency persisting across climate and ecological zones. The depth dependency is consistent with a model in which mixing intensity decreases linearly or exponentially with depth, although our data do not resolve between these cases. Our findings support the long-suspected idea that depth-dependent mixing is a spatially and temporally persistent feature of soils. Evidence for a climate control on the patterns and intensities of soil mixing with depth remains elusive and requires the further study of soil mixing processes.

","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.1914140117","collaboration":"","usgsCitation":"Gray, H., Keen-Zebert, A., Furbish, D., Tucker, G.E., and Mahan, S.A., 2020, Depth-dependent soil mixing persists across climate zones: PNAS, v. 117, no. 16, p. 8750-8756, https://doi.org/10.1073/pnas.1914140117.","productDescription":"7 p.","startPage":"8750","endPage":"8756","ipdsId":"IP-114039","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457155,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7183219","text":"Publisher Index Page"},{"id":374155,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"16","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Gray, Harrison J. 0000-0002-4555-7473","orcid":"https://orcid.org/0000-0002-4555-7473","contributorId":207019,"corporation":false,"usgs":true,"family":"Gray","given":"Harrison J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":787487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keen-Zebert, Amanda","contributorId":224228,"corporation":false,"usgs":false,"family":"Keen-Zebert","given":"Amanda","email":"","affiliations":[{"id":40841,"text":"University of Nevada Reno / Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":787488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Furbish, David","contributorId":189086,"corporation":false,"usgs":false,"family":"Furbish","given":"David","affiliations":[],"preferred":false,"id":787489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tucker, Gregory E.","contributorId":177811,"corporation":false,"usgs":false,"family":"Tucker","given":"Gregory","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":787490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":787491,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209415,"text":"70209415 - 2020 - Sea turtle conservation: 10 ways you can help","interactions":[],"lastModifiedDate":"2020-05-19T14:28:59.388526","indexId":"70209415","displayToPublicDate":"2020-04-07T09:24:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5946,"text":"EDIS","active":true,"publicationSubtype":{"id":10}},"title":"Sea turtle conservation: 10 ways you can help","docAbstract":"Five species of sea turtle rely on Florida’s coastal and nearshore habitats for nesting during the summer months and foraging throughout the year (Figure 1). \n- Loggerhead turtles, named for their large, block-shaped heads with strong jaw muscles for crushing benthic invertebrates, are the most common sea turtle species on Florida’s nesting beaches. They nest on beaches throughout much of the state. \n- Green turtles are unique among sea turtles in that they are largely vegetarian, and can be spotted foraging in seagrass meadows.\n- Leatherbacks, the largest species of sea turtle, are different from other turtles in that they are covered with a somewhat flexible “leathery” shell, rather than a hard shell. Leatherbacks can be seen in Florida’s coastal waters, but nest much less frequently in the state than loggerheads and green turtles. \n- Kemp’s ridley turtles are the smallest and most endangered marine turtle. They can be seen foraging in nearshore areas, but rarely nest on Florida’s beaches. \n- Lastly, hawksbill turtles are named for their pointed beak. 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\"}}]}","volume":"2020","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Swindall, Jessica E.","contributorId":219304,"corporation":false,"usgs":false,"family":"Swindall","given":"Jessica","email":"","middleInitial":"E.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":786423,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ober, Holly K.","contributorId":219305,"corporation":false,"usgs":false,"family":"Ober","given":"Holly","email":"","middleInitial":"K.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":786424,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamont, Margaret 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":206817,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":786425,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carthy, Raymond R. 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":223853,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","email":"","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":786426,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209327,"text":"tm6A60 - 2020 - One-Water Hydrologic Flow Model: A MODFLOW based conjunctive-use simulation software","interactions":[],"lastModifiedDate":"2023-03-31T18:33:38.4397","indexId":"tm6A60","displayToPublicDate":"2020-04-07T00:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-A60","displayTitle":"One-Water Hydrologic Flow Model: A MODFLOW Based Conjunctive-Use Simulation Software","title":"One-Water Hydrologic Flow Model: A MODFLOW based conjunctive-use simulation software","docAbstract":"

The U.S. Geological Survey’s (USGS) Modular Ground-Water Flow Model (MODFLOW-2005) is a computer program that simulates groundwater flow by using finite differences. The MODFLOW-2005 framework uses a modular design that allows for the easy development and incorporation of new features called processes and packages that work with or modify inputs to the groundwater-flow equation. A process solves a flow equation or set of equations. For example, the central part of MODFLOW is the groundwater-flow process that solves the groundwater-flow equation; the surface-water routing process is an additional process that solves the surface-water flow equation. Packages are code related to the groundwater-flow process. For example, the subsidence package modifies the groundwater-flow process by including aquifer compaction effects on flow. With the development of new packages and processes, the MODFLOW-2005 base framework diverged into multiple independent versions designed for specific simulation needs. This divergence limited each independent MODFLOW release to its specific purpose, so that there was no longer a single, comprehensive, general-purpose hydraulic-simulation framework.

The MODFLOW One-Water Hydrologic Flow Model (MF-OWHM, also informally known as OneWater) is an integrated hydrologic flow model that combines multiple MODFLOW-2005 variants in one cohesive simulation software; changes were made to enable multiple capabilities in one code. This fusion of the MODFLOW-2005 versions resulted in a simulation software that can be used to address and analyze a wide class of conjunctive-use, water-management, water-food-security, and climate-crop-water scenarios. As a second core version of MODFLOW-2005, MF-OWHM maintains backward compatibility with existing MODFLOW-2005 versions, with features that include the following:

MF-OWHM is a MODFLOW-2005 based integrated hydrologic model that can simulate and analyze varying environmental conditions to allow for the evaluation of management options from many components of human and natural water movement through a physically based, supply and demand framework. The term “integrated,” in the context of this report, refers to the tight coupling of groundwater flow, surface-water flow, landscape processes, aquifer compaction and subsidence, reservoir operations, and conduit (karst) flow. Another benefit of this integrated hydrologic model is that models developed to run by MODFLOW-2005, MODFLOW-NWT, MODFLOW-CFP, or MODFLOW-FMP can also be simulated with MF-OWHM. At the time of this report’s publication, MF-OWHM version 2 (MF-OWHM2) does not include a direct internal simulation of snowmelt, advanced mountainous watershed rainfall-runoff simulation, detailed shallow soil-moisture accounting, or atmospheric moisture content. Atmospheric moisture may be accounted for indirectly by, optionally, specifying a pan-evaporation rate, reference evapotranspiration, and precipitation. These features are not included to ensure that simulation runtime remains short enough to enable the use of automated methods of calibrating model parameters to field observations, which typically require many simulation model runs. The MF-OWHM approach is to include as much detail as possible to simulate hydrological processes, providing the simulation runtimes remain reasonable enough to allow for robust parameter estimation and model calibration.

To represent both natural and human-influenced flow, MF-OWHM integrates physically based flow processes derived from MODFLOW-2005 in a supply and demand framework. From this integration, the physically based movement of groundwater, surface water, imported water, and precipitation serve as supply to meet consumptive demands associated with irrigated and non-irrigated agriculture, natural vegetation, and urban water uses. Water consumption is determined by balancing the available water supply with water demand, leading to the concept of a demand-driven, supply-constrained simulation.

The MF-OWHM Supply-and-Demand Framework is especially useful for the analysis of agricultural water use, where there are often few data available to describe changes in land-use through time, such as crop type and distribution, and the associated changes in groundwater pumpage. This framework attempts to satisfy each land-use water demand with available water supplies—that is, groundwater uptake, precipitation, and irrigation. An option provided in MF-OWHM2 is to automatically increase groundwater pumping for irrigation, which often is unknown, by the calculated residual between demand and the other available sources of supply. From large- to small-scale applications, the physically based supply and demand framework provides key capabilities for simulating and analyzing historical, current, and future conjunctive-use of surface water and groundwater.

To achieve the physically based supply and demand framework, the MODFLOW-2005 standard of no inter-package and -process communication was relaxed for MF-OWHM2. Traditional MODFLOW simulation models required that all packages and processes interact through the groundwater-flow equation or by removing the water flow from the simulation domain. For example, the MODFLOW-2005 representation of a groundwater well extracts water from the groundwater-flow equation (by subtraction) and removes it from the simulation domain. This feature is available in the MF-OWHM framework, but options have been added to allow the specification of a use or destination of pumped groundwater within the model domain, for example, it can be used for irrigation, managed aquifer recharge, or return-flow to streams.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A60","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Boyce, S.E., Hanson, R.T., Ferguson, I., Schmid, W., Henson, W., Reimann, T., Mehl, S.M., and Earll, M.M., 2020, One-Water Hydrologic Flow Model: A MODFLOW based conjunctive-use simulation software: U.S. Geological Survey Techniques and Methods 6–A60, 435 p., https://doi.org/10.3133/tm6A60.","productDescription":"Report: xvii, 435 p.; Application Site","numberOfPages":"435","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071159","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437036,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K2IQ6Y","text":"USGS data release","linkHelpText":"Batteries Included Fortran Library (BiF-lib), version 1.0.0"},{"id":437035,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P8I8GS","text":"USGS data release","linkHelpText":"MODFLOW One-Water Hydrologic Flow Model (MF-OWHM) Conjunctive Use and Integrated Hydrologic Flow Modeling Software"},{"id":374113,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix8.pdf","text":"Appendix 8","size":"300 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Conduit Flow Process (CFP2) Input File Documentation for New Capabilities of CFP2 Mode 1—Discrete Conduits"},{"id":374112,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix7.pdf","text":"Appendix 7","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Conduit Flow Process Updates and Upgrades (CFP2)"},{"id":374111,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix6.pdf","text":"Appendix 6","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Farm Process Version 4 (FMP)"},{"id":374110,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix5.pdf","text":"Appendix 5","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Landscape and Root-Zone Processes and Water Demand and Supply"},{"id":374109,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix4.pdf","text":"Appendix 4","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Consumptive Use and Evapotranspiration in the Farm Process"},{"id":374108,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix3.pdf","text":"Appendix 3","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Modflow Upgrades and Updates"},{"id":374107,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix2.pdf","text":"Appendix 2","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Separation of Spatial and Temporal Input Options"},{"id":374106,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix1.pdf","text":"Appendix 1","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- New Input Formats and Utilities"},{"id":374105,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix0.pdf","text":"Appendix 0","size":"500 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Report Syntax Highlighting and Custom Font Styles"},{"id":374104,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_body.pdf","text":"Main body","size":"3 MB - Main body","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60 Main body"},{"id":373682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a60/coverthb.jpg"},{"id":373683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60.pdf","text":"Full report","size":"30 MB - Full report","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60 Full report"},{"id":373696,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://www.usgs.gov/software/modflow-owhm-one-water-hydrologic-flow-model"}],"contact":"

Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2020-04-07","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanson, Randall T. 0000-0002-9819-7141 rthanson@usgs.gov","orcid":"https://orcid.org/0000-0002-9819-7141","contributorId":801,"corporation":false,"usgs":true,"family":"Hanson","given":"Randall","email":"rthanson@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferguson, Ian","contributorId":205394,"corporation":false,"usgs":false,"family":"Ferguson","given":"Ian","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":786098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmid, Wolfgang","contributorId":84020,"corporation":false,"usgs":false,"family":"Schmid","given":"Wolfgang","affiliations":[{"id":13040,"text":"Department of Hydrology and Water Resources, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":786099,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henson, Wesley R. 0000-0003-4962-5565 whenson@usgs.gov","orcid":"https://orcid.org/0000-0003-4962-5565","contributorId":384,"corporation":false,"usgs":true,"family":"Henson","given":"Wesley","email":"whenson@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786100,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reimann, Thomas","contributorId":45536,"corporation":false,"usgs":true,"family":"Reimann","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":786101,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mehl, Steffen W. swmehl@usgs.gov","contributorId":975,"corporation":false,"usgs":true,"family":"Mehl","given":"Steffen","email":"swmehl@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":true,"id":786102,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Earll, Marisa M. 0000-0002-4367-2013 mearll@usgs.gov","orcid":"https://orcid.org/0000-0002-4367-2013","contributorId":223723,"corporation":false,"usgs":true,"family":"Earll","given":"Marisa","email":"mearll@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786103,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211203,"text":"70211203 - 2020 - Models with environmental drivers offer a plausible mechanism for the rapid spread of infectious disease outbreaks in marine organisms","interactions":[],"lastModifiedDate":"2020-07-17T17:37:36.702241","indexId":"70211203","displayToPublicDate":"2020-04-06T12:28:25","publicationYear":"2020","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":"Models with environmental drivers offer a plausible mechanism for the rapid spread of infectious disease outbreaks in marine organisms","docAbstract":"

The first signs of sea star wasting disease (SSWD) epidemic occurred in just few months in 2013 along the entire North American Pacific coast. Disease dynamics did not manifest as the typical travelling wave of reaction-diffusion epidemiological model, suggesting that other environmental factors might have played some role. To help explore how external factors might trigger disease, we built a coupled oceanographic-epidemiological model and contrasted three hypotheses on the influence of temperature on disease transmission and pathogenicity. Models that linked mortality to sea surface temperature gave patterns more consistent with observed data on sea star wasting disease, which suggests that environmental stress could explain why some marine diseases seem to spread so fast and have region-wide impacts on host populations.

","language":"English","publisher":"Springer Nature Limited","doi":"10.1038/s41598-020-62118-4","usgsCitation":"Aalto, E.A., Lafferty, K.D., Sokolow, S.H., Grewelle, R.E., Ben-Horin, T., Boch, C., Raimondi, P.T., Bograd, S.J., Hazen, E.L., Jacox, M., Micheli, F., and De Leo, G., 2020, Models with environmental drivers offer a plausible mechanism for the rapid spread of infectious disease outbreaks in marine organisms: Scientific Reports, v. 10, 5975, 10 p., https://doi.org/10.1038/s41598-020-62118-4.","productDescription":"5975, 10 p.","ipdsId":"IP-086788","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":457157,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-62118-4","text":"Publisher Index Page"},{"id":376468,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","state":"Baja California, British Columbia, California, Oregon, Washington","otherGeospatial":"Pacific Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.64257812499999,\n 28.536274512989916\n ],\n [\n -119.70703125,\n 35.88905007936091\n ],\n [\n -123.134765625,\n 39.16414104768742\n ],\n [\n -123.662109375,\n 43.32517767999296\n ],\n [\n -121.025390625,\n 48.40003249610685\n ],\n [\n -123.48632812499999,\n 50.62507306341435\n ],\n [\n -130.25390625,\n 55.02802211299252\n ],\n [\n -134.6484375,\n 54.97761367069628\n ],\n [\n -132.890625,\n 51.83577752045248\n ],\n [\n -126.298828125,\n 47.931066347509784\n ],\n [\n -125.771484375,\n 42.74701217318067\n ],\n [\n -124.892578125,\n 38.54816542304656\n ],\n [\n -120.673828125,\n 33.358061612778876\n ],\n [\n -115.13671875,\n 28.14950321154457\n ],\n [\n -113.466796875,\n 26.194876675795218\n ],\n [\n -113.64257812499999,\n 28.536274512989916\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Aalto, E. 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Despite decades of research into air and stream temperature dynamics, paired air-water annual temperature signals have been underutilized to characterize watershed processes. Annual stream temperature dynamics are useful in classifying fundamental thermal regimes and can enhance process-based interpretation of stream temperature controls, including deep and shallow groundwater discharge, when paired with air signals. In this study, we investigated multi-scale variability in annual paired air-water temperature patterns using sine-wave linear regressions of multi-year daily temperature data from streams of various sizes. A total of 311 sites from two spatially intensive regional datasets (Shenandoah National Park and Olympic Experimental State Forest) and a spatially extensive national dataset spanning the contiguous United States (U.S. Geological Survey gages) were evaluated. We calculated three annual air-water thermal metrics (mean ratio, phase lag, and amplitude ratio) to deduce the influence of groundwater and other watershed processes on stream thermal regimes at multiple spatial scales. Site-specific values of the three annual air-water thermal metrics ranged from 0.69 to 5.29 (mean ratio), −9 to 40 days (phase lag), and 0.29 to 1.12 (amplitude ratio). Regional patterns in the annual thermal metrics revealed persistent yet spatially variable influences of shallow groundwater discharge and high levels of thermal variability within watersheds, indicating the importance of local hydrogeological controls on stream temperature. Furthermore, annual thermal metric patterns from the regional datasets were generally concordant with the national dataset suggesting the utility of these annual thermal metrics for analysis at multiple scales. Analysis of the national dataset showed that previously defined thermal regimes based on water temperature alone could be further refined using air-water metrics and these metrics were related to physiographic watershed characteristics such as contributing area, elevation, and slope. This research demonstrates the importance of spatial scale and heterogeneity for inferring hydrological process in streams and provides guidance for the interpretation of annual air-water temperature metrics that can be efficiently applied to the growing database of multi-year temperature records. Results from this research can aid in the prediction of future thermal habitat suitability for coldwater-adapted species at ecologically and management-relevant spatial scales with readily available data.

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bdjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-8837-997X","contributorId":245863,"corporation":false,"usgs":false,"family":"Johnson","given":"Brittany","email":"bdjohnson@usgs.gov","middleInitial":"G.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":814786,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":814787,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Devine, Warren 0000-0003-2520-0925","orcid":"https://orcid.org/0000-0003-2520-0925","contributorId":245864,"corporation":false,"usgs":false,"family":"Devine","given":"Warren","email":"","affiliations":[{"id":37093,"text":"Washington State Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":814788,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Snyder, Craig D. 0000-0002-3448-597X csnyder@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-597X","contributorId":2568,"corporation":false,"usgs":true,"family":"Snyder","given":"Craig","email":"csnyder@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":814789,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":814790,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hare, D.","contributorId":255088,"corporation":false,"usgs":false,"family":"Hare","given":"D.","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":814791,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Minkova, T.","contributorId":257641,"corporation":false,"usgs":false,"family":"Minkova","given":"T.","email":"","affiliations":[{"id":37093,"text":"Washington State Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":814792,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70210071,"text":"70210071 - 2020 - Probabilistic regional-scale liquefaction triggering modeling using 3D Gaussian processes","interactions":[],"lastModifiedDate":"2020-05-13T14:18:36.270936","indexId":"70210071","displayToPublicDate":"2020-04-06T09:15:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3418,"text":"Soil Dynamics and Earthquake Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Probabilistic regional-scale liquefaction triggering modeling using 3D Gaussian processes","docAbstract":"

Liquefaction is a major cause of coseismic damages, occurring irregularly over hundreds or thousands of square kilometers in large earthquakes. Large variations in the extent and location of liquefaction have been observed in recent earthquakes, motivating the need for prediction methods that consider the spatial heterogeneity of geologic deposits at a regional scale. Contemporary regional-scale liquefaction hazard analyses are typically performed using only surficial data, which does not address the complicated subsurface mechanics and spatial variability associated with artificial fill and natural soil deposits.

In this study, we develop a probabilistic, regional-scale, subsurface model using data from hundreds of borings to better understand subsurface conditions that could influence liquefaction. We then use this subsurface sample database to train Gaussian process models, yielding 3D independent random fields of groundwater depth, soil plasticity, and penetration resistance for each geologic unit. We incorporate the Gaussian process models into probabilistic liquefaction triggering procedures, producing 3D estimates of the probability of liquefaction for an example study area in Portland, Oregon. Near sampling locations, the variance of the Gaussian process models approaches the variance of site-specific liquefaction triggering procedures. Conversely, when no sample data are nearby to condition a Gaussian process, the variance approaches the marginal variance of the entire recorded dataset. Thus, the procedure described in this study unifies probabilistic site-specific and regional-scale liquefaction triggering procedures and provides an important step towards quantitative liquefaction hazard assessments for regionally distributed infrastructures, such as levees, pipelines, roadways, and electrical transmission facilities.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.soildyn.2020.106159","collaboration":"","usgsCitation":"Greenfield, M., and Grant, A.R., 2020, Probabilistic regional-scale liquefaction triggering modeling using 3D Gaussian processes: Soil Dynamics and Earthquake Engineering, v. 134, 106159, 10 p., https://doi.org/10.1016/j.soildyn.2020.106159.","productDescription":"106159, 10 p.","ipdsId":"IP-110234","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":374751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","city":"Portland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.8656005859375,\n 45.31739181570158\n ],\n [\n -122.431640625,\n 45.31739181570158\n ],\n [\n -122.431640625,\n 45.74069339553309\n ],\n [\n -122.8656005859375,\n 45.74069339553309\n ],\n [\n -122.8656005859375,\n 45.31739181570158\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"134","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Greenfield, Michael","contributorId":224657,"corporation":false,"usgs":false,"family":"Greenfield","given":"Michael","affiliations":[{"id":40903,"text":"Greenfield Geotechnical, Portland, OR","active":true,"usgs":false}],"preferred":false,"id":788985,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":788986,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210189,"text":"70210189 - 2020 - Surface methane concentrations along the mid-Atlantic bight driven by aerobic subsurface production rather than seafloor gas seeps","interactions":[],"lastModifiedDate":"2020-05-20T12:40:04.192663","indexId":"70210189","displayToPublicDate":"2020-04-04T07:26:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2315,"text":"Journal of Geophysical Research C: Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Surface methane concentrations along the mid-Atlantic bight driven by aerobic subsurface production rather than seafloor gas seeps","docAbstract":"Relatively minor amounts of methane, a potent greenhouse gas, are currently emitted from the oceans to the atmosphere, but such methane emissions have been hypothesized to increase as oceans warm. Here, we investigate the source, distribution, and fate of methane released from the upper continental slope of the U.S. Mid-Atlantic Bight, where hundreds of gas seeps have been discovered between the shelf-break and ~1600 m water depth. Using physical, chemical, and isotopic analyses, we identify two main sources of methane in the water column: seafloor gas seeps and in situ aerobic methanogenesis which primarily occurs at 100 – 200 m depth in the water column. Stable isotopic analyses reveal that water samples collected at all depths were significantly impacted by aerobic methane oxidation, the dominant methane sink in this region, with more than 50% of the methane being oxidized, on average. Due to methane oxidation in the deeper water column, below 200 m depth, surface concentrations of methane are influenced more by methane sources found near the surface (0 – 10 m depth) and in the subsurface (10 - 200 m depth), rather than seafloor emissions at greater depths.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JC015989","usgsCitation":"Leonte, M., Ruppel, C.D., Ruiz-Angelo, A., and Kessler, J.D., 2020, Surface methane concentrations along the mid-Atlantic bight driven by aerobic subsurface production rather than seafloor gas seeps: Journal of Geophysical Research C: Oceans, v. 125, no. 5, e2019JC015989, 13 p., https://doi.org/10.1029/2019JC015989.","productDescription":"e2019JC015989, 13 p.","ipdsId":"IP-114697","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":457161,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2019jc015989","text":"External Repository"},{"id":374952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Atlantic margin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.76220703125,\n 39.232253141714885\n ],\n [\n -74.794921875,\n 37.71859032558816\n ],\n [\n -75.1904296875,\n 35.7286770448517\n ],\n [\n -76.22314453125,\n 34.27083595165\n ],\n [\n -76.11328125,\n 34.17999758688084\n ],\n [\n -75.322265625,\n 33.99802726234877\n ],\n [\n -73.916015625,\n 33.97980872872457\n ],\n [\n -72.333984375,\n 36.03133177633187\n ],\n [\n -71.56494140625,\n 38.565347844885466\n ],\n [\n -71.47705078125,\n 39.842286020743394\n ],\n [\n -73.14697265625,\n 39.50404070558415\n ],\n [\n -73.76220703125,\n 39.232253141714885\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Leonte, Mihai 0000-0003-1582-5606","orcid":"https://orcid.org/0000-0003-1582-5606","contributorId":224782,"corporation":false,"usgs":false,"family":"Leonte","given":"Mihai","email":"","affiliations":[{"id":40676,"text":"University of Rochester, NY","active":true,"usgs":false}],"preferred":false,"id":789479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":195778,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn","email":"cruppel@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":789480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruiz-Angelo, Angel","contributorId":224783,"corporation":false,"usgs":false,"family":"Ruiz-Angelo","given":"Angel","email":"","affiliations":[{"id":40940,"text":"Icelandic Meteorological Office","active":true,"usgs":false}],"preferred":false,"id":789481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kessler, John D. 0000-0003-1097-6800","orcid":"https://orcid.org/0000-0003-1097-6800","contributorId":184241,"corporation":false,"usgs":false,"family":"Kessler","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":789482,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212842,"text":"70212842 - 2020 - The impact is in the details: Evaluating a standardized protocol and scale for determining non-native insect impact","interactions":[],"lastModifiedDate":"2020-08-31T14:31:47.909529","indexId":"70212842","displayToPublicDate":"2020-04-03T09:27:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5071,"text":"NeoBiota","active":true,"publicationSubtype":{"id":10}},"title":"The impact is in the details: Evaluating a standardized protocol and scale for determining non-native insect impact","docAbstract":"

Assessing the ecological and economic impacts of non-native species is crucial to providing managers and policymakers with the information necessary to respond effectively. Most non-native species have minimal impacts on the environment in which they are introduced, but a small fraction are highly deleterious. The definition of ‘damaging’ or ‘high-impact’ varies based on the factors determined to be valuable by an individual or group, but interpretations of whether non-native species meet particular definitions can be influenced by the interpreter’s bias or level of expertise, or lack of group consensus. Uncertainty or disagreement about an impact classification may delay or otherwise adversely affect policymaking on management strategies. One way to prevent these issues would be to have a detailed, nine-point impact scale that would leave little room for interpretation and then divide the scale into agreed upon categories, such as low, medium, and high impact. Following a previously conducted, exhaustive search regarding non-native, conifer-specialist insects, the authors independently read the same sources and scored the impact of 41 conifer-specialist insects to determine if any variation among assessors existed when using a detailed impact scale. Each of the authors, who were selected to participate in the working group associated with this study because of their diverse backgrounds, also provided their level of expertise and uncertainty for each insect evaluated. We observed 85% congruence in impact rating among assessors, with 27% of the insects having perfect inter-rater agreement. Variance in assessment peaked in insects with a moderate impact level, perhaps due to ambiguous information or prior assessor perceptions of these specific insect species. The authors also participated in a joint fact-finding discussion of two insects with the most divergent impact scores to isolate potential sources of variation in assessor impact scores. We identified four themes that could be experienced by impact assessors: ambiguous information, discounted details, observed versus potential impact, and prior knowledge. To improve consistency in impact decision-making, we encourage groups to establish a detailed scale that would allow all observed and published impacts to fall under a particular score, provide clear, reproducible guidelines and training, and use consensus-building techniques when necessary.

","language":"English","publisher":"PenSoft","doi":"10.3897/neobiota.55.38981","usgsCitation":"Schulz, A.N., Mech, A.M., Allen, C., Ayres, M.P., Gandhi, K., Gurevitch, J., Havill, N.P., Herms, D.A., Hufbauer, R.A., Liebhold, A.M., Raffa, K.F., Raupp, M.J., Thomas, K.A., Tobin, P.C., and Marsico, T.D., 2020, The impact is in the details: Evaluating a standardized protocol and scale for determining non-native insect impact: NeoBiota, v. 55, p. 61-83, https://doi.org/10.3897/neobiota.55.38981.","productDescription":"13 p.","startPage":"61","endPage":"83","ipdsId":"IP-099057","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":457164,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/neobiota.55.38981","text":"Publisher Index Page"},{"id":378028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","noUsgsAuthors":false,"publicationDate":"2020-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Schulz, Ashley N.","contributorId":219894,"corporation":false,"usgs":false,"family":"Schulz","given":"Ashley","email":"","middleInitial":"N.","affiliations":[{"id":40088,"text":"Department of Biological Sciences, Arkansas State University, Jonesboro, AR","active":true,"usgs":false}],"preferred":false,"id":797634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mech, Angela M.","contributorId":219892,"corporation":false,"usgs":false,"family":"Mech","given":"Angela","email":"","middleInitial":"M.","affiliations":[{"id":40087,"text":"School of Environmental and Forest Sciences, University of Washington, Seattle, WA. Corresponding email: ammech@wcu.edu. Present address: Department of Geosciences and Natural Resources, Western Carolina University, Cullowhee, NC","active":true,"usgs":false}],"preferred":false,"id":797635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Craig 0000-0001-8655-8227 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8227","contributorId":219896,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":797636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ayres, Matthew P.","contributorId":219897,"corporation":false,"usgs":false,"family":"Ayres","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":35787,"text":"Department of Biological Sciences, Dartmouth College, Hanover, NH","active":true,"usgs":false}],"preferred":false,"id":797637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gandhi, Kamal J.K.","contributorId":219898,"corporation":false,"usgs":false,"family":"Gandhi","given":"Kamal J.K.","affiliations":[{"id":40090,"text":"D.B. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA","active":true,"usgs":false}],"preferred":false,"id":797638,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gurevitch, Jessica","contributorId":219899,"corporation":false,"usgs":false,"family":"Gurevitch","given":"Jessica","email":"","affiliations":[{"id":33447,"text":"Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY","active":true,"usgs":false}],"preferred":false,"id":797639,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Havill, Nathan P.","contributorId":219900,"corporation":false,"usgs":false,"family":"Havill","given":"Nathan","email":"","middleInitial":"P.","affiliations":[{"id":40091,"text":"Northern Research Station, USDA Forest Service, Hamden, CT","active":true,"usgs":false}],"preferred":false,"id":797640,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Herms, Daniel A.","contributorId":219895,"corporation":false,"usgs":false,"family":"Herms","given":"Daniel","email":"","middleInitial":"A.","affiliations":[{"id":40089,"text":"The Davey Tree Expert Company, Kent, OH","active":true,"usgs":false}],"preferred":false,"id":797641,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hufbauer, Ruth A.","contributorId":219901,"corporation":false,"usgs":false,"family":"Hufbauer","given":"Ruth","email":"","middleInitial":"A.","affiliations":[{"id":40092,"text":"Department of Bioagricultural Science and Pest Management, Colorado State University, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":797642,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Liebhold, Andrew M.","contributorId":219902,"corporation":false,"usgs":false,"family":"Liebhold","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":40093,"text":"USDA Forest Service Northern Research Station, Morgantown, WV","active":true,"usgs":false}],"preferred":false,"id":797643,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Raffa, Kenneth F.","contributorId":219903,"corporation":false,"usgs":false,"family":"Raffa","given":"Kenneth","email":"","middleInitial":"F.","affiliations":[{"id":40094,"text":"Department of Entomology, University of Wisconsin, Madison, WI","active":true,"usgs":false}],"preferred":false,"id":797644,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Raupp, Michael J.","contributorId":239692,"corporation":false,"usgs":false,"family":"Raupp","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":47979,"text":"University of Maryland, Department of Entomology, 4112 Plant Sciences Building, College Park, MD 20742, USA","active":true,"usgs":false}],"preferred":false,"id":797645,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thomas, Kathryn A. 0000-0002-7131-8564 kathryn_a_thomas@usgs.gov","orcid":"https://orcid.org/0000-0002-7131-8564","contributorId":167,"corporation":false,"usgs":true,"family":"Thomas","given":"Kathryn","email":"kathryn_a_thomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":797646,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Tobin, Patrick C.","contributorId":200172,"corporation":false,"usgs":false,"family":"Tobin","given":"Patrick","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":797647,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Marsico, Travis D.","contributorId":219893,"corporation":false,"usgs":false,"family":"Marsico","given":"Travis","email":"","middleInitial":"D.","affiliations":[{"id":40088,"text":"Department of Biological Sciences, Arkansas State University, Jonesboro, AR","active":true,"usgs":false}],"preferred":false,"id":797648,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70209420,"text":"70209420 - 2020 - Thermal heterogeneity, migration, and consequences for spawning potential of female bull trout in a river-reservoir system","interactions":[],"lastModifiedDate":"2020-06-04T17:09:46.699712","indexId":"70209420","displayToPublicDate":"2020-04-03T08:18:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Thermal heterogeneity, migration, and consequences for spawning potential of female bull trout in a river-reservoir system","docAbstract":"

The likelihood that fish will initiate spawning, spawn successfully, or skip spawning in a given year is conditioned in part on availability of energy reserves. We evaluated the consequences of spatial heterogeneity in thermal conditions on the energy accumulation and spawning potential of migratory bull trout (Salvelinus confluentus) in a regulated river–reservoir system. Based on existing data, we identified a portfolio of thermal exposures and migratory patterns and then estimated their influence on energy reserves of female bull trout with a bioenergetics model. Spawning by females was assumed to be possible if postspawning energy reserves equaled or exceeded 4 kJ/g. Given this assumption, results suggested up to 70% of the simulated fish could spawn each year. Fish that moved seasonally between a cold river segment and a warmer reservoir downstream had a greater growth rate and higher propensity to spawn in a given year (range: 40%–70%) compared with fish that resided solely in the cold river segment (25%–40%). On average, fish that spawned lost 30% of their energy content relative to their prespawn energy. In contrast, fish that skipped spawning accumulated, on average, 16% energy gains that could be used toward future gamete production. Skipped spawning occurred when water temperatures were relatively low or high, and if upstream migration occurred relatively late (mid-July or later) or early (early-May or earlier). Overall, our modeling effort suggests the configuration of thermal exposures, and the ability of bull trout to exploit this spatially and temporally variable thermal conditions can strongly influence energy reserves and likelihood of successful spawning.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6184","usgsCitation":"Benjamin, J.R., Vidergar, D., and Dunham, J.B., 2020, Thermal heterogeneity, migration, and consequences for spawning potential of female bull trout in a river-reservoir system: Ecology and Evolution, v. 10, no. 9, p. 4128-4142, https://doi.org/10.1002/ece3.6184.","productDescription":"15 p.","startPage":"4128","endPage":"4142","ipdsId":"IP-111770","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457165,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.6184","text":"External Repository"},{"id":373838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Boise River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.4111328125,\n 42.97250158602597\n ],\n [\n -113.99414062499999,\n 42.97250158602597\n ],\n [\n -113.99414062499999,\n 44.4808302785626\n ],\n [\n -116.4111328125,\n 44.4808302785626\n ],\n [\n -116.4111328125,\n 42.97250158602597\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":786443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vidergar, Dmitri T","contributorId":223858,"corporation":false,"usgs":false,"family":"Vidergar","given":"Dmitri T","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":786444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":365,"text":"Leetown Science Center","active":true,"usgs":true},{"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":786445,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209443,"text":"70209443 - 2020 - Long-term trends of Lake Michigan benthos with emphasis on the southern basin","interactions":[],"lastModifiedDate":"2020-06-04T17:08:34.997292","indexId":"70209443","displayToPublicDate":"2020-04-03T07:50:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Long-term trends of Lake Michigan benthos with emphasis on the southern basin","docAbstract":"Lake Michigan benthic macrofauna have been studied for almost a century, allowing for a unique analysis of long-term changes in community structure. We examined changes in abundances of three major taxonomic groups of benthic macroinvertebrates (Diporeia, Oligochaeta, and Sphaeriidae) in southern Lake Michigan from 1931-2015, and identified the most likely causes for these changes. Abundances of all three groups increased during 1931-1980, with the bulk of these increases occurring in nearshore (≤ 50 m) waters and coincident with increasing loading of phosphorus (P) to the lake. Abundances of all three taxa declined during 1980-2000 again mostly in nearshore waters and coincident with decreased P loading. The quagga mussel (Dreissena rostriformis bugensis) invasion was associated with a further decline in phytoplankton primary production during 2000-2015. Both Diporeia and Sphaeriidae declined in abundance during that time, with Diporeia exhibiting the more pronounced decrease of the two groups. In contrast, Oligochaeta increased in abundance during 2000-2015. The quagga mussel has become, by far, the most abundant benthic macroinvertebrate species in terms of density and biomass. Overall, the primary driver of changes in the abundances of the three major taxa during this 85-year period appeared to be changes in phytoplankton primary production due to changing P loadings and, later in the time series, Dreissena filtering. The dreissenid mussel invasions coincided with a rapid decline of Diporeia abundance, but the mechanism of this negative effect remains unidentified. In contrast, Oligochaeta likely benefitted from the quagga mussel invasion; perhaps via quagga-generated food supplies.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.03.011","usgsCitation":"Mehler, K., Burlakova, L.E., Karatayev, A.Y., Elgin, A.K., Nalepa, T.F., Madenjian, C.P., and Hinchey, E.K., 2020, Long-term trends of Lake Michigan benthos with emphasis on the southern basin: Journal of Great Lakes Research, v. 46, no. 3, p. 528-537, https://doi.org/10.1016/j.jglr.2020.03.011.","productDescription":"10 p.","startPage":"528","endPage":"537","ipdsId":"IP-111303","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":457166,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2020.03.011","text":"Publisher Index Page"},{"id":373836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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F.","contributorId":211819,"corporation":false,"usgs":false,"family":"Nalepa","given":"Thomas","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":786500,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":786501,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hinchey, Elizabeth K.","contributorId":197957,"corporation":false,"usgs":false,"family":"Hinchey","given":"Elizabeth","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":786502,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209988,"text":"70209988 - 2020 - Understanding the golden eagle and bald eagle sensory worlds to enhance detection and response to wind turbines","interactions":[],"lastModifiedDate":"2020-05-08T12:43:14.492553","indexId":"70209988","displayToPublicDate":"2020-04-03T07:35:55","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Understanding the golden eagle and bald eagle sensory worlds to enhance detection and response to wind turbines","docAbstract":"The objective for this study was to measure the auditory and visual physiology of Golden and Bald Eagles in order to use eagle sensory capabilities to inform the design of potential deterrent stimuli that could be used to reduce eagle/turbine collisions with wind turbines. The rationale for this approach is that sensory systems of any organism will limit the capability of that organism to perceive aspects of the world around it. Moreover, species can differ dramatically in their sensory physiology so it is important to examine these characteristics in the species of concern, rather than relying on data from similar birds. Our project consisted of two main phases. The first phase was the acquisition and analysis of visual and auditory information from Golden and Bald Eagles in rehabilitation centers. This was performed in order to identify light and sound stimuli tuned to sensitive areas in the eagle’s sensory systems. The second phase of the project was to present these stimuli to both species of eagles in a behavioral experiment to identify which stimuli would be the most effective in changing the behaviors of the eagles. \nResults of phase one indicated that the visual system of the Golden Eagle strongly absorbs ultraviolet light, making it unlikely the Golden Eagle (and most likely the Bald Eagle) will detect ultraviolet light signals. The Golden and Bald Eagles have differences in the sensitivities of their visual systems to light within the eye, but mathematical models indicate that both species are able to detect indigo/blue and orange/red light produced by LEDs (light emitting diodes) very well. We also found that both species of eagles have a blind spot above their head. This blind spot is particularly large in Golden Eagles due to a pronounced brow ridge above the eyes. This blind spot will result in the inability of a Golden Eagle to see something in front of it when its head is pointed down during flight – as might happen while hunting (i.e. searching the ground for prey). As such, the blind spot may increase the chance of collision with wind turbines if the eagle is actively hunting. This problem is less pronounced in Bald Eagles because their blind spot is smaller than in the Golden Eagles and their foraging strategy is different.\nResults of phase one also indicated that the auditory systems of the Golden and Bald Eagles respond differently to a variety of sounds (static tones, static chords (i.e. stacked tones), and sounds with dynamic changes through amplitude modulation or frequency modulation). Both species’ auditory systems responded strongly to tones across a wide range of frequencies (0.5 – 5kHz ), however the Bald Eagles’ auditory system was much better at processing complex sounds with dynamic rapid changes in amplitude or frequency modulation than the Golden Eagle. All of these sounds were then played with two types of noise in the background (white or pink). White noise more closely resembles the sound of wind and pink noise more closely resembles wind turbines or other sources of anthropogenic noise. Most sounds were more strongly masked by pink noise than by white noise, but several sounds (especially sounds with rapid modulation changes) showed little or no masking, indicating these were good candidate signals. However, even though rapidly changing sounds are less subject to noise masking, they are also less strongly processed by the Golden Eagle auditory system. This tradeoff does not exist in Bald Eagles because individuals of this species are very good at processing rapidly changing sounds. Given that Golden Eagle populations are at greater risk than Bald Eagle populations, we suggest that the most efficient alerting sound stimuli used in deterrent systems should be complex sounds that do not change very rapidly. \nWe identified candidate light (indigo/blue and orange/red LED lights) and sound (sinusoidal frequency modulated sound, linear frequency sweeps, amplitude modulated sound, and a mistuned harmonic stack) stimuli that both eagle species sensory systems are highly sensitive to. Results of phase two, in which we presented these stimuli to eagles in a behavioral experiment, indicated that eagles behaviorally responded to all the stimuli presented, but at varying degrees. The Golden Eagles, especially, elicited higher rates of visual exploratory behavior with a flashing blue light stimulus and all sound stimuli. Our results suggest using these stimuli in field-testing of light/sound eagle deterrent systems on wind turbines. The eagles showed lower rates of behavior over the course of an experiment, suggesting either that they habituated to our stimuli or were initially stressed by the setup of the behavioral tests. These results underscore the need to test for habituation effects. Nonetheless, habitation to the stimuli in these field tests would likely be reduced by the use of random presentations of the four sounds and if possible random presentation of the candidate lights.","language":"English","publisher":"U.S. Department of Energy","doi":"","collaboration":"Purdue University","usgsCitation":"Fernandez-Juricic, E., Lucas, J., Katzner, T., Goller, B., Baumhardt, P., and Lovko, N., 2020, Understanding the golden eagle and bald eagle sensory worlds to enhance detection and response to wind turbines, 181 p., https://doi.org/.","productDescription":"181 p.","ipdsId":"IP-118356","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":374571,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":374570,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://tethys.pnnl.gov/publications/understanding-golden-eagle-bald-eagle-sensory-worlds-enhance-detection-response-wind"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fernandez-Juricic, Esteban","contributorId":224607,"corporation":false,"usgs":false,"family":"Fernandez-Juricic","given":"Esteban","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":788720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lucas, Jeffrey","contributorId":224608,"corporation":false,"usgs":false,"family":"Lucas","given":"Jeffrey","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":788721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":788722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goller, B.","contributorId":224609,"corporation":false,"usgs":false,"family":"Goller","given":"B.","affiliations":[],"preferred":false,"id":788739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baumhardt, P.","contributorId":224610,"corporation":false,"usgs":false,"family":"Baumhardt","given":"P.","affiliations":[],"preferred":false,"id":788740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lovko, N.","contributorId":224611,"corporation":false,"usgs":false,"family":"Lovko","given":"N.","email":"","affiliations":[],"preferred":false,"id":788741,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}