Empirical thresholds for landslide warning systems have benefitted from the incorporation of soil‐hydrologic monitoring data, but the mechanistic basis for their predictive capabilities is limited. Although physically based hydrologic models can accurately simulate changes in soil moisture and pore pressure that promote landslides, their utility is restricted by high computational costs and nonunique parameterization issues. We construct a deep learning model using soil moisture, pore pressure, and rainfall monitoring data acquired from landslide‐prone hillslopes in Oregon, USA, to predict the timing and magnitude of hydrologic response at multiple soil depths for 36‐hr intervals. We find that observation records as short as 6 months are sufficient for accurate predictions, and our model captures hydrologic response for high‐intensity rainfall events even when those storm types are excluded from model training. We conclude that machine learning can provide an accurate and computationally efficient alternative to empirical methods or physical modeling for landslide hazard warning.
Assessments of groundwater and surface water budgets at a large scale, such as the contiguous United States, often separately analyze the complex dynamics linking the surface and subsurface categories of water resources. These dynamics include recharge and groundwater contributions to streamflow. The time-varying simulation of these complex hydrologic dynamics, across large spatial and temporal scales, remains a scientific challenge due to the complexity of the processes and data availability. In this study, groundwater fluxes and surface hydrologic processes are simulated across the contiguous US for 1950-2010. The simulation estimates the monthly water budget components, such as groundwater recharge, surface runoff, and evapotranspiration; streamflow in major rivers is routed while accounting for groundwater exchange. Human impacts are included through groundwater pumping, and climate variability is included, including variability in precipitation, temperature and potential evapotranspiration. The simulated groundwater level and river discharge have strong correlation with USGS observation wells and streamflow gages, with R2 values of 0.992 and 0.946, respectively. The simulated evapotranspiration is compared with three other published estimation methods, showing that it is able to capture the magnitude and seasonality of evapotranspiration over the Mississippi River basin. As such, the model is able to reasonably simulate the surface and groundwater budgets over the US, allowing for questions of the relative importance of climate and human impacts to be explored in the future.
The U.S. Army Corps of Engineers, Jacksonville District, plans to deepen the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel, beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the third annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project and contains information pertinent to the data collection during the 2018 water year, from October 2017 to September 2018. Changes to the network on the main stem of the St. Johns River include the addition of (1) three new stations to monitor water temperature and salinity at Racy Point, Shands Bridge, and above Buckman Bridge; (2) stage data collection at both Buckman Bridge and Dames Point Bridge; and (3) three additional parameters, namely stage, velocity, and streamflow direction, to the St. Johns River at Jacksonville and Dames Point Bridge.
Discharge and salinity varied widely during the data collection period, which included residual effects from Hurricane Irma in September 2017 and above-average rainfall for all counties in the project area over the 4-month period from April to July. The annual mean discharge at Durbin Creek was greatest among the tributaries, followed by annual mean discharges at Ortega River, Trout River, Cedar River, Julington Creek, Clapboard Creek, Broward River, Pottsburg Creek, and Dunn Creek. The annual mean discharge for each of the main-stem sites was higher in the 2018 water year than that of the previous 2 years of this study. Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and lowest at Durbin Creek and Ortega River, the sites farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which is expected. Relative to annual mean salinity calculated since the 2016 water year, annual mean salinity at all monitoring locations was lower for the 2018 water year, except for Durbin Creek, which was the same.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201061","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2020, Continuous stream discharge, salinity, and associated data collected in the Lower St. Johns River and its tributaries, Florida, 2018: U.S. Geological Survey Open-File Report 2020–1061, 34 p., https://doi.org/10.3133/ofr20201061.","productDescription":"viii, 34 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-107711","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":375991,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1061/coverthb.jpg"},{"id":375992,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1061/ofr20201061.pdf","text":"Report","size":"25.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1061"}],"country":"United States","state":"Florida","otherGeospatial":"Lower St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.947021484375,\n 29.504159065872624\n ],\n [\n -81.00769042968749,\n 29.504159065872624\n ],\n [\n -81.00769042968749,\n 30.488917676126846\n ],\n [\n -81.947021484375,\n 30.488917676126846\n ],\n [\n -81.947021484375,\n 29.504159065872624\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Fatty acids are well-established biomarkers used to characterize trophic ecology, food-web linkages, and the ecological niche of many different taxa. Most often, fatty acids that are examined include only those previously identified as “dietary” or “extended dietary” biomarkers. Fatty acids considered as nondietary biomarkers, however, represent numerous fatty acids that can be extracted. Some studies may include nondietary fatty acids (i.e., combined with dietary fatty acids), but do not specifically assess them, whereas in other studies, these data are discarded. In this study, we explored whether nondietary biomarker fatty acids can provide worthwhile information by assessing their ability to discriminate intraspecific diversity within and between lakes. Nondietary fatty acids used as biomarkers delineated variation among regions, among locations within a lake, and among ecotypes within a species. Physiological differences that arise from differences in energy processing can be adaptive and linked to habitat use by a species’ ecotype and likely explains why nondietary fatty acid biomarkers can be a relevant tool to delineate intraspecific diversity. Little is known about the nondietary-mediated differences in fatty acid composition, but our results showed that nondietary fatty acid biomarkers can be useful tool in identifying variation.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0343","usgsCitation":"Chavarie, L., Hoffmann, J., Muir, A.M., Krueger, C.C., Bronte, C., Howland, K., Gallagher, S., Sitar, S.P., Hansen, M., Vinson, M., Baker, L., Loseto, L., Tonn, W.M., and Swanson, H., 2020, Dietary versus nondietary fatty acid profiles of lake trout ecotypes from Lake Superior and Great Bear Lake: Are fish really what they eat?: Canadian Journal of Fisheries and Aquatic Sciences, v. 77, no. 7, p. 1209-1220, https://doi.org/10.1139/cjfas-2019-0343.","productDescription":"12 p.","startPage":"1209","endPage":"1220","ipdsId":"IP-117041","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":456118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2019-0343","text":"Publisher Index Page"},{"id":382870,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.22021484375,\n 48.99463598353405\n ],\n [\n -88.516845703125,\n 48.857487002645485\n ],\n [\n -88.626708984375,\n 48.516604348867475\n ],\n [\n -88.857421875,\n 48.334343174592014\n ],\n [\n -88.79150390625,\n 48.574789910928864\n ],\n [\n -89.23095703125,\n 48.42920055556841\n ],\n [\n -89.241943359375,\n 48.3416461723746\n ],\n [\n -89.58251953125,\n 48.026672195436014\n ],\n [\n -90.95581054687499,\n 47.57652571374621\n ],\n [\n -92.076416015625,\n 46.76244305208004\n ],\n [\n -91.82373046875,\n 46.649436163350245\n ],\n [\n -90.823974609375,\n 46.9502622421856\n ],\n [\n -90.867919921875,\n 46.717268685073954\n ],\n [\n -90.977783203125,\n 46.63435070293566\n ],\n [\n -90.68115234375,\n 46.649436163350245\n ],\n [\n -90.37353515625,\n 46.59661864884465\n ],\n [\n -89.791259765625,\n 46.81509864599243\n ],\n [\n -88.9453125,\n 47.03269459852135\n ],\n [\n -88.143310546875,\n 47.487513008956554\n ],\n [\n -87.73681640625,\n 47.45037978769006\n ],\n [\n -88.077392578125,\n 47.32393057095941\n ],\n [\n -88.5498046875,\n 46.882723010671945\n ],\n [\n -88.472900390625,\n 46.76996843356982\n ],\n [\n -88.1982421875,\n 46.92025531537451\n ],\n [\n -87.615966796875,\n 46.78501604269254\n ],\n [\n -87.29736328125,\n 46.5286346952717\n ],\n [\n -87.000732421875,\n 46.50595444552049\n ],\n [\n -86.66015624999999,\n 46.53619267489863\n ],\n [\n -86.7919921875,\n 46.45299704748289\n ],\n [\n -86.0009765625,\n 46.68713141244413\n ],\n [\n -84.979248046875,\n 46.76996843356982\n ],\n [\n -85.078125,\n 46.46813299215554\n ],\n [\n -84.57275390625,\n 46.475699386607516\n ],\n [\n -84.48486328124999,\n 46.717268685073954\n ],\n [\n -84.825439453125,\n 46.98025235521883\n ],\n [\n -84.605712890625,\n 47.27922900257082\n ],\n [\n -85.01220703125,\n 47.628380027447136\n ],\n [\n -84.869384765625,\n 47.98256841921405\n ],\n [\n -85.67138671875,\n 47.931066347509784\n ],\n [\n -86.11083984375,\n 48.151428143221224\n ],\n [\n -86.37451171875,\n 48.748945343432936\n ],\n [\n -88.22021484375,\n 48.99463598353405\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chavarie, Louise","contributorId":156227,"corporation":false,"usgs":false,"family":"Chavarie","given":"Louise","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":809605,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoffmann, John P.","contributorId":207031,"corporation":false,"usgs":false,"family":"Hoffmann","given":"John P.","affiliations":[{"id":12608,"text":"USGS, retired","active":true,"usgs":false}],"preferred":false,"id":809606,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muir, A. 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P.","contributorId":248505,"corporation":false,"usgs":false,"family":"Sitar","given":"S.","email":"","middleInitial":"P.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":809612,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hansen, M.J.","contributorId":248626,"corporation":false,"usgs":false,"family":"Hansen","given":"M.J.","affiliations":[],"preferred":false,"id":809613,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vinson, Mark R. 0000-0001-5256-9539 mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809614,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Baker, L.F.","contributorId":248633,"corporation":false,"usgs":false,"family":"Baker","given":"L.F.","email":"","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":809615,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Loseto, L.L.","contributorId":248683,"corporation":false,"usgs":false,"family":"Loseto","given":"L.L.","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":809616,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Tonn, William M.","contributorId":204532,"corporation":false,"usgs":false,"family":"Tonn","given":"William","email":"","middleInitial":"M.","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":809617,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Swanson, H.","contributorId":152186,"corporation":false,"usgs":false,"family":"Swanson","given":"H.","email":"","affiliations":[],"preferred":false,"id":809618,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70217801,"text":"70217801 - 2020 - Improved fish counting method accurately quantifies high‐density fish movement in dual‐frequency identification sonar data files from a coastal wetland environment","interactions":[],"lastModifiedDate":"2021-02-03T21:20:22.369695","indexId":"70217801","displayToPublicDate":"2020-07-07T06:46:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Improved fish counting method accurately quantifies high‐density fish movement in dual‐frequency identification sonar data files from a coastal wetland environment","docAbstract":"There are many ways to quantify fish movement through shallow‐water habitats, but most noninvasive methods (e.g., visual counts) are not effective in turbid coastal wetland waters of the Great Lakes. Dual‐frequency identification sonar (DIDSON) technology (Sound Metrics) offers a noninvasive, hydroacoustic‐based approach to characterize fish movement in wetlands and other habitats by collecting highly detailed fish movement data regardless of light and water quality conditions. High‐resolution data can be analyzed to estimate fish movement in areas where visual observations are difficult. However, enumerating a complex mix of fish sizes by manually counting fish visible in echogram files requires training and is very time consuming. Therefore, four counting techniques were tested to estimate fish abundance from DIDSON echograms that were collected at a hydrologically reconnected coastal wetland in the Great Lakes. Briefly, the four counting methods were (1) manually viewing the entire length of the echogram (full‐hour manual count), (2) manually viewing subsections of the echogram before generating fish estimates by per‐minute average (subsample manual count), (3) using Echoview automated software to generate automated estimates, and (4) using DIDSON viewer software to generate automated estimates. Over 800 echogram‐hours were recorded over a 9‐month period at an open‐flow water control structure connecting a coastal wetland to a tributary to Lake Erie. Commercial fish tracking software (Echoview) and custom software scripts from Milne Technologies were used to semi‐automate fish count estimates for a small subset of data. Semi‐automated software counts were compared to manual counts of identical data files to assess differences in accuracy, cost, processing time, and counter effort. Semi‐automated fish count estimates using Echoview and custom pre‐ and postprocessing software scripts did not differ from baseline manual counts, suggesting that the semi‐automated count process could be a reliable tool to increase efficiency when processing large DIDSON data sets.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10451","usgsCitation":"Eggleston, M., Milne, S.W., Ramsay, M., and Kowalski, K., 2020, Improved fish counting method accurately quantifies high‐density fish movement in dual‐frequency identification sonar data files from a coastal wetland environment: North American Journal of Fisheries Management, v. 40, no. 4, p. 883-892, https://doi.org/10.1002/nafm.10451.","productDescription":"10 p.","startPage":"883","endPage":"892","ipdsId":"IP-108651","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":436893,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CMU62C","text":"USGS data release","linkHelpText":"DIDSON video collection of Coastal Lake Erie Wetland, Lucas Co, Ohio in 2011"},{"id":382918,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.59277343749999,\n 40.78054143186033\n ],\n [\n -75.6298828125,\n 40.78054143186033\n ],\n [\n -75.6298828125,\n 49.55372551347579\n ],\n [\n -92.59277343749999,\n 49.55372551347579\n ],\n [\n -92.59277343749999,\n 40.78054143186033\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Eggleston, Michael R. 0000-0003-1068-3290","orcid":"https://orcid.org/0000-0003-1068-3290","contributorId":248759,"corporation":false,"usgs":true,"family":"Eggleston","given":"Michael R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809797,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milne, Scott W.","contributorId":248760,"corporation":false,"usgs":false,"family":"Milne","given":"Scott","email":"","middleInitial":"W.","affiliations":[{"id":40886,"text":"Milne Technologies","active":true,"usgs":false}],"preferred":false,"id":809798,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramsay, Maxwell","contributorId":248761,"corporation":false,"usgs":false,"family":"Ramsay","given":"Maxwell","email":"","affiliations":[],"preferred":false,"id":809799,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809800,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210956,"text":"70210956 - 2020 - Accidental chlorophacinone exposure of lactating ewes: Clinical follow-up and human health dietary implications","interactions":[],"lastModifiedDate":"2020-08-04T14:22:37.376039","indexId":"70210956","displayToPublicDate":"2020-07-06T10:25:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1685,"text":"Food and Chemical Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Accidental chlorophacinone exposure of lactating ewes: Clinical follow-up and human health dietary implications","docAbstract":"Anticoagulant rodenticides are widely used for rodent control in agricultural and urban settings. Their intense use can sometimes result in accidental exposure and even poisoning of livestock. Can milk, eggs or meat derived from such accidentally exposed animals be consumed by humans? Data on the pharmacokinetics of chlorophacinone in milk of accidentally exposed ewes were used to estimate the risk associated with its consumption. Three days after accidental ingestion, chlorophacinone was detected in plasma of 18 ewes, with concentrations exceeding 100 ng/mL in 11 animals. Chlorophacinone was detected in milk on day 2 post-exposure and remained quantifiable for at least 7 days in milk of these 11 ewes. Concentrations in milk were much lower than in plasma and decreased quickly (mean half-life of 2 days). This study demonstrated dose-dependent mammary transfer of ingested chlorophacinone. Variation in prothrombin time (PT) on Day 3 suggested that some of the ewes that ingested chlorophacinone may have been adversely affected, but PT did not facilitate estimation of the quantity of chlorophacinone consumed. Using safety factors described in the literature, consumption of dairy products derived from these ewes after a one-week withdrawal period would pose low risk to consumers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fct.2020.111518","usgsCitation":"Moriceau, M., Lefebvre, S., Fourel, I., Benoit, E., Rattner, B.A., and Lattard, V., 2020, Accidental chlorophacinone exposure of lactating ewes: Clinical follow-up and human health dietary implications: Food and Chemical Toxicology, v. 143, 111518, 8 p., https://doi.org/10.1016/j.fct.2020.111518.","productDescription":"111518, 8 p.","ipdsId":"IP-117861","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":456122,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-02896032","text":"External Repository"},{"id":376204,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"France","otherGeospatial":"Creuse","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n 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tale","interactions":[],"lastModifiedDate":"2020-07-22T14:40:18.175529","indexId":"70211293","displayToPublicDate":"2020-07-06T09:37:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Dating silica sinter (geyserite): A cautionary tale","docAbstract":"We describe a new effort to date hydrothermal silica sinter deposits (geyserite) from the Upper Geyser Basin of Yellowstone National Park using 14C of co-deposited organic matter, U-series and cosmogenic 10Be methods. A majority of the samples were collected from stratigraphic sections, mainly at Riverside, Giant, and Castle Geysers. Ages obtained from 41 14C analyses range from modern to 12.1 cal ka BP. Nearly all the 14C ages show inconsistencies with their stratigraphic positions, and several replicate 14C analyses from the same sample result in significantly different ages. The δ13C values of the organic material in the sinter range from -26.6‰ to -12.7‰. The more enriched values are attributed to microbial fixation of dissolved inorganic carbon (DIC), which has heavier δ13C values and is 14C-depleted relative to atmospheric CO2, leading to apparent older ages. U-series analyses on 4 samples yielded ages between 2.2 and 7.4 ka. Large 230Th/U age uncertainties in the sinter, due to low uranium concentrations along with elevated 232Th and associated initial 230Th, make these ages imprecise for use on Holocene deposits. A single cosmogenic 10Be exposure age of 596±18 ka is considerably older than the age of underlying rhyolite and is thus unreliable. This apparent old age results from contamination by meteoric 10Be trapped in the opal that overprints the very small amount of cosmogenic 10Be. By presenting the problems we encountered and discussing their probable cause, this paper highlights the difficulty in obtaining reliable, high-precision geochronological data necessary to use sinter deposits as paleoenvironmental and paleo-hydrothermal archives.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.106991","usgsCitation":"Churchill, D.M., Manga, M., Hurwitz, S., Peek, S., Licciardi, J., and Paces, J.B., 2020, Dating silica sinter (geyserite): A cautionary tale: Journal of Volcanology and Geothermal Research, v. 402, 106991, 12 p., https://doi.org/10.1016/j.jvolgeores.2020.106991.","productDescription":"106991, 12 p.","ipdsId":"IP-119376","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":376631,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.060791015625,\n 43.88205730390537\n ],\n [\n -109.3304443359375,\n 43.88205730390537\n ],\n [\n -109.3304443359375,\n 44.999767019181284\n ],\n [\n -111.060791015625,\n 44.999767019181284\n ],\n [\n -111.060791015625,\n 43.88205730390537\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"402","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Churchill, Dakota M.","contributorId":229593,"corporation":false,"usgs":false,"family":"Churchill","given":"Dakota","email":"","middleInitial":"M.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":793593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manga, Michael","contributorId":229594,"corporation":false,"usgs":false,"family":"Manga","given":"Michael","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":793594,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":793595,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peek, Sara 0000-0002-9770-6557","orcid":"https://orcid.org/0000-0002-9770-6557","contributorId":209971,"corporation":false,"usgs":true,"family":"Peek","given":"Sara","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":793596,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Licciardi, Joseph","contributorId":229595,"corporation":false,"usgs":false,"family":"Licciardi","given":"Joseph","affiliations":[{"id":41689,"text":"U. 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A water-availability study of the Mississippi River alluvial plain, and particularly the Mississippi River Valley alluvial aquifer (MRVA), is ongoing. Software (visGWDBmrva) has been released as part of the study that demonstrates groundwater informatics for the aquifer. Considerable water-level data collected by multiple agencies over a seven-state area exist (18,903 wells; 287,272 measurements [April 22, 2019]). Data and metadata quality assurance methods, basic statistics, hydrograph visualization, outlier identification, hypothesis testing, and time-series modeling are described. Two approaches (generalized additive models [GAMs] and support vector machines [SVMs]) are used for data interpolation and extension to monthly water-level estimates. Numerical congruence between GAM and SVM estimates will be useful to limit inclusion of monthly estimates from subsequent science activities.
The US Geological Survey (USGS) is currently (2020) integrating its water science programs to better address the nation’s greatest water resource challenges now and into the future. This integration will rely, in part, on data from 10 or more intensively monitored river basins from across the USA. A team of USGS scientists was convened to develop a systematic, quantitative approach to prioritize candidate basins for this monitoring investment to ensure that, as a group, the 10 basins will support the assessment and forecasting objectives of the major USGS water science programs. Candidate basins were the level-4 hydrologic units (HUC04) with some of the smaller HUC04s being combined; median candidate-basin area is 46,600 km2. Candidate basins for the contiguous United States (CONUS) were grouped into 18 hydrologic regions. Ten geospatial variables representing land use, climate change, water use, water-balance components, streamflow alteration, fire risk, and ecosystem sensitivity were selected to rank candidate basins within each of the 18 hydrologic regions. The two highest ranking candidate basins in each of the 18 regions were identified as finalists for selection as “Integrated Water Science Basins”; final selection will consider input from a variety of stakeholders. The regional framework, with only one basin selected per region, ensures that as a group, the basins represent the range in major drivers of the hydrologic cycle. Ranking within each region, primarily based on anthropogenic stressors of water resources, ensures that settings representing important water-resource challenges for the nation will be studied.
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Currently, the Monitoring Trends in Burn Severity (MTBS) project maps the fire perimeter and burn severity of all large fires on public lands. Although the MTBS project maps a large proportion of the fire acreage, it maps a smaller proportion of the actual number of fires in the United States, thereby creating a data gap. To fill this data gap, fire scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS; Sioux Falls, South Dakota) proposed creating an open-source Fire Mapping Tool (FMT; available at https://mtbs.gov/qgis-fire-mapping-tool) as part of a two-phase National Aeronautics and Space Administration (NASA) Applied Fire Science Program grant. Phase II developed the FMT to map burn perimeters and severity not included in the MTBS database. This paper will focus on Phase II and will explain the algorithms that enhance the FMT’s functionality, demonstrate fire mapping procedures, and provide an example comparison between MTBS analyst fire products and those mapped using the FMT. The overall goal in the production of the FMT was to provide a freely available tool that can be used to map fires anywhere in the world.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the fire continuum- Preparing for the future of wildland fire","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"U.S. Forest Service","usgsCitation":"Picotte, J.J., 2020, Development of a new open-source tool to map burned area and burn severity, in Proceedings of the fire continuum- Preparing for the future of wildland fire, p. 182-194.","productDescription":"13 p.","startPage":"182","endPage":"194","ipdsId":"IP-097770","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":377462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377460,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.fs.fed.us/rm/pubs_series/rmrs/proc/rmrs_p078.pdf"}],"country":"United States","state":"Washington","otherGeospatial":"Paradise Fire","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.16404724121094,\n 47.5369059030354\n ],\n [\n -123.94020080566408,\n 47.62467785241324\n ],\n [\n -123.87187957763672,\n 47.68018294648414\n ],\n [\n -123.88011932373045,\n 47.70352370383532\n ],\n [\n -123.94191741943358,\n 47.674866264152534\n ],\n [\n -123.98929595947264,\n 47.64943116129891\n ],\n [\n -124.07718658447266,\n 47.636246278753234\n ],\n [\n -124.16130065917969,\n 47.60500565066204\n ],\n [\n -124.20387268066405,\n 47.5820839916191\n ],\n [\n -124.23099517822266,\n 47.56216409801383\n ],\n [\n -124.16439056396483,\n 47.535746978239125\n ],\n [\n -124.16404724121094,\n 47.5369059030354\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":796088,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211681,"text":"70211681 - 2020 - The potential of using dynamic strains in earthquake early warning applications","interactions":[],"lastModifiedDate":"2020-09-10T20:25:08.380546","indexId":"70211681","displayToPublicDate":"2020-07-01T17:56:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"The potential of using dynamic strains in earthquake early warning applications","docAbstract":"We investigate the potential of using borehole strainmeter data from the Network of the Americas (NOTA) and the U.S. Geological Survey networks to estimate earthquake moment magnitudes for earthquake early warning (EEW) applications. We derive an empirical equation relating peak dynamic strain, earthquake moment magnitude, and hypocentral distance, and investigate the effects of different types of instrument calibration on model misfit. We find that raw (uncalibrated) strains fit the model as accurately as calibrated strains. We test the model by estimating moment magnitudes of the largest two earthquakes in the July 2019 Ridgecrest earthquake sequence—the M 6.4 foreshock and the M 7.1 mainshock—using two strainmeters located within ∼50 km of the rupture. In both the cases, the magnitude based on the dynamic strain component is within ∼0.1–0.4 magnitude units of the catalog moment magnitude. We then compare the temporal evolution of our strain‐derived magnitudes for the largest two Ridgecrest events to the real‐time performance of the ShakeAlert EEW System (SAS). The final magnitudes from NOTA borehole strainmeters are close to SAS real‐time estimates for the M 6.4 foreshock, and significantly more accurate for the M 7.1 mainshock.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190385","usgsCitation":"Farghal, N.S., Barbour, A.J., and Langbein, J., 2020, The potential of using dynamic strains in earthquake early warning applications: Seismological Research Letters, v. 91, no. 5, p. 2817-2827, https://doi.org/10.1785/0220190385.","productDescription":"11 p.","startPage":"2817","endPage":"2827","ipdsId":"IP-112135","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":377141,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 32.54681317351514\n ],\n [\n -114.345703125,\n 32.76880048488168\n ],\n [\n -114.345703125,\n 34.34343606848294\n ],\n [\n -120.05859375,\n 39.232253141714885\n ],\n [\n -120.14648437499999,\n 41.96765920367816\n ],\n [\n -119.61914062499999,\n 48.83579746243093\n ],\n [\n -123.96972656249999,\n 49.38237278700955\n ],\n [\n -126.3427734375,\n 49.866316729538674\n ],\n [\n -127.08984375000001,\n 48.22467264956519\n ],\n [\n -124.8486328125,\n 47.39834920035926\n ],\n [\n -124.76074218749999,\n 44.74673324024678\n ],\n [\n -125.33203125,\n 41.343824581185686\n ],\n [\n -124.01367187499999,\n 38.238180119798635\n ],\n [\n -121.728515625,\n 35.28150065789119\n ],\n [\n -119.66308593749999,\n 33.7243396617476\n ],\n [\n -117.158203125,\n 32.54681317351514\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"91","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Farghal, Noha Sameh Ahmed 0000-0001-8423-5066","orcid":"https://orcid.org/0000-0001-8423-5066","contributorId":237040,"corporation":false,"usgs":true,"family":"Farghal","given":"Noha","email":"","middleInitial":"Sameh Ahmed","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":795045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barbour, Andrew J 0000-0001-6473-5493 abarbour@usgs.gov","orcid":"https://orcid.org/0000-0001-6473-5493","contributorId":237041,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew","email":"abarbour@usgs.gov","middleInitial":"J","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":795046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langbein, John 0000-0002-7821-8101","orcid":"https://orcid.org/0000-0002-7821-8101","contributorId":212735,"corporation":false,"usgs":true,"family":"Langbein","given":"John","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":795047,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228337,"text":"70228337 - 2020 - Living on the edge: Multi-scale analyses of bird habitat use in coastal marshes of Barataria Basin, Louisiana, USA","interactions":[],"lastModifiedDate":"2022-02-09T22:45:53.971165","indexId":"70228337","displayToPublicDate":"2020-07-01T16:38:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Living on the edge: Multi-scale analyses of bird habitat use in coastal marshes of Barataria Basin, Louisiana, USA","docAbstract":"Coastal marsh loss, combined with expected sea-level rise, will cause inundation and extensive shifts to vegetation and salinity regimes that may affect the bird species dependent on coastal ecosystems worldwide. Within coastal marsh habitats, birds provide key targets for coastal management goals. However, limited information on bird-habitat relationships within coastal marshes inhibits the development of restoration projects targeted to bird species. We surveyed birds bi-monthly within Barataria Basin, LA from July 2014 to December 2015 to compare their use between fresh and saline coastal marshes. Additionally, we examined habitat use at finer spatial scales to assess preference for marsh edge microhabitats. Edge habitat supported 1.8 times more bird species (guild) richness than emergent and open water habitat. We concluded that future modelling efforts would be improved if models incorporate edge effects for birds in coastal marshes that extend 20 m from emergent vegetation into open water, with a reduced effect if marsh types convert from fresh to saline. Our data will be useful to simulate the effects of changes in marsh type, area, and edge on habitat quality for birds in coastal Louisiana and will inform habitat restoration and management decisions aimed at optimizing bird use.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-020-01324-2","usgsCitation":"Patton, B., Nyman, J.A., and La Peyre, M., 2020, Living on the edge: Multi-scale analyses of bird habitat use in coastal marshes of Barataria Basin, Louisiana, USA: Wetlands, v. 40, p. 2041-2054, https://doi.org/10.1007/s13157-020-01324-2.","productDescription":"14 p.","startPage":"2041","endPage":"2054","ipdsId":"IP-098169","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499826,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.lsu.edu/agrnr_pubs/602","text":"External Repository"},{"id":395743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Barataria Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.8349609375,\n 28.714678586705976\n ],\n [\n -89.033203125,\n 28.714678586705976\n ],\n [\n -89.033203125,\n 30.32547125932808\n ],\n [\n -90.8349609375,\n 30.32547125932808\n ],\n [\n -90.8349609375,\n 28.714678586705976\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2020-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Patton, Brett 0000-0002-7396-3452 pattonb@usgs.gov","orcid":"https://orcid.org/0000-0002-7396-3452","contributorId":5458,"corporation":false,"usgs":true,"family":"Patton","given":"Brett","email":"pattonb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":833827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nyman, J. A.","contributorId":275213,"corporation":false,"usgs":false,"family":"Nyman","given":"J.","email":"","middleInitial":"A.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":833828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833829,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211872,"text":"70211872 - 2020 - Regionally continuous Miocene rhyolites beneath the eastern Snake River Plain reveal localized flexure at its western margin: Idaho National Laboratory and vicinity","interactions":[],"lastModifiedDate":"2020-12-15T20:23:40.067951","indexId":"70211872","displayToPublicDate":"2020-07-01T16:07:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6000,"text":"The Mountain Geologist","active":true,"publicationSubtype":{"id":10}},"title":"Regionally continuous Miocene rhyolites beneath the eastern Snake River Plain reveal localized flexure at its western margin: Idaho National Laboratory and vicinity","docAbstract":"The eastern Snake River Plain (ESRP) is a northeast-trending topographic basin interpreted to be the result of the time-transgressive track of the North American plate above the Yellowstone hotspot. The track is defined by the age progression of silicic volcanic rocks exposed along the margins of the ESRP. However, the bulk of these silicic rocks are buried under 1 to 3 kilometers of younger basalts. Here, silicic volcanic rocks recovered from boreholes that penetrate below the basalts, including INEL-1, WO-2 and new deep borehole USGS-142, are correlated with one another and to surface exposures to assess various models for ESRP subsidence. These correlations are established on U/Pb zircon and 40Ar/39Ar sanidine age determinations, phenocryst assemblages, major and trace element geochemistry, δ18O isotopic data from selected phenocrysts, and initial εHf values of zircon. These data suggest a correlation of: (1) the newly documented 8.1 ± 0.2 Ma rhyolite of Butte Quarry (sample 17KS03), exposed near Arco, Idaho to the upper-most Picabo volcanic field rhyolites found in borehole INEL-1; (2) the 6.73 ± 0.02 Ma East Arco Hills rhyolite (sample 16KS02) to the Blacktail Creek Tuff, which was also encountered at the bottom of borehole WO-2; and (3) the 6.42 ± 0.07 Ma rhyolite of borehole USGS-142 to the Walcott Tuff B encountered in deep borehole WO-2. These results show that rhyolites found along the western margin of the ESRP dip ~20º south-southeast toward the basin axis, and then gradually tilt less steeply in the subsurface as the axis is approached. This subsurface pattern of tilting is consistent with a previously proposed crustal flexural model of subsidence based only on surface exposures, but is inconsistent with subsidence models that require accommodation of ESRP subsidence on either a major normal fault or strike-slip fault.","language":"English","publisher":"Rocky Mountain Association of Geologists","doi":"10.31582/rmag.mg.57.3.241","usgsCitation":"Schusler, K.L., Pearson, D.M., McCurry, M.J., Bartholomay, R.C., and Anders, M.H., 2020, Regionally continuous Miocene rhyolites beneath the eastern Snake River Plain reveal localized flexure at its western margin: Idaho National Laboratory and vicinity: The Mountain Geologist, v. 57, no. 3, p. 241-270, https://doi.org/10.31582/rmag.mg.57.3.241.","productDescription":"30 p.","startPage":"241","endPage":"270","ipdsId":"IP-112371","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":377936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.18002319335938,\n 43.41302868475145\n ],\n [\n -111.93145751953125,\n 43.41302868475145\n ],\n [\n -111.93145751953125,\n 43.55651037504758\n ],\n [\n -112.18002319335938,\n 43.55651037504758\n ],\n [\n -112.18002319335938,\n 43.41302868475145\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Schusler, Kyle L.","contributorId":237858,"corporation":false,"usgs":false,"family":"Schusler","given":"Kyle","email":"","middleInitial":"L.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":795484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearson, David M.","contributorId":237860,"corporation":false,"usgs":false,"family":"Pearson","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":795485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCurry, Michael J.","contributorId":237861,"corporation":false,"usgs":false,"family":"McCurry","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":795486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartholomay, Roy C. 0000-0002-4809-9287 rcbarth@usgs.gov","orcid":"https://orcid.org/0000-0002-4809-9287","contributorId":1131,"corporation":false,"usgs":true,"family":"Bartholomay","given":"Roy","email":"rcbarth@usgs.gov","middleInitial":"C.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anders, Mark H.","contributorId":237862,"corporation":false,"usgs":false,"family":"Anders","given":"Mark","email":"","middleInitial":"H.","affiliations":[{"id":39266,"text":"St. Lawrence University","active":true,"usgs":false}],"preferred":false,"id":795488,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210894,"text":"sir20205064 - 2020 - A summary of water-quality monitoring in San Francisco Bay in water year 2017","interactions":[],"lastModifiedDate":"2020-07-01T21:11:28.152523","indexId":"sir20205064","displayToPublicDate":"2020-07-01T12:33:01","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5064","displayTitle":"A Summary of Water-Quality Monitoring in San Francisco Bay in Water Year 2017","title":"A summary of water-quality monitoring in San Francisco Bay in water year 2017","docAbstract":"This report summarizes the activities of the U.S. Geological Survey (USGS) San Francisco Bay Water-Quality Monitoring and Sediment Transport Project during water year 2017, including an explanation of methods employed, stations operated, and a graphical summary of data for the period of record for stations operational in water year 2017. In cooperation with partner agencies, the USGS maintains a network of sensors that continuously and autonomously measures water-quality parameters in San Francisco Bay including water temperature, specific conductance, turbidity, and suspended-sediment concentration. Data are collected at several locations in the estuary by a network of water-quality sondes sampled at 15-minute intervals. Methods of data collection are presented along with documentation of the regression models utilized to estimate suspended-sediment concentration from observed turbidity, a commonly utilized surrogate to estimate suspended-sediment concentration. The goals of the data collection effort are to (1) obtain long-term, high-frequency, and high-quality data to describe San Francisco Bay water quality; (2) make the data publicly available on the USGS National Water Information System data portal; and (3) help improve understanding of the spatial and temporal variability of water quality in the estuary, informing management decisions regarding restoration, water supply, navigation, and ecology.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205064","usgsCitation":"Livsey, D., and Downing-Kunz, M., 2020, A summary of water-quality monitoring in San Francisco Bay in water year 2017: U.S. Geological Survey Scientific Investigations Report 2020–5064, 78 p., https://doi.org/10.3133/sir20205064.","productDescription":"Report: vi, 78 p.; Data Release","numberOfPages":"78","onlineOnly":"Y","ipdsId":"IP-104269","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376068,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"National Water Information System"},{"id":376066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5064/coverthb.jpg"},{"id":376067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5064/sir20205064.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.62390136718749,\n 37.35050947036205\n ],\n [\n -121.7340087890625,\n 37.35050947036205\n ],\n [\n -121.7340087890625,\n 38.22307753495298\n ],\n [\n -122.62390136718749,\n 38.22307753495298\n ],\n [\n -122.62390136718749,\n 37.35050947036205\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Hydrographic features derived from U.S. Geological Survey (USGS) 3D Elevation Program data, and collected for use by the USGS, must meet the specifications described in this document. The specifications described herein pertain to the final product delivered to the USGS, not to methods used to derive the hydrographic features. The specifications describe the collection area, spatial reference system, attribute table structure, feature codes and values, delineation of hydrographic features, topology, positional assessment, metadata, and delivery formats. A companion document, Elevation-Derived Hydrography—Representation, Extraction, Attribution, and Delineation Rules, defines the fields, domains, and minimum feature collection requirements for hydrography features derived from elevation data. Hydrographic features collected to this specification will be suitable for using as breaklines to hydroflatten digital elevation models, processing for preconflation of features to the National Hydrography Dataset, and using for hydroenforcement of digital elevation models.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11 Collection and Delineation of Spatial Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B11","usgsCitation":"Terziotti, S., and Archuleta, C.M., 2020, Elevation-Derived Hydrography Acquisition Specifications: U.S. Geological Survey Techniques and Methods, book 11, chap. B11, 74 p., https://doi.org/10.3133/tm11B11.","productDescription":"Report: vii, 74 p.; Companion Report","numberOfPages":"86","onlineOnly":"Y","ipdsId":"IP-111691","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":376021,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/tm11B12","text":"T&M 11–B12","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 11–B12","linkHelpText":"— Elevation-Derived Hydrography—Representation, Extraction, Attribution, and Delineation Rules"},{"id":376020,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b11/tm11b11.pdf","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 11–B11"},{"id":376019,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b11/coverthb.jpg"}],"contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
With the increasing availability of 3D Elevation Program (3DEP) quality high resolution elevation data across the United States and the pressing need for better integrated elevation and hydrography data, the U.S. Geological Survey is developing guidance to improve the horizontal and vertical alignment of these datasets. The U.S. Geological Survey is providing the Elevation-Derived Hydrography—Acquisition Specifications for the acquisition of elevation-derived hydrography for the United States, and the companion document The Elevation-Derived Hydrography—Representation, Extraction, Attribution, and Delineation (READ) Rules, which describes the parameters for the portrayal of hydrography features as derived from elevation data. The READ Rules provide a definition, example, attribute value list, delineation instructions, representation rules, and data extraction rules for each hydrography feature required to meet the Elevation-Derived Hydrography—Acquisition Specifications.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11 Collection and Delineation of Spatial Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B12","usgsCitation":"Archuleta, C.M., and Terziotti, S., 2020, Elevation-Derived Hydrography—Representation, Extraction, Attribution, and Delineation Rules (ver. 1.1, January 2023): U.S. Geological Survey Techniques and Methods, book 11, chap. B12, 60 p., https://doi.org/10.3133/tm11B12.","productDescription":"Report: ix, 60 p.; Companion Report","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-111692","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":410927,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/11/b12/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":376005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b12/coverthb3.jpg"},{"id":376018,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/tm11B11","text":"T&M 11–B11","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 11–B11","linkHelpText":"— Elevation-Derived Hydrography Acquisition Specifications"},{"id":376006,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b12/tm11b12.pdf","text":"Report","size":"36.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 11–B12"}],"edition":"Version 1.0: July 1, 2020; Version 1.1: January 3, 2023","contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
To explore connections between rock strength and rock falls, we undertook a comprehensive rock mechanics testing program for six granitic rock types in Yosemite Valley (California, USA) where rock falls are a common geomorphic and sometimes hazardous process. We collected samples from boulders located at the base of cliffs, with the inherent assumption that the intact boulders should provide reasonable estimates of full-strength values. Our testing program included unconfined compressive strength tests, triaxial compressive strength tests, Brazilian tensile strength tests, and Mode I fracture toughness strength testing using two different types of samples – chevron bend (CB) and cracked chevron notched Brazilian disk (CCNBD). Our results, consisting of 88 individual tests, provide the most detailed evaluation of rock strength in Yosemite Valley to date. These results provide the data needed to evaluate the various failure modes (e.g., shear failure of wedge instabilities, tensile failure of overhangs) that might be expected for rock falls from cliffs in Yosemite. We expect that these data will provide an important resource for the evaluation of rock falls and other geomorphological studies in Yosemite National Park.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"54th US Rock Mechanics/Geomechanics Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"American Rock Mechanics Association","collaboration":"National Park Service, University of Lausanne, École Polytechnique Fédérale de Lausanne – EPFL","usgsCitation":"Collins, B.D., Sandrone, F., Gastaldo, L., Stock, G.M., and Jaboyedoff, M., 2020, A new data set of granitic rock strength values from Yosemite Valley, California: Applications to rock fall assessment, in 54th US Rock Mechanics/Geomechanics Symposium, 7 p.","productDescription":"7 p.","ipdsId":"IP-116600","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":378919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":378800,"type":{"id":15,"text":"Index Page"},"url":"https://www.onepetro.org/conference-paper/ARMA-2020-1412"}],"country":"United States","state":"California","otherGeospatial":"Yosemite Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.11627197265624,\n 37.60335225883687\n ],\n [\n -118.92974853515624,\n 37.60335225883687\n ],\n [\n -118.92974853515624,\n 38.151837403006766\n ],\n [\n -120.11627197265624,\n 38.151837403006766\n ],\n [\n -120.11627197265624,\n 37.60335225883687\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":799727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sandrone, Federica","contributorId":225125,"corporation":false,"usgs":false,"family":"Sandrone","given":"Federica","email":"","affiliations":[{"id":27718,"text":"Ecole Polytechnique Federale de Lausanne","active":true,"usgs":false}],"preferred":true,"id":799728,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gastaldo, Laurent","contributorId":225126,"corporation":false,"usgs":false,"family":"Gastaldo","given":"Laurent","email":"","affiliations":[{"id":27718,"text":"Ecole Polytechnique Federale de Lausanne","active":true,"usgs":false}],"preferred":true,"id":799729,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stock, Greg M.","contributorId":202873,"corporation":false,"usgs":false,"family":"Stock","given":"Greg","email":"","middleInitial":"M.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":799730,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jaboyedoff, Michel","contributorId":205586,"corporation":false,"usgs":false,"family":"Jaboyedoff","given":"Michel","affiliations":[{"id":37117,"text":"University of Lausanne (Switzerland)","active":true,"usgs":false}],"preferred":false,"id":799731,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210975,"text":"70210975 - 2020 - Mortality and cholinesterase inhibition in butterflies following aerial naled applications for mosquito control on the National Key Deer Refuge","interactions":[],"lastModifiedDate":"2020-08-04T14:21:41.420781","indexId":"70210975","displayToPublicDate":"2020-07-01T10:07:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":887,"text":"Archives of Environmental Contamination and Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Mortality and cholinesterase inhibition in butterflies following aerial naled applications for mosquito control on the National Key Deer Refuge","docAbstract":"Natural resource managers are concerned about the impacts of aerial ultra-low volume spray (ULV) of insecticides for mosquito control (i.e., mosquito adulticides) and seek science-driven management recommendations that reduce risk but allow vector control for nearby human populations. Managers at the National Key Deer Refuge (Florida Keys, FL) are concerned for ULV effects upon conservation efforts for imperiled butterflies (Florida leafwing [Anaea troglodyta floridalis] and Bartram’s hairstreak [Strymon acis bartrami] butterflies). No-spray zones were designated for protection of those butterflies, but their effectiveness for mitigation is unclear. To address this uncertainty, cholinesterase activity (ChE) and mortality were monitored for caged butterflies gulf fritillary [Agraulis vanilla] and great southern white [Ascia monuste]) deployed on the Refuge during three aerial ULV applications of the insecticide naled. Residue samplers also were deployed to estimate butterfly exposure. Spray efficacy against mosquitoes was assessed by deploying caged mosquitoes at the same locations as the butterflies. Average naled residue levels on filter paper samplers in the target area (1882–2898 µg/m2) was significantly greater than in the no-spray zone (9–1562 µg/m2). Differences between the no-spray zone and target area for butterfly mortality and ChE were inconsistent. Average mortality was significantly lower, and average ChE was significantly higher in the no-spray zone for larvae of one species but not for larvae of the other species. Mosquito mortality did not differ significantly between the two areas. Data from the present study reflect the inconsistent effectiveness of no-spray zones on the Refuge using standard methods employed at the time by the vector control agency in the Florida Keys and possibly by other vector control agencies in similar coastal environments. Furthermore, these findings helped to guide the design and to improve the conservation value of future no-spray zone delineations while allowing for treatment in areas where mosquito control is necessary for vector-borne disease reduction.
","language":"English","publisher":"Springer","doi":"10.1007/s00244-020-00745-8","usgsCitation":"Bargar, T., Anderson, C., and Sowers, A., 2020, Mortality and cholinesterase inhibition in butterflies following aerial naled applications for mosquito control on the National Key Deer Refuge: Archives of Environmental Contamination and Toxicology, v. 79, p. 233-245, https://doi.org/10.1007/s00244-020-00745-8.","productDescription":"13 p.","startPage":"233","endPage":"245","ipdsId":"IP-117059","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":436900,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74X55ZP","text":"USGS data release","linkHelpText":"Cholinesterase inhibition in butterflies on the National Key Deer Refuge following aerial application of a mosquito control pesticide"},{"id":376201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"National Key Deer Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.45092010498047,\n 24.63671928411111\n ],\n [\n -81.31050109863281,\n 24.63671928411111\n ],\n [\n -81.31050109863281,\n 24.778318518683687\n ],\n [\n -81.45092010498047,\n 24.778318518683687\n ],\n [\n -81.45092010498047,\n 24.63671928411111\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"79","noUsgsAuthors":false,"publicationDate":"2020-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Bargar, Timothy 0000-0001-8588-3436","orcid":"https://orcid.org/0000-0001-8588-3436","contributorId":211833,"corporation":false,"usgs":true,"family":"Bargar","given":"Timothy","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":792323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Chad","contributorId":222871,"corporation":false,"usgs":false,"family":"Anderson","given":"Chad","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":792324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sowers, Anthony 0000-0002-9654-5341","orcid":"https://orcid.org/0000-0002-9654-5341","contributorId":222872,"corporation":false,"usgs":false,"family":"Sowers","given":"Anthony","email":"","affiliations":[{"id":40611,"text":"U.S. Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":792325,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216660,"text":"70216660 - 2020 - Gambel’s quail survey variability and implications for survey design in the Mohave Desert","interactions":[],"lastModifiedDate":"2020-11-27T13:36:19.520157","indexId":"70216660","displayToPublicDate":"2020-07-01T07:36:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Gambel’s quail survey variability and implications for survey design in the Mohave Desert","docAbstract":"Careful design of a wildlife population monitoring strategy is necessary to obtain accurate and precise results whether the purpose of the survey is development of habitat suitability models, to estimate abundance, or assess site occupancy. Important characteristics to consider in survey design are sources of elevated variability, particularly within‐subject variability, which increases the amount of data needed to achieve statistical certainty either in terms of population trend analysis, hypothesis testing, or statistical power. However, alternative objectives, such as associating counts with habitat characteristics, may benefit from increased variation among counts when differences covary with habitat measures. This difference can result in competing needs when developing survey protocols. We investigated the relative precision of differing gamebird monitoring protocols to identify methods with the greatest statistical efficiency. We assessed call‐count transects using standard Breeding Bird Survey protocols (Passive call‐counts) and modified by including longer survey periods and call playback (Active call‐counts), autonomous recording units with supervised call detection (ARU‐recorded calls), camera traps, and roadside covey‐counts for Gambel's quail (Callipepla gambelii) in the Mojave Desert (CA, USA) during the spring of 2016. Active call‐counts had the lowest within‐site variation relative to estimated population index values, but Passive call‐count transects may be more efficient for some purposes because more survey stations can be completed within a single survey timeframe. The ARU‐recorded calls may provide a suitable alternative despite larger sample size needs, especially for occupancy surveys because multiple units can be deployed concurrently. The ultimate sample size required will depend on specific study objectives and scope of interest, but camera traps and breeding‐season covey counts are not likely to meet objectives in desert environments.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1105","usgsCitation":"Overton, C.T., Casazza, M.L., Connelley, D., and Gardner, S.C., 2020, Gambel’s quail survey variability and implications for survey design in the Mohave Desert: Wildlife Society Bulletin, v. 44, no. 3, p. 493-501, https://doi.org/10.1002/wsb.1105.","productDescription":"9 p.","startPage":"493","endPage":"501","ipdsId":"IP-104330","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436903,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SVPK0N","text":"USGS data release","linkHelpText":"Comparisons of Gambel's quail survey methods conducted in 2016 within the Mohave Desert of California with results and summaries"},{"id":380834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave National Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.597900390625,\n 33.8247936182649\n ],\n [\n -114.378662109375,\n 33.8247936182649\n ],\n [\n -114.378662109375,\n 35.576916524038616\n ],\n [\n -116.597900390625,\n 35.576916524038616\n ],\n [\n -116.597900390625,\n 33.8247936182649\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Overton, Cory T. 0000-0002-5060-7447 coverton@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-7447","contributorId":3262,"corporation":false,"usgs":true,"family":"Overton","given":"Cory","email":"coverton@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805779,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805780,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Connelley, Daniel","contributorId":245293,"corporation":false,"usgs":false,"family":"Connelley","given":"Daniel","email":"","affiliations":[{"id":49140,"text":"Pheasants Forever, California","active":true,"usgs":false}],"preferred":false,"id":805781,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gardner, Scott C.","contributorId":192081,"corporation":false,"usgs":false,"family":"Gardner","given":"Scott","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":805782,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243722,"text":"70243722 - 2020 - Substantially greater carbon emissions estimated based on annual land-use transition data","interactions":[],"lastModifiedDate":"2023-05-18T11:48:21.304527","indexId":"70243722","displayToPublicDate":"2020-07-01T06:40:15","publicationYear":"2020","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":"Substantially greater carbon emissions estimated based on annual land-use transition data","docAbstract":"Quantifying land-use and land-cover change (LULCC) effects on carbon sources and sinks has been very challenging because of the availability and quality of LULCC data. As the largest estuary in the United States, Chesapeake Bay is a rapidly changing region and is affected by human activities. A new annual land-use and land-cover (LULC) data product developed by the U.S. Geological Survey Land Change Monitoring and Analysis Program (LCMAP) from 2001 to 2011 was analyzed for transitions between agricultural land, developed land, grassland, forest land and wetland. The Land Use and Carbon Scenario Simulator was used to simulate effects of LULCC and ecosystem disturbance in the south of the Chesapeake Bay Watershed (CBW) on carbon storage and fluxes, with carbon parameters derived from the Integrated Biosphere Simulator. We found that during the study period: (1) areas of forest land, disturbed land, agricultural land and wetland decreased by 90, 82, 57, and 65 km2, respectively, but developed lands gained 293 km2 (29 km2 annually); (2) total ecosystem carbon stock in the CBW increased by 13 Tg C from 2001 to 2011, mainly due to carbon sequestration of the forest ecosystem; (3) carbon loss was primarily attributed to urbanization (0.224 Tg C·yr−1) and agricultural expansion (0.046 Tg C·yr−1); and (4) estimated carbon emissions and harvest wood products were greater when estimated with the annual LULC input. We conclude that a dense time series of LULCC, such as that of the LCMAP program, may provide a more accurate accounting of the effects of land use change on ecosystem carbon, which is critical to understanding long-term ecosystem carbon dynamics.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12071126","usgsCitation":"Diao, J., Liu, J., Zhu, Z., Li, M., and Sleeter, B.M., 2020, Substantially greater carbon emissions estimated based on annual land-use transition data: Remote Sensing, v. 12, no. 7, 15 p., https://doi.org/10.3390/rs12071126.","productDescription":"15 p.","ipdsId":"IP-105541","costCenters":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":456187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12071126","text":"Publisher Index Page"},{"id":417197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, Virginia","otherGeospatial":"Chesapeake Bay Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.76743643889431,\n 39.33891670961805\n ],\n [\n -77.39396479275541,\n 38.21620000341724\n ],\n [\n -75.61512040539517,\n 37.61260838958307\n ],\n [\n -75.0537501361022,\n 38.83411126864999\n ],\n [\n -76.76743643889431,\n 39.33891670961805\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-04-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Diao, Jiaojiao","contributorId":305505,"corporation":false,"usgs":false,"family":"Diao","given":"Jiaojiao","email":"","affiliations":[{"id":33416,"text":"Nanjing Forestry University, China","active":true,"usgs":false}],"preferred":false,"id":873061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":873063,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Mingshi","contributorId":202731,"corporation":false,"usgs":false,"family":"Li","given":"Mingshi","email":"","affiliations":[],"preferred":false,"id":873065,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873066,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263639,"text":"70263639 - 2020 - California Historical Intensity Mapping Project (CHIMP): A consistently reinterpreted dataset of seismic intensities for the past 162 years and implications for seismic hazard maps","interactions":[],"lastModifiedDate":"2025-02-19T16:21:00.999385","indexId":"70263639","displayToPublicDate":"2020-07-01T00:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"California Historical Intensity Mapping Project (CHIMP): A consistently reinterpreted dataset of seismic intensities for the past 162 years and implications for seismic hazard maps","docAbstract":"Historical seismic intensity data are useful for myriad reasons, including assessment of the performance of Probabilistic Seismic Hazard Assessment (PSHA) models and corresponding hazard maps by comparing their predictions to a dataset of historically observed intensities in the region. To assess PSHA models for California, a long and consistently interpreted intensity record is necessary. For this purpose, the California Historical Intensity Mapping Project (CHIMP) has compiled a dataset that combines and reinterprets intensity information that has been stored in disparate and sometimes hard-to-access locations. The CHIMP dataset also includes new observations of intensity from archival research and oral history collection. Version 1 of the dataset includes 46,502 intensity observations for 62 earthquakes with estimated magnitudes ranging from 4.7 to 7.9. The 162 years of shaking data show observed shaking lower than expected from seismic hazard models. This discrepancy is reduced, but persists, if historical intensity data for the largest earthquakes are smoothed to reduce the effects of spatial under-sampling. Possible reasons for this discrepancy include other limitations of the CHIMP dataset, the hazard models, and the possibility that California seismicity throughout the historical period has been lower than the long-term average. Some of these issues may also explain similar discrepancies observed for Italy and Japan.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220200065","usgsCitation":"Salditch, L., Gallahue, M.M., Lucas, M.C., Neely, J.S., Hough, S.E., and Stein, S., 2020, California Historical Intensity Mapping Project (CHIMP): A consistently reinterpreted dataset of seismic intensities for the past 162 years and implications for seismic hazard maps: Seismological Research Letters, v. 91, no. 5, p. 2631-2650, https://doi.org/10.1785/0220200065.","productDescription":"20 p.","startPage":"2631","endPage":"2650","ipdsId":"IP-119084","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482223,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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108 National Center
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As part of the model improvement effort for the 2023 Coastal Master Plan, the wetland processes captured by the morphology and vegetation models used during previous master plans were reevaluated to assess how Integrated Compartment Model (ICM) subroutines could be improved. This process considered technical reviews, comments, and suggested improvements provided by model developers, advisory groups, and other experts during previous master plan cycles. The availability of new data and information that could be used to make model improvements was also considered. In many cases, the team considered and tested multiple options or approaches. As a result of this effort, recommended improvements are provided here.
The improvements recommended to be included in the 2023 Coastal Master Plan include: adjusting marsh collapse thresholds, refining organic matter accretion calculations, developing an unstructured grid for modeling vegetation, improving flotant marsh and forested wetlands algorithms, creating and applying an updated map of existing vegetation, adjusting model code, and updating the submerged aquatic vegetation (SAV) module.
This report describes the team’s work through a series of 7 distinct activities to identify and test options for model improvements to ensure the updated ICM used for the 2023 Coastal Master Plan appropriately captures ecological and morphological processes observed in Coastal Louisiana. As appropriate, relevant literature and data are discussed. Test runs to evaluate how changes influence model outputs are also documented. A final list of recommended updates, taking into account consideration of all options and results from test runs, is summarized at the end of the report. A later report will describe the final ICM-LAVegMod and ICM-Morph subroutines for the 2023 Coastal Master Plan, detailing the updates that have been incorporated.
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