Clumped isotope paleothermometry using pedogenic carbonates is a powerful tool for investigating past climate changes. However, location-specific seasonal patterns of precipitation and soil moisture cause systematic biases in the temperatures they record, hampering comparison of data across large areas or differing climate states. To account for biases, more systematic studies of carbonate forming processes are needed. We measured modern soil temperatures within the San Luis Valley of the Rocky Mountains and compared them to paleotemperatures determined using clumped isotopes. For Holocene-age samples, clumped isotope results indicate carbonate accumulated at a range of temperatures with site averages similar to the annual mean. Paleotemperatures for late Pleistocene-age samples (ranging 19–72 ka in age) yielded site averages only 2°C lower, despite evidence that annual temperatures during glacial periods were 5–9°C colder than modern. We use a 1D numerical model of soil physics to support the idea that differences in hydrologic conditions in interglacial versus glacial periods promote differences in the seasonal distribution of soil carbonate accumulation. Model simulations of modern (Holocene) conditions suggest that soil drying under low soil pCO2 favors year-round carbonate accumulation in this region but peaking during post-monsoon soil drying. During a “glacial” simulation with lowered temperatures and added snowpack, more carbonate accumulation shifted to the summer season. These experiments show that changing hydrologic regimes could change the seasonality of carbonate accumulation, which in this study blunts the use of clumped isotopes to quantify glacial-interglacial temperature changes. This highlights the importance of understanding seasonal biases of climate proxies for accurate paleoenvironmental reconstruction.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GC011221","usgsCitation":"Hudson, A.M., Kelson, J.R., Paces, J., Ruleman, C.A., Huntington, K.W., and Schauer, A.J., 2024, Clumped isotopes record a glacial-interglacial shift in seasonality of soil carbonate accumulation in the San Luis Valley, southern Rocky Mountains, USA: Geochemistry, Geophysics, Geosystems, v. 25, no. 4, e2023GC011221, 23 p., https://doi.org/10.1029/2023GC011221.","productDescription":"e2023GC011221, 23 p.","ipdsId":"IP-114357","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":440002,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gc011221","text":"Publisher Index Page"},{"id":435011,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TJF3PZ","text":"USGS data release","linkHelpText":"Isotopic, geochronologic and soil temperature data for Holocene and late Pleistocene soil carbonates of the San Luis Valley, Colorado and New Mexico, USA"},{"id":427311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, new Mexico","otherGeospatial":"San Luis Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107,\n 38.5\n ],\n [\n -107,\n 35.5\n ],\n [\n -105,\n 35.5\n ],\n [\n -105,\n 38.5\n ],\n [\n -107,\n 38.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-03-30","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":897780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelson, Julia R.","contributorId":335224,"corporation":false,"usgs":false,"family":"Kelson","given":"Julia","email":"","middleInitial":"R.","affiliations":[{"id":80344,"text":"Department of Geosciences, University of Michigan, Ann Arbor, MI, USA, Department of Earth and Atmospheric Sciences, Indiana University, Indianapolis, Indiana, USA","active":true,"usgs":false}],"preferred":false,"id":897781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paces, James B. 0000-0002-9809-8493","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":118216,"corporation":false,"usgs":true,"family":"Paces","given":"James B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":897782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":897783,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huntington, Katharine W.","contributorId":195423,"corporation":false,"usgs":false,"family":"Huntington","given":"Katharine","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":897784,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schauer, Andrew J.","contributorId":140713,"corporation":false,"usgs":false,"family":"Schauer","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":897785,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252668,"text":"70252668 - 2024 - Timing and source of recharge to the Columbia River Basalt groundwater system in northeastern Oregon","interactions":[],"lastModifiedDate":"2024-09-11T16:11:18.161455","indexId":"70252668","displayToPublicDate":"2024-03-30T06:44:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Timing and source of recharge to the Columbia River Basalt groundwater system in northeastern Oregon","docAbstract":"Recharge to and flow within the Columbia River Basalt Group (CRBG) groundwater flow system of northeastern Oregon were characterized using isotopic, gas, and age-tracer samples from wells completed in basalt, springs, and stream base flow. Most groundwater samples were late-Pleistocene to early-Holocene; median age of well samples was 11,100 years. The relation between mean groundwater age and completed well depth across the eastern portion of the study area was similar despite differences in precipitation, topographic position, incision, thickness of the sedimentary overburden, and CRBG geologic unit. However, the lateral continuity in groundwater age was disrupted across large regional fault zones indicating these structures are substantial impediments to groundwater flow from the high-precipitation uplands to adjacent lower-precipitation and lower-elevation portions of the study area. Recharge rates calculated from the age-depth relations were <3 mm/yr and independent of the modern precipitation gradient across the study area. The age-constrained recharge rates to the CRBG groundwater system are considerably smaller than previously published estimates and highlight the uncertainty of prevailing models used to estimate recharge to the CRBG groundwater system across the Columbia Plateau in Oregon and Washington. Age tracer and isotopic evidence indicate recharge to the CRBG groundwater system is an exceedingly slow and localized process.
Eight springs and seeps at Fort Irwin National Training Center were described and categorized by their general characteristics, discharge, geophysical properties, and water quality between 2015 and 2017. The data collected establish a modern (2017) baseline of hydrologic conditions at the springs. Two types of springs were identified: (1) precipitation-fed upland springs (Cave, Desert King, Devouge, No Name, and Panther Springs) and (2) groundwater discharge-fed basin springs (Garlic, Bitter, and Jack Springs). Comparison of electrical resistivity tomography data collected at groundwater basin springs from 2015 to 2017 indicated that spring discharge and connection to the underlying groundwater system is highly focused, although the springs themselves appear diffuse and are spread out over a large area.
Spring discharge was consistently less than reported by Thompson (1929), except at Garlic Spring where discharges and vegetation have increased in recent years. Multiple discrete flume and seepage meter measurements taken between October 2015 and April 2016 indicated that discharge changed predictably on diurnal and seasonal timescales in response to evapotranspiration. These preliminary results and the lush vegetation noted at some of the springs, particularly at Bitter, Garlic, and Jack Springs, indicated plant evapotranspiration accounts for a substantial part of the discharge from these springs.
The quality of water ranges from fresh in precipitation-fed upland springs (Cave, Desert King, Devouge, and Panther Springs) to slightly saline (Garlic and Jack Springs) and moderately saline (Bitter Spring) in groundwater-fed discharge springs. Nitrate concentrations from water at most of the springs were less than 3 milligrams per liter, except for samples from Devouge and Desert King Springs and one sample from Jack Spring. An analysis of delta nitrogen-15 in nitrate (δ15N-NO3) and delta oxygen-18 in nitrate (δ18O-NO3) indicates high nitrate concentrations in excess of the U.S. Environmental Protection Agency maximum contaminant level at Jack Spring and Desert King Spring resulting from the dissolution of nitrate-bearing caliche deposits; nitrate concentrations at Devouge Spring are a result of algal growth within the spring, and the source of nitrate concentrations in Garlic Spring are consistent with a treated wastewater origin from Langford Valley-Irwin subbasin upgradient. The source of water in upland springs, indicated by values of delta oxygen-18 (δ18O) and delta deuterium (δD) are consistent with recharge from winter precipitation. In groundwater basin springs, values of δ18O and δD are consistent with groundwater sampled from nearby wells. Summer monsoonal precipitation appears to contribute little water to spring flow. Most springs contain low levels of tritium and appear to be primarily older (pre-1950s) groundwater. Groundwater basin springs with detectable tritium may result from occasional streamflow in nearby washes. These springs could be susceptible to decreases in flow during extended dry periods when the localized recharge may be reduced due to the loss of focused recharge through nearby washes.
Groundwater samples from Garlic and Bitter Springs contained arsenic concentrations above the U.S. Environmental Protection Agency maximum contaminant level. Groundwater samples from all springs, except Cave, Desert King, and Devouge Springs, exceeded the State of California maximum contaminant level for fluoride. Garlic Spring was the only sampled spring that contained vanadium concentrations that exceeded the State of California notification level. Only a single water sample from Jack Spring contained uranium at a concentration that exceeded the U.S. Environmental Protection Agency maximum contaminant level.
Many other constituents of concern were analyzed, including those from anthropogenic sources that may be a result of military activities. Most of these constituents were not detected above their respective reporting levels in spring water; only 15 were detected in spring waters. Diesel and gasoline degradants, many of which also occur naturally, were the most commonly detected compounds. Several other organic compounds, primarily solvents or their degradants, were detected in groundwater basin springs. These constituents, in order of decreasing detection frequency, were carbon disulfide; perchlorate; mercury; acetone; methylnaphthalene; toluene; methyl ethyl ketone; cyanide; and styrene; 4-iso-propyl-toluene; isopropylbenzene; methyl salicylate; and phenol. Except for Garlic Spring, which is affected by discharges of treated wastewater, the quality of water from most springs appears to be relatively unaffected by activities at the Fort Irwin National Training Center.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235142","collaboration":"Prepared in cooperation with the U.S. Army Fort Irwin National Training Center","programNote":"Water Availability and Use Science Program","usgsCitation":"Densmore, J.N., Thayer, D.C., Dick, M.C., Swarzenski, P.W., Ball, L.B., Rosecrans, C.Z., and Johnson, C., 2024, Evaluation of the characteristics, discharge, and water quality of selected springs at Fort Irwin National Training Center, San Bernardino County, California: U.S. Geological Survey Scientific Investigations Report 2023–5142, 87 p., https://doi.org/10.3133/sir20235142.","productDescription":"Report: xii, 87 p.; 2 Data Releases","numberOfPages":"87","onlineOnly":"Y","ipdsId":"IP-098665","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":426854,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P901E9C2","text":"USGS Data Release","description":"Mesmer, R.D., Dick, M.C., and Densmore, J.N., 2024, Temperature and discharge data of selected springs at Fort Irwin National Training Center, San Bernardino County, California: U.S. Geological Survey data release, available at https://doi.org/10.5066/P901E9C2.","linkHelpText":"Temperature and discharge data of selected springs at Fort Irwin National Training Center, San Bernardino County, California"},{"id":499404,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116216.htm","linkFileType":{"id":5,"text":"html"}},{"id":426868,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5142/images"},{"id":426867,"rank":6,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5142/covrthb.jpg"},{"id":426866,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235142/full"},{"id":426865,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5142/sir20235142.xml"},{"id":426864,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5142/sir20235142.pdf","text":"Report","size":"25.9 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426853,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77W6BF0","text":"USGS Data Release","description":"Thayer, D.C., Ball, L.B., Densmore, J.N., Swarzenski, P.W., and Johnson, C., 2018, Electrical resistivity tomography data at Fort Irwin National Training Center, San Bernardino County, California, 2015 and 2017: U.S. Geological Survey data release, available at https://doi.org/10.5066/F77W6BF0.","linkHelpText":"Electrical resistivity tomography data at Fort Irwin National Training Center, San Bernardino County, California, 2015 and 2017"}],"country":"United States","state":"California","otherGeospatial":"Fort Irwin National Training Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.65077467771744,\n 36.01045506303355\n ],\n [\n -117.65077467771744,\n 34.68622540325404\n ],\n [\n -115.49481045780325,\n 34.68622540325404\n ],\n [\n -115.49481045780325,\n 36.01045506303355\n ],\n [\n -117.65077467771744,\n 36.01045506303355\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The Arizona Toad (Anaxyrus microscaphus) is restricted to riverine corridors and adjacent uplands in the arid southwestern United States. As with numerous amphibians worldwide, populations are declining and face various known or suspected threats, from disease to habitat modification resulting from climate change. The Arizona Toad has been petitioned to be listed under the U.S. Endangered Species Act and was considered “warranted but precluded” citing the need for additional information – particularly regarding natural history (e.g., connectivity and dispersal ability). The objectives of this study were to characterize population structure and genetic diversity across the species’ range. We used reduced-representation genomic sequencing to genotype 3,601 single nucleotide polymorphisms in 99 Arizona Toads from ten drainages across its range. Multiple analytical methods revealed two distinct genetic groups bisected by the Colorado River; one in the northwestern portion of the range in southwestern Utah and eastern Nevada and the other in the southeastern portion of the range in central and eastern Arizona and New Mexico. We also found subtle substructure within both groups, particularly in central Arizona where toads at lower elevations were less connected than those at higher elevations. The northern and southern parts of the Arizona Toad range are not well connected genetically and could be managed as separate units. Further, these data could be used to identify source populations for assisted migration or translocations to support small or potentially declining populations.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-024-01606-w","usgsCitation":"Oyler-McCance, S.J., Ryan, M.J., Sullivan, B.K., Fike, J., Cornman, R.S., Giermakowski, J.T., Zimmerman, S.J., Harrow, R.L., Hedwell, S., Hossack, B., Latella, I., Lovish, R.E., Siefken, S., Sigafus, B., and Muths, E., 2024, Genetic Connectivity in the Arizona toad (Anaxyrus microscaphus): implications for conservation of a stream dwelling amphibian in the arid Southwestern U.S.: Conservation Genetics, v. 25, p. 835-848, https://doi.org/10.1007/s10592-024-01606-w.","productDescription":"14 p.","startPage":"835","endPage":"848","ipdsId":"IP-154561","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":440011,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-024-01606-w","text":"Publisher Index Page"},{"id":427560,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Nevada, New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.0000406770512,\n 38.468528390736736\n ],\n [\n -118.0000406770512,\n 30.615684527609147\n ],\n [\n -106.80078434694725,\n 30.615684527609147\n ],\n [\n -106.80078434694725,\n 38.468528390736736\n ],\n [\n -118.0000406770512,\n 38.468528390736736\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2024-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":898325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ryan, Mason J.","contributorId":266045,"corporation":false,"usgs":false,"family":"Ryan","given":"Mason","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":898326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Brian K.","contributorId":177225,"corporation":false,"usgs":false,"family":"Sullivan","given":"Brian","email":"","middleInitial":"K.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":898327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":898328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":898329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Giermakowski, J. T.","contributorId":335421,"corporation":false,"usgs":false,"family":"Giermakowski","given":"J.","email":"","middleInitial":"T.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":898330,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zimmerman, Shawna J 0000-0003-3394-6102 szimmerman@usgs.gov","orcid":"https://orcid.org/0000-0003-3394-6102","contributorId":238076,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Shawna","email":"szimmerman@usgs.gov","middleInitial":"J","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":898331,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Harrow, R. L.","contributorId":335422,"corporation":false,"usgs":false,"family":"Harrow","given":"R.","email":"","middleInitial":"L.","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":898332,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hedwell, S.J.","contributorId":335423,"corporation":false,"usgs":false,"family":"Hedwell","given":"S.J.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":898333,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":898334,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Latella, I. M.","contributorId":335424,"corporation":false,"usgs":false,"family":"Latella","given":"I. M.","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":898335,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lovish, R. E.","contributorId":335425,"corporation":false,"usgs":false,"family":"Lovish","given":"R.","email":"","middleInitial":"E.","affiliations":[{"id":80401,"text":"Naval Facilities Engineering Systems Command Southwest","active":true,"usgs":false}],"preferred":false,"id":898336,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Siefken, S.","contributorId":335427,"corporation":false,"usgs":false,"family":"Siefken","given":"S.","affiliations":[{"id":65571,"text":"Utah Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":898337,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sigafus, Brent H. 0000-0002-7422-8927","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":264740,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898338,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":245922,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":898339,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70251912,"text":"70251912 - 2024 - Summary of the discussions during 2023 SSA topical meeting on “Future Directions for Physics-Based Ground Motion Modeling”","interactions":[],"lastModifiedDate":"2026-03-25T16:02:49.42664","indexId":"70251912","displayToPublicDate":"2024-03-29T11:01:26","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Summary of the discussions during 2023 SSA topical meeting on “Future Directions for Physics-Based Ground Motion Modeling”","docAbstract":"The Seismological Society of America (SSA) topical conference, Future Directions for Physics‐Based Ground Motion Modeling, was held in Vancouver, Canada, on 10–13 October 2023, co‐sponsored by the Seismological Society of Japan and co‐chaired by Annemarie Baltay of the U.S. Geological Survey and Hiroshi Kawase of Kyoto University. This meeting brought together many researchers and practitioners interested in modeling, observing, and utilizing ground‐motion models (GMMs). Scientists gathered to discuss complex kinematic and dynamic rupture simulation approaches, empirical representations of the earthquake source, site and path effects, physical modeling of the recording site, challenges for model extrapolation, and overall prediction accuracy and simulation validation. The four‐day meeting included many posters as well as oral presentations, with each session followed by lively discussion sections, upon which we report here.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240084","usgsCitation":"Kawase, H., and Baltay Sundstrom, A.S., 2024, Summary of the discussions during 2023 SSA topical meeting on “Future Directions for Physics-Based Ground Motion Modeling”: Seismological Research Letters, v. 95, no. 3, p. 2026-2030, https://doi.org/10.1785/0220240084.","productDescription":"5 p.","startPage":"2026","endPage":"2030","ipdsId":"IP-163467","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":501503,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"95","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Kawase, Hiroshi","contributorId":267868,"corporation":false,"usgs":false,"family":"Kawase","given":"Hiroshi","email":"","affiliations":[{"id":36662,"text":"Kyoto University","active":true,"usgs":false}],"preferred":false,"id":896049,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":896050,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252670,"text":"70252670 - 2024 - Post-wildfire debris flows","interactions":[],"lastModifiedDate":"2024-04-02T15:03:04.719071","indexId":"70252670","displayToPublicDate":"2024-03-29T10:00:07","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Post-wildfire debris flows","docAbstract":"Post-wildfire debris flows pose severe hazards to communities and infrastructure near and within recently burned mountainous terrain. Intense heat of wildfires changes the runoff characteristics of a watershed by combusting the vegetative canopy, litter, and duff, introducing ash into the soil and creating water repellant soils. Following wildfire, rainfall on bare ground is less able to infiltrate into the fire-altered soils and overland flow is less impeded by vegetation. Rainfall runoff in recently burned areas can erode hillslopes owing to the removal of soil binding organic matter near the soil surface by fire. In channels, loose, dry-ravel deposits composed of sand and gravel are readily entrained by concentrated runoff in channels. Entrainment of soil on hillslopes and in channels bulks up the sediment concentration of the rainfall runoff to generate debris flows capable of transporting boulders and large woody debris. Post-wildfire debris flows can be triggered by rainfall conditions that would typically produce little runoff during unburned conditions. The primary rainfall trigger for post-wildfire debris flows is high intensity rainfall during short duration convective rainstorms or periods of high rainfall intensity embedded within a long-duration frontal storm. Numerous observations of debris flows triggered by storms lasting less than an hour following periods of little to no rainfall indicate that antecedent rainfall is not a requirement for initiation of post-wildfire debris flows. Post-wildfire debris-flow hazard assessment entails estimating probability and magnitude of debris flows in the burned area, estimating debris-flow runout and intensity, and defining rainfall intensity-duration thresholds for debris-flow initiation. In the United States, probability and magnitude is estimated using empirically derived models largely based on data collected in southern California. The models provide maps to identify watersheds and drainage paths where post-wildfire hazards are most pronounced. Rainfall intensity-duration thresholds can be incorporated into flood hazard forecasting tools. Currently, work is underway to identify how to best implement debris-flow runout models in burned areas with efficiency and accuracy. Post-wildfire debris flows have been a long-recognized process in the Transverse Ranges of southern California; however, climate change is driving more frequent wildfires to burn more mountainous terrain throughout the western United States and worldwide. As a result, post-wildfire debris flows are becoming a more common threat in areas where they were once infrequent. As the threat of post-wildfire debris flow expands into new areas, evaluating the hazard becomes challenging because the degree to which wildfire increases debris-flow susceptibility varies from region to region. This chapter summarizes the knowledge to date for evaluating post-wildfire debris-flow susceptibility and hazard assessment. We summarize the characteristics of wildfire burn severity, topography, underlying soil and geology, and rainfall conditions that contribute to making a watershed most likely to produce post-wildfire debris flows. Methods for hazard assessment in the United States and other countries are summarized. We highlight knowledge gaps for how post-wildfire debris-flow susceptibility varies throughout the western United States and worldwide and identify research needs to improve hazard assessment methods in different geographies.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Advances in Debris-flow Science and Practice","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-031-48691-3_11","usgsCitation":"Gartner, J., Kean, J.W., Rengers, F.K., McCoy, S., Oakley, N.S., and Sheridan, G.J., 2024, Post-wildfire debris flows, chap. of Advances in Debris-flow Science and Practice, p. 309-345, https://doi.org/10.1007/978-3-031-48691-3_11.","productDescription":"37 p.","startPage":"309","endPage":"345","ipdsId":"IP-144910","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":427315,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Gartner, Joseph","contributorId":335250,"corporation":false,"usgs":false,"family":"Gartner","given":"Joseph","affiliations":[{"id":78476,"text":"BGC Engineering","active":true,"usgs":false}],"preferred":false,"id":897864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCoy, Scott W.","contributorId":267182,"corporation":false,"usgs":false,"family":"McCoy","given":"Scott W.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":897867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oakley, Nina S.","contributorId":197885,"corporation":false,"usgs":false,"family":"Oakley","given":"Nina","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":897868,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sheridan, Gary J.","contributorId":210293,"corporation":false,"usgs":false,"family":"Sheridan","given":"Gary","email":"","middleInitial":"J.","affiliations":[{"id":13336,"text":"University of Melbourne","active":true,"usgs":false}],"preferred":false,"id":897869,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261888,"text":"70261888 - 2024 - Lahars: Origins, behavior and hazards","interactions":[],"lastModifiedDate":"2024-12-31T16:01:22.063764","indexId":"70261888","displayToPublicDate":"2024-03-29T09:58:59","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Lahars: Origins, behavior and hazards","docAbstract":"Volcanic debris flows that originate at potentially active volcanoes are called lahars. Lahars are like debris flows in non-volcanic terrain but can most notably differ in origin and size. Primary lahars occur during eruptions and may have novel origins such as turbulent mixing of hot rock moving across ice- and snow-clad volcanoes and eruptions through crater lakes. Lahars range in volume to more than a cubic kilometer (109 m3), with the biggest ones caused by huge deep-seated flank collapses of water-saturated edifice rock. Because they can be so voluminous, can have high water contents, and commonly can be clay rich, these lahars can travel tens to even hundreds of kilometers. Long transport causes evolution of flow types from flood flow to hyperconcentrated flow to debris flow. Lahars capable of traveling far downstream are commonly sufficiently liquefied that they drape valley slopes and leave behind thin deposits as they pass downstream. Only in valley bottoms are lahars likely to emplace thick deposits, and even there the deposits are apt to be much thinner than peak flow depths. Flows with long transport change character with time and distance downstream. Deposits, especially those in valley bottoms, can accrete during intervals that represent a significant proportion of the time it takes the flow to pass (typically minutes). The combination of flows changing character and their progressive accretion imposes distinctive characteristics on their deposits such as normal and inverse grading. Historically, lahars have caused thousands of fatalities and destroyed entire towns. Perhaps the most disastrous known lahar occurred in 1985 at Nevado del Ruiz in Colombia and killed more than 23,000 people. Since that disaster, an increasing awareness of lahar hazards has led to efforts to mitigate them. In recent decades, improved land-use decisions, monitoring and communication have improved hazard responses and saved many lives. Lahar hazard maps and development of lahar inundation models have helped planners and people at risk to better understand the nature of the risk owing to lahars.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Advances in debris-flow science and practice","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-031-48691-3_12","usgsCitation":"Vallance, J.W., 2024, Lahars: Origins, behavior and hazards, chap. of Advances in debris-flow science and practice, p. 347-381, https://doi.org/10.1007/978-3-031-48691-3_12.","productDescription":"35 p.","startPage":"347","endPage":"381","ipdsId":"IP-149909","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":465567,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-03-29","publicationStatus":"PW","contributors":{"editors":[{"text":"Jakob, Matthias","contributorId":82179,"corporation":false,"usgs":true,"family":"Jakob","given":"Matthias","email":"","affiliations":[],"preferred":false,"id":922171,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"McDougall, Scott","contributorId":194908,"corporation":false,"usgs":false,"family":"McDougall","given":"Scott","email":"","affiliations":[],"preferred":false,"id":922172,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Santi, Paul","contributorId":347682,"corporation":false,"usgs":false,"family":"Santi","given":"Paul","affiliations":[],"preferred":false,"id":922173,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Vallance, James W. 0000-0002-3083-5469 jvallance@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5469","contributorId":547,"corporation":false,"usgs":true,"family":"Vallance","given":"James","email":"jvallance@usgs.gov","middleInitial":"W.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":922161,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70254474,"text":"70254474 - 2024 - Numerical modeling of debris flows: A conceptual assessment","interactions":[],"lastModifiedDate":"2024-05-28T11:49:50.632421","indexId":"70254474","displayToPublicDate":"2024-03-29T06:48:23","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Numerical modeling of debris flows: A conceptual assessment","docAbstract":"Real-world hazard evaluation poses many challenges for the development and application of numerical models of debris flows. In this chapter we provide a conceptual overview of physically based, depth-averaged models designed to simulate debris-flow motion across three-dimensional terrain. When judiciously formulated and applied, these models can provide useful information about anticipated depths, speeds, and extents of debris-flow inundation as well as debris interactions with structures such as levees and dams. Depth-averaged debris-flow models can differ significantly from one another, however. Some of the greatest differences result from simulation of one-phase versus two-phase flow, use of parsimonious versus information-intensive initial and boundary conditions, use of tuning coefficients versus physically measureable parameters, application of dissimilar numerical solution techniques, and variations in computational speed and model accessibility. This overview first addresses these and related attributes of depth-averaged debris-flow models. It then describes model testing and application to hazard evaluation, with a focus on our own model, D-Claw. The overview concludes with a discussion of outstanding challenges for development of improved debris-flow models and suggestions for prospective model users.
The Arctic National Wildlife Refuge is a unique landscape in northern Alaska with limited water resources, substantial biodiversity of rare and threatened species, as well as oil and gas resources. The region has unique hydrology related to perennial springs, and the formation of large aufeis fields—sheets of ice that grow in the river channels where water reaches the surface in the winter and freezes. This work aims to update our understanding of water resources and water quality in the springs, streams, rivers, and lakes of this region, returning to sites sampled by the U.S. Geological Survey in the 1970s. We resampled eight streams, four springs, and six lakes for hydrological metrics, water quality, and macroinvertebrates, and recalculated flood-frequency metrics for rivers using updated data and modern techniques. Aufeis field melt rates were also assessed for the past several decades. Although the available data preclude trend determinations in most cases, our analysis and comparison to the historical sampling indicates an increase in dissolved ions for streams and springs, faster and earlier aufeis melt, and similar macroinvertebrate populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245008","usgsCitation":"Koch, J.C., Best, H., Baughman, C., Couvillion, C., Carey, M.P., and Conaway, J., 2024, A comparison of contemporary and historical hydrology and water quality in the foothills and coastal plain of the Arctic National Wildlife Refuge, Arctic Slope, northern Alaska: U.S. Geological Survey Scientific Investigations Report 2024–5008, 24 p., https://doi.org/10.3133/sir20245008.","productDescription":"Report: viii, 24 p.; 2 Data Releases; Correction Note","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-151990","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":499422,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116206.htm","linkFileType":{"id":5,"text":"html"}},{"id":498378,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2024/5008/correctionNote.txt","text":"Correction note","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":430506,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KK98VP","text":"USGS data release","description":"USGS data release","linkHelpText":"Rasters of observed aufeis deposits within rivers of the 1002 Area based on historical Landsat imagery, 1985-2022"},{"id":430429,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7X34VHM","text":"USGS data release","description":"USGS data release","linkHelpText":"Macroinvertebrates from streams and springs in the 1002 region of the Arctic National Wildlife Refuge, Alaska, 2021"},{"id":427077,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5008/sir20245008.XML"},{"id":427076,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5008/images"},{"id":427075,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245008/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5008"},{"id":427074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5008/sir20245008.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5008"},{"id":427073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5008/sir20245008.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.35991062435545,\n 70.47943246978403\n ],\n [\n -147.35991062435545,\n 68.94103321326239\n ],\n [\n -141.60307468685548,\n 68.94103321326239\n ],\n [\n -141.60307468685548,\n 70.47943246978403\n ],\n [\n -147.35991062435545,\n 70.47943246978403\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
There is a significant need to develop decision support tools capable of delivering accurate representations of environmental conditions, such as ground and surface water solute concentrations, in a timely and computationally efficient manner. Such tools can be leveraged to assess a large number of potential management strategies for mitigating non-point source pollutants. Here, we assess the effectiveness of the impulse-response emulation approach to approximate process-based groundwater model estimates of solute transport from MODFLOW and MT3D over a wide range of model inputs and parameters, with the goal of assessing where in parameter space the assumptions underlying this emulation approach are valid. The impulse-response emulator was developed using the sensitivity analysis utilities in the PEST++ software suite and is capable of approximating MODFLOW/MT3D estimates of solute transport over a large portion of the parameter space tested, except in cases where the Courant number is above 0.5. Across all runs tested, the highest percent errors were at the plume fronts. These results suggest that the impulse-response approach may be suitable for emulation of solute transport models for a wide range of cases, except when high-resolution outputs are needed, or when very low concentrations at plume edges are of particular interest.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13405","usgsCitation":"Heerspink, B.P., Fienen, M., and Reeves, H.W., 2024, Evaluation of an impulse-response emulator for groundwater contaminant transport modeling: Groundwater, v. 62, no. 6, p. 945-956, https://doi.org/10.1111/gwat.13405.","productDescription":"12 p.","startPage":"945","endPage":"956","ipdsId":"IP-153543","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":498231,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13405","text":"Publisher Index Page"},{"id":429517,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":435013,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13J3TCG","text":"USGS data release","linkHelpText":"Model Archive for an Impulse Response Emulator of Groundwater Contaminant Transport Models"}],"volume":"62","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Heerspink, Brent Porter 0000-0001-7591-5115","orcid":"https://orcid.org/0000-0001-7591-5115","contributorId":337146,"corporation":false,"usgs":true,"family":"Heerspink","given":"Brent","email":"","middleInitial":"Porter","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902087,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reeves, Howard W. 0000-0001-8057-2081 hwreeves@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-2081","contributorId":2307,"corporation":false,"usgs":true,"family":"Reeves","given":"Howard","email":"hwreeves@usgs.gov","middleInitial":"W.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902089,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252583,"text":"70252583 - 2024 - Performance-based earthquake early warning for tall buildings","interactions":[],"lastModifiedDate":"2024-05-07T14:38:41.09501","indexId":"70252583","displayToPublicDate":"2024-03-28T06:52:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Performance-based earthquake early warning for tall buildings","docAbstract":"Point counts (PCs) are widely used in biodiversity surveys but, despite numerous advantages, simple PCs suffer from several problems: detectability, and therefore abundance, is unknown; systematic spatiotemporal variation in detectability yields biased inferences, and unknown survey area prevents formal density estimation and scaling-up to the landscape level. We introduce integrated distance sampling (IDS) models that combine distance sampling (DS) with simple PC or detection/nondetection (DND) data to capitalize on the strengths and mitigate the weaknesses of each data type. Key to IDS models is the view of simple PC and DND data as aggregations of latent DS surveys that observe the same underlying density process. This enables the estimation of separate detection functions, along with distinct covariate effects, for all data types. Additional information from repeat or time-removal surveys, or variable survey duration, enables the separate estimation of the availability and perceptibility components of detectability with DS and PC data. IDS models reconcile spatial and temporal mismatches among data sets and solve the above-mentioned problems of simple PC and DND data. To fit IDS models, we provide JAGS code and the new “IDS()” function in the R package unmarked. Extant citizen-science data generally lack the information necessary to adjust for detection biases, but IDS models address this shortcoming, thus greatly extending the utility and reach of these data. In addition, they enable formal density estimation in hybrid designs, which efficiently combine DS with distance-free, point-based PC or DND surveys. We believe that IDS models have considerable scope in ecology, management, and monitoring.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.4292","usgsCitation":"Kery, M., Royle, A., Hallman, T., Robinson, D., Strebel, N., and Kellner, K.F., 2024, Integrated distance sampling models for simple point counts: Ecology, v. 105, no. 5, e4292, 14 p., https://doi.org/10.1002/ecy.4292.","productDescription":"e4292, 14 p.","ipdsId":"IP-147069","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":492866,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.4292","text":"Publisher Index Page"},{"id":492538,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"105","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-03-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Kery, Marc","contributorId":168361,"corporation":false,"usgs":false,"family":"Kery","given":"Marc","affiliations":[{"id":12551,"text":"Swiss Ornithological Institute, Sempach, Switzerland","active":true,"usgs":false}],"preferred":false,"id":943421,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":943422,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hallman, Tyler","contributorId":358288,"corporation":false,"usgs":false,"family":"Hallman","given":"Tyler","affiliations":[{"id":85597,"text":"Swiss Ornithological Institute; University of Charlotte; Bangor University","active":true,"usgs":false}],"preferred":false,"id":943423,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robinson, Doug","contributorId":358289,"corporation":false,"usgs":false,"family":"Robinson","given":"Doug","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":943424,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Strebel, Nicolas","contributorId":358290,"corporation":false,"usgs":false,"family":"Strebel","given":"Nicolas","affiliations":[{"id":67146,"text":"Swiss Ornithological Institute","active":true,"usgs":false}],"preferred":false,"id":943425,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kellner, Kenneth F.","contributorId":310338,"corporation":false,"usgs":false,"family":"Kellner","given":"Kenneth","email":"","middleInitial":"F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":943426,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252216,"text":"sir20245010 - 2024 - Evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes region, New York, 2019 and 2020","interactions":[],"lastModifiedDate":"2026-02-02T22:18:32.06379","indexId":"sir20245010","displayToPublicDate":"2024-03-26T10:10:00","publicationYear":"2024","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":"2024-5010","displayTitle":"Evaluation of Sensors for Continuous Monitoring of Harmful Algal Blooms in the Finger Lakes Region, New York, 2019 and 2020","title":"Evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes region, New York, 2019 and 2020","docAbstract":"In response to the increasing frequency of cyanobacterial harmful algal blooms (CyanoHABs) in the Finger Lakes region of New York State, a pilot study by the U.S. Geological Survey, in collaboration with the New York State Department of Environmental Conservation, was conducted to enhance CyanoHAB monitoring and understanding. High-frequency sensors were deployed on open water monitoring-station platforms at Seneca Lake in 2019–20, at Owasco Lake in 2019–20, and at Skaneateles Lake in 2019. One of the goals of this study was to evaluate the ability of in-place sensors to make representative measurements of dissolved organic matter, nutrients, and algal pigments (as indicators of phytoplankton biomass) while collecting routine field parameters (water temperature, specific conductance, pH, dissolved oxygen, turbidity, weather, and light) to provide additional information about environmental conditions.
Despite challenges like power issues and sensor fouling, the sensors performed well overall. However, correlation analyses between sensor readings and laboratory measurements revealed variable performance. Results indicate the relation between the fluorescent dissolved organic matter sensor and laboratory-measured dissolved organic carbon was weak at all study lakes. The nitrate sensors can be sensitive to ambient temperature and have a substantial power requirement, and the relation between sensor- and laboratory-measured nitrate values differed among lakes. The orthophosphate sensors, which were complex and prone to data loss, yielded results that were difficult to interpret because orthophosphate detections are rare in the study lakes. The multichannel fluorometer was also complex to use and required several unique procedures for its operation.
Chlorophyll measurements from the fluorometers correlated moderately well with laboratory-measured chlorophyll-a, although relations with total phytoplankton biovolume were weaker. Relations between phycocyanin concentration measurements from the dual-channel fluorometers and cyanobacterial biovolume were not significant; however, the cyanobacterial biovolume correlation was moderately strong with chlorophyll contribution from cyanobacteria measurements from the multichannel fluorometer. Of all collected parameters, water temperature was among the strongest correlated with chlorophyll-a, total phytoplankton biovolume, and cyanobacterial biovolume.
Stepwise regression analysis was used to identify the best parameters for modeling variance in laboratory measures of phytoplankton biomass. This analysis included factors such as chlorophyll fluorescence, pH, water temperature, and others, which varied by lake. Overall, the models had limited explanatory power for chlorophyll-a and other biovolumes, possibly due to the absence of CyanoHABs at the open-water monitoring locations. Multivariate models did not outperform simple fluorescence-based models. Notably, turbidity was a more significant indicator of cyanobacterial biovolume variability than phycocyanin from dual-channel fluorometers.
The study concludes that while single and multivariate models based on sensor data are useful, they did not explain any more variance than fluorescence-based models. Broader data collection, including more CyanoHAB events, is necessary to refine these models. Integrating machine learning could leverage large, complex datasets to improve CyanoHAB predictions, thereby enhancing the management and understanding of these blooms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245010","usgsCitation":"Johnston, B.D., Finkelstein, K.M., Gifford, S.R., Stouder, M.D., Nystrom, E.A., Savoy, P.R., Rosen, J.J., and Jennings, M.B., 2024, Evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes region, New York, 2019 and 2020: U.S. Geological Survey Scientific Investigations Report 2024–5010, 54 p., https://doi.org/10.3133/sir20245010.","productDescription":"Report: vii, 54 p.; 2 Data Releases","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151193","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":426806,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TP9T1D","text":"USGS data release","linkHelpText":"Phytoplankton data from Owasco, Seneca, and Skaneateles Lakes, Finger Lakes region, New York, 2019–2020 (ver. 2.1, June 2023)"},{"id":426805,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9046YOS","text":"USGS data release","linkHelpText":"Field data for an evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes, New York, 2019 and 2020"},{"id":426804,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5010/images/"},{"id":426803,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5010/sir20245010.XML"},{"id":426802,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245010/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5010 HTML"},{"id":499423,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116207.htm","linkFileType":{"id":5,"text":"html"}},{"id":426800,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5010/coverthb.jpg"},{"id":426801,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5010/sir20245010.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5010 PDF"}],"country":"United States","state":"New York","otherGeospatial":"Finger Lakes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.18392460037663,\n 43.28424086245511\n ],\n [\n -78.18392460037663,\n 42.10263922827107\n ],\n [\n -76.00088907460236,\n 42.10263922827107\n ],\n [\n -76.00088907460236,\n 43.28424086245511\n ],\n [\n -78.18392460037663,\n 43.28424086245511\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Management efforts to reduce cyanobacterial harmful algal blooms (cHABs) in the Great Lakes have focused on decreasing tributary inputs of phosphorus (P). Recent research has indicated that reduction of both P and nitrogen (N) can lessen cHABs severity. Microbially mediated N cycling in streambed sediment may reduce N riverine loads, yet little is known about in-stream N processing rates in the Maumee River Basin, a major source of nutrients to Lake Erie. During summer of 2019 and 2021, we sampled streambed sediment to measure potential nitrification and denitrification rates using the acetylene block method at 78 sites throughout the Maumee River network. We used structural equation models to identify indirect and direct drivers of denitrification. Precipitation was greater in 2019, resulting in a 67 % increase in mean discharge, 41 % of farm fields to be fallow, and a 50 % reduction in fertilizer use. During summer field surveys, median stream-water nitrate concentrations were not different between 2019 and 2021. Median denitrification rates were 13.3 mg N/m2/h and 31.2 mg N/m2/h, respectively, indicating high potential to remove N. Nitrate concentrations and nitrification rates were strong direct drivers of denitrification, especially in 2019 when coupled nitrification–denitrification sustained denitrification. Nitrate concentrations varied with land use. Notably, nitrate concentrations increased with the area of fallow land, which may indicate the presence of a legacy N source. These findings indicate that promoting streambed denitrification could reduce N loads to Lake Erie, but legacy N currently stored in the system may mask N reduction efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102284","usgsCitation":"Kreiling, R.M., Bartsch, L., Perner, P.M., Breckner, K.J., Williamson, T.N., Hood, J.M., Manning, N., and Johnson, L.T., 2024, Controls on in-stream nitrogen loss in western Lake Erie tributaries: Journal of Great Lakes Research, v. 50, no. 2, 102284, https://doi.org/10.1016/j.jglr.2024.102284.","productDescription":"102284","ipdsId":"IP-155751","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":427346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana, Michigan, Ohio","otherGeospatial":"Maumee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.1,\n 42\n ],\n [\n -85.1,\n 40.52\n ],\n [\n -83,\n 40.52\n ],\n [\n -83,\n 42\n ],\n [\n -85.1,\n 42\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kreiling, Rebecca M. 0000-0002-9295-4156","orcid":"https://orcid.org/0000-0002-9295-4156","contributorId":202193,"corporation":false,"usgs":true,"family":"Kreiling","given":"Rebecca","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartsch, Lynn A. 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":149360,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn A.","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perner, Patrik Mathis 0000-0002-6142-518X","orcid":"https://orcid.org/0000-0002-6142-518X","contributorId":261675,"corporation":false,"usgs":true,"family":"Perner","given":"Patrik","email":"","middleInitial":"Mathis","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breckner, Kenna Jean 0000-0002-8358-7825","orcid":"https://orcid.org/0000-0002-8358-7825","contributorId":301096,"corporation":false,"usgs":true,"family":"Breckner","given":"Kenna","email":"","middleInitial":"Jean","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897944,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williamson, Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hood, James M.","contributorId":267332,"corporation":false,"usgs":false,"family":"Hood","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":897946,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Manning, Nathan F.","contributorId":211818,"corporation":false,"usgs":false,"family":"Manning","given":"Nathan F.","affiliations":[],"preferred":false,"id":897947,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Laura T.","contributorId":301097,"corporation":false,"usgs":false,"family":"Johnson","given":"Laura","email":"","middleInitial":"T.","affiliations":[{"id":16990,"text":"Heidelberg University","active":true,"usgs":false}],"preferred":false,"id":897948,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70252248,"text":"sir20245011 - 2024 - A conceptual site model of contaminant transport pathways from the Bremerton Naval Complex to Sinclair Inlet, Washington, 2011–21","interactions":[],"lastModifiedDate":"2026-02-02T22:20:20.744408","indexId":"sir20245011","displayToPublicDate":"2024-03-26T05:50:08","publicationYear":"2024","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":"2024-5011","displayTitle":"A Conceptual Site Model of Contaminant Transport Pathways from the Bremerton Naval Complex to Sinclair Inlet, Washington, 2011–21","title":"A conceptual site model of contaminant transport pathways from the Bremerton Naval Complex to Sinclair Inlet, Washington, 2011–21","docAbstract":"Historical activities on the Bremerton Naval Complex (BNC) in Puget Sound, Washington, have resulted in Sinclair Inlet sediments with elevated concentrations of contaminants, including organic contaminants such as polychlorinated biphenyls and trace elements including mercury. Six U.S. Geological Survey–U.S. Navy datasets have been collected since the last major assessment, in 2013, of soil and groundwater contaminant transport pathways and mercury loading estimates from the BNC to Sinclair Inlet. These include:
The results were incorporated into an updated Conceptual Site Model and used to update contaminant load estimates from the terrestrial BNC to Sinclair Inlet. The results from these studies provide data to the U.S. Navy to support prioritization of on-going remediation actions to manage contamination on the BNC that reduce potential impacts to Sinclair Inlet sediment, surface water, and fish and shellfish tissue.
Mercury isotope analysis of surface sediments and particulate material indicated that a similar industrial mercury profile is present throughout Puget Sound, including terrestrial and marine BNC samples and in other Sinclair Inlet sediments and persists across regions with low and elevated mercury concentrations. Two sources of mercury at the BNC are Sites 1 and 2 subsurface soils/fill material, with total mercury concentrations in particulates collected from the bottom of monitoring wells drilled in these materials ranging from 18,000 to 44,000 nanograms per gram (as compared to the Washington State Marine Sediment Cleanup Screening Level of 590 nanograms per gram).
Contaminants are transported from the terrestrial BNC to Sinclair Inlet via three primary pathways, (1) stormwater outfalls, (2) dry dock discharges, and (3) direct discharge along unwalled shorelines.
Previous loading estimates (based on filtered total mercury) ranked stormwater outfalls, particularly outfall PSNS015 in Site 2 soils, as the largest soil and groundwater contaminant transport pathway from the terrestrial BNC to Sinclair Inlet. Updated loading estimates in this report suggest that the dry dock systems may be a larger pathway of mercury from the terrestrial BNC to Sinclair Inlet than previously thought, within the same order of magnitude as the PSNS015 storm-drain system.
Trace-element loads via direct shoreline discharge are difficult to estimate due to the large and dynamic tidal mixing zone of groundwater and seawater in the nearshore along unwalled shorelines. However, current best estimated ranges suggest that direct shoreline discharge is one of the three main pathways and may contribute smaller mercury loads than the stormwater and the dry dock systems. Along unwalled shorelines, direct groundwater discharge of terrestrial contaminants may be less important than recirculating seawater in the nearshore mixing zone that can extract contaminants from nearshore subsurface material. Total estimated mercury loads from the terrestrial BNC to Sinclair Inlet range from approximately 40 to 200 grams of filtered total mercury per year and a minimum of 70–350 grams of particulate total mercury per year, for a minimum total of 110–525 grams of whole (filtered plus particulate) total mercury per year. Data gaps are identified that, if filled, would further refine the Conceptual Site Model and contaminant loading estimates from the terrestrial BNC to Sinclair Inlet.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245011","collaboration":"Prepared in cooperation with U.S. Department of the Navy","usgsCitation":"Conn, K.E., Janssen, S.E., Opatz, C.C., and Bright, V.A.L., 2024, A conceptual site model of contaminant transport pathways from the Bremerton Naval Complex to Sinclair Inlet, Washington, 2011–21 (ver. 1.1): U.S. Geological Survey Scientific Investigations Report 2024–5011, 111 p., https://doi.org/10.3133/sir20245011.","productDescription":"Report: x, 111 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-142440","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499424,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116205.htm","linkFileType":{"id":5,"text":"html"}},{"id":426852,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5011/sir20245011.XML"},{"id":426851,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5011/images"},{"id":427851,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2024/5011/VersionHistory.txt","description":"Version History"},{"id":426850,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K9P8G2","text":"USGS data release","description":"USGS data release","linkHelpText":"Particulate mercury isotope results, fiber optic thermal survey data, and nearshore surface sediment results at the Bremerton Naval Complex, Washington, USA, 2020-21"},{"id":426849,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94FCYGV","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-NWT model to simulate the groundwater flow system at Puget Sound Naval Shipyard, Naval Base Kitsap, Bremerton, Washington"},{"id":426848,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245011/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5011"},{"id":426847,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5011/sir20245011.pdf","text":"Report","size":"36.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5011"},{"id":426846,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5011/sir20245011.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Sinclair Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.75191725882988,\n 47.580405375032115\n ],\n [\n -122.75191725882988,\n 47.50997977336348\n ],\n [\n -122.60639127716968,\n 47.50997977336348\n ],\n [\n -122.60639127716968,\n 47.580405375032115\n ],\n [\n -122.75191725882988,\n 47.580405375032115\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The U.S. Geological Survey (USGS), in cooperation with the National Park Service, investigated groundwater gains and losses on the upper White River within Mount Rainier National Park in Washington. This investigation was conducted using stream discharge measurements at 14 locations within 7 reaches over a 6.5-mile river length from near the White River’s origin at the terminus of the Emmons Glacier on Mount Rainier to the White River Entrance near the northeast boundary of Mount Rainier National Park. Locations selected for the stream discharge measurements were on the main channel of the White River and on tributary streams near their confluence with the White River.
A soil-water-balance (SWB) model analysis was also performed on the White River basin to estimate groundwater recharge throughout the basin during the time of the study. Analyses were made for the White River basin at the sub-basin (zone) scale to determine groundwater input to the stream for individual stream reaches. The gridded SWB model was simulated at a 10-meter (m) horizontal resolution, where recharge simulations were constructed using five spatially distributed datasets. Daily climate data as input for the simulation included gridded daily precipitation and air temperature.
Upon analysis of the seepage run results, three of the seven reaches showed groundwater gains in this study. The SWB model results were used in conjunction with the baseflow gain totals in the reaches to estimate the length of time for recharge to become base flow. Further analysis estimated the rates of groundwater flow in the zones with adjacent gaining reaches. A streamflow gain curve was created from a simple flow model for each of the zones to relate the recharge from the zones to the adjacent reaches on the White River and tributaries. The fit of the streamflow gain curve to the calculated streamflow gain during the seepage run was used to analyze where the recharge from each zone resulted as streamflow gain. Consecutive reach losses from zones D and L were immediately followed downstream by a relatively large gain in zone GH, indicating that the gain in the reach adjacent to zone GH could be from the recharge in zones D and L.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245015","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fuhrig, L.T., Long, A.J., and Headman, A.O., 2024, Evaluation of groundwater resources in the Upper White River Basin within Mount Rainier National Park, Washington State, 2020 (ver. 1.1, March 2024): U.S. Geological Survey Scientific Investigations Report 2024–5015, 19 p., https://doi.org/10.3133/sir20245015.","productDescription":"Report: vi, 19 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-148848","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":499425,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116204.htm","linkFileType":{"id":5,"text":"html"}},{"id":426941,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5015/sir20245015.XML"},{"id":426940,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5015/images"},{"id":426939,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KI310W","text":"USGS data release","description":"USGS data release","linkHelpText":"Soil water balance model of the White River basin, Mount Rainier National Park, Washington, USA"},{"id":427249,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2024/5015/versionHistory.txt"},{"id":426938,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245015/full","linkFileType":{"id":5,"text":"html"}},{"id":426937,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5015/sir20245015.pdf","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5015"},{"id":426936,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5015/sir20245015.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.66827303334932,\n 47.34261069492973\n ],\n [\n -122.66827303334932,\n 46.07710849497087\n ],\n [\n -120.72369295522444,\n 46.07710849497087\n ],\n [\n -120.72369295522444,\n 47.34261069492973\n ],\n [\n -122.66827303334932,\n 47.34261069492973\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: March 25, 2024; Version 1.1: March 29, 2024","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Intermediate sized earthquakes (≈M4–6.5) are often measured using the teleseismic body‐wave magnitude (\uD835\uDC5Ab). \uD835\uDC5Ab measurements are especially critical at the lower end of this range when teleseismic waveform modeling techniques (i.e., moment tensor analysis) are difficult. The U.S. Geological Survey National Earthquake Information Center (NEIC) determines the location and magnitude of all M 5 and greater earthquakes worldwide within 20 min of the rupture time, and therefore accurate \uD835\uDC5Ab magnitude estimates are essential to fulfill its mission. To better understand how network geometry and noise levels affect the global response capabilities, we developed a method to spatially estimate the minimum measurable \uD835\uDC5Ab. To do this, we compare expected \uD835\uDC5Ab amplitudes at every station to the station’s background noise level. We find that using NEIC’s current network geometry and these idealized thresholds, NEIC can potentially estimate \uD835\uDC5Ab magnitudes down to M 4.5 globally. Low‐latitude regions in the Southern Hemisphere present the biggest opportunity to improve monitoring capabilities. However, logistically they also present the biggest hurdles for network operators. Finally, to test the resiliency of the network we removed the 20 most important stations and found the \uD835\uDC5Ab threshold remains \uD835\uDC5Ab 4.5. However, the region where only \uD835\uDC5Ab 4.5 and greater can be estimated increases and is again restricted to the Southern Hemisphere.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230246","usgsCitation":"Ringler, A.T., Wilson, D.C., Earle, P.S., Yeck, W.L., Mason, D.B., and Wilgus, J., 2024, Noise constraints on global body‐wave measurement thresholds: Bulletin of the Seismological Society of America, v. 114, no. 4, p. 1765-1776, https://doi.org/10.1785/0120230246.","productDescription":"13 p.","startPage":"1765","endPage":"1776","ipdsId":"IP-160503","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":427200,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"114","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897439,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897440,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897441,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897442,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mason, David B. 0000-0003-0313-3370 dmason@usgs.gov","orcid":"https://orcid.org/0000-0003-0313-3370","contributorId":265781,"corporation":false,"usgs":true,"family":"Mason","given":"David","email":"dmason@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897443,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilgus, Justin T.","contributorId":206263,"corporation":false,"usgs":false,"family":"Wilgus","given":"Justin T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":897444,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269056,"text":"70269056 - 2024 - Limited evidence of late Quaternary tectonic surface deformation in the eastern Tennessee seismic zone, USA","interactions":[],"lastModifiedDate":"2025-07-15T15:24:44.875998","indexId":"70269056","displayToPublicDate":"2024-03-25T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Limited evidence of late Quaternary tectonic surface deformation in the eastern Tennessee seismic zone, USA","docAbstract":"The ~300-km-long eastern Tennessee seismic zone (ETSZ), USA, is the second-most seismically active region east of the Rocky Mountains. Seismicity generally occurs below the Paleozoic fold-and-thrust belt within the Mesoproterozoic basement, at depths of 5–26 km, and earthquake magnitudes during the instrumental record have been moment magnitude (Mw)≤4.8. Evidence of surface deformation may not exist or be difficult to detect because of the vegetated and soil-mantled landscape, landslides, locally steep topography, anthropogenic landscape modification, or long, irregular recurrence intervals between surface-rupturing earthquakes. Despite the deep seismicity, analog models indicate that accumulation of strike-slip or oblique-slip displacement at depth could be expected to propagate upward through the Paleozoic section, producing a detectable surficial signal of distributed faulting. To identify potential surface deformation, we interrogated the landscape at different spatial scales. We evaluated morphotectonic and channel metrics, such as channel sinuosity and catchment-scale hypsometry. Additionally, we mapped possible fault-related topographic features on 1-m lidar. Finally, we integrated our observations with available bedrock and Quaternary surficial mapping and subsurface geophysical data. At a regional scale, most morphotectonic and channel metrics have a strong lithologic control. Within smaller regions of similar lithology, we observe changes in landscape metrics like channel sinuosity and catchment-scale hypsometry that spatially correlate with new lineaments identified in this study and previously mapped east–west Cenozoic faults. These faults have apparent left-lateral offsets, are optimally oriented to slip in the current stress field, and match kinematics from recent focal mechanisms, but do not clearly preserve evidence of late Pleistocene or Holocene tectonic surface deformation. Most newly mapped lineaments might be explained by either tectonic or non-tectonic origins, such as fluvial or karst processes. We also re-evaluated a previously described paleoseismic site and interpret that the exposure does not record evidence of late Pleistocene faulting but instead is explained by fluvial stratigraphy.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230094","usgsCitation":"Jobe, J.A., Briggs, R.W., Gold, R.D., Bauer, L., and Collett, C., 2024, Limited evidence of late Quaternary tectonic surface deformation in the eastern Tennessee seismic zone, USA: Bulletin of the Seismological Society of America, v. 114, no. 4, p. 1920-1940, https://doi.org/10.1785/0120230094.","productDescription":"21 p.","startPage":"1920","endPage":"1940","ipdsId":"IP-154193","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":492246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"eastern Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.17028696488822,\n 36.643021837336434\n ],\n [\n -86.17028696488822,\n 35.0924378490018\n ],\n [\n -82.53465655915278,\n 35.0924378490018\n ],\n [\n -82.53465655915278,\n 36.643021837336434\n ],\n [\n -86.17028696488822,\n 36.643021837336434\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"114","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Jobe, Jessica Ann Thompson 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Jobe","given":"Jessica","email":"","middleInitial":"Ann Thompson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943170,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bauer, Laurel","contributorId":266056,"corporation":false,"usgs":false,"family":"Bauer","given":"Laurel","email":"","affiliations":[{"id":54872,"text":"Office of Nuclear Reactor Regulation, U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":943171,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collett, Camille 0000-0003-4836-0243","orcid":"https://orcid.org/0000-0003-4836-0243","contributorId":310393,"corporation":false,"usgs":false,"family":"Collett","given":"Camille","affiliations":[{"id":67175,"text":"Formerly: U.S. Geological Survey, Geologic Hazards Science Center","active":true,"usgs":false}],"preferred":false,"id":943172,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70253018,"text":"70253018 - 2024 - Fair graph learning using constraint-aware priority adjustment and graph masking in river networks","interactions":[],"lastModifiedDate":"2024-04-16T16:15:32.996155","indexId":"70253018","displayToPublicDate":"2024-03-24T11:08:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10143,"text":"Proceedings of the AAAI Conference on Artificial Intelligence","active":true,"publicationSubtype":{"id":10}},"title":"Fair graph learning using constraint-aware priority adjustment and graph masking in river networks","docAbstract":"Accurate prediction of water quality and quantity is crucial for sustainable development and human well-being. However, existing data-driven methods often suffer from spatial biases in model performance due to heterogeneous data, limited observations, and noisy sensor data. To overcome these challenges, we propose Fair-Graph, a novel graph-based recurrent neural network that leverages interrelated knowledge from multiple rivers to predict water flow and temperature within large-scale stream networks. Additionally, we introduce node-specific graph masks for information aggregation and adaptation to enhance prediction over heterogeneous river segments. To reduce performance disparities across river segments, we introduce a centralized coordination strategy that adjusts training priorities for segments. We evaluate the prediction of water temperature within the Delaware River Basin, and the prediction of streamflow using simulated data from U.S. National Water Model in the Houston River network. The results showcase improvements in predictive performance and highlight the proposed model's ability to maintain spatial fairness over different river segments.
","language":"English","publisher":"Association for the Advancement of Artificial Intelligence","doi":"10.1609/aaai.v38i20.30212","usgsCitation":"He, E., Xie, Y., Sun, A.Y., Zwart, J.A., Yang, J., Jin, Z., Wang, Y., Karimi, H.A., and Jia, X., 2024, Fair graph learning using constraint-aware priority adjustment and graph masking in river networks: Proceedings of the AAAI Conference on Artificial Intelligence, v. 38, no. 20, p. 22087-22095, https://doi.org/10.1609/aaai.v38i20.30212.","productDescription":"9 p.","startPage":"22087","endPage":"22095","ipdsId":"IP-158367","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":440050,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1609/aaai.v38i20.30212","text":"Publisher Index Page"},{"id":427821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.19185307052085,\n 38.71562965387949\n ],\n [\n -74.4374506373952,\n 38.7070541171185\n ],\n [\n -74.13043021159795,\n 41.597382404326765\n ],\n [\n -75.720357416618,\n 41.63871233834436\n ],\n [\n -76.19185307052085,\n 38.71562965387949\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","issue":"20","noUsgsAuthors":false,"publicationDate":"2024-03-24","publicationStatus":"PW","contributors":{"authors":[{"text":"He, Erhu","contributorId":329980,"corporation":false,"usgs":false,"family":"He","given":"Erhu","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":898944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xie, Yiqun","contributorId":297447,"corporation":false,"usgs":false,"family":"Xie","given":"Yiqun","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":898945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sun, Alexander Y. 0000-0002-6365-8526","orcid":"https://orcid.org/0000-0002-6365-8526","contributorId":302987,"corporation":false,"usgs":false,"family":"Sun","given":"Alexander","email":"","middleInitial":"Y.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":898946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":898947,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yang, Jie","contributorId":335648,"corporation":false,"usgs":false,"family":"Yang","given":"Jie","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":898948,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jin, Zhenong","contributorId":297865,"corporation":false,"usgs":false,"family":"Jin","given":"Zhenong","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":898949,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Yang","contributorId":173071,"corporation":false,"usgs":false,"family":"Wang","given":"Yang","email":"","affiliations":[],"preferred":false,"id":898950,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Karimi, Hassan Ali","contributorId":335649,"corporation":false,"usgs":false,"family":"Karimi","given":"Hassan","email":"","middleInitial":"Ali","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":898951,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":898952,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70253890,"text":"70253890 - 2024 - Association of water arsenic with incident diabetes in U.S. adults: The Multi-Ethnic Study of Atherosclerosis and The Strong Heart Study","interactions":[],"lastModifiedDate":"2024-07-01T14:43:11.240846","indexId":"70253890","displayToPublicDate":"2024-03-24T10:11:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17625,"text":"Diabetes Care","active":true,"publicationSubtype":{"id":10}},"title":"Association of water arsenic with incident diabetes in U.S. adults: The Multi-Ethnic Study of Atherosclerosis and The Strong Heart Study","docAbstract":"We examined the association of arsenic in federally regulated community water systems (CWSs) and unregulated private wells with type 2 diabetes (T2D) incidence in the Strong Heart Family Study (SHFS), a prospective study of American Indian communities, and the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of racially and ethnically diverse urban U.S. communities.
We evaluated 1,791 participants from SHFS and 5,777 participants from MESA who had water arsenic estimates available and were free of T2D at baseline (2001–2003 and 2000–2002, respectively). Participants were followed for incident T2D until 2010 (SHFS cohort) or 2019 (MESA cohort). We used Cox proportional hazards mixed-effects models to account for clustering by family and residential zip code, with adjustment for sex, baseline age, BMI, smoking status, and education.
T2D incidence was 24.4 cases per 1,000 person-years (mean follow-up, 5.6 years) in SHFS and 11.2 per 1,000 person-years (mean follow-up, 14.0 years) in MESA. In a meta-analysis across the SHFS and MESA cohorts, the hazard ratio (95% CI) per doubling in CWS arsenic was 1.10 (1.02, 1.18). The corresponding hazard ratio was 1.09 (0.95, 1.26) in the SHFS group and 1.10 (1.01, 1.20) in the MESA group. The corresponding hazard ratio (95% CI) for arsenic in private wells and incident T2D in SHFS was 1.05 (0.95, 1.16). We observed statistical interaction and larger magnitude hazard ratios for participants with BMI <25 kg/m2 and female participants.
Low to moderate water arsenic levels (<10 µg/L) were associated with T2D incidence in the SHFS and MESA cohorts.
","language":"English","publisher":"American Diabetes Association","doi":"10.2337/dc23-2231","usgsCitation":"Spaur, M., Galvez-Fernandez, M., Chen, Q., Lombard, M.A., Bostick, B., Factor-Litvak, P., Fretts, A., Shea, S., Navas-Acien, A., and Nigra, A., 2024, Association of water arsenic with incident diabetes in U.S. adults: The Multi-Ethnic Study of Atherosclerosis and The Strong Heart Study: Diabetes Care, v. 47, no. 7, p. 1143-1151, https://doi.org/10.2337/dc23-2231.","productDescription":"9 p.","startPage":"1143","endPage":"1151","ipdsId":"IP-159826","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":440052,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.2337/dc23-2231","text":"External Repository"},{"id":428359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Spaur, Maya","contributorId":257947,"corporation":false,"usgs":false,"family":"Spaur","given":"Maya","email":"","affiliations":[{"id":52179,"text":"Columbia University Mailman School of Public Health","active":true,"usgs":false}],"preferred":false,"id":900006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Galvez-Fernandez, Marta","contributorId":336125,"corporation":false,"usgs":false,"family":"Galvez-Fernandez","given":"Marta","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":900007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chen, Qixuan","contributorId":318224,"corporation":false,"usgs":false,"family":"Chen","given":"Qixuan","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":900008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lombard, Melissa A. 0000-0001-5924-6556 mlombard@usgs.gov","orcid":"https://orcid.org/0000-0001-5924-6556","contributorId":198254,"corporation":false,"usgs":true,"family":"Lombard","given":"Melissa","email":"mlombard@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - 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2024 - Plant-derived products selectively suppress growth of the harmful alga Prymnesium parvum","interactions":[],"lastModifiedDate":"2024-08-22T16:38:45.585477","indexId":"70256586","displayToPublicDate":"2024-03-23T11:38:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Plant-derived products selectively suppress growth of the harmful alga Prymnesium parvum","title":"Plant-derived products selectively suppress growth of the harmful alga Prymnesium parvum","docAbstract":"Prymnesium parvum is a harmful alga found in brackish waters worldwide whose toxins can be lethal to aquatic organisms. Established field methods to control blooms of this species, however, are unavailable. Earlier studies showed that various extracts of giant reed (Arundo donax) can suppress P. parvum growth and that ellipticine, an allelochemical present in giant reed, is a potent algicide against this species. The unintended effects of giant reed products on nontarget organisms, however, are not fully understood. This study determined the effects of giant reed leachate (aqueous extract of dried chips) and ellipticine on growth of P. parvum and the green microalga Chlorella sorokiniana; survival and reproduction of the planktonic crustacean Daphnia pulex; and hatching success, larval survival, and larval swimming behavior of the teleost fish Danio rerio. Leachate made with 3 g chips L−1 was lethally toxic to P. parvum and D. pulex, stimulated C. sorokiniana growth, and impaired D. rerio behavior. Leachate at 1 g L−1 fully suppressed P. parvum growth, had moderate effects on D. pulex reproductive output, and had no effects on D. rerio. Ellipticine at 0.01 mg L−1 irreversibly inhibited P. parvum growth, acutely but reversibly inhibited C. sorokiniana growth, slightly delayed D. pulex reproduction, and had no effects on D. rerio. These observations suggest that when applied at appropriate concentrations, natural products derived from giant reed can be used as tools to specifically control P. parvum growth with minimal effects on nontarget species.
","language":"English","publisher":"MDPI","doi":"10.3390/w16070930","usgsCitation":"Mary, M.A., Tabora-Sarmiento, S., Nash, S., Mayer, G.D., Crago, J., and Patino, R., 2024, Plant-derived products selectively suppress growth of the harmful alga Prymnesium parvum: Water, v. 16, no. 7, 930, 12 p., https://doi.org/10.3390/w16070930.","productDescription":"930, 12 p.","ipdsId":"IP-159702","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":440054,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w16070930","text":"Publisher Index Page"},{"id":433072,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Mary, Mousumi A.","contributorId":341256,"corporation":false,"usgs":false,"family":"Mary","given":"Mousumi","email":"","middleInitial":"A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tabora-Sarmiento, Shisbeth","contributorId":341257,"corporation":false,"usgs":false,"family":"Tabora-Sarmiento","given":"Shisbeth","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nash, Sarah","contributorId":341258,"corporation":false,"usgs":false,"family":"Nash","given":"Sarah","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908153,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mayer, Gregory D.","contributorId":172783,"corporation":false,"usgs":false,"family":"Mayer","given":"Gregory","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":908154,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crago, Jordan","contributorId":341260,"corporation":false,"usgs":false,"family":"Crago","given":"Jordan","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908155,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908156,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261324,"text":"70261324 - 2024 - Exploring and integrating differences in niche characteristics across regional and global scales to better understand plant invasions in Hawaiʻi","interactions":[],"lastModifiedDate":"2024-12-05T15:54:04.297803","indexId":"70261324","displayToPublicDate":"2024-03-23T09:50:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Exploring and integrating differences in niche characteristics across regional and global scales to better understand plant invasions in Hawaiʻi","docAbstract":"The spread of ecosystem modifying invasive plant (EMIP) species is one of the largest threats to native ecosystems in Hawaiʻi. However, differences in niche characteristics between Hawaiʻi’s isolated insular environment and the wider global distribution of these species have not been carefully examined. We used species distribution modeling (SDM) methods to assess similarities and differences in niche characteristics between global and regional scales for 17 EMIPs present in Hawaiʻi. With a clearer understanding of the global context of regional plant invasion, we combined two SDM methods to better understand the potential future regional spread: (1) a nested modeling approach to integrate global and regional invasive species distribution projections; and (2) integrating all available agency and citizen science data to minimize the effect of monitoring gaps and biases. Our results show there are multiple similarities in niche characteristics across regional and global scales for most species, such as similar sets of climatic determinants of distribution, similar responses along environmental gradients, and moderate to high niche overlap between global and regional models. However, some differences were apparent and likely due to several factors including incomplete regional spread, community assembly or diversity effects. Invaders that established earlier showed a higher degree of niche overlap and similar environmental gradient responses when comparing global and regional models. This pattern, coupled with the tendency for regionally-based projections to predict narrower distributions than global projections, indicates a potential for continued spread of several invasive species across the Hawaiian landscape. Our study has broader implications for understanding the distribution and spread of invasive species in other regions, as similar analyses and models, including a novel way to characterize environmental gradient response differences across regions or scales, can likely provide valuable information for conservation and management efforts.
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Habitat of Nevada and Northeastern California—Integrating Space Use, Habitat Selection, and Survival Indices to Guide Areas for Habitat Management","title":"Greater sage-grouse habitat of Nevada and northeastern California—Integrating space use, habitat selection, and survival indices to guide areas for habitat management","docAbstract":"Greater sage-grouse populations (Centrocercus urophasianus; hereafter sage-grouse) are threatened by a suite of disturbances and anthropogenic factors that have contributed to a net loss of sagebrush-dominant shrub cover in recent decades. Declines in sage-grouse populations are largely linked to habitat loss across their range. A key component of conservation and land use planning efforts for sage-grouse involves the continued monitoring and modeling of habitat requirements and suitability across its range. The Bureau of Land Management (BLM) is addressing the management of sage-grouse habitats on BLM-authorized public lands throughout the western United States through a land use planning amendment and associated environmental impact statement (86 FR 66331). More than 25 percent of the range-wide distribution of sage-grouse is within Nevada and northeastern California, and information on sage-grouse distribution and habitat requirements is important to guide appropriate management decisions. Therefore, the BLM has identified the need for updated spatially explicit information on sage-grouse habitat in Nevada and northeastern California to guide the land use planning amendment and associated management decisions.
To address this need, researchers with the U.S. Geological Survey, in close cooperation with multiple State and Federal resource agency partners, including BLM, Nevada Department of Wildlife (NDOW) and California Department of Fish and Wildlife (CDFW), sought to map sage-grouse distribution and produce example habitat designations in these states. Herein, we report results of our primary study objective, which was to map sage-grouse habitat and create example habitat management areas, based on more than a decade of location and survival data collected from marked sage-grouse across the study region coupled with lek count survey data managed by the NDOW and the CDFW.
We expanded on previously developed methodology to incorporate information on habitat selection and survival during reproductive life stages and specific seasons with updated sage-grouse location and known fate datasets, while also including brood-rearing areas that are understood to be threatened and important for population persistence. We combined predictive habitat map surfaces for each life stage and season with updated information on current occupancy patterns to classify habitat based on its suitability and probability of occupancy. We carried out additional steps to delineate specific example habitat management areas, specifically (1) incorporated corridors connecting key nesting and brood-rearing habitat, (2) corrected outputs for pre-wildfire habitat conditions within areas burned in the last 16 years, and (3) masked out areas of anthropogenic development. Our methodological example of deriving habitat management areas was intended to help inform decisions by BLM and other land managers regarding conservation and management of sage-grouse. Associated data products in the form of habitat maps provide updated, detailed, and comprehensive information about the status of habitats and can be useful to partner agencies in their efforts to designate and rank habitats for this species of high conservation concern in Nevada and California, with full recognition that on-the-ground field data and local sources of information and expertise should be used in conjunction with inferences from these models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241018","collaboration":"Prepared in cooperation with the Bureau of Land Management, Nevada Department of Wildlife, and California Department of Fish and Wildlife","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Milligan, M.C., Coates, P.S., O’Neil, S.T., Brussee, B.E., Chenaille, M.P., Friend, D., Steele, K., Small, J.R., Bowden, T.S., Kosic, A.D., and Miller, K., 2024, Greater sage-grouse habitat of Nevada and northeastern California—Integrating space use, habitat selection, and survival indices to guide areas for habitat management: U.S. Geological Survey Open-File Report 2024–1018, 70 p., https://doi.org/10.3133/ofr20241018.","productDescription":"Report: viii, 70 p.: Data Release","numberOfPages":"70","onlineOnly":"Y","ipdsId":"IP-157608","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":427111,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241018/full"},{"id":426917,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P933VE6W","text":"USGS Data Release","description":"Coates, P.S., Milligan, M.C., O’Neil, S.T., Brussee, B.E., and Chenaille, M.P., 2024, Rasters representing Greater sage-grouse space use, habitat selection, and survival to inform habitat management: U.S. Geological Survey data release, https://doi.org/10.5066/P933VE6W.","linkHelpText":"Rasters representing Greater sage-grouse space use, habitat selection, and survival to inform habitat management"},{"id":426916,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1018/images"},{"id":426914,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1018/ofr20241018.xml"},{"id":426913,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1018/ofr20241018.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426912,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1018/covrthb.jpg"}],"country":"United States","state":"California, Idaho, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114,\n 43\n ],\n [\n -121,\n 43\n ],\n [\n -121,\n 38\n ],\n [\n -114,\n 38\n ],\n [\n -114,\n 43\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
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The Parkfield transitional segment of the San Andreas Fault (SAF) is characterized by the production of frequent quasi-periodical M6 events that break the very same asperity. The last Parkfield mainshock occurred on 28 September 2004, 38 years after the 1966 earthquake, and after the segment showed a ∼22 years average recurrence time. The main reason for the much longer interevent period between the last two earthquakes is thought to be the reduction of the Coulomb stress from the M6.5 Coalinga earthquake of 2 May 1983, and the M6 Nuñez events of June 11th and 22 July 1983. Plausibly, the transitional segment of the SAF at Parkfield is now in the late part of its seismic cycle and current observations may all be relative to a state of stress close to criticality. However, the behavior of the attenuation parameter in the last few years seems substantially different from the one that characterized the years prior to the 2004 mainshock. A few questions arise: (i) Does a detectable preparation phase for the Parkfield mainshocks exist, and is it the same for all events? (ii) How dynamically/kinematically similar are the quasi-periodic occurrences of the Parkfield mainshocks? (iii) Are some dynamic/kinematic characteristics of the next mainshock predictable from the analysis of current data? (e.g., do we expect the epicenter of the next failure to be co-located to that of 2004?) (iv) Should we expect the duration of the current interseismic period to be close to the 22-year “undisturbed” average value? We respond to the questions listed above by analyzing the non-geometric attenuation of direct S-waves along the transitional segment of the SAF at Parkfield, in the close vicinity of the fault plane, between January 2001 and November 2023. Of particular interest is the preparatory behavior of the attenuation parameter as the 2004 mainshock approached, on both sides of the SAF. We also show that the non-volcanic tremor activity modulates the seismic attenuation in the area, and possibly the seismicity along the Parkfield fault segment, including the occurrence of the mainshocks.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2024.1349425","usgsCitation":"Malagnini, L., Nadeau, R., and Parsons, T.E., 2024, Seismic attenuation and stress on the San Andreas Fault at Parkfield: Are we critical yet?: Frontiers in Earth Science, v. 12, 1349425; 16 p., https://doi.org/10.3389/feart.2024.1349425.","productDescription":"1349425; 16 p.","ipdsId":"IP-161211","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":440062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3389/feart.2024.1349425","text":"Publisher Index Page"},{"id":426966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Parkfield","otherGeospatial":"Sand Andreas Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.8,\n 36.2\n ],\n [\n -120.8,\n 35.7\n ],\n [\n -120.2,\n 35.7\n ],\n [\n -120.2,\n 36.2\n ],\n [\n -120.8,\n 36.2\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Malagnini, Luca 0000-0001-5809-9945","orcid":"https://orcid.org/0000-0001-5809-9945","contributorId":245308,"corporation":false,"usgs":false,"family":"Malagnini","given":"Luca","email":"","affiliations":[{"id":5113,"text":"INGV","active":true,"usgs":false}],"preferred":false,"id":897204,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nadeau, Robert M. 0000-0003-1255-0643","orcid":"https://orcid.org/0000-0003-1255-0643","contributorId":264609,"corporation":false,"usgs":false,"family":"Nadeau","given":"Robert M.","affiliations":[{"id":54514,"text":"Berkeley Seismological Laboratory, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":897205,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":897206,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}