Geophysical investigations of Mountain Pass and vicinity were begun as part of an effort to study regional crustal structures as an aid to understanding the geologic framework and mineral resources of the eastern Mojave Desert. The study area encompasses Mountain Pass, host to one of the world’s largest rare earth element carbonatite deposits. The deposit is found along a north-northwest-trending, fault-bounded block that extends along the eastern parts of the Clark Mountain Range, Mescal Range, and Ivanpah Mountains. This Paleoproterozoic block is composed of a 1.7-Ga metamorphic complex of gneiss and schist that underwent widespread metamorphism and associated plutonism during the Ivanpah orogeny. The Paleoproterozoic rocks were intruded by a Mesoproterozoic (1.4 Ga) ultrapotassic alkaline intrusive suite and carbonatite body. The intrusive rocks include, from oldest to youngest, shonkinite, mesosyenite, syenite, quartz syenite, potassic granite, carbonatite, carbonatite dikes, and late shonkinite dikes.
The diverse physical properties of rocks that underlie the study area are well suited to geophysical investigations. Contrasts in radiogenic signatures between Paleoproterozoic crystalline basement, rocks of the Mesoproterozoic carbonatite body and the associated alkaline intrusive suite, Paleozoic carbonate rocks, Mesozoic granitoids, Tertiary volcanic rocks, and unconsolidated alluvium, for example, produce a distinctive pattern of radiometric anomalies that can aid in understanding the geologic framework and mineral resource potential of the eastern Mojave Desert.
A high-resolution radiometric survey of Mountain Pass was flown by helicopter over parts of the Clark Mountain Range, Mescal Range, and Ivanpah Mountains. Aeroradiometric surveys measure the intensity and energy spectrum of gamma-ray radiation from the three most common naturally occurring radioelements: potassium (40K), thorium (232Th), and uranium (238U). For 232Th and 238U, the source of the gamma-rays comes from their thallium (208Tl) and bismuth (214Bi) decay products, respectively, and, thus, concentrations for Th and U are referred to as “equivalent concentration,” assuming radioactive equilibrium. The concentrations of these radioelements can be used together to estimate changes in geochemistry and lithology.
Carbonatite deposits typically have distinctive geophysical signatures because they are relatively dense, magnetic, and radiogenic. Specifically, the carbonatite and alkaline intrusive suite at Mountain Pass is ultrapotassic and contains relatively significant amounts of K, Th, and U, which can be delineated using airborne radiometric surveys.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3412C","usgsCitation":"Ponce, D.A., and Denton, K.M. (D.A. Ponce, ed.), 2019, Airborne radiometric maps of Mountain Pass, California: U.S. Geological Survey Scientific Investigations Map 3412–C, scale 1:62,500, https://doi.org/10.3133/sim3412C.","productDescription":"1 Plate: 39 x 32 inches; Data Release","numberOfPages":"1","onlineOnly":"Y","ipdsId":"IP-100413","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":366599,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ENLS6D","linkHelpText":"High-resolution airborne radiometric survey of Mountain Pass, California"},{"id":366597,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3412/c/coverthb.jpg"},{"id":366598,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3412/c/sim3412C_.pdf","text":"Report","size":"3 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Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Changing Arctic climate may alter freshwater ecosystems as a result of warmer surface waters, longer open-water periods, reduced wintertime lake ice growth, and altered hydrologic connectivity. This study aims to characterize zooplankton community composition and size structure in the context of hydrologic connectivity and ice regimes in Arctic lakes. Between 2011 and 2016, we sampled the phytoplankton, zooplankton, and fish communities from a set of representative lakes on the Arctic Coastal Plain (ACP) of northern Alaska to determine potential food web responses to changing Arctic ecosystems. Multivariate analyses showed that time from ice-out had a strong influence on zooplankton community structure and that seasonal succession of zooplankton differed between lakes with varying hydrologic connectivity. Trends were observed suggesting that large-bodied zooplankton (Daphnia, calanoid copepods) may be more prevalent in poorly connected lakes with low fish diversity. Large-bodied zooplankton displayed higher biomass in lakes with high occurrences of bedfast ice, while small-bodied zooplankton (Bosmina, rotifers) displayed highest biomass in deeper lakes with low occurrences of bedfast ice. Our results contribute to limited knowledge of zooplankton in remote lakes of the ACP and suggest that the anticipated changes to aquatic ecosystems in the Arctic may include energetically less efficient plankton food webs.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/15230430.2019.1643210","usgsCitation":"Beaver, J.R., Arp, C.D., Tausz, C.E., Jones, B.M., Whitman, M.S., Renicker, T.R., Samples, E.E., Ordosch, D.M., and Scotese, K.C., 2019, Potential shifts in zooplankton community structure in response to changing ice regimes and hydrologic connectivity: Arctic, Antarctic, and Alpine Research, v. 51, no. 1, p. 327-345, https://doi.org/10.1080/15230430.2019.1643210.","productDescription":"19 p.","startPage":"327","endPage":"345","ipdsId":"IP-086427","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":467357,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15230430.2019.1643210","text":"Publisher Index Page"},{"id":408649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -150.4555084446034,\n 70.42307829371518\n ],\n [\n -154.7694812572369,\n 70.42307829371518\n ],\n [\n -154.7694812572369,\n 68.72452581295417\n ],\n [\n -150.4555084446034,\n 68.72452581295417\n ],\n [\n -150.4555084446034,\n 70.42307829371518\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Beaver, John R. 0000-0003-0091-2387","orcid":"https://orcid.org/0000-0003-0091-2387","contributorId":202089,"corporation":false,"usgs":false,"family":"Beaver","given":"John","email":"","middleInitial":"R.","affiliations":[{"id":36339,"text":"BSA Environmental Services, Inc.","active":true,"usgs":false}],"preferred":false,"id":855597,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":855598,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tausz, Claudia E.","contributorId":202091,"corporation":false,"usgs":false,"family":"Tausz","given":"Claudia","email":"","middleInitial":"E.","affiliations":[{"id":36339,"text":"BSA Environmental Services, Inc.","active":true,"usgs":false}],"preferred":false,"id":855715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":855599,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whitman, Matthew S.","contributorId":67961,"corporation":false,"usgs":false,"family":"Whitman","given":"Matthew","email":"","middleInitial":"S.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":855600,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Renicker, Thomas R.","contributorId":202090,"corporation":false,"usgs":false,"family":"Renicker","given":"Thomas","email":"","middleInitial":"R.","affiliations":[{"id":36339,"text":"BSA Environmental Services, Inc.","active":true,"usgs":false}],"preferred":false,"id":855601,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Samples, Erin E","contributorId":298429,"corporation":false,"usgs":false,"family":"Samples","given":"Erin","email":"","middleInitial":"E","affiliations":[{"id":64577,"text":"BSA Environmental Services","active":true,"usgs":false}],"preferred":false,"id":855602,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ordosch, David M","contributorId":298430,"corporation":false,"usgs":false,"family":"Ordosch","given":"David","email":"","middleInitial":"M","affiliations":[{"id":64577,"text":"BSA Environmental Services","active":true,"usgs":false}],"preferred":false,"id":855603,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scotese, Kyle C.","contributorId":201592,"corporation":false,"usgs":false,"family":"Scotese","given":"Kyle","email":"","middleInitial":"C.","affiliations":[{"id":36339,"text":"BSA Environmental Services, Inc.","active":true,"usgs":false}],"preferred":true,"id":855604,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70205022,"text":"70205022 - 2019 - The effects of seasonal temperature and photoperiod manipulation on reproduction in the eastern elliptio Elliptio complanata","interactions":[],"lastModifiedDate":"2019-08-28T10:33:59","indexId":"70205022","displayToPublicDate":"2019-08-20T10:30:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2455,"text":"Journal of Shellfish Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The effects of seasonal temperature and photoperiod manipulation on reproduction in the eastern elliptio Elliptio complanata","title":"The effects of seasonal temperature and photoperiod manipulation on reproduction in the eastern elliptio Elliptio complanata","docAbstract":"The eastern elliptio Elliptio complanata is a species of freshwater mussel common to streams and rivers of the Atlantic Coast. Egg fertilization, larval brooding, and glochidial release are reported to occur within a period of several weeks during early to midsummer. In this study, mussels were exposed to manipulated photoperiod and water temperatures to prolong the availability of glochidia for use in artificial propagation and research. Brooding mussels were collected from Pine Creek, Tioga County, PA, in late December and were housed in groups subjected to one of four environmental treatments: natural temperature and photoperiod, 6-wk delay in natural conditions, 12-wk delay in natural conditions, and natural temperature and photoperiod with a winter low of 10°C. Reproductive activity was monitored for 1 y. Mussels subjected to natural conditions released mature glochidia between 16°C and 19°C with a photoperiod of 15 h of light. Temperature and photoperiod delays of 6 and 12 wk delayed reproduction proportional to the treatment, and constant 10°C winter low temperatures slightly shifted the timing of glochidial release. Survival during the study was high (96%–100%). Data indicate that the seasonal availability of E. complanata glochidia can be extended 3-fold using photoperiod and temperature manipulation.
","language":"English","publisher":"BioOne","doi":"10.2983/035.038.0219","usgsCitation":"Blakeslee, C.J., and Lellis, W.A., 2019, The effects of seasonal temperature and photoperiod manipulation on reproduction in the eastern elliptio Elliptio complanata: Journal of Shellfish Research, v. 38, no. 2, p. 379-384, https://doi.org/10.2983/035.038.0219.","productDescription":"6 p.","startPage":"379","endPage":"384","ipdsId":"IP-105915","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":367002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Blakeslee, Carrie J. 0000-0002-0801-5325 cblakeslee@usgs.gov","orcid":"https://orcid.org/0000-0002-0801-5325","contributorId":5462,"corporation":false,"usgs":true,"family":"Blakeslee","given":"Carrie","email":"cblakeslee@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":769592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lellis, William A. 0000-0001-7806-2904 wlellis@usgs.gov","orcid":"https://orcid.org/0000-0001-7806-2904","contributorId":2369,"corporation":false,"usgs":true,"family":"Lellis","given":"William","email":"wlellis@usgs.gov","middleInitial":"A.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":769593,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208499,"text":"70208499 - 2019 - New insights into the ecology of adfluvial Bull Trout and the population response to the Endangered Species Act in the North Fork Lewis River, Washington","interactions":[],"lastModifiedDate":"2020-02-14T06:31:16","indexId":"70208499","displayToPublicDate":"2019-08-20T08:31:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"New insights into the ecology of adfluvial Bull Trout and the population response to the Endangered Species Act in the North Fork Lewis River, Washington","docAbstract":"Like many other salmonids, Bull Trout Salvelinus confluentus migratory life-history expressions are becoming increasingly rare. A critical step in effectively refining management and conservation strategies is a robust assessment of the effectiveness of such strategies and key biological information used in monitoring and recovery planning. To address this need, we integrated a variety of methods to evaluate the population demographics (abundance), vital rates (survival), and life-history characteristics (ageing, growth, spawning migrations and iteroparity) of an adfluvial Bull Trout population. We also employed our mark-recapture data to quantify if recruitment or adult survival had a greater contribution to population trends from year to year. Our results indicated Bull Trout spawning migrations vary with body size, as a considerable portion of smaller adults (<650 mm) did not spawn each year. Additionally, most spawning individuals made only one spawning migration, while <13% made three or more spawning migrations. Our abundance and survival data, which extends to the early 1990s, illustrated positive responses in survival and abundance following the protection of Bull Trout under the Endangered Species Act (1998). Over this period, we found high interannual variability in both survival and abundance, and adult survival (average = 0.45, SE = 0.04) was surprisingly lower than subadult individuals (average = 0.66, SE = 0.04), suggesting limitations at this important life stage. Our mark-recapture data also suggested the attributes driving the Bull Trout population trend (i.e., recruitment to the adult stage or adult survival) has varied through time, with declining trends in the relative contribution of recruitment. Our results provide new insights into the life-history patterns of adfluvial Bull Trout and can serve as a template to consider factors potentially limiting this and other native trout populations.","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10201","usgsCitation":"Al-Chokhachy, R.K., Doyle, J., and Lampierth, J., 2019, New insights into the ecology of adfluvial Bull Trout and the population response to the Endangered Species Act in the North Fork Lewis River, Washington: Transactions of the American Fisheries Society, v. 148, no. 6, p. 1102-1116, https://doi.org/10.1002/tafs.10201.","productDescription":"15 p.","startPage":"1102","endPage":"1116","ipdsId":"IP-097753","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":372305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"North Fork Lewis River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.12353515624999,\n 45.43700828867391\n ],\n [\n -122.06909179687501,\n 45.43700828867391\n ],\n [\n -122.06909179687501,\n 46.4605655457854\n ],\n [\n -124.12353515624999,\n 46.4605655457854\n ],\n [\n -124.12353515624999,\n 45.43700828867391\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"148","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Al-Chokhachy, Robert K. 0000-0002-2136-5098 ral-chokhachy@usgs.gov","orcid":"https://orcid.org/0000-0002-2136-5098","contributorId":1674,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","email":"ral-chokhachy@usgs.gov","middleInitial":"K.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":782173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doyle, Jeremiah","contributorId":214617,"corporation":false,"usgs":false,"family":"Doyle","given":"Jeremiah","email":"","affiliations":[{"id":39086,"text":"PacifiCorp","active":true,"usgs":false}],"preferred":false,"id":782174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lampierth, James","contributorId":222448,"corporation":false,"usgs":false,"family":"Lampierth","given":"James","email":"","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":782175,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222960,"text":"70222960 - 2019 - Evaluating the temperature difference parameter in the SSEBop model with satellite observed land surface temperature data","interactions":[],"lastModifiedDate":"2021-08-10T13:19:36.379901","indexId":"70222960","displayToPublicDate":"2019-08-20T08:11:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the temperature difference parameter in the SSEBop model with satellite observed land surface temperature data","docAbstract":"The Operational Simplified Surface Energy Balance (SSEBop) model uses the principle of satellite psychrometry to produce spatially explicit actual evapotranspiration (ETa) with remotely sensed and weather data. The temperature difference (dT) in the model is a predefined parameter quantifying the difference between surface temperature at bare soil and air temperature at canopy level. Because dT is derived from the average-sky net radiation based primarily on climate data, validation of the dT estimation is critical for assuring a high-quality ETa product. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) data to evaluate the SSEBop dT estimation for the conterminous United States. MODIS data (2008–2017) were processed to compute the 10-year average land surface temperature (LST) and normalized difference vegetation index (NDVI) at 1 km resolution and 8-day interval. The observed dT (dTo) was computed from the LST difference between hot (NDVI < 0.25) and cold (NDVI > 0.7) pixels within each 2° × 2° sampling block. There were enough hot and cold pixels within each block to create dTo timeseries in the West Coast and South-Central regions. The comparison of dTo and modeled dT (dTm) showed high agreement, with a bias of 0.8 K and a correlation coefficient of 0.88 on average. This study concludes that the dTm estimation from the SSEBop model is reliable, which further assures the accuracy of the ETa estimation.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11161947","usgsCitation":"Ji, L., Senay, G.B., Velpuri, N., and Kagone, S., 2019, Evaluating the temperature difference parameter in the SSEBop model with satellite observed land surface temperature data: Remote Sensing, v. 11, no. 6, 1947, 16 p., https://doi.org/10.3390/rs11161947.","productDescription":"1947, 16 p.","ipdsId":"IP-108754","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467358,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11161947","text":"Publisher Index Page"},{"id":387802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": 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Alewife remain the primary prey of Chinook Salmon but have experienced substantial declines in abundance due to reduced food resources and salmonine predation pressure. The movements of Chinook Salmon have been linked to the density and spatial distribution of Alewife, but spatial patterns in Chinook Salmon growth have not been well documented and the temporal relationship between growth and Alewife density has not been evaluated during the current period of low Alewife abundance. We evaluated spatial and temporal variation in growth of Chinook Salmon in Lake Michigan and the U.S. waters of Lake Huron and explored linkages with Alewife density. Von Bertalanffy growth parameters were generally similar for recaptured coded‐wire‐tagged Chinook Salmon from different stocking locations and different recovery locations. Only a few small differences among stocking and recovery regions were evident, with regions divided into two subtly different groups with shared growth parameters. The small regional differences may be attributable to unique habitat and/or stocking characteristics of specific regions. In Lake Michigan average Chinook Salmon length at age also varied across years and was tightly coupled with annual lakewide densities of age‐1 and older Alewife, suggesting that Chinook Salmon growth from 2012 to 2016 was constrained by Alewife density. Our findings are consistent with evidence of lakewide movements associated with foraging and support continued management of Chinook Salmon in Lake Michigan as a single population. Furthermore, similar growth in Chinook Salmon from Lakes Michigan and Huron corroborates evidence that Chinook Salmon move from U.S. waters of Lake Huron to Lake Michigan to feed and reinforces the recent decision to include most fish stocked in northwestern Lake Huron in the Lake Michigan population when managing for predator–prey balance.","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10349","usgsCitation":"Kornis, M., Simpkins, D., Lane, A., Warner, D.M., and Bronte, C., 2019, Growth of hatchery‐reared chinook salmon in Lakes Michigan and Huron exhibits limited spatial variation but Is temporally linked to alewife abundance: North American Journal of Fisheries Management, v. 39, no. 6, p. 1155-1174, https://doi.org/10.1002/nafm.10349.","productDescription":"20 p.","startPage":"1155","endPage":"1174","ipdsId":"IP-101157","costCenters":[{"id":324,"text":"Great Lakes Science 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The streamflow and loads are used to develop Spatially Referenced Regressions on Watershed Attributes (SPARROW) models. SPARROW models help describe the distribution, sources, and transport of streamflow, nutrients, and sediment in streams throughout five regions of the conterminous United States. After the data were screened, approximately 5,200 streamflow, 3,000 sediment, and 3,300 nutrient sites, sampled by 137 agencies and organizations were identified as having suitable data for calculating the long-term mean daily streamflow and annual nutrient and sediment loads required for SPARROW model estimation. These sites are representative of a wide range in terms of watershed size, contaminant source types, and land-use and other important watershed characteristics. The methods used to estimate long-term mean annual loads include the Beale ratio estimator and Fluxmaster regression method with Kalman smoothing.
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U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
This fact sheet describes water quality and geochemistry of the Little Arkansas River and Equus Beds aquifer during 2001 through 2016 as part of the City of Wichita’s Equus Beds aquifer storage and recovery project in south-central Kansas. The Equus Beds aquifer storage and recovery project was developed to help meet future water demand by pumping water out of the Little Arkansas River (during above-base-flow conditions), treating it using National Primary Drinking Water Regulations as a guideline, and injecting it into the aquifer for later use. Water-quality data were collected and analyzed by the U.S. Geological Survey from 2 Little Arkansas River surface-water sites and 63 Equus Beds groundwater sites, including 38 areal assessment index wells, each of which has a shallow well and a deep well. About 4,700 surface and groundwater samples were collected and analyzed for more than 300 water-quality constituents. About 1,300 groundwater chemistry samples were geochemically modeled. Constituents of concern in the Equus Beds aquifer exceeded their respective Federal criteria throughout the study period and included chloride, sulfate, nitrate plus nitrite, Escherichia coli (E. coli), total coliforms, and dissolved iron and arsenic species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193017","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., Klager, B.J., and Ziegler, A.C., 2019, Water-quality and geochemical variability in the Little Arkansas River and Equus Beds aquifer, south-central Kansas, 2001–16: U.S. Geological Survey Fact Sheet 2019–3017, 6 p., https://doi.org/10.3133/fs20193017.","productDescription":"Report: 6 p.; Companion Files","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-097042","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":364768,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3017/coverthb.jpg"},{"id":364769,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3017/fs20193017.pdf","text":"Report","size":"5.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3017"},{"id":364770,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026.pdf","text":"SIR 2019–5026","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5026","linkHelpText":" – Water-Quality and Geochemical Variability in the Little Arkansas River and Equus Beds Aquifer, South-Central Kansas, 2001–16"},{"id":364797,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026_appendix01.xlsx","text":"SIR 2019–5026 Appendix Tables","size":"236 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5026 Appendix Tables","linkHelpText":"– Table 1.1 through Table 1.14"}],"country":"United States","state":"Kansas","otherGeospatial":"Equus Beds Aquifer, Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.83462524414062,\n 37.884608857503785\n ],\n [\n -97.82844543457031,\n 37.85859141570558\n ],\n [\n -97.76664733886719,\n 37.79296501804014\n ],\n [\n -97.57919311523438,\n 37.66805980224121\n ],\n [\n -97.33749389648438,\n 37.684907136008846\n ],\n [\n -97.33062744140625,\n 37.74248523826606\n ],\n [\n -97.35397338867188,\n 37.859675659210005\n ],\n [\n -97.34230041503906,\n 38.03619406237626\n ],\n [\n -97.3443603515625,\n 38.17829073458205\n ],\n [\n -97.40684509277344,\n 38.17613163876633\n ],\n [\n -97.8826904296875,\n 38.171273439283084\n ],\n [\n -97.89985656738281,\n 38.149137543764894\n ],\n [\n -97.83462524414062,\n 37.884608857503785\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Dr.
Lawrence, KS 66049
The city of Wichita’s water supply currently (2019) comes from two primary sources: Cheney Reservoir and the Equus Beds aquifer. The Equus Beds aquifer storage and recovery project was developed to help the city of Wichita meet increasing future water demands. Source water for artificial recharge comes from the Little Arkansas River during above-base-flow conditions, is treated using National Primary Drinking Water Regulations as a guideline, and is injected into the Equus Beds aquifer through recharge wells or surface spreading basins for later use. The Equus Beds aquifer storage and recovery project currently (2019) consists of two coexisting phases. Phase I began in 2007 and captures Little Arkansas River water and indirect streambank diversion well water for aquifer recharge using 4 wells and 2 recharge basins. Phase II began in 2013 and currently (2019) includes a surface-water treatment facility, a river intake facility, eight recharge injection wells, and a third recharge basin. The U.S. Geological Survey, in cooperation with the City of Wichita, completed this study to summarize water-quality and geochemical variability of the Equus Beds aquifer. Data in this report can be used to establish baseline conditions before implementing artificial aquifer recharge further, document groundwater quality, evaluate changing conditions, identify environmental factors affecting groundwater, provide science-based information for decision making, and help meet regulatory monitoring requirements.
Physicochemical properties were measured and water-quality data were collected from 2 Little Arkansas River surface-water sites and 63 Equus Beds aquifer groundwater sites, including 38 areal assessment index wells (IWs) during 2001 through 2016. Data collection included discrete samples and additional continuous measurements at selected sites. Discretely collected samples were analyzed for physicochemical properties, dissolved solids, primary ions, nutrients (nitrogen and phosphorus species), organic carbon, indicator bacteria, trace elements, arsenic species, organic compounds, and radioactivity. This report focuses discussion on aquifer water quality. Federal drinking-water criteria were used to evaluate aquifer water quality. Primary drinking-water criteria are those that are enforceable for public drinking water. Secondary criteria are those that can cause aesthetics or tastes that are unpleasant.
Continuously collected data at a subset of sites included streamflow, groundwater levels, water temperature, specific conductance, pH, oxidation-reduction potential (ORP), dissolved oxygen, turbidity, nitrate plus nitrite, and fluorescent dissolved organic matter. Continuous measurement of physicochemical properties in near-real time allowed characterization of Little Arkansas River surface water and Equus Beds aquifer groundwater during conditions and time scales that would not have been possible otherwise and served as a complement to discrete water-quality sampling. During 2001 through 2016, less than 1 percent of chloride and nitrate plus nitrite, 7 percent of dissolved iron, 48 percent of dissolved manganese, 12 percent of dissolved arsenic, and 39 percent of atrazine detections in surface-water samples exceeded their respective Federal primary or secondary drinking-water criteria. None of the surface-water samples collected exceeded the Federal sulfate criterion, and every sample had detections of total coliform bacteria during the study.
Constituents of concern in the Equus Beds aquifer exceeded their respective Federal criteria throughout the study period and included chloride, sulfate, nitrate plus nitrite, Escherichia coli (E. coli), total coliforms, and dissolved iron and arsenic species. About 5 percent of shallow (less than 80 feet) and 7 percent of deep (greater than 80 feet) IW chloride sample concentrations exceeded the secondary Federal criterion of 250 milligrams per liter (mg/L). Chloride tended to exceed its criterion in shallow and deep wells along the Arkansas River and near Burrton, Kansas, an area with past oil and gas activities. Chloride concentrations near Burrton were larger in the deep parts of the aquifer. About 18 percent of shallow and 13 percent of deep IW sulfate sample concentrations exceeded the secondary Federal criterion of 250 mg/L. Mean sulfate concentrations tended to exceed the criterion in the central part of the study area. Shallow IW mean nitrate plus nitrite (hereafter referred to as “nitrate”) was substantially larger than mean deep IW nitrate. Geochemical conditions in the deeper aquifer reduced forms of nitrogen to species such as ammonia. About 15 percent of shallow and less than 1 percent of deep IW nitrate sample concentrations exceeded the Federal criterion of 10 mg/L. Mean shallow IW nitrate concentrations exceeded the criterion in the northeastern and southeastern parts of the study area; on average, deep IW nitrate concentrations did not exceed the criterion. E. coli and fecal coliform bacteria detections were usually at or near the detection limit. E. coli was detected in 3 percent of shallow and deep IWs, and fecal coliform bacteria were detected in 8 percent of shallow and 6 percent of deep IWs. Total coliforms were detected in 24 percent of shallow and 12 percent of deep IWs. E. coli coliphage was detected in two shallow IW samples (1 percent of samples) at the detection limit and was not detected in deep IW samples.
Dissolved iron was detected in 51 percent of shallow and 62 percent of deep IW samples. Dissolved iron concentrations exceeded the secondary Federal criterion of 0.3 mg/L in 38 percent of shallow and 46 percent of deep IW samples. Mean dissolved iron concentrations were largest mostly in the central and northwest part of the study area corresponding to an area of the aquifer where aquifer material is more clay-rich. The distribution of large dissolved iron concentrations was similar to that of large sulfate concentrations. About 55 percent of shallow and 92 percent of deep IW dissolved manganese samples exceeded the secondary Federal criterion of 0.05 mg/L. Almost all samples from the central and northern parts of the study area had mean dissolved manganese concentrations that exceeded the Federal criterion in the shallow part of the aquifer. Mean dissolved manganese concentrations in the shallow part of the aquifer were substantially large (greater than 1,000 micrograms per liter [μg/L]) in wells near the Little Arkansas River and in the central part of the study area because of chemically reducing conditions in the aquifer that likely related to larger percentages of clay in the aquifer material.
Concentrations of dissolved arsenic species generally were larger in the deep parts of the aquifer. Arsenite was the dominant form of arsenic on average in shallow (52 percent) and deep (55 percent) IWs. About 12 percent of shallow and 34 percent of deep IW dissolved arsenic sample concentrations exceeded the Federal primary drinking criterion of 10 μg/L. Shallow IW dissolved arsenic concentrations were larger near the Little Arkansas River and the center of the study area; large shallow IW dissolved arsenic concentrations (10–50 μg/L) in the center of the study area correspond to areas that have had the most water-level recovery since the historical low in 1993. Mean ORP in shallow IWs generally decreased with increasing water-level depths and were inversely related to mean dissolved arsenic concentrations because of more reducing conditions (smaller ORP) at larger depths below the land surface. Larger dissolved arsenic concentrations in the shallow parts of the aquifer were associated with decreases in water levels and a subsequent decrease in ORP and thus more reducing conditions.
Atrazine was detected in about 58 percent of shallow and 28 percent of deep IWs and did not exceed the primary Federal criterion of 3 μg/L in any groundwater samples. Atrazine concentrations in shallow IWs generally were largest in the northwest part of the study area near the North Branch Kisiwa Creek, and atrazine concentrations in deep IWs generally were largest most often in the southern part of the study area. Gross α radioactivity concentrations exceeded the primary Federal criterion of 15 picocuries per liter in 4 percent of shallow IW samples. Gross α and gross β radioactivity concentrations generally were larger in the southern third of the aquifer.
Most groundwater-sample-simulated minerals saturation indices (SIs) were consistently negative (undersaturated). Minerals that had SI values that were consistently or typically positive (oversaturated) included iron oxide, hydroxide, and quartz-group minerals. Several SI values for arsenic- and manganese-bearing minerals were consistently negative. Some manganese-bearing mineral SI values ranged from undersaturated to oversaturated in shallow and deep IWs during the study. Several carbonate minerals in shallow and deep IWs varied across their equilibrium state. Calcite SI values were larger more often in the deep parts of the aquifer and did not show a clear distributional pattern. Mean and median calcite SI values for shallow and deep IWs were negative (undersaturated) indicating the potential for calcite dissolution if calcite is present for a substantial part of the study period. However, some individual calcite SI values in this study indicated saturation and subsequent calcite precipitation may occur in the study area, potentially resulting in formation of calcite mineral deposits that may reduce efficiency of injection wells. SI values with respect to iron hydroxide varied across their equilibrium states. Mean and median SI values with respect to iron hydroxide were undersaturated in shallow and deep IWs; however, some samples had positive SI values indicating there is potential for iron hydroxide precipitation, possibly caused by leaching and oxidation of iron-containing minerals, like pyrite, in the aquifer material.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195026","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., Klager, B.J., and Ziegler, A.C., 2019, Water-quality and geochemical variability in the Little Arkansas River and Equus Beds aquifer, south-central Kansas, 2001–16: U.S. Geological Survey Scientific Investigations Report 2019–5026, 79 p., https://doi.org/10.3133/sir20195026.","productDescription":"Report: viii, 79 p.; Appendix Tables: Table 1.1 to Table 1.14; Companion Files","numberOfPages":"92","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097040","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":364760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5026/coverthb.jpg"},{"id":364761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026.pdf","text":"Report","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5026"},{"id":364771,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026_appendix01.xlsx","text":"Appendix Tables","size":"236 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5026 Appendix Tables","linkHelpText":" – Table 1.1 through Table 1.14"},{"id":364762,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/fs/2019/3017/fs20193017.pdf","text":"Fact Sheet 2019–3017","size":"4.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3017","linkHelpText":" – Water-Quality and Geochemical Variability in the Little Arkansas River and Equus Beds Aquifer, South-Central Kansas, 2001–16"}],"country":"United States","state":"Kansas","otherGeospatial":"Equus Beds Aquifer, Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.83462524414062,\n 37.884608857503785\n ],\n [\n -97.82844543457031,\n 37.85859141570558\n ],\n [\n -97.76664733886719,\n 37.79296501804014\n ],\n [\n -97.57919311523438,\n 37.66805980224121\n ],\n [\n -97.33749389648438,\n 37.684907136008846\n ],\n [\n -97.33062744140625,\n 37.74248523826606\n ],\n [\n -97.35397338867188,\n 37.859675659210005\n ],\n [\n -97.34230041503906,\n 38.03619406237626\n ],\n [\n -97.3443603515625,\n 38.17829073458205\n ],\n [\n -97.40684509277344,\n 38.17613163876633\n ],\n [\n -97.8826904296875,\n 38.171273439283084\n ],\n [\n -97.89985656738281,\n 38.149137543764894\n ],\n [\n -97.83462524414062,\n 37.884608857503785\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Dr.
Lawrence, KS 66049
Migration is a widespread but highly diverse component of many animal life histories. Fish migrate throughout the world's oceans, within lakes and rivers, and between the two realms, transporting matter, energy, and other species (e.g., microbes) across boundaries. Migration is therefore a process responsible for myriad ecosystem services. Many human populations depend on the presence of predictable migrations of fish for their subsistence and livelihoods. Although much research has focused on fish migration, many questions remain in our rapidly changing world. We assembled a diverse team of fundamental and applied scientists who study fish migrations in marine and freshwater environments to identify pressing unanswered questions. Our exercise revealed questions within themes related to understanding the migrating individual's internal state, navigational mechanisms, locomotor capabilities, external drivers of migration, the threats confronting migratory fish including climate change, and the role of migration. In addition, we identified key requirements for aquatic animal management, restoration, policy, and governance. Lessons revealed included the difficulties in generalizing among species and populations, and in understanding the levels of connectivity facilitated by migrating fishes. We conclude by identifying priority research needed for assuring a sustainable future for migratory fishes.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2019.00286","usgsCitation":"Lennox, R.J., Paukert, C.P., Aarestrup, K., Auger-Methe, M., Baumgartner, L.J., Birnie-Gauvin, K., Boe, K., Brink, K., Brownscombe, J.W., Chen, Y., Davidsen, J., Eliason, E.J., Filous, A., Gillanders, B., Palm Helland, I., Horodysky, A.Z., Januchowski-Hartley, S.R., Lowerre-Barbieri, S.K., Lucas, M.C., Martins, E.G., Murchie, K.J., Pompeu, P.S., Power, M., Raghavan, R., Rahel, F.J., Secor, D., Thiem, J., Thorstad, E.B., Ueda, H., Whoriskey, F.G., and Cooke, S.J., 2019, One hundred pressing questions on the future of global fish migration science, conservation, and policy: Frontiers in Ecology and Evolution, v. 7, 286, 16 p., https://doi.org/10.3389/fevo.2019.00286.","productDescription":"286, 16 p.","ipdsId":"IP-108920","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":467360,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00286","text":"Publisher Index Page"},{"id":395049,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","noUsgsAuthors":false,"publicationDate":"2019-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Lennox, Robert J.","contributorId":198273,"corporation":false,"usgs":false,"family":"Lennox","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":832111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832044,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aarestrup, Kim","contributorId":203992,"corporation":false,"usgs":false,"family":"Aarestrup","given":"Kim","email":"","affiliations":[{"id":36789,"text":"Danmarks Tekniske Universitet","active":true,"usgs":false}],"preferred":false,"id":832112,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Auger-Methe, Marie","contributorId":272553,"corporation":false,"usgs":false,"family":"Auger-Methe","given":"Marie","email":"","affiliations":[],"preferred":false,"id":832113,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baumgartner, Lee J.","contributorId":203990,"corporation":false,"usgs":false,"family":"Baumgartner","given":"Lee","email":"","middleInitial":"J.","affiliations":[{"id":36787,"text":"Charles Sturt University, Institute for Land, Water, and Society","active":true,"usgs":false}],"preferred":false,"id":832114,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Birnie-Gauvin, Kim","contributorId":272554,"corporation":false,"usgs":false,"family":"Birnie-Gauvin","given":"Kim","email":"","affiliations":[],"preferred":false,"id":832115,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boe, Kristin","contributorId":272555,"corporation":false,"usgs":false,"family":"Boe","given":"Kristin","email":"","affiliations":[],"preferred":false,"id":832116,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brink, Kerry","contributorId":272556,"corporation":false,"usgs":false,"family":"Brink","given":"Kerry","email":"","affiliations":[],"preferred":false,"id":832117,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brownscombe, Jacob W","contributorId":215060,"corporation":false,"usgs":false,"family":"Brownscombe","given":"Jacob","email":"","middleInitial":"W","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":832118,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Chen, Yushun","contributorId":146569,"corporation":false,"usgs":false,"family":"Chen","given":"Yushun","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":832119,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Davidsen, J. 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Rajeev","contributorId":250656,"corporation":false,"usgs":false,"family":"Raghavan","given":"Rajeev","email":"","affiliations":[{"id":50216,"text":"Kerala University of Fisheries and Ocean Studies","active":true,"usgs":false}],"preferred":false,"id":832133,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Rahel, Frank J.","contributorId":171824,"corporation":false,"usgs":false,"family":"Rahel","given":"Frank","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":832134,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Secor, David","contributorId":272560,"corporation":false,"usgs":false,"family":"Secor","given":"David","affiliations":[],"preferred":false,"id":832135,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Thiem, Jason","contributorId":203991,"corporation":false,"usgs":false,"family":"Thiem","given":"Jason","affiliations":[{"id":36788,"text":"Department of Primary Industries, Narrandera Fisheries Centre","active":true,"usgs":false}],"preferred":false,"id":832136,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Thorstad, Eva B.","contributorId":95367,"corporation":false,"usgs":true,"family":"Thorstad","given":"Eva","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":832137,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Ueda, Hiroshi","contributorId":100238,"corporation":false,"usgs":true,"family":"Ueda","given":"Hiroshi","email":"","affiliations":[],"preferred":false,"id":832138,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Whoriskey, Fred G.","contributorId":265635,"corporation":false,"usgs":false,"family":"Whoriskey","given":"Fred","email":"","middleInitial":"G.","affiliations":[{"id":54743,"text":"Ocean Tracking Network, Department of Biology, Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":832139,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Cooke, Stephen J.","contributorId":172747,"corporation":false,"usgs":false,"family":"Cooke","given":"Stephen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":832140,"contributorType":{"id":1,"text":"Authors"},"rank":31}]}} ,{"id":70217322,"text":"70217322 - 2019 - Development and implementation of an empirical habitat change model and decision support tool for estuarine ecosystems","interactions":[],"lastModifiedDate":"2021-01-19T12:55:34.568699","indexId":"70217322","displayToPublicDate":"2019-08-19T07:07:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Development and implementation of an empirical habitat change model and decision support tool for estuarine ecosystems","docAbstract":"Widespread land use change in coastal ecosystems has led to a decline in the amount of habitat available for fish and wildlife, lower production of ecosystem goods and services, and loss of recreational and aesthetic value. This has prompted global efforts to restore the natural hydrologic regimes of developed shorelines, especially resource-rich estuaries, but the resilience of these restored ecosystems in the face of accelerated sea-level rise (SLR) remains uncertain. We implemented a Monitoring-based Simulation of Accretion in Coastal Estuaries (MOSAICS) in R statistical software to address uncertainty in the resilience of modified estuarine habitats, using the Nisqually River Delta in the Pacific Northwest USA as a case study. MOSAICS is a spatially explicit model with a numerical foundation that uses empirical monitoring datasets to forecast habitat change in response to rising tidal levels. Because it accounts for the crucial ecomorphodynamic feedbacks between tidal inundation, vegetative growth, and sediment accretion, MOSAICS can be used to determine whether alternative management scenarios, such as enhanced sediment inputs, will bolster estuarine resilience to SLR. Under moderate SLR (0.62 m), the model predicted that a two-fold increase in mean daily suspended sediment during the rainy season was sufficient to maintain Nisqually’s emergent marshes through 2100, but under high SLR (1.35 m) MOSAICS indicated that greater sediment additions would be necessary to prevent submergence. A comparison between a restored marsh with subsided and high-elevation areas and a relict marsh demonstrated that the subsided restoration area was highly susceptible to SLR. Findings from the MOSAICS model highlight the importance of a site’s initial elevation, capacity for producing above and belowground biomass, and suspended sediment availability when considering management actions in estuaries and other coastal ecosystems.
Ilopango volcano (El Salvador) erupted violently during the Maya Classic Period (250–900 CE) in a densely-populated and intensively-cultivated region of the southern Maya realm, causing regional abandonment of an area covering more than 20,000 km2. However, neither the regional nor global impacts of the Tierra Blanca Joven (TBJ) eruption in Mesoamerica have been well appraised due to limitations in available volcanological, chronological, and archaeological observations. Here we present new evidence of the age, magnitude and sulfur release of the TBJ eruption, establishing it as one of the two hitherto unidentified volcanic triggers of a period of stratospheric aerosol loading that profoundly impacted Northern Hemisphere climate and society between circa 536 and 550 CE. Our chronology is derived from 100 new radiocarbon measurements performed on three subfossil tree trunks enveloped in proximal TBJ pyroclastic deposits. We also reassess the eruption magnitude using terrestrial (El Salvador, Guatemala, Honduras) and near-shore marine TBJ tephra deposit thickness measurements. Together, our new constraints on the age, eruption size (43.6 km3 Dense Rock Equivalent of magma, magnitude = 7.0) and sulfur yield (∼9–90 Tg), along with Ilopango's latitude (13.7° N), squarely frame the TBJ as the major climate-forcing eruption of 539 or 540 CE identified in bipolar ice cores and sourced to the tropics. In addition to deepening appreciation of the TBJ eruption's impacts in Mesoamerica, linking it to the major Northern Hemisphere climatic downturn of the mid-6th century CE offers another piece in the puzzle of understanding Eurasian history of the period.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2019.07.037","usgsCitation":"Dull, R.A., Southon, J.R., Kutterolf, S., Anchukaitis, K.J., Freundt, A., Wahl, D., Sheets, P., Amaroli, P., Hernandez, W., Weimann, M.C., and Oppenheimer, C., 2019, Radiocarbon and geologic evidence reveal Ilopango volcano as source of the colossal 'mystery' eruption of 539/40 CE: Quaternary Science Reviews, v. 222, 105855, 17 p., https://doi.org/10.1016/j.quascirev.2019.07.037.","productDescription":"105855, 17 p.","ipdsId":"IP-107480","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467363,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://oceanrep.geomar.de/47558/1/Kopie%20von%20Dull2018_radicarbon_data_TBJ.xlsx","text":"Publisher Index Page"},{"id":366663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"El 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C.","contributorId":218215,"corporation":false,"usgs":false,"family":"Weimann","given":"Micheal","email":"","middleInitial":"C.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":768711,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Oppenheimer, Clive","contributorId":174445,"corporation":false,"usgs":false,"family":"Oppenheimer","given":"Clive","email":"","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":768712,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70203946,"text":"sir20195062 - 2019 - Evaluation of groundwater resources in the Spanish Valley Watershed, Grand and San Juan Counties, Utah","interactions":[],"lastModifiedDate":"2020-10-06T20:20:52.39653","indexId":"sir20195062","displayToPublicDate":"2019-08-16T12:14:34","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5062","displayTitle":"Evaluation of Groundwater Resources in the Spanish Valley Watershed, Grand and San Juan Counties, Utah","title":"Evaluation of groundwater resources in the Spanish Valley Watershed, Grand and San Juan Counties, Utah","docAbstract":"Groundwater resources in the Spanish Valley watershed in southern Utah were quantified for the first time since the early 1970s. The primary objectives of this study were (1) to better understand sources of recharge to, groundwater flow directions within, and discharge points for both the valley-fill and Glen Canyon Group aquifers (VFA and GCGA), and (2) to quantify groundwater budget components of the combined VFA and GCGA, including both recharge and discharge. Based on both groundwater chemistry (stable isotopes, major ions, and noble gases) and environmental tracers in vadose-zone pore water of the Navajo Sandstone outcrop along Sand Flats Road, most recharge to the GCGA occurs high in the La Sal Mountains, and not on the sandstone outcrop area. The same groundwater chemistry and environmental tracer evidence from the saturated zone indicates that Pack Creek, rather than GCGA groundwater, is the primary source of recharge to the VFA. Groundwater recharge in the study area occurs mostly from infiltration of precipitation (in the form of snowmelt) at high altitudes. Additional recharge occurs from the infiltration of runoff along losing reaches of stream channels, or as unconsumed surface-water and groundwater irrigation. Average annual recharge to the Moab-Spanish Valley watershed part of the Spanish Valley study area was estimated to be between 9,550 and 30,000 acre-feet. Based on water-levels collected in the current study, groundwater in both the GCGA and the VFA generally moves downgradient parallel to the topographic slope of the watershed towards the Colorado River. Groundwater discharge measurements, and hydraulic-flux estimates at the lower end of Spanish Valley, provide a more robust estimate of the groundwater budget than evaluating recharge. The primary base-flow discharge components in the study area include groundwater discharge to gaining reaches of streams, groundwater discharge to springs, and well withdrawals. Based on 3 years of measurements (2014–16) and hydraulic-flux calculations at the lower end of Spanish Valley, total groundwater discharge was estimated to be 14,000 to 16,000 acre-feet per year (acre-ft/yr) for the entire watershed, or 13,000 to 15,000 acre-ft/yr, excluding the watershed areas of Grandstaff (formerly Negro Bill) and Ice Box Canyons (compared to the 1971 Sumsion estimate of 22,000 acre-ft/yr). The primary difference is this study’s estimate of subsurface outflow to the Colorado River of only 300 to 1,000 acre-ft/yr, compared to 11,000 acre-ft/yr estimated by Sumsion. Because the study period (2014–16) experienced above average precipitation for 2 of the 3 years, the discharge estimates may be slightly higher than long-term average annual discharge from the groundwater system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195062","collaboration":"Prepared in cooperation with the Utah Division of Water Rights, City of Moab, Grand and San Juan Counties, Grand Water and Sewer Service Agency, Utah School and Institutional Trust Lands Administration, The Nature Conservancy, Utah Division of Wildlife Resources, Living Rivers, San Juan Spanish Valley Special Service District, U.S. Bureau of Land Management, and U.S. Forest Service","usgsCitation":"Masbruch, M.D., Gardner, P.M., Nelson, N.C., Heilweil, V.M., Solder, J.E., Hess, M.D., McKinney, T.S., Briggs, M.A., and Solomon, D.K., 2019, Evaluation of groundwater resources in the Spanish Valley Watershed, Grand and San Juan Counties, Utah: U.S. Geological Survey Scientific Investigations Report 2019–5062, 86 p., https://doi.org/10.3133/sir20195062.","productDescription":"Report: x, 86 p.; 3 Plates: 24.00 x 38.00 inches or smaller","numberOfPages":"86","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080057","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":437367,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z39TII","text":"USGS data release","linkHelpText":"Lumped parameter models of groundwater age, Spanish Valley Watershed, Grand and San Juan Counties, Utah"},{"id":366484,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5062/coverthb.jpg"},{"id":366487,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062_plate2.pdf","text":"Plate 2","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062 Plate 2","linkHelpText":"- Stiff Diagrams Showing Major-Ion Composition from Hydrogeologic Units in the Spanish Valley Study Area, Utah"},{"id":366485,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062.pdf","text":"Report","size":"14.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062"},{"id":366488,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062_plate3.pdf","text":"Plate 3","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062 Plate 3","linkHelpText":"- Water-Level Surface Map and General Direction of Groundwater Movement in the Spanish Valley Study Area, Utah"},{"id":366486,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062_plate1.pdf","text":"Plate 1","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062 Plate 1","linkHelpText":"- Surficial Extent and Cross Sections of Hydrogeologic Units for Selected Locations in the Spanish Valley Study Area, Utah"}],"country":"United States","state":"Utah","county":"Grand County, San Juan County","otherGeospatial":"Spanish Valley Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.74037170410156,\n 38.357810999675664\n ],\n [\n -109.31259155273438,\n 38.357810999675664\n ],\n [\n -109.31259155273438,\n 38.61043215866372\n ],\n [\n -109.74037170410156,\n 38.61043215866372\n ],\n [\n -109.74037170410156,\n 38.357810999675664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
801-908-5000
Air pollution has been identified as one of the most challenging health issues in urban areas worldwide.
The aim of this study was to investigate the association between short-term exposure to ambient air pollution and respiratory disease over a long-term period in Shiraz, one of the largest cities in Iran. Methods: hospital admissions due to respiratory diseases (asthma, pneumonia, chronic obstructive pulmonary disease(COPD) and pleural effusion) in residents of Shiraz between March 21, 2009 and March 20, 2015 were included. Demographics for each patient and meta data to include principal meteorological variables (temperature and relative humidity) and five ambient pollutants (CO, O3, SO2, NO2, and PM10) were also included. Statistical analysis was performed by Poisson regression in single-pollutant generalized linear model with principal component analysis (GLPCA) to analyze the relationship between pollutants and hospital admissions at the 0–9 cumulative lag day period. Pearson correlation test was used to determine the relationship between different pollutants, temperature and humidity.
It was found that the highest increase in asthma admission was related to PM10(relative risk (RR) = 1.31, 95% CI = 1.17, 1.47). For COPD, the rate of hospital visits significantly increased with the increase in NO2 concentration (RR = 1.17, 95% CI = 1.09.1.27). In the children's hospital, O3 (RR = 1.25, 95% CI = 1.06, 1.47) and SO2(RR = 1.17, 95% CI = 1.07, 1.28) affected the asthma admissions and all contaminants (highest RR observed was for NO2 (RR = 1.28 95% CI = 1.18, 1.40) affected pneumonia admissions on cumulative lag days of 0–9. Conclusions: These data confirm an association between ambient air pollutants and hospital admissions due to respiratory disease.
Thiamine Deficiency Complex (TDC) limits early life stage survival of salmonines. Consuming fatty prey has been hypothesized as a cause of thiamine deficiency; however, this relationship has not been evaluated in the Laurentian Great Lakes where TDC occurs. We found that alewife (Alosa pseudoharengus) have higher lipid content than other common Lake Ontario prey fish. In addition, alewife were predicted as the most consumed prey for brown trout (Salmo trutta), Chinook salmon (Oncorhynchus tshawytscha), coho salmon (O. kisutch), lake trout (Salvelinus namaycush), and steelhead trout (O. mykiss); however, the relative importance of alewife in diet composition varied within and among species. Overall, species with greater predicted consumption of alewife had lower egg and muscle thiamine concentrations. Negative correlations between thiamine concentrations and both lipid content and fatty acid concentrations (mg/mg of wet tissue) were limited to brown trout. Similarly, negative correlations between fatty acid proportions (i.e., cumulative proportions of polyunsaturated fatty acids [PUFA] and monounsaturated fatty acids [MUFA]) and thiamine concentrations were only observed for brown and lake trout. Combining data from all species produced curvilinear correlations between thiamine concentrations (egg and muscle) and fatty acid composition (eggs and belly flap). Proportions of PUFAs had negative correlations with thiamine concentrations while proportions of MUFAs had positive correlations. These results provide evidence that, in some cases, salmonine fatty acid composition negatively correlates with thiamine concentrations in Lake Ontario; however, additional research is needed to confirm that this mechanism causes TDC in salmonines, and to understand additional factors potentially associated with TDC.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2019.08.005","usgsCitation":"Futia, M.H., Connerton, M., Weidel, B., and Rinchard, J., 2019, Diet predictions of Lake Ontario salmonines based on fatty acids and correlations between their fat content and thiamine concentrations: Journal of Great Lakes Research, v. 45, no. 5, p. 934-948, https://doi.org/10.1016/j.jglr.2019.08.005.","productDescription":"15 p.","startPage":"934","endPage":"948","ipdsId":"IP-105375","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":373572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.256103515625,\n 44.134913443750726\n ],\n [\n -75.9814453125,\n 44.25700308645885\n ],\n [\n -76.1627197265625,\n 44.402391829093915\n ],\n [\n -76.871337890625,\n 44.20583500104184\n ],\n [\n -77.156982421875,\n 44.01257086123085\n ],\n [\n -77.5799560546875,\n 44.08758502824516\n ],\n [\n -78.936767578125,\n 43.929549935614595\n ],\n [\n -79.5135498046875,\n 43.6599240747891\n ],\n [\n -79.9969482421875,\n 43.27720532212024\n ],\n [\n -80.0244140625,\n 43.205175817237304\n ],\n [\n -79.793701171875,\n 43.197167282501276\n ],\n [\n -79.2828369140625,\n 43.13306116240612\n ],\n [\n -78.673095703125,\n 43.29320031385282\n ],\n [\n -78.046875,\n 43.345154990451135\n ],\n [\n -77.574462890625,\n 43.21718664827096\n ],\n [\n -77.025146484375,\n 43.23319741022136\n ],\n [\n -76.6845703125,\n 43.28920196020127\n ],\n [\n -76.387939453125,\n 43.49676775343911\n ],\n [\n -76.1956787109375,\n 43.492782808225\n ],\n [\n -76.13525390624999,\n 43.65197548731187\n ],\n [\n -76.17919921875,\n 43.84245116699039\n ],\n [\n -75.98693847656249,\n 44.000717834282774\n ],\n [\n -76.256103515625,\n 44.134913443750726\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"5","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Futia, Matthew H.","contributorId":208498,"corporation":false,"usgs":false,"family":"Futia","given":"Matthew","email":"","middleInitial":"H.","affiliations":[{"id":37810,"text":"Department of Environmental Science and Ecology, The College at Brockport – State University of New York, 350 New Campus Drive, Brockport, New York","active":true,"usgs":false}],"preferred":false,"id":785834,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connerton, Michael J.","contributorId":25495,"corporation":false,"usgs":false,"family":"Connerton","given":"Michael J.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":785835,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":785833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rinchard, Jacques","contributorId":208500,"corporation":false,"usgs":false,"family":"Rinchard","given":"Jacques","affiliations":[{"id":37810,"text":"Department of Environmental Science and Ecology, The College at Brockport – State University of New York, 350 New Campus Drive, Brockport, New York","active":true,"usgs":false}],"preferred":false,"id":785836,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205170,"text":"70205170 - 2019 - Interseismic quiescence and triggered slip of active normal faults of Kīlauea Volcano’s south flank during 2001-2018","interactions":[],"lastModifiedDate":"2019-10-28T10:16:29","indexId":"70205170","displayToPublicDate":"2019-08-16T08:53:17","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Interseismic quiescence and triggered slip of active normal faults of Kīlauea Volcano’s south flank during 2001-2018","docAbstract":"The mobile south flank of Kīlauea Volcano hosts two normal fault systems, the Koa'e fault system (KFS) and the Hilina fault system (HFS). In historical time, at least three M>6.5 earthquakes\nhave occurred on the basal detachment of the Kīlauea Volcano's south flank, with the most recent being the 4 May 2018 M6.9 earthquake. Here we analyze kinematic Global Positioning System data collected from 2001 to 2017 and interferometric synthetic aperture radar data before, during, and after the 2018 M6.9 earthquake to determine the crustal motion across the HFS and KFS faults. Our results indicate that the HFS faults did not significantly slip during the interseismic period from 2007 to 2011. Despite its substantial magnitude, interferometric synthetic aperture radar (InSAR) data show that the 2018 M6.9 earthquake triggered subcentimeter level slip along sections of the previously mapped HFS branches. Up to 20 cm of offset occurred on what appears to be a newly formed (or previously unknown) fault near the eastern end of the HFS. During the 3 months following the M6.9 earthquake, up to ~30 cm of slip occurred along the KFS, which helps accommodate rapid large‐scale subsidence of Kīlauea's summit region as large volumes of summit reservoir magma fed the lower East Rift Zone eruption. The HFS appears to activate only in concert with large earthquakes on the basal detachment. The KFS, on the other hand, moves both seismically during small local earthquakes and aseismically in response to nearby earthquakes and caldera subsidence.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB017419","usgsCitation":"Wang, K., MacArthur, H., Johanson, I.A., Montgomery-Brown, E.K., Poland, M.P., Cannon, E., d’Alessio, M., and Bürgmann, R., 2019, Interseismic quiescence and triggered slip of active normal faults of Kīlauea Volcano’s south flank during 2001-2018: Journal of Geophysical Research B: Solid Earth, v. 124, no. 9, p. 9780-9794, https://doi.org/10.1029/2019JB017419.","productDescription":"15 p.","startPage":"9780","endPage":"9794","ipdsId":"IP-104626","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467364,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2019jb017419","text":"External Repository"},{"id":367210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.4393768310547,\n 19.18862319930604\n ],\n [\n -154.99717712402344,\n 19.18862319930604\n ],\n [\n -154.99717712402344,\n 19.445874298215937\n ],\n [\n -155.4393768310547,\n 19.445874298215937\n ],\n [\n -155.4393768310547,\n 19.18862319930604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Kang","contributorId":197483,"corporation":false,"usgs":false,"family":"Wang","given":"Kang","email":"","affiliations":[],"preferred":false,"id":770210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"MacArthur, Hayden","contributorId":218774,"corporation":false,"usgs":false,"family":"MacArthur","given":"Hayden","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":770211,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johanson, Ingrid A. 0000-0002-6049-2225","orcid":"https://orcid.org/0000-0002-6049-2225","contributorId":215613,"corporation":false,"usgs":true,"family":"Johanson","given":"Ingrid","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":770209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Montgomery-Brown, Emily K. 0000-0001-6787-2055","orcid":"https://orcid.org/0000-0001-6787-2055","contributorId":214074,"corporation":false,"usgs":true,"family":"Montgomery-Brown","given":"Emily","email":"","middleInitial":"K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":770212,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":770213,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cannon, Eric","contributorId":218775,"corporation":false,"usgs":false,"family":"Cannon","given":"Eric","affiliations":[{"id":34755,"text":"Golder Associates Inc.","active":true,"usgs":false}],"preferred":false,"id":770214,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"d’Alessio, Matthew","contributorId":218776,"corporation":false,"usgs":false,"family":"d’Alessio","given":"Matthew","email":"","affiliations":[{"id":39477,"text":"California State University Northridge","active":true,"usgs":false}],"preferred":false,"id":770215,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bürgmann, Roland","contributorId":195087,"corporation":false,"usgs":false,"family":"Bürgmann","given":"Roland","affiliations":[],"preferred":false,"id":770216,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70204786,"text":"70204786 - 2019 - Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests","interactions":[],"lastModifiedDate":"2019-08-19T13:52:04","indexId":"70204786","displayToPublicDate":"2019-08-16T06:59:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests","docAbstract":"Abstract \nWildfires raise risks of floods, debris flows, major geomorphologic and sedimentologic change, and water quality and quantity shifts. A principal control on the magnitude of these changes is field-saturated hydraulic conductivity (Kfs), which dictates surface runoff generation and is a key input into numerical models. This work synthesizes 73 Kfs datasets from the literature in the first year following fire at the plot scale (≤ 10 m2). A meta-analysis using a random effects analysis showed significant differences between burned and unburned Kfs. The reductions in Kfs after fire, expressed by the ratio of Kfs Burned / Kfs Unburned, were 0.46 (95% confidence interval of 0.31-0.70) combining wildfire and prescribed fire and 0.3 (95% confidence interval of 0.13-0.71) for wildfire. No significant differences for Kfs were observed between wildfire and prescribed fire or moderate and high fire severity. Both Kfs magnitude and variability depended more on measurement method than measurement support area at the plot scale, with methods applying head ≥0.5 cm producing larger estimates of Kfs. It is recommended that post-fire efforts to characterize Kfs for modeling or process-based interpretations use methods that reflect the dominant infiltration processes: tension infiltrometers and simulated rainfall methods when soil matrix flow dominates and ponded head methods when macropore flow is critical.","language":"English","publisher":"Wiley","doi":"10.1002/esp.4621","usgsCitation":"Ebel, B.A., 2019, Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests: Earth Surface Processes and Landforms, v. 44, no. 10, p. 1945-1956, https://doi.org/10.1002/esp.4621.","productDescription":"12 p.","startPage":"1945","endPage":"1956","ipdsId":"IP-101329","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":366581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Ebel, Brian A. 0000-0002-5413-3963 bebel@usgs.gov","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":218151,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian","email":"bebel@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":768477,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70215778,"text":"70215778 - 2019 - Phosphorus and the Chesapeake Bay: Lingering issues and emerging concerns for agriculture","interactions":[],"lastModifiedDate":"2020-10-29T21:50:50.764784","indexId":"70215778","displayToPublicDate":"2019-08-15T16:39:50","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Phosphorus and the Chesapeake Bay: Lingering issues and emerging concerns for agriculture","docAbstract":"Hennig Brandt's discovery of phosphorus (P) occurred during the early European colonization of the Chesapeake Bay region. Today, P, an essential nutrient on land and water alike, is one of the principal threats to the health of the bay. Despite widespread implementation of best management practices across the Chesapeake Bay watershed following the implementation in 2010 of a total maximum daily load (TMDL) to improve the health of the bay, P load reductions across the bay's 166,000‐km2 watershed have been uneven, and dissolved P loads have increased in a number of the bay's tributaries. As the midpoint of the 15‐yr TMDL process has now passed, some of the more stubborn sources of P must now be tackled. For nonpoint agricultural sources, strategies that not only address particulate P but also mitigate dissolved P losses are essential. Lingering concerns include legacy P stored in soils and reservoir sediments, mitigation of P in artificial drainage and stormwater from hotspots and converted farmland, manure management and animal heavy use areas, and critical source areas of P in agricultural landscapes. While opportunities exist to curtail transport of all forms of P, greater attention is required toward adapting P management to new hydrologic regimes and transport pathways imposed by climate change.
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B.","contributorId":191824,"corporation":false,"usgs":false,"family":"Bryant","given":"R.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":803488,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Shenk, Gary","contributorId":244194,"corporation":false,"usgs":false,"family":"Shenk","given":"Gary","affiliations":[],"preferred":false,"id":803489,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70227922,"text":"70227922 - 2019 - Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context","interactions":[],"lastModifiedDate":"2022-02-03T12:08:31.632737","indexId":"70227922","displayToPublicDate":"2019-08-15T14:38:12","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context","docAbstract":"Hybridization can profoundly affect the genomic composition and phenotypes of closely related species, and provides an opportunity to identify mechanisms that maintain reproductive isolation between species. Recent evidence suggests that hybridization outcomes within a species pair can vary across locations. However, we still don’t know how extensive variation in outcomes of hybridization is across geographic replicates, and what mechanisms drive that variation. In this study, we described hybridization outcomes across 27 locations in the North Fork Shoshone River basin (Wyoming, USA) where native Yellowstone cutthroat trout and introduced rainbow trout co-occur. We used genomic data and hierarchical Bayesian models to precisely identify ancestry of hybrid individuals. Hybridization outcomes varied across locations. In some locations, only rainbow trout and advanced backcrossed hybrids towards rainbow trout were present, while other locations had a broader range of ancestry, including both parental species and first-generation hybrids. Using an individual-based simulation, we found that outcomes of hybridization in the North Fork Shoshone River basin deviate substantially from what we would expect under assumptions of random mating and no selection against hybrids. Since this implies that some mechanisms of reproductive isolation function to maintain parental taxa and a diversity of hybrid types, we then modeled hybridization outcomes as a function of environmental variables and stocking history that are likely to affect prezygotic barriers to hybridization. Variables associated with history of fish stocking were the strongest predictors of hybridization outcomes, followed by environmental variables that might affect overlap in spawning time and location.","language":"English","publisher":"Wiley","doi":"10.1111/mec.15175","usgsCitation":"Mandeville, E., Walters, A.W., Nordberg, B.J., Higgins, K.H., Burckhardt, J.C., and Wagner, C.E., 2019, Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context: Molecular Ecology, v. 28, no. 16, p. 3738-3755, https://doi.org/10.1111/mec.15175.","productDescription":"18 p.","startPage":"3738","endPage":"3755","ipdsId":"IP-099309","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467366,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6775767","text":"External Repository"},{"id":395304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Buffalo Bill Reservoir, North Fork Shoshone River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.57077026367188,\n 44.26683800273895\n ],\n [\n -108.96102905273438,\n 44.26683800273895\n ],\n [\n -108.96102905273438,\n 44.6579085850145\n ],\n [\n -109.57077026367188,\n 44.6579085850145\n ],\n [\n -109.57077026367188,\n 44.26683800273895\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"16","noUsgsAuthors":false,"publicationDate":"2019-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mandeville, Elizabeth G.","contributorId":270691,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth G.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":832577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nordberg, Brittany J.","contributorId":270690,"corporation":false,"usgs":false,"family":"Nordberg","given":"Brittany","email":"","middleInitial":"J.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":832579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Higgins, Karly H.","contributorId":273111,"corporation":false,"usgs":false,"family":"Higgins","given":"Karly","email":"","middleInitial":"H.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":832580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burckhardt, Jason C.","contributorId":270692,"corporation":false,"usgs":false,"family":"Burckhardt","given":"Jason","email":"","middleInitial":"C.","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":832581,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Catherine E.","contributorId":270693,"corporation":false,"usgs":false,"family":"Wagner","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":832582,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204253,"text":"sir20195068 - 2019 - Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018","interactions":[],"lastModifiedDate":"2019-08-16T06:55:10","indexId":"sir20195068","displayToPublicDate":"2019-08-15T13:46:58","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5068","displayTitle":"Flood-Inundation Maps for Joachim Creek, De Soto, Missouri, 2018","title":" Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018","docAbstract":"Digital flood-inundation maps for a 6.7-mile reach of Joachim Creek, De Soto, Missouri, were created by the U.S. Geological Survey (USGS) in cooperation with the city of De Soto and Jefferson County, Missouri. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps2/hydrograph.php?wfo=lsx&gage=desm7, which also forecasts flood hydrographs at this site (site DESM7).
Flood profiles were computed for the stream reach using a one-dimensional model for simulation of water-surface profiles with steady-state (gradually varied) or unsteady-state flow computation options. The model was calibrated by using the theoretical stage-discharge relation at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500), and documented high-water marks from the flood of April 18, 2013.
The hydraulic model was then used to compute 10 water surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum. The profiles ranged from 8.0 ft, or near bankfull, to 17.0 ft, which exceeds the stage that corresponds to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.60-ft vertical accuracy and 1.97-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures and for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195068","collaboration":"Prepared in cooperation with the city of De Soto, Missouri, and Jefferson County, Missouri","usgsCitation":"Heimann, D.C., Voss, J.D., and Rydlund, P.H., Jr., 2019, Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5068, 10 p., https://doi.org/10.3133/sir20195068.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-105218","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":366556,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5068/sir20195068.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Demand for freshwater on the Island of Maui is expected to increase by 45 percent between 2015 and 2035. Groundwater availability on Maui is affected by changes in climate and agricultural irrigation. To evaluate the availability of fresh groundwater under projected future climate conditions and changing agricultural irrigation practices, estimates of groundwater recharge are needed. A water-budget model with a daily computation interval was used to estimate the spatial distribution of recharge on Maui for one present-day and two future-climate scenarios. All three scenarios used 2017 land cover. The two future-climate scenarios, including one wetter than the present-day scenario and one drier than the present-day scenario, were developed using available high-resolution downscaled climate projections. The drier future scenario was developed using projections for a Representative Concentration Pathway warming scenario during 2071–99 with total radiative forcing of 8.5 Watts per square meter by the year 2100 (RCP8.5 2071–99 scenario), whereas the wetter future scenario was developed using projections for a “Special Report on Emissions Scenarios” A1B emission scenario during 2080–99 (A1B 2080–99 scenario). For the RCP8.5 2071–99 scenario, projected mean annual recharge decrease for Maui is about 172 million gallons per day, or about 14 percent less than present-day recharge, which is estimated to be 1,232 million gallons per day. Recharge for the RCP8.5 2071–99 scenario is projected to decrease in 22 of Maui’s 25 aquifer systems, which are defined by the Hawaiʻi Commission on Water Resource Management. For the A1B 2080–99 future scenario, projected mean annual recharge increase for Maui is about 144 million gallons per day, or about 12 percent more than present-day recharge. Recharge for the A1B 2080–99 scenario is projected to increase in 17 of Maui’s 25 aquifer systems. Between the two future scenarios, a total of 11 aquifer systems show similar direction in drying (Kahului, Kama‘ole, Lualaʻilua, Makawao, Olowalu, Pāʻia, Ukumehame, Waikapū) or wetting (Honopou, Kawaipapa, and Waikamoi) changes for recharge. Selectively modifying the climate inputs for the A1B 2080–99 scenario indicates that the projected changes in rainfall account for most of the projected changes in recharge for Maui’s 25 aquifer systems. However, projected changes in reference evapotranspiration and forest-canopy evaporation also can account for a substantial part of the projected changes in recharge where changes in reference evapotranspiration are relatively large and where changes in forest-canopy evaporation extend across large forested areas. Projected changes in daily rainfall frequency have a relatively small but non-negligible impact on recharge estimates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195064","collaboration":"Prepared in cooperation with the County of Maui Department of Water Supply and the Pacific Regional Integrated Sciences and Assessments Program","usgsCitation":"Mair, A., Johnson A.G., Rotzoll, Kolja, and Oki, D.S., 2019, Estimated groundwater recharge from a water-budget model incorporating selected climate projections, Island of Maui, Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2019–5064, 46 p., https://doi.org/10.3133/sir20195064.","productDescription":"Report: vi, 46 p., 3 data releases","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-100732","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":366544,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98W9ABX","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for projected climate conditions, CMIP5 RCP8.5 2071-99 scenario rainfall and 2017 land cover"},{"id":366541,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5064/sir20195064.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5064"},{"id":366542,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91WSOFO","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for average climate conditions, 1978-2007 rainfall and 2017 land cover"},{"id":366540,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5064/coverthb.jpg"},{"id":366543,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9437T2F","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for projected climate conditions, CMIP3 A1B 2080-99 scenario climate and 2017 land cover"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.7474365234375,\n 20.52478875041428\n ],\n [\n -155.8685302734375,\n 20.52478875041428\n ],\n [\n -155.8685302734375,\n 21.099875492701216\n ],\n [\n -156.7474365234375,\n 21.099875492701216\n ],\n [\n -156.7474365234375,\n 20.52478875041428\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
ubsurface coal environments, where biogenic coal-to-methane conversion occurs, are difficult to access, resulting in inherent challenges and expenses for in situexperiments. Previous batch reactor studies provided insights into specific processes, pathways, kinetics, and engineering strategies, but field-relevance is restricted due to limited substrate availability or byproduct accumulation that may influence reactions or metabolisms. In this study, continuous-flow column reactors were used to overcome some batch limitations, improve the understanding of in situconditions, and increase field-relevance for subsurface engineering technology development. The bench-scale reactor system was constructed to investigate the addition of algal amendment for enhancing microbial coal-to-methane conversion previously developed in batch systems. Four reactor columns were packed with coal and inoculated with a microbial consortium from the same Flowers-Goodale coal bed. Two reactors were amended with 13C-labeled algal amendment on day 0, and two were unamended. On day 61, one previously amended and one previously unamended reactor were re-amended. Produced gases were captured in a gas trap, and CH4 and CO2 were quantified. The reactor amended twice produced 1712.6 µmol CH4 (4.6% as 13CH4). The reactor amended only on day 0 produced 1485.5 µmol CH4 (2.6% as 13CH4). The reactor amended only on day 61 produced 278.9 µmol CH4 (3.9% as 13CH4). The reactor with no amendment produced no measurable gases for the duration of the 172-day experiment. Amendment increased the rate of coal-to-methane conversion and total gas production; most of the produced gases were due to coal conversion with only small contributions (<7%) from amendment conversion.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fuel.2019.115905","usgsCitation":"Davis, K.J., Platt, G.A., Barnhart, E.P., Hiebart, R., Hyatt, R., Fields, M.W., and Gerlach, R., 2019, Biogenic coal-to-methane conversion can be enhanced with small additions of algal amendment in field-relevant upflow column reactors: Fuel, v. 256, 115905, 8 p., https://doi.org/10.1016/j.fuel.2019.115905.","productDescription":"115905, 8 p.","ipdsId":"IP-106712","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":467367,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1557363","text":"Publisher Index Page"},{"id":366902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"256","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Katherine J.","contributorId":203246,"corporation":false,"usgs":false,"family":"Davis","given":"Katherine","email":"","middleInitial":"J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":769184,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Platt, George A.","contributorId":218404,"corporation":false,"usgs":false,"family":"Platt","given":"George","email":"","middleInitial":"A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":769185,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":769183,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hiebart, Randy","contributorId":218422,"corporation":false,"usgs":false,"family":"Hiebart","given":"Randy","email":"","affiliations":[],"preferred":false,"id":769186,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hyatt, Robert","contributorId":218406,"corporation":false,"usgs":false,"family":"Hyatt","given":"Robert","email":"","affiliations":[{"id":39839,"text":"Montana Emergent Technologies","active":true,"usgs":false}],"preferred":false,"id":769187,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fields, Matthew W.","contributorId":172391,"corporation":false,"usgs":false,"family":"Fields","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":769188,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gerlach, Robin","contributorId":203247,"corporation":false,"usgs":false,"family":"Gerlach","given":"Robin","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":769189,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70207520,"text":"70207520 - 2019 - Elevated heterozygosity in adults relative to juveniles provides evidence of viability selection on eagles and falcons","interactions":[],"lastModifiedDate":"2019-12-21T10:30:56","indexId":"70207520","displayToPublicDate":"2019-08-15T10:27:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2333,"text":"Journal of Heredity","active":true,"publicationSubtype":{"id":10}},"title":"Elevated heterozygosity in adults relative to juveniles provides evidence of viability selection on eagles and falcons","docAbstract":"Viability selection yields adult populations that are more genetically variable than those of juveniles, producing a positive correlation between heterozygosity and survival. Viability selection could be the result of decreased heterozygosity across many loci in inbred individuals and a subsequent decrease in survivorship resulting from the expression of the deleterious alleles. Alternatively, locus-specific differences in genetic variability between adults and juveniles may be driven by forms of balancing selection, including heterozygote advantage, frequency-dependent selection, or selection across temporal and spatial scales. We use a pooled-sequencing approach to compare genome-wide and locus-specific genetic variability between 74 golden eagle (Aquila chrysaetos), 62 imperial eagle (Aquila heliaca), and 69 prairie falcon (Falco mexicanus) juveniles and adults. Although genome-wide genetic variability is comparable between juvenile and adult golden eagles and prairie falcons, imperial eagle adults are significantly more heterozygous than juveniles. This evidence of viability selection may stem from a relatively smaller imperial eagle effective population size and potentially greater genetic load. We additionally identify ~2000 single-nucleotide polymorphisms across the 3 species with extreme differences in heterozygosity between juveniles and adults. Many of these markers are associated with genes implicated in immune function or olfaction. These loci represent potential targets for studies of how heterozygote advantage, frequency-dependent selection, and selection over spatial and temporal scales influence survivorship in avian species. Overall, our genome-wide data extend previous studies that used allozyme or microsatellite markers and indicate that viability selection may be a more common evolutionary phenomenon than often appreciated.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jhered/esz048","usgsCitation":"Doyle, J.M., Willoughby, J.R., Bell, D.A., Bloom, P.H., Bragin, E.A., Fernandez, N.B., Katzner, T., Leonard, K., and DeWoody, J., 2019, Elevated heterozygosity in adults relative to juveniles provides evidence of viability selection on eagles and falcons: Journal of Heredity, v. 110, no. 6, p. 696-706, https://doi.org/10.1093/jhered/esz048.","productDescription":"11 p.","startPage":"696","endPage":"706","ipdsId":"IP-105731","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":467368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jhered/esz048","text":"Publisher Index Page"},{"id":370601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"110","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Doyle, Jacqueline M.","contributorId":175099,"corporation":false,"usgs":false,"family":"Doyle","given":"Jacqueline","email":"","middleInitial":"M.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":778350,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willoughby, Janna R 0000-0002-0176-1878","orcid":"https://orcid.org/0000-0002-0176-1878","contributorId":221484,"corporation":false,"usgs":false,"family":"Willoughby","given":"Janna","email":"","middleInitial":"R","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":778351,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, Douglas A.","contributorId":199739,"corporation":false,"usgs":false,"family":"Bell","given":"Douglas","email":"","middleInitial":"A.","affiliations":[{"id":24634,"text":"East Bay Regional Park District","active":true,"usgs":false}],"preferred":false,"id":778352,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bloom, Peter H.","contributorId":191356,"corporation":false,"usgs":false,"family":"Bloom","given":"Peter","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":778353,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bragin, Evgeny A.","contributorId":194894,"corporation":false,"usgs":false,"family":"Bragin","given":"Evgeny","email":"","middleInitial":"A.","affiliations":[{"id":35656,"text":"Science Department, Naurzum National Nature Reserve, Kostanay Oblast, Naurzumski Raijon, Karamendy, Kazakhstan","active":true,"usgs":false}],"preferred":false,"id":778354,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fernandez, Nadia B.","contributorId":175100,"corporation":false,"usgs":false,"family":"Fernandez","given":"Nadia","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":778355,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778349,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Leonard, Kolbe","contributorId":204166,"corporation":false,"usgs":false,"family":"Leonard","given":"Kolbe","email":"","affiliations":[{"id":36867,"text":"Department of Computer and Information Sciences, Towson University","active":true,"usgs":false}],"preferred":false,"id":778356,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"DeWoody, J Andrew","contributorId":221485,"corporation":false,"usgs":false,"family":"DeWoody","given":"J Andrew","affiliations":[{"id":33107,"text":"Towson University","active":true,"usgs":false}],"preferred":false,"id":778357,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ]}