Among the hazards posed by volcanoes are the emissions of gases and particles that can affect air quality and damage agriculture and infrastructure. A recent intense episode of volcanic degassing associated with severe impacts on air quality accompanied the 2018 lower East Rift Zone (LERZ) eruption of Kīlauea volcano, Hawai'i. This resulted in a major increase in gas emission rates with respect to usual emission values for this volcano, along with a shift in the source of the dominant plume to a populated area on the lower flank of the volcano. This led to reduced air quality in downwind communities. We analyse open-access data from the permanent air quality monitoring networks operated by the Hawai'i Department of Health (HDOH) and National Park Service (NPS), and report on measurements of atmospheric sulfur dioxide (SO2) between 2007 and 2018 and PM2.5 (aerosol particulate matter with diameter <2.5 μm) between 2010 and 2018. Additional air quality data were collected through a community-operated network of low-cost PM2.5 sensors during the 2018 LERZ eruption. From 2007 to 2018 the two most significant escalations in Kīlauea's volcanic emissions were: the summit eruption that began in 2008 (Kīlauea emissions averaged 5–6 kt/day SO2 from 2008 until summit activity decreased in May 2018) and the LERZ eruption in 2018 when SO2 emission rates reached a monthly average of 200 kt/day during June. In this paper we focus on characterizing the airborne pollutants arising from the 2018 LERZ eruption and the spatial distribution and severity of volcanic air pollution events across the Island of Hawai'i. The LERZ eruption caused the most frequent and severe exceedances of the Environmental Protection Agency (EPA) PM2.5 air quality threshold (35 μg/m3 as a daily average) in Hawai'i in the period 2010–2018. In Kona, for example, the maximum 24-h-mean mass concentration of PM2.5 was recorded as 59 μg/m3 on the twenty-ninth of May 2018, which was one of eight recorded exceedances of the EPA air quality threshold during the 2018 LERZ eruption, where there had been no exceedances in the previous 8 years as measured by the HDOH and NPS networks. SO2 air pollution during the LERZ eruption was most severe in communities in the south and west of the island, as measured by selected HDOH and NPS stations in this study, with a maximum 24-h-mean mass concentration of 728 μg/m3 recorded in Ocean View (100 km west of the LERZ emission source) in May 2018. Data from the low-cost sensor network correlated well with data from the HDOH PM2.5 instruments, confirming that these low-cost sensors provide a robust means to augment reference-grade instrument networks.
Bogoslof volcano, a back-arc volcano in Alaska’s Aleutian arc, began an eruptive sequence in mid-December 2016 that ended in late August 2017, with 70 individual eruptive episodes. Because there were no local seismic or infrasound stations on the island, the Alaska Volcano Observatory (AVO) relied on distant geophysical networks and remote sensing techniques to assess activity during the eruption. AVO maintains six infrasound arrays to monitor activity along the Aleutian arc: Adak, the Island of Four Mountains, Okmok, Akutan, Sand Point, and Dillingham. Eruption detection at infrasound arrays is subject to local as well as mesoscale meteorological conditions that vary greatly over both short and long timescales. Infrasound detections from the array nearest to Bogoslof (Okmok), with a latency of about 3 min, played a crucial role in monitoring activity during the eruption. Despite the relative proximity of the Okmok array to Bogoslof (60 km), infrasound detections were not uniformly observed with only about two-thirds of the events successfully detected. The farthest array at Dillingham (816 km) detected approximately half of the explosive events, with all other arrays detecting less than half of the events. We compare observations with infrasound propagation model predictions, using both normal mode and parabolic equation forward models, to interpret the variation in detections of the 70 explosive events across the AVO infrasound network. The forward models utilize the newly created, publicly available AVO-G2S atmospheric reconstruction using numerical weather predictions data for the lower atmosphere, coupled with upper atmosphere empirical models of wind speeds and temperature. We find that long-range detections (> 100 km) of Bogoslof events are largely aligned with seasonal variability in favorable propagation conditions, while regional detections (< 100 km) are less consistent with propagation modeling. Understanding the output of numerical models in comparison to past observations will facilitate their use in future operational settings for AVO and other observatories.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-020-1360-3","usgsCitation":"Schwaiger, H., Lyons, J.J., Iezzi, A., Fee, D., and Haney, M.M., 2020, Evolving infrasound detections from Bogoslof volcano, Alaska: Insights from atmospheric propagation modeling: Bulletin of Volcanology, v. 82, 27, 14 p., https://doi.org/10.1007/s00445-020-1360-3.","productDescription":"27, 14 p.","ipdsId":"IP-108990","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":397149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bogoslof Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -179.5166015625,\n 48.40003249610685\n ],\n [\n -157.5,\n 48.40003249610685\n ],\n [\n -157.5,\n 58.768200159239576\n ],\n [\n -179.5166015625,\n 58.768200159239576\n ],\n [\n -179.5166015625,\n 48.40003249610685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2020-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Schwaiger, Hans 0000-0001-7397-8833","orcid":"https://orcid.org/0000-0001-7397-8833","contributorId":214983,"corporation":false,"usgs":true,"family":"Schwaiger","given":"Hans","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":838173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":838174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":196436,"corporation":false,"usgs":false,"family":"Iezzi","given":"Alexandra M.","affiliations":[],"preferred":false,"id":838175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fee, David 0000-0002-0936-9977","orcid":"https://orcid.org/0000-0002-0936-9977","contributorId":267231,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":838176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":838177,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208644,"text":"70208644 - 2020 - Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony","interactions":[],"lastModifiedDate":"2022-03-31T18:52:42.924432","indexId":"70208644","displayToPublicDate":"2020-02-22T06:42:15","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":"Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony","docAbstract":"Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016–2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers.","language":"English","publisher":"MDPI","doi":"10.3390/rs12040725","usgsCitation":"Pastick, N., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., and Wu, Z., 2020, Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony: Remote Sensing, v. 12, no. 4, 725, 17 p.; Data release, https://doi.org/10.3390/rs12040725.","productDescription":"725, 17 p.; Data release","ipdsId":"IP-114798","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457631,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12040725","text":"Publisher Index Page"},{"id":437093,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91NJ2PD","text":"USGS data release","linkHelpText":"Near real time estimation of annual exotic herbaceous fractional cover in the sagebrush ecosystem 30m, USA, July 2020"},{"id":437092,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KKPT07","text":"USGS data release","linkHelpText":"Weekly cloud free Harmonized Landsat Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) data for western United States (2016 &amp;ndash; 2019)."},{"id":437091,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XT1BV2","text":"USGS data release","linkHelpText":"Fractional estimates of exotic annual grass cover in dryland ecosystems of western United States (2016 - 2019)"},{"id":372534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397944,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZZSX5Q","text":"USGS data release","description":"USGS data release","linkHelpText":"Early estimates of Annual Exotic Herbaceous Fractional Cover in the Sagebrush Ecosystem, USA, May 2020"}],"country":"United States","state":"California, Idaho, Nevada, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.728515625,\n 40.97989806962013\n ],\n [\n -114.7412109375,\n 40.97989806962013\n ],\n [\n -114.7412109375,\n 44.18220395771566\n ],\n [\n -121.728515625,\n 44.18220395771566\n ],\n [\n -121.728515625,\n 40.97989806962013\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Pastick, Neal 0000-0002-4321-6739","orcid":"https://orcid.org/0000-0002-4321-6739","contributorId":222683,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":782880,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":782883,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":782881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parajuli, Sujan 0000-0002-1652-3063","orcid":"https://orcid.org/0000-0002-1652-3063","contributorId":222684,"corporation":false,"usgs":true,"family":"Parajuli","given":"Sujan","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":782882,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","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":782884,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":782885,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209323,"text":"70209323 - 2020 - The NASA hydrological forecast system for food and water security applications","interactions":[],"lastModifiedDate":"2020-08-05T13:51:35.378688","indexId":"70209323","displayToPublicDate":"2020-02-21T16:42:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"The NASA hydrological forecast system for food and water security applications","docAbstract":"Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development’s (USAID) Famine Early Warning System Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multi-model, remote sensing-based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET’s current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage); and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean-land-atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multi-model ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its one-to-five month forecasts successfully capture known historic drought events. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-18-0264.1","usgsCitation":"Arsenault, K., Shukla, S., Hazra, A., Getirana, A., McNally, A., Kumar, S., Koster, R., Peters-Lidard, C., Zaitchik, B., Badr, H., Jung, H.C., Narapusetty, B., , N., Wang, S., Mocko, D.M., Funk, C., Harrison, L., Husak, G.J., Adoum, A., Galu, G., Magadzire, T., Roningen, J., Shaw, M.J., Eylander, J., Bergaoui, K., McDonnell, R.A., and Verdin, J., 2020, The NASA hydrological forecast system for food and water security applications: Bulletin of the American Meteorological Society, v. 101, no. 7, p. E1007-E1025, https://doi.org/10.1175/BAMS-D-18-0264.1.","productDescription":"19 p.","startPage":"E1007","endPage":"E1025","ipdsId":"IP-117242","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457635,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/bams-d-18-0264.1","text":"Publisher Index Page"},{"id":373697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Africa, Middle East","volume":"101","issue":"7","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Arsenault, Kristi","contributorId":198836,"corporation":false,"usgs":false,"family":"Arsenault","given":"Kristi","affiliations":[],"preferred":false,"id":786063,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shukla, 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worldwide and are found on every continent except Antarctica. They contribute to overall ecosystem health by suppressing pest insects and pollinating plants and spreading seeds. Eight North American bat species are listed as federally endangered or threatened, and more than one-half are of current conservation concern in the United States, Canada, or Mexico.
The U.S. Geological Survey (USGS) leads, manages, and coordinates the multinational North American Bat Monitoring Program (NABat) as well as conducts scientific research on bats. USGS and NABat partners help resource managers and policymakers make informed decisions regarding the conservation of bats across North America. USGS science also helps inform decision making with respect to WNS surveillance and bat vulnerability; mitigation of potential impacts of energy development on bats; prelisting conservation efforts for regulatory agencies; and land management practices.
Partners are essential to the success of NABat. The data contributed by NABat partners provide baseline knowledge on the distribution and abundance of bats, which is used to evaluate the impacts of the threats to bats across North America. These data are also the foundation for the scientific information used to set conservation priorities and evaluate the effectiveness of management actions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203008","usgsCitation":"Reichert, B.E., and Soileau, S.C., 2020, U.S. Geological Survey science in support of the North American Bat Monitoring Program (NABat): U.S. Geological Survey Fact Sheet 2020–3008, 2 p., https://doi.org/10.3133/fs20203008.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-113139","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":372513,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://www.nabatmonitoring.org/","text":"North American Bat Monitoring Program (NABat)"},{"id":372512,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3008/fs20203008.pdf","text":"Report","size":"3.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3008"},{"id":372461,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3008/coverthb.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.28515625,\n 14.944784875088372\n ],\n [\n -90.263671875,\n 16.46769474828897\n ],\n [\n -91.318359375,\n 17.308687886770034\n ],\n [\n -87.890625,\n 18.22935133838668\n ],\n [\n -87.099609375,\n 21.207458730482642\n ],\n [\n -90.087890625,\n 21.12549763660628\n ],\n [\n -92.724609375,\n 18.979025953255267\n ],\n [\n -95.09765625,\n 18.646245142670608\n ],\n [\n -97.91015624999999,\n 24.206889622398023\n ],\n [\n -96.240234375,\n 28.76765910569123\n ],\n [\n -90.615234375,\n 29.611670115197377\n ],\n [\n -88.76953125,\n 28.844673680771795\n ],\n [\n -84.814453125,\n 29.6880527498568\n ],\n [\n -81.73828125,\n 25.403584973186703\n ],\n [\n -80.15625,\n 24.926294766395593\n ],\n [\n -79.716796875,\n 26.745610382199022\n ],\n [\n -80.947265625,\n 30.751277776257812\n ],\n [\n -75.234375,\n 35.38904996691167\n ],\n [\n -73.740234375,\n 39.36827914916014\n ],\n [\n -67.587890625,\n 44.08758502824516\n ],\n [\n -65.478515625,\n 42.87596410238256\n ],\n [\n -59.58984374999999,\n 45.706179285330855\n ],\n [\n -52.55859375,\n 46.558860303117164\n ],\n [\n -55.72265625,\n 53.69670647530323\n ],\n [\n -64.423828125,\n 60.58696734225869\n ],\n [\n -73.65234375,\n 62.59334083012024\n ],\n [\n -77.95898437499999,\n 62.71446210149774\n ],\n [\n -97.998046875,\n 68.78414378041504\n ],\n [\n -126.91406249999999,\n 70.31873847853124\n ],\n [\n -143.173828125,\n 70.19999407534661\n ],\n [\n -157.32421875,\n 71.44117085172385\n ],\n [\n -166.9921875,\n 68.8159271333607\n ],\n [\n -168.22265625,\n 65.62202261510642\n ],\n [\n -164.267578125,\n 59.7563950493563\n ],\n [\n -170.5078125,\n 52.96187505907603\n ],\n [\n -151.34765625,\n 57.27904276497778\n ],\n [\n -145.8984375,\n 60.19615576604439\n ],\n [\n -138.515625,\n 58.722598828043374\n ],\n [\n -134.296875,\n 54.97761367069628\n ],\n [\n -132.71484375,\n 52.3755991766591\n ],\n [\n -125.68359374999999,\n 48.40003249610685\n ],\n [\n -124.541015625,\n 39.90973623453719\n ],\n [\n -121.28906250000001,\n 33.797408767572485\n ],\n [\n -118.91601562499999,\n 32.84267363195431\n ],\n [\n -116.103515625,\n 30.06909396443887\n ],\n [\n -115.6640625,\n 27.059125784374068\n ],\n [\n -110.0390625,\n 22.67484735118852\n ],\n [\n -107.22656249999999,\n 22.755920681486405\n ],\n [\n -105.8203125,\n 19.31114335506464\n ],\n [\n -102.74414062499999,\n 17.5602465032949\n ],\n [\n -96.6796875,\n 15.029685756555674\n ],\n [\n -94.658203125,\n 15.707662769583518\n ],\n [\n -92.28515625,\n 14.944784875088372\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NABat Program Coordinator
USGS Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526–8118
The Organ Mountains-Desert Peaks National Monument (Monument) in southern New Mexico was established in 2014. Given anticipated future demands in the Monument for recreation, livestock grazing, and maintenance of rights-of-way (for example, pipelines and powerlines), the Bureau of Land Management (BLM) needs a better understanding of the current soil and water resources and how infrastructure improvements could affect these resources and the watershed. Specifically, the BLM is concerned with infiltration and erosion and their relations to existing or planned infrastructure, such as roads, campgrounds, location of livestock grazing, and rights-of-way. Alternatives to the current land-use conditions, land-management practices, and infrastructure will be assessed by BLM to best protect Monument resources. The U.S. Geological Survey, in cooperation with the BLM, conducted a study to assess the soil and water resources within the Monument to provide an inventory and compilation of natural-resource information needed by resource managers for the BLM’s land-use planning process for this new national monument. The overall objectives of this study were to (1) compile and interpret existing soil- and water-resource data for the Monument and (2) provide a basic assessment of the surface hydrological effects of selected alternatives to current land use and infrastructure. Data were compiled by using geographic information system software and evaluated for hydrologic and landscape properties that influence infiltration, runoff, and erosion. The effects of changing vegetation were simulated by using different scenarios in the Rangeland Hydrology and Erosion Model. Results of this model indicate areas where soil loss or runoff may occur.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195142","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Blake, J.M., Mitchell, A.C., Shephard, Z., Ball, G., Chavarria, S., and Douglas-Mankin, K.R., 2020, Assessment of soil and water resources in the Organ Mountains-Desert Peaks National Monument, New Mexico: U.S. Geological Survey Scientific Investigations Report 2019–5142, 64 p., https://doi.org/10.3133/sir20195142.","productDescription":"Report: x, 64 p.; Data Release","numberOfPages":"78","onlineOnly":"Y","ipdsId":"IP-098054","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":372464,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5142/sir20195142.pdf","text":"Report","size":"87.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5142"},{"id":399617,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109724.htm"},{"id":372465,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JVHA4Z","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Database associated with the assessment of soil and water resources in the Organ Mountains-Desert Peaks National Monument"},{"id":372463,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5142/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Organ Mountains-Desert Peaks National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.3,\n 31.8511\n ],\n [\n -106.4639,\n 31.8511\n ],\n [\n -106.4639,\n 32.6628\n ],\n [\n -107.3,\n 32.6628\n ],\n [\n -107.3,\n 31.8511\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113
Restoration guidelines increasingly recognize the importance of genetic attributes in translocating native plant materials (NPMs). However, when species‐specific genetic information is unavailable, seed transfer guidelines use climate‐informed seed transfer zones (CSTZs) as an approximation. While CSTZs may improve how NPMs are developed and/or matched to restoration sites, they overlook genetic factors that can diminish restoration success and/or deteriorate natural patterns of genetic diversity and environmental factors that may introduce unexpected variation. Here, we analyze molecular data and geographic patterns of environmental variability across the western United States and demonstrate how they can refine CSTZs. Using genetic data available for 13 relevant plant species, we found that the probability of mixing genetically differentiated individuals (i.e. from different evolutionary lineages, or populations) was approximately 8% when considering locations separated by 50 km and reached nearly 80% by 500 km, which are distances relevant to ecoregionally constrained CSTZs. Furthermore, climate analyses revealed that geographically proximate locations are likely to maintain environmental similarity, regardless of CSTZ or ecoregion assignment. These results suggest constraining CSTZ‐informed seed transfer decisions by distance may mitigate the opportunity for negative genetic outcomes. Furthermore, environmental variability and/or specificity of NPMs (depending upon the restoration strategy) should be achieved by sourcing NPMs from geographically proximate locations to avoid introducing excessive genetic differentiation. Our results highlight the utility of combining molecular genetic data with other genetic inferences (i.e. of adaptation) to determine how best to transfer seed across restoration species' ranges and develop new restoration materials.
The U.S. Geological Survey (USGS), in cooperation with the California State Water Resources Control Board, is evaluating several questions about oil and gas development and groundwater resources in California, including (1) the location of groundwater resources; (2) the proximity of oil and gas operations and groundwater and the geologic materials between them; (3) the location of evidence (or no evidence) of fluids from oil and gas sources in groundwater; and (4) the pathways or processes responsible when fluids from oil and gas sources are present in groundwater (U.S. Geological Survey, 2019). As part of this evaluation, the USGS installed a multiple-well monitoring site in the southern San Joaquin Valley near Lost Hills, California, adjacent to the Lost Hills oil field. Data collected at the Lost Hills multiple-well monitoring site (LHSP) provide information about the geology, hydrology, geophysics, and geochemistry of the aquifer system, thus enhancing understanding of relations between adjacent groundwater and the Lost Hills oil field in an area where there is little groundwater data. This report presents construction information for the LHSP and initial geohydrologic data collected from the site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191114","collaboration":"Prepared in cooperation with California State Water Resources Control Board","usgsCitation":"Everett, R.R., Kjos, A., Brown, A.A., Gillespie, J.M., and McMahon, P.B., 2020, Multiple-well monitoring site adjacent to the Lost Hills oil field, Kern County, California: U.S. Geological Survey Open-File Report 2019–1114, 8 p., https://doi.org/10.3133/ofr20191114.","productDescription":"8 p.","numberOfPages":"8","onlineOnly":"Y","ipdsId":"IP-104714","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437100,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LGXIN8","text":"USGS data release","linkHelpText":"Aquifer test data for multiple-well monitoring site LHSP, Kern County, California"},{"id":399418,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109721.htm"},{"id":372427,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1114/coverthb.jpg"},{"id":372428,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1114/ofr20191114.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"Lost Hills Oil Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.0167,\n 35.5294\n ],\n [\n -119.4833,\n 35.5294\n ],\n [\n -119.4833,\n 35.7667\n ],\n [\n -120.0167,\n 35.7667\n ],\n [\n -120.0167,\n 35.5294\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
As runoff flows over the land or impervious surfaces (paved streets, parking lots, and building roofs), it accumulates debris, chemicals, sediment, and other contaminants that can adversely affect water quality if the runoff discharge remains untreated. Pathogens, commonly measured using fecal indicator bacteria such as Escherichia coli, enterococci, or fecal coliform, are the most-frequent cause of water-quality impairment in rivers and streams in the United States. Rapid Creek originates in the western Black Hills area and flows east through Rapid City, South Dakota, to its mouth at the Cheyenne River. The water quality of Rapid Creek is important because the reach that flows through Rapid City is a valuable spawning area for a self-sustaining trout fishery, is actively used for recreation, and is a seasonal municipal water supply for the City of Rapid City. These uses (fishery, recreation, and water supply) are considered beneficial uses by the South Dakota Department of Environment and Natural Resources. Numerical criteria have been established for total suspended solids and Escherichia coli concentrations, among other water-quality constituents, for these beneficial uses. The objectives of this study were to improve the method by which fecal indicator bacteria and total suspended solids are quantified in the urban drainages within Rapid City and to provide information that helps identify origins of fecal indicator bacteria and total suspended solids. This information can be used in hydrologic models to estimate fecal indicator bacteria and total suspended solid loading from certain infrastructure elements in urban environments.
Stormwater samples analyzed for Escherichia coli, total suspended solids, specific conductance, and pH were collected in three drainage basin flowpaths within Rapid City: Jackson, Wildwood, and the Eco Prayer Park. Data-collection activities for this study focused on upgradient urban flowpath elements during rainfall events. This approach builds upon previous stormwater assessments that characterized the water quality in urban basin outlets near the downstream end of the stormwater flowpaths. Within each flowpath group, 4–6 sites were selected to represent the various infrastructure elements of the runoff process. These elements included roof downspouts, parking lots, street curbs and gutters, open channels, underground storm sewers, and stormwater ponds or best-management practice facilities.
In general, the concentrations of Escherichia coli and total suspended solids increased in the downstream direction for all flowpath sites. The wash-off process after the first flush is evident for total suspended solids and specific conductance; however, Escherichia coli concentrations did not necessarily follow the same pattern. Escherichia coli concentrations in the latter part of the runoff period were similar to or greater than the initial concentrations of the first set of samples. Stormwater-quality data were summarized by infrastructure type (roof downspout, parking lot, street curb, and channel/storm sewer) to provide information about approximate water-quality concentrations originating at the upper end of urban flowpaths. Escherichia coli and total suspended solid concentrations were lowest in samples collected from locations most isolated from human influence (roof downspouts); the median concentrations at these sites were 4 most probable number per 100 milliliters and 15 milligrams per liter, respectively. The delivery potential of fecal indicator bacteria and sediment from parking lots and street curbs was similar; median concentrations of Escherichia coli and total suspended solids were around 150–220 most probable number per 100 milliliters and 56–86 milligrams per liter, respectively. The downstream receiving channels and storm sewers where stormwater was aggregated typically contained the highest Escherichia coli concentrations (median was 1,800 most probable number per 100 milliliters), but the total suspended solid concentrations were similar to upstream elements in the flowpath (median was 69 milligrams per liter). The data collected from this study demonstrate that stormwater is contaminated with fecal indicator bacteria upon initial contact with impervious surfaces and highlight the importance of controlling the volume of stormwater discharges into receiving waterbodies via storage structures and pervious elements. Diluting stormwater with high concentrations of Escherichia coli with the receiving water’s (Rapid Creek) lower concentration of Escherichia coli is likely the primary mechanism for meeting the beneficial-use criterion threshold of 235 most probable number per 100 milliliters. Although total suspended solid concentrations in the upper parts of the basin (parking lots and street curbs) also begin at concentrations (56 to 86 milligrams per liter) above the beneficial-use criterion for Rapid Creek (53 milligrams per liter), current stormwater-control practices (storage ponds, swales, and wetlands) may be able to reduce suspended-sediment concentrations to meet this threshold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205004","collaboration":"Prepared in cooperation with the City of Rapid City","usgsCitation":"Hoogestraat, G.K., 2020, Stormwater quality of infrastructure elements in Rapid City, South Dakota, 2016–18: U.S. Geological Survey Scientific Investigations Report 2020–5004, 24 p., https://doi.org/10.3133/sir20205004.","productDescription":"Report: vii, 24 p.; Appendix; Dataset","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108184","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":399627,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109723.htm"},{"id":372437,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System database","linkHelpText":"– USGS water data for the Nation"},{"id":372436,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5004/sir20205004_appendix1.csv","text":"Appendix 1","size":"12.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5004 Appendix 1"},{"id":372434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5004/coverthb.jpg"},{"id":372435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5004/sir20205004.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5004"}],"country":"United States","state":"South Dakota","city":"Rapid City","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.32,\n 44.0111\n ],\n [\n -103.1364,\n 44.0111\n ],\n [\n -103.1364,\n 44.125\n ],\n [\n -103.32,\n 44.125\n ],\n [\n -103.32,\n 44.0111\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Groundwater levels and stream stage were monitored by the U.S. Geological Survey, in cooperation with the Friends of Herring River, at 19 sites in the Mill Creek Basin, a tributary of the Herring River in Wellfleet, Massachusetts, on outer Cape Cod, to provide baseline data prior to a proposed restoration of tidal flow to the Herring River estuary at the Cape Cod National Seashore. Tidal flow in the Herring River has been restricted by a tide-control structure since 1909. Baseline data are necessary to understand current conditions and provide information on water levels for comparison to future water levels under the proposed Herring River restoration, which includes restoration of salt marshes by enhancing tidal flow to the Herring River and construction of a tide-control structure on Mill Creek to prevent the flooding of upstream private properties, including a golf course.
Analysis of data collected during monitoring-well installation at eight locations on or near the golf course and Mill Creek, along with analysis of existing information, determined that parts of the study area are underlain by salt marsh deposits up to 18 feet (ft) thick. These marsh deposits are directly underlain by estuarine sediments, and adjacent upland areas are underlain by medium to very coarse sand. The freshwater lens on the golf course is 70 ft thick or more.
Groundwater levels at individual wells in the study area fluctuated by 1.3 to 2.6 ft during the study period (June 1, 2017, to June 14, 2018). Total precipitation during this period was 60.8 inches, about 10 inches greater than the long-term (2000–17) annual average (50.3 inches). Groundwater levels on Cape Cod generally were normal to above normal during the study owing to the higher than normal precipitation. Tidal amplitudes of groundwater levels caused by daily fluctuations at nearby tidal waterbodies (M2 tidal harmonic) were as large as 0.12 ft at a well 105 ft from the tidally restricted Herring River and as large as 0.06 ft at a well 575 ft from Wellfleet Harbor. Tidal fluctuations in groundwater levels were generally limited to areas about 1,500 ft from the nearest tidal waterbody. Under the initial proposed restoration, where mean tides would be maintained similar to current conditions, tidal fluctuations would be restored to parts of Mill Creek, and subsequent tidal fluctuations in groundwater levels could increase at some of the areas closest to the proposed tide-control structure, but the fluctuations would be less than about 0.06 ft in magnitude.
Regression models were used to describe the variability of daily mean tidally filtered groundwater levels and daily maximum stream stage in Mill Creek. Significant independent variables for the groundwater-level model included daily tidally filtered Wellfleet Harbor stage with a lag time of zero to 2 days, 7-day precipitation, the growing degree days (50 degrees Fahrenheit), and the quartile of groundwater levels relative to a long period of record at a nearby observation well.
Significant independent variables to predict the Mill Creek stage included daily mean groundwater levels in nearby wells, 7-day precipitation, growing degree days (50 degrees Fahrenheit), and a binary indicator of either a flooded or nonflooded condition on the golf course near Mill Creek. Flooding in Mill Creek occurred primarily when groundwater levels at nearby wells reached certain thresholds, when the precipitation in the preceding 7 days was at least 0.92–1.04 inches, and during the nongrowing season.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195145","collaboration":"Prepared in cooperation with the Friends of Herring River","usgsCitation":"Mullaney, J.R., Barclay, J.R., Laabs, K.L., and Lavallee, K.D., 2020, Hydrogeology and interactions of groundwater and surface water near Mill Creek and the Herring River, Wellfleet, Massachusetts, 2017–18: U.S. Geological Survey Scientific Investigations Report 2019–5145, 60 p., https://doi.org/10.3133/sir20195145.","productDescription":"Report: viii, 60 p.; Data Release; Project Site","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103306","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437103,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P903HI9K","text":"USGS data release","linkHelpText":"Data on Models to Describe Groundwater Levels and Stream Stage near the Herring River, Wellfleet, Cape Cod, Massachusetts, 2017-2022"},{"id":399619,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109683.htm"},{"id":372270,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/centers/new-england-water/science/groundwater-and-surface-water-monitoring-mill-creek-watershed","text":"Project site","linkHelpText":"- Groundwater and Surface-Water Monitoring in the Mill Creek Watershed, Wellfleet and Truro, Massachusetts"},{"id":372269,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T167II","text":"USGS data release","linkHelpText":"Data on Tidally Filtered Groundwater and Estuary Water Levels, and Climatological Data Near Mill Creek and the Herring River, Cape Cod, Wellfleet, Massachusetts, 2017–2018"},{"id":372451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5145/sir20195145.pdf","text":"Report","size":"6.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5145"},{"id":372267,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5145/coverthb2.jpg"}],"country":"United States","state":"Massachusetts","county":"Barnstable County","city":"Wellfleet","otherGeospatial":"Mill Creek, Herring River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.06719589233398,\n 41.92412111618309\n ],\n [\n -70.04968643188475,\n 41.92412111618309\n ],\n [\n -70.04968643188475,\n 41.9377858285046\n ],\n [\n -70.06719589233398,\n 41.9377858285046\n ],\n [\n -70.06719589233398,\n 41.92412111618309\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, New Hampshire 03275
Fossil rodent middens are powerful tools in paleoecology. In arid parts of western North America, packrat (Neotoma spp.) middens preserve plant and animal remains for tens of thousands of years. Midden contents are so well preserved that fragments of endogenous ancient DNA (aDNA) can be extracted and analyzed across millennia. Here, we explore the use of shotgun metagenomics to study the aDNA obtained from packrat middens up to 32,000 C14 years old. Eleven Illumina HiSeq 2500 libraries were successfully sequenced, and between 0.11% and 6.7% of reads were classified using Centrifuge against the NCBI “nt” database. Eukaryotic taxa identified belonged primarily to vascular plants with smaller proportions mapping to ascomycete fungi, arthropods, chordates, and nematodes. Plant taxonomic diversity in the middens is shown to change through time and tracks changes in assemblages determined by morphological examination of the plant remains. Amplicon sequencing of ITS2 and rbcL provided minimal data for some middens, but failed at amplifying the highly fragmented DNA present in others. With repeated sampling and deep sequencing, analysis of packrat midden aDNA from well-preserved midden material can provide highly detailed characterizations of past communities of plants, animals, bacteria, and fungi present as trace DNA fossils. The prospects for gaining more paleoecological insights from aDNA for rodent middens will continue to improve with optimization of laboratory methods, decreasing sequencing costs, and increasing computational power.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6082","usgsCitation":"Moore, G., Tessler, M., Cunningham, S., Betancourt, J.L., and Harbert, R., 2020, Paleo-metagenomics of North American fossil packrat middens: Past biodiversity revealed by ancient DNA: Ecology and Evolution, v. 10, p. 2530-2544, https://doi.org/10.1002/ece3.6082.","productDescription":"15 p.","startPage":"2530","endPage":"2544","ipdsId":"IP-116757","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":457653,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.6082","text":"Publisher Index Page"},{"id":423176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2020-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Grace","contributorId":332110,"corporation":false,"usgs":false,"family":"Moore","given":"Grace","email":"","affiliations":[{"id":79384,"text":"Smith College (Northampton, MA)","active":true,"usgs":false}],"preferred":false,"id":889462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tessler, Michael","contributorId":247608,"corporation":false,"usgs":false,"family":"Tessler","given":"Michael","email":"","affiliations":[{"id":49589,"text":"Division of Invertebrate Zoology, American Museum of Natural History, Central Park West at 79th Street, New York, NY 10024, USA","active":true,"usgs":false}],"preferred":false,"id":889463,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cunningham, Seth","contributorId":332111,"corporation":false,"usgs":false,"family":"Cunningham","given":"Seth","email":"","affiliations":[{"id":79386,"text":"Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY","active":true,"usgs":false}],"preferred":false,"id":889464,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Betancourt, Julio L. 0000-0002-7165-0743 jlbetanc@usgs.gov","orcid":"https://orcid.org/0000-0002-7165-0743","contributorId":3376,"corporation":false,"usgs":true,"family":"Betancourt","given":"Julio","email":"jlbetanc@usgs.gov","middleInitial":"L.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":889465,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harbert, Robert","contributorId":332112,"corporation":false,"usgs":false,"family":"Harbert","given":"Robert","email":"","affiliations":[{"id":79387,"text":"Stonehill College (MA)","active":true,"usgs":false}],"preferred":false,"id":889466,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212528,"text":"70212528 - 2020 - Six-axis ground motion measurements of caldera collapse at Kīlauea Volcano, Hawaiʻi - More data, more puzzles?","interactions":[],"lastModifiedDate":"2020-08-19T13:36:23.530445","indexId":"70212528","displayToPublicDate":"2020-02-20T08:29:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Six-axis ground motion measurements of caldera collapse at Kīlauea Volcano, Hawaiʻi - More data, more puzzles?","docAbstract":"Near‐field recordings of large earthquakes and volcano‐induced events using traditional seismological instrumentation often suffer from unaccounted effects of local tilt and saturation of signals. Recent hardware advances have led to the development of the blueSeis‐3A, a very broadband, highly sensitive rotational motion sensor. We installed this sensor in close proximity to permanently deployed classical instrumentation (i.e., translational seismometer, accelerometer, and tiltmeter) at the Hawaiian Volcano Observatory (USGS). There, we were able to record three ~Mw 5 earthquakes associated with large collapse events during the later phase of the 2018 Kīlauea summit eruption. Located less than 2 km from the origins of these sources, the combined six‐axis translational and rotational measurements revealed clear static rotations around all three coordinate axes. With these six component recordings, we have been able to reconstruct the complete time history of ground motion of a fixed point during an earthquake for the first time.
We present new 3-D P wave and S wave velocity models of the upper 20 km of the Mount St. Helens (MSH) region. These were obtained using local-source arrival time tomography from earthquakes and explosions recorded at 70 broadband stations deployed as part of the imaging Magma Under St. Helens (iMUSH) project and augmented by several data sets. Principal features of our models include (1) low P wave and S wave velocities along the St. Helens seismic zone to depths of at least 20 km corresponding to high conductivity imaged by iMUSH magnetotelluric studies. This delineates a zone of weakness that magma can exploit at the location of MSH; (2) a 5- to 7-km diameter, 6–15 km deep, 3–6% negative P wave and S wave velocity anomaly beneath MSH, consistent with previous estimates of the source region for recent eruptions. We interpret this as a magma storage region containing up to 15–20 km3 of partial melt, which is about 5 times more than the largest documented eruption at MSH; (3) a broad region of low P wave velocity below 10-km depth extending between Mount Adams and Mount Rainier along and to the east of the main Cascade arc, which is likely due to high-temperature arc crust and possible presence of fluids or melt; (4) several anomalies associated with surface-mapped features, including high-velocity igneous units such as the Spud Mountain and Spirit Lake plutons and low velocities in the Chehalis sedimentary basin and the Indian Heaven volcanic field. Our results place further constraints on the geometry of these features at depth.
Understanding the impact of magmatic plumbing systems on explosive volcanic activity is important for hazard management. This study describes phase equilibria experiments using a high-silica andesite (HSA; SiO2 = 62.5 wt%) from the 2006 eruption of Augustine Volcano, Alaska. Experiments were conducted under H2O saturated conditions,
There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects. This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes.
","language":"English","publisher":"Wiley","doi":"10.1016/j.ecolind.2020.106172","usgsCitation":"Rappaport, D.I., Royle, J.A., and Morton, D.C., 2020, Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes: Ecological Indicators, v. 113, 106172, 9 p., https://doi.org/10.1016/j.ecolind.2020.106172.","productDescription":"106172, 9 p.","ipdsId":"IP-113750","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457701,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2020.106172","text":"Publisher Index Page"},{"id":375463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","state":"Mato Grosso","city":"Feliz Natal, Nova Ubirita","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -59.64477539062499,\n -13.667338259654947\n ],\n [\n -53.2177734375,\n -13.667338259654947\n ],\n [\n -53.2177734375,\n -10.055402736564224\n ],\n [\n -59.64477539062499,\n -10.055402736564224\n ],\n [\n -59.64477539062499,\n -13.667338259654947\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"113","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rappaport, Danielle I.","contributorId":225138,"corporation":false,"usgs":false,"family":"Rappaport","given":"Danielle","email":"","middleInitial":"I.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":790497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":790498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morton, Douglas C.","contributorId":225139,"corporation":false,"usgs":false,"family":"Morton","given":"Douglas","email":"","middleInitial":"C.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":790499,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209565,"text":"70209565 - 2020 - Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model","interactions":[],"lastModifiedDate":"2020-04-14T11:20:35.897788","indexId":"70209565","displayToPublicDate":"2020-02-15T06:14:26","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":"Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model","docAbstract":"The Delmarva Peninsula in the eastern United States is dominated by thousands of small, forested depressional wetlands that are highly sensitive to climate change and climate variability but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art deep semantic segmentation network to map forested wetland inundation in the Delmarva region by integrating leaf-off Worldview-3 (WV3) multispectral data with fine resolution light detection and ranging (lidar) intensity and topographic data, including digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation maps generated from lidar intensity were used for model calibration and validation. The wetland inundation map results were also validated by field polygons and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% against field data and high overlap with lidar mapped inundation. The integration of topographic metrics in deep learning model can improve classification accuracy in depressional wetlands. This study highlights the great potential of deep learning models to map wetland inundation through use of high resolution optical and lidar remote sensing datasets.","language":"English","publisher":"MDPI","doi":"10.3390/rs12040644","collaboration":"","usgsCitation":"Du, L., McCarty, G.W., Zhang, X., Lang, M.W., Vanderhoof, M.K., Lin, X., Huang, C., Lee, S., and Zou, Z., 2020, Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model: Remote Sensing, v. 12, no. 4, 644, 19 p., https://doi.org/10.3390/rs12040644.","productDescription":"644, 19 p.","ipdsId":"IP-114826","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457706,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12040644","text":"Publisher Index Page"},{"id":373937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Delmarva Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.26434326171875,\n 38.46649284538942\n ],\n [\n -75.71502685546875,\n 38.46649284538942\n ],\n [\n -75.71502685546875,\n 39.08530414503412\n ],\n [\n -76.26434326171875,\n 39.08530414503412\n ],\n [\n -76.26434326171875,\n 38.46649284538942\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-02-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Du, Ling","contributorId":224056,"corporation":false,"usgs":false,"family":"Du","given":"Ling","email":"","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":786898,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarty, Greg W.","contributorId":131149,"corporation":false,"usgs":false,"family":"McCarty","given":"Greg","email":"","middleInitial":"W.","affiliations":[{"id":7262,"text":"USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":786899,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Xinhow","contributorId":177143,"corporation":false,"usgs":false,"family":"Zhang","given":"Xinhow","email":"","affiliations":[],"preferred":false,"id":786900,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lang, Megan W.","contributorId":131150,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":7264,"text":"USDA Forest Service, Northern Research Station, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":786901,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":786902,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lin, Xian-Dan","contributorId":171991,"corporation":false,"usgs":false,"family":"Lin","given":"Xian-Dan","email":"","affiliations":[],"preferred":false,"id":786903,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, Chengquan 0000-0003-0055-9798","orcid":"https://orcid.org/0000-0003-0055-9798","contributorId":198972,"corporation":false,"usgs":false,"family":"Huang","given":"Chengquan","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":786904,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lee, Sangchul","contributorId":201237,"corporation":false,"usgs":false,"family":"Lee","given":"Sangchul","email":"","affiliations":[],"preferred":false,"id":786905,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zou, Zhenhua","contributorId":224062,"corporation":false,"usgs":false,"family":"Zou","given":"Zhenhua","email":"","affiliations":[],"preferred":false,"id":786946,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70211911,"text":"70211911 - 2020 - Batch extraction method to estimate total dissolved solids (TDS) release from coal refuse and overburden","interactions":[],"lastModifiedDate":"2020-08-11T18:13:39.125122","indexId":"70211911","displayToPublicDate":"2020-02-14T13:06:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Batch extraction method to estimate total dissolved solids (TDS) release from coal refuse and overburden","docAbstract":"A rapid batch extraction method was evaluated to estimate potential for total dissolved solids (TDS) release by 65 samples of rock from coal and gas-bearing strata of the Appalachian Basin in eastern USA. Three different extractant solutions were considered: deionized water (DI), DI equilibrated with 10% CO2 atmosphere (DI + CO2), or 30% H2O2 under 10% CO2 (H2O2+CO2). In all extractions, 10 g of pulverized rock (<0.5-mm) were mixed with 20 mL of extractant solution and shaken for 4 h at 50 rpm and 20–22 °C. The 65 rock samples were classified as coal (n=3), overburden (n = 17), coal refuse that had weathered in the field (n = 14), unleached coal refuse that had oxidized during indoor storage (n = 20), gas-bearing shale (n = 10), and pyrite (n = 1). Extracts were analyzed for specific conductance (SC), TDS, pH, and major and trace elements, and subsequently speciated to determine ionic contributions to SC. The pH of extractant blanks decreased in the order DI (6.0), DI + CO2 (5.1), and H2O2+CO2 (2.6). The DI extractant was effective for mobilizing soluble SO4 and Cl salts. The DI + CO2 extractant increased weathering of carbonates and resulted in equivalent or greater TDS than the DI leach of the same material. The H2O2+CO2 extractant increased weathering of sulfides (and carbonates) and resulted in the greatest TDS production and lowest pH values. Of the 65 samples, 19 had leachate chemistry data from previous column experiments and 35 were paired to 10 field sites with leachate chemistry data. When accounting for the water-to-rock ratio, TDS from DI and DI + CO2 extractions were correlated to TDS from column experiments while TDS from H2O2+CO2 extractions was not. In contrast to column experiments, field SC was better correlated to SC measured from H2O2+CO2 extractions than DI extractions. The field SC and SC from H2O2+CO2 extractions were statistically indistinguishable for 7 of 9 paired data sets while SC from DI extractions underestimated field SC in 5 of 9 cases. Upscaling comparisons suggest that (1) weathering reactions in the field are more aggressive than DI water or synthetic rainwater extractants used in batch or column tests, and (2) a batch extraction method utilizing 30% H2O2 (which is mildly acidic without CO2 enrichment) could be effective for identifying rocks that will release high amounts of TDS.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2020.104540","usgsCitation":"Castillo-Meza, L.E., Cravotta, C., Tasker, T.L., Warner, N.R., Daniels, W.L., Orndorff, Z.W., Bergstresser, T., Douglass, A., Kimble, G., Streczywilk, J., Barton, C., Thompson, A., and Burgos, W.D., 2020, Batch extraction method to estimate total dissolved solids (TDS) release from coal refuse and overburden: Applied Geochemistry, v. 115, 104540, 16 p., https://doi.org/10.1016/j.apgeochem.2020.104540.","productDescription":"104540, 16 p.","ipdsId":"IP-106585","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":467297,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/102448","text":"External Repository"},{"id":377359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"115","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Castillo-Meza, L. E.","contributorId":237999,"corporation":false,"usgs":false,"family":"Castillo-Meza","given":"L.","email":"","middleInitial":"E.","affiliations":[{"id":47676,"text":"Department of Civil and Environmental Engineering, The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":795778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tasker, T. 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L.","contributorId":238002,"corporation":false,"usgs":false,"family":"Daniels","given":"W.","email":"","middleInitial":"L.","affiliations":[{"id":47677,"text":"Department of Crop and Soil Science, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":795782,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Orndorff, Z. 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In this study, we evaluated the effect of existing protocols and a revised protocol on the precision of diatom species counts. The revised protocol adjusts four elements of sample preparation, taxon identification and enumeration, and quality control (QC). We used six independent datasets to assess the effect of the adjustments on analytical outcomes. The first dataset was produced by three laboratories with a total of five analysts following established protocols (Charles et al. 2002), or their slight variations. The remaining datasets were produced by 1-3 laboratories with a total of 2-3 analysts following a revised protocol. The revised protocol included the following modifications: 1) development of coordinated pre-count voucher floras based on morphological operational taxonomic units (mOTUs), 2) random assignment of samples to analysts, 3) post-count identification and documentation of taxa (as opposed to an approach in which analysts assign names while they enumerate), and 4) increased use of QC samples. The revised protocol reduced taxonomic bias, as measured by reduction in analyst signal, and improved similarity among QC samples. Reduced taxonomic bias improves the performance of biological assessments, facilitates transparency across studies, and refines estimates of diatom species distributions.","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lom3.10350","usgsCitation":"Tyree, M., Bishop, I., Hawkins, C.P., Mitchell, R., and Spaulding, S.A., 2020, Reduction of taxonomic bias in diatom species data: Limnology and Oceanography: Methods, v. 18, no. 6, p. 271-279, https://doi.org/10.1002/lom3.10350.","productDescription":"9 p.","startPage":"271","endPage":"279","ipdsId":"IP-112071","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":457724,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lom3.10350","text":"Publisher Index Page"},{"id":373082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Tyree, Meredith","contributorId":207506,"corporation":false,"usgs":false,"family":"Tyree","given":"Meredith","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":784463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bishop, Ian W.","contributorId":207505,"corporation":false,"usgs":false,"family":"Bishop","given":"Ian W.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":784464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawkins, Charles P.","contributorId":198331,"corporation":false,"usgs":false,"family":"Hawkins","given":"Charles","email":"","middleInitial":"P.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":784465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mitchell, Richard M.","contributorId":215406,"corporation":false,"usgs":false,"family":"Mitchell","given":"Richard M.","affiliations":[{"id":39239,"text":"USEPA, Washington D.C.","active":true,"usgs":false}],"preferred":false,"id":784466,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spaulding, Sarah A. 0000-0002-9787-7743","orcid":"https://orcid.org/0000-0002-9787-7743","contributorId":212796,"corporation":false,"usgs":true,"family":"Spaulding","given":"Sarah","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":784462,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215612,"text":"70215612 - 2020 - OpenCLC: An open-source software tool for similarity assessment of linear hydrographic features","interactions":[],"lastModifiedDate":"2020-10-26T14:47:58.968907","indexId":"70215612","displayToPublicDate":"2020-02-13T09:43:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5923,"text":"SoftwareX","active":true,"publicationSubtype":{"id":10}},"title":"OpenCLC: An open-source software tool for similarity assessment of linear hydrographic features","docAbstract":"The National Hydrography Dataset (NHD) is a foundational geospatial data source in the United States that enables extensive and diverse environmental research and supports decision-making in numerous contexts. However, the NHD requires regular validation and update given possible inconsistent initial collection and hydrographic changes. Furthermore, systems or tools that use NHD data must manage regular updates that occur within the high-resolution version of the NHD (NHD HR). This research contributes to filling this gap by establishing an open-source software tool named OpenCLC, which automatically identifies matching and mismatching line features between two sets of hydrographic flowlines. Aside from identifying differences among two version of NHD lines, results can be applied to improve the quality of NHD HR content. OpenCLC significantly outperforms the best available commercial off-the-shelf software in computational scalability, and it is made widely available as part of the CyberGIS Toolkit to benefit broad environmental and geospatial science communities.