Estimates of riparian vegetation water use are important for hydromorphological assessment, partitioning within human and natural environments, and informing environmental policy decisions. The objectives of this study were to calculate the actual evapotranspiration (ETa) (mm/day and mm/year) and derive riparian vegetation annual consumptive use (CU) in acre-feet (AF) for select riparian areas of the Little Colorado River watershed within the Navajo Nation, in northeastern Arizona, USA. This was accomplished by first estimating the riparian land cover area for trees and shrubs using a 2019 summer scene from National Agricultural Imagery Program (NAIP) (1 m resolution), and then fusing the riparian delineation with Landsat-8 OLI (30-m) to estimate ETa for 2014–2020. We used indirect remote sensing methods based on gridded weather data, Daymet (1 km) and PRISM (4 km), and Landsat measurements of vegetation activity using the two-band Enhanced Vegetation Index (EVI2). Estimates of potential ET were calculated using Blaney-Criddle. Riparian ETa was quantified using the Nagler ET(EVI2) approach. Using both vector and raster estimates of tree, shrub, and total riparian area, we produced the first CU measurements for this region. Our best estimate of annual CU is 36,983 AF with a range between 31,648–41,585 AF and refines earlier projections of 25,387–46,397 AF.
","language":"English","publisher":"MDPI","doi":"10.3390/rs15010052","usgsCitation":"Nagler, P.L., Barreto-Muñoz, A., Sall, I., Lurtz, M.R., and Didan, K., 2022, Riparian plant evapotranspiration and consumptive use for selected areas of the Little Colorado River watershed on the Navajo Nation: Remote Sensing, v. 15, no. 1, 52, 37 p.; Data Release, https://doi.org/10.3390/rs15010052.","productDescription":"52, 37 p.; Data Release","ipdsId":"IP-143742","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445627,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs15010052","text":"Publisher Index Page"},{"id":435592,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EFZWPP","text":"USGS data release","linkHelpText":"Uncultivated plant water use (riparian evapotranspiration) and consumptive use data for selected areas of the Little Colorado River watershed on the Navajo Nation, Arizona"},{"id":411050,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","otherGeospatial":"Hopi Reservation, Little Colorado River Watershed, Navajo Nation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.27451835472442,\n 37.936722934098526\n ],\n [\n -112.27451835472442,\n 33.63417184178236\n ],\n [\n -108.7808660109742,\n 33.63417184178236\n ],\n [\n -108.7808660109742,\n 37.936722934098526\n ],\n [\n -112.27451835472442,\n 37.936722934098526\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":859997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":859998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sall, Ibrahima 0000-0002-7526-636X","orcid":"https://orcid.org/0000-0002-7526-636X","contributorId":251750,"corporation":false,"usgs":false,"family":"Sall","given":"Ibrahima","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":859999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lurtz, Matthew R.","contributorId":300337,"corporation":false,"usgs":false,"family":"Lurtz","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":65088,"text":"Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, 80523 USA","active":true,"usgs":false}],"preferred":false,"id":860000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":860001,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238957,"text":"70238957 - 2022 - New larger benthic foraminifera from the subsurface Lower to Middle Eocene Oldsmar Formation of southeastern Florida (USA)","interactions":[],"lastModifiedDate":"2022-12-28T15:09:23.794846","indexId":"70238957","displayToPublicDate":"2022-12-25T09:02:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12981,"text":"Carnets Geol.","active":true,"publicationSubtype":{"id":10}},"title":"New larger benthic foraminifera from the subsurface Lower to Middle Eocene Oldsmar Formation of southeastern Florida (USA)","docAbstract":"We describe two larger benthic foraminiferal taxa collected from wells drilled in the subsurface Eocene rocks of southeastern Florida that are new to peninsular Florida and the Caribbean region. Saudia floridana n.sp. is characteristic of a foraminiferal assemblage, along with Helicostegina gyralis, wide forms of the Cushmania americana group, and Gunteria floridana, in an upper part of the Oldsmar Formation. Globogypsinoides browardensis n.gen. n.sp. occurs in a second foraminiferal assemblage, along with Borelis cf. floridanus, Coskinolina cf. yucatanensis, and as-yet undescribed large rotaliids, in a middle part of the Oldsmar Formation. The foraminiferal assemblage of the middle Oldsmar unit is ascribed an Ypresian age and the assemblage of the upper Oldsmar unit a Lutetian age. These two assemblages indicate inner shelf water depths of 40 m or less on the Florida Platform during the Early to Middle Eocene deposition of the middle to upper part of the Oldsmar Formation.
","language":"English","publisher":"Carnet Geol.","doi":"10.2110/carnets.2022.2221","usgsCitation":"Robinson, E., and Cunningham, K., 2022, New larger benthic foraminifera from the subsurface Lower to Middle Eocene Oldsmar Formation of southeastern Florida (USA): Carnets Geol., v. 22, no. 1, p. 857-865, https://doi.org/10.2110/carnets.2022.2221.","productDescription":"9 p.","startPage":"857","endPage":"865","ipdsId":"IP-136586","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":489218,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2110/carnets.2022.2221","text":"Publisher Index Page"},{"id":411120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80,\n 26.25\n ],\n [\n -80.5,\n 26.25\n ],\n [\n -80.5,\n 25.4\n ],\n [\n -80,\n 25.4\n ],\n [\n -80,\n 26.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Edward 0000-0002-5377-3248","orcid":"https://orcid.org/0000-0002-5377-3248","contributorId":300068,"corporation":false,"usgs":false,"family":"Robinson","given":"Edward","email":"","affiliations":[{"id":52507,"text":"University of West Indies","active":true,"usgs":false}],"preferred":false,"id":859368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cunningham, Kevin J. 0000-0002-2179-8686","orcid":"https://orcid.org/0000-0002-2179-8686","contributorId":214677,"corporation":false,"usgs":true,"family":"Cunningham","given":"Kevin J.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859369,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237221,"text":"70237221 - 2022 - Distributions of Cisco (Coregonus artedi) in the upper Great Lakes in the mid-twentieth century, when populations were in decline","interactions":[],"lastModifiedDate":"2022-12-28T15:22:01.887259","indexId":"70237221","displayToPublicDate":"2022-12-22T09:17:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Distributions of Cisco (Coregonus artedi) in the upper Great Lakes in the mid-twentieth century, when populations were in decline","title":"Distributions of Cisco (Coregonus artedi) in the upper Great Lakes in the mid-twentieth century, when populations were in decline","docAbstract":"The restoration of the once abundant Cisco (Coregonus artedi) is a management interest across the Laurentian Great Lakes. To inform the restoration, we (1) described historical distributions of Cisco and (2) explored whether non-indigenous Rainbow Smelt (Osmerus mordax) and Alewife (Alosa pseudoharengus) played a role in the decline of Cisco populations across the upper Great Lakes (i.e., Lakes Superior, Michigan, and Huron). Our source data were collected from fishery-independent surveys conducted by the U.S. Fish and Wildlife Service’s research vessel R/V Cisco in 1952–1962. By analyzing data collected by gill-net surveys, we confirmed the importance of embayment and shallow-water habitats to Cisco. We found that Cisco was abundant in Whitefish Bay and Keweenaw Bay, Lake Superior, and in Green Bay, Lake Michigan, but we also found a sign of Cisco extirpation in Saginaw Bay, Lake Huron. Our results also showed that Ciscoes generally stayed in waters <80 m in bottom depth throughout the year. However, a substantial number of Ciscoes stayed in very deep waters (>150 m in bottom depth) in summer and fall in Lake Michigan, although we cannot exclude the possibility that these Ciscoes had hybridized with the other Coregonus species. By comparing complementary data collected from bottom-trawl surveys, we concluded that the spatiotemporal overlap between Rainbow Smelt and Cisco likely occurred across the upper Great Lakes throughout 1952–1962. These data were consistent with the hypothesis that Rainbow Smelt played a role in the decline of Cisco populations across the upper Great Lakes in the period. We also found that the spatiotemporal overlap between Alewife and Cisco likely occurred only in Saginaw Bay in fall 1956 and in Lake Michigan after 1960. Thus, any potential recovery of Cisco after the 1950s could have been inhibited by Alewife in Lakes Michigan and Huron.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0276109","usgsCitation":"Kao, Y., Renauer, R.E., Bunnell, D.B., Gorman, O., and Eshenroder, R.L., 2022, Distributions of Cisco (Coregonus artedi) in the upper Great Lakes in the mid-twentieth century, when populations were in decline: PLoS ONE, v. 17, no. 12, e0276109, 25 p., https://doi.org/10.1371/journal.pone.0276109.","productDescription":"e0276109, 25 p.","ipdsId":"IP-135228","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":445633,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0276109","text":"Publisher Index Page"},{"id":411121,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Huron, Lake Michigan, Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.24148564929357,\n 43.128343171405305\n ],\n [\n -81.69977245414111,\n 43.52059584985696\n ],\n [\n -81.42700623343956,\n 44.25333933406159\n ],\n [\n -79.80453385458382,\n 44.51028095403615\n ],\n [\n -80.99815392086296,\n 45.99485239535679\n ],\n [\n -83.91796617690204,\n 46.19077525766917\n ],\n [\n -84.54872679917949,\n 47.12705115608435\n ],\n [\n -85.10022888988864,\n 48.09695257760933\n ],\n [\n -86.43397848611653,\n 48.75155320246097\n ],\n [\n -88.25635596998222,\n 48.965015855752114\n ],\n [\n -92.33180171691794,\n 46.737107095509884\n ],\n [\n -91.70097016267727,\n 46.524212304806326\n ],\n [\n -91.0536206819973,\n 46.76635468639478\n ],\n [\n -90.37207232878637,\n 46.25533549070198\n ],\n [\n -88.50379699624864,\n 46.98573246037074\n ],\n [\n -87.65889504261429,\n 46.526095292109034\n ],\n [\n -88.32356869398052,\n 44.39020963763622\n ],\n [\n -87.81012235518037,\n 42.070121155945685\n ],\n [\n -87.25729165091205,\n 41.48537550687672\n ],\n [\n -86.05717581130553,\n 42.496480418600555\n ],\n [\n -86.15119743624166,\n 44.56181928857606\n ],\n [\n -84.72498338239201,\n 45.46700776336178\n ],\n [\n -83.50860043565015,\n 45.06851264959553\n ],\n [\n -83.53084834434623,\n 44.323034056272434\n ],\n [\n -84.13069369486993,\n 43.633977187301525\n ],\n [\n -83.67414099221224,\n 43.71352469230504\n ],\n [\n -83.06777587170279,\n 43.91450808618606\n ],\n [\n -82.24148564929357,\n 43.128343171405305\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Kao, Yu-Chun 0000-0001-5552-909X ykao@usgs.gov","orcid":"https://orcid.org/0000-0001-5552-909X","contributorId":192240,"corporation":false,"usgs":true,"family":"Kao","given":"Yu-Chun","email":"ykao@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":853667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renauer, Renee Elizabeth 0000-0003-4641-9141","orcid":"https://orcid.org/0000-0003-4641-9141","contributorId":297220,"corporation":false,"usgs":true,"family":"Renauer","given":"Renee","email":"","middleInitial":"Elizabeth","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":853668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunnell, David B. 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":216540,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","middleInitial":"B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":853669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gorman, Owen 0000-0003-0451-110X","orcid":"https://orcid.org/0000-0003-0451-110X","contributorId":216889,"corporation":false,"usgs":true,"family":"Gorman","given":"Owen","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":853670,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eshenroder, Randy L.","contributorId":297221,"corporation":false,"usgs":false,"family":"Eshenroder","given":"Randy","email":"","middleInitial":"L.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":853671,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257013,"text":"70257013 - 2022 - Do unpublished data help to redraw distributions? The case of the spectacled bear in Peru","interactions":[],"lastModifiedDate":"2024-09-04T15:45:23.281028","indexId":"70257013","displayToPublicDate":"2022-12-22T08:39:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5278,"text":"Mammal Research","active":true,"publicationSubtype":{"id":10}},"title":"Do unpublished data help to redraw distributions? The case of the spectacled bear in Peru","docAbstract":"Data availability remains a principal factor limiting the use of species distribution models (SDMs) as tools for wildlife conservation and management of rare species. Although data collected in systematic and rigorous fashion are preferable, available data for most species of conservation interest are usually low in both quality and number. Here we show that combining records published in peer-reviewed journals and gray literature sources (e.g., theses, government, and NGO reports) with unpublished records obtained by personal communications from relevant stakeholders affect the predicted distribution of spectacled bears (Tremarctos ornatus) in Peru. We built SDMs using generalized linear models, random forest, and Maxent, first using a dataset that only included published records, and second with a dataset using both published and unpublished records. All models were replicated ten times with random subsets with controlled sample size. Models that combined published and unpublished spectacled bear records had a better performance, irrespective of with SDM method used, increasing the connectivity of the species’ range, and increasing the overall predicted distribution area than models that only included published records. This was because unpublished records added key new localities, reducing spatial sampling biases. Our study shows that the inclusion of commonly disregarded data such as opportunistic records, reports from natural park rangers, student theses, and data-deficient small studies can make an important contribution to the overall ecological knowledge of rare and difficult-to-study species such as the spectacled bear.
","language":"English","publisher":"Springer Link","doi":"10.1007/s13364-022-00664-0","usgsCitation":"Falconi, N., Finn, J.T., Fuller, T., and Organ, J.F., 2022, Do unpublished data help to redraw distributions? The case of the spectacled bear in Peru: Mammal Research, v. 68, p. 143-150, https://doi.org/10.1007/s13364-022-00664-0.","productDescription":"8 p.","startPage":"143","endPage":"150","ipdsId":"IP-119469","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-69.59042,-17.58001],[-69.85844,-18.09269],[-70.37257,-18.34798],[-71.37525,-17.7738],[-71.46204,-17.36349],[-73.44453,-16.35936],[-75.23788,-15.26568],[-76.00921,-14.64929],[-76.42347,-13.82319],[-76.25924,-13.53504],[-77.10619,-12.22272],[-78.09215,-10.37771],[-79.03695,-8.38657],[-79.44592,-7.93083],[-79.76058,-7.19434],[-80.53748,-6.54167],[-81.25,-6.13683],[-80.92635,-5.69056],[-81.41094,-4.73676],[-81.09967,-4.03639],[-80.30256,-3.40486],[-80.18401,-3.82116],[-80.46929,-4.05929],[-80.44224,-4.42572],[-80.02891,-4.34609],[-79.62498,-4.4542],[-79.20529,-4.95913],[-78.6399,-4.54778],[-78.45068,-3.8731],[-77.8379,-3.00302],[-76.63539,-2.60868],[-75.545,-1.56161],[-75.23372,-0.91142],[-75.37322,-0.15203],[-75.10662,-0.05721],[-74.4416,-0.53082],[-74.1224,-1.00283],[-73.6595,-1.26049],[-73.07039,-2.30895],[-72.32579,-2.43422],[-71.77476,-2.16979],[-71.41365,-2.3428],[-70.81348,-2.25686],[-70.04771,-2.72516],[-70.69268,-3.74287],[-70.39404,-3.76659],[-69.89364,-4.29819],[-70.79477,-4.25126],[-70.92884,-4.40159],[-71.74841,-4.59398],[-72.89193,-5.27456],[-72.96451,-5.74125],[-73.21971,-6.08919],[-73.12003,-6.62993],[-73.72449,-6.9186],[-73.7234,-7.341],[-73.98724,-7.52383],[-73.57106,-8.42445],[-73.01538,-9.03283],[-73.22671,-9.46221],[-72.56303,-9.52019],[-72.18489,-10.0536],[-71.30241,-10.07944],[-70.48189,-9.49012],[-70.54869,-11.00915],[-70.09375,-11.12397],[-69.52968,-10.95173],[-68.66508,-12.5613],[-68.88008,-12.89973],[-68.92922,-13.60268],[-68.94889,-14.45364],[-69.33953,-14.9532],[-69.16035,-15.32397],[-69.38976,-15.66013],[-68.95964,-16.5007],[-69.59042,-17.58001]]]},\"properties\":{\"name\":\"Peru\"}}]}","volume":"68","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Falconi, Nereyda","contributorId":272944,"corporation":false,"usgs":false,"family":"Falconi","given":"Nereyda","email":"","affiliations":[],"preferred":false,"id":909147,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finn, John T.","contributorId":43398,"corporation":false,"usgs":false,"family":"Finn","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":16720,"text":"Department of Environmental Conservation, University of Massachusetts, Amherst, MA 01003-9485, USA","active":true,"usgs":false}],"preferred":false,"id":909148,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Todd K.","contributorId":270781,"corporation":false,"usgs":false,"family":"Fuller","given":"Todd K.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":909149,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Organ, John F. 0000-0002-0959-0639 jorgan@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-0639","contributorId":189047,"corporation":false,"usgs":true,"family":"Organ","given":"John","email":"jorgan@usgs.gov","middleInitial":"F.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":909150,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239113,"text":"70239113 - 2022 - Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions","interactions":[],"lastModifiedDate":"2022-12-28T14:04:34.673006","indexId":"70239113","displayToPublicDate":"2022-12-22T08:00:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions","docAbstract":"Predicting species geographic distributions is key to managing invasive species, conserving biodiversity, and understanding species' environmental requirements. Species distribution models (SDMs) commonly focus on climatic predictors, but other environmental factors can also be essential, particularly for species with specialized habitats defined by hydrologic, topographic, or edaphic conditions (e.g., riparian, wetland, alpine, coastal, serpentine). Here, we demonstrate a novel approach for capturing strong effects of both hydrologic and climatic predictors in SDMs for riparian plants, by merging analyses targeted at environmental drivers within riparian ecosystems and across the western USA (3.8 × 106 km2). We developed presence-background SDMs from five algorithms for three invasive riparian trees (Tamarix ramossisima/chinensis [saltcedar], Elaeagnus angustifolia [Russian olive], and Ulmus pumila [Siberian elm]) and three native Populus spp. (cottonwoods). We used separate background datasets to develop models with different spatial scales of inference: (1) spatially filtered random points to represent available habitat across the study area and (2) target-group points from Salix (willow) occurrences to represent available riparian habitat. Random-background models captured hydrologic drivers of riparian tree distributions relative to the largely upland western USA, whereas Salix-background models captured climatic drivers within the context of riparian ecosystems. Combining predictions from the two backgrounds identified hydrologically suitable habitats within climatically suitable regions, resulting in fewer false “absences” than either background alone, improving predictions over previous SDMs, and providing more complete information to guide management decisions. Surprisingly, the predicted habitat for U. pumila, a newly recognized riparian invader, was as or more extensive than Populus deltoides/fremontii, T. ramossisima/chinensis, and E. angustifolia, the most common riparian tree complexes in the western USA. Watersheds constituting 20% of U. pumila predicted habitat contained no occurrence records, indicating high risk of future and unrecognized invasions. Combining models from random and ecosystem-specific target-group backgrounds may improve SDMs for species from many specialized habitats, providing a method to link predicted distributions to localized geographic features while capturing broad-scale climatic requirements.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4305","usgsCitation":"Perry, L.G., Jarnevich, C.S., and Shafroth, P., 2022, Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions: Ecosphere, v. 13, no. 12, e4305, 22 p., https://doi.org/10.1002/ecs2.4305.","productDescription":"e4305, 22 p.","ipdsId":"IP-133461","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":445636,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4305","text":"Publisher Index Page"},{"id":435593,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LIB2TF","text":"USGS data release","linkHelpText":"Occurrence data and models for woody riparian native and invasive plant species in the conterminous western USA"},{"id":411118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100,\n 49\n ],\n [\n -124,\n 49\n ],\n [\n -124,\n 28\n ],\n [\n -100,\n 28\n ],\n [\n -100,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Perry, Laura G","contributorId":177873,"corporation":false,"usgs":false,"family":"Perry","given":"Laura","email":"","middleInitial":"G","affiliations":[],"preferred":false,"id":860091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":860092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":860093,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239342,"text":"70239342 - 2022 - Analysis of per capita contributions from a spatial model provides strategies for controlling spread of invasive carp","interactions":[],"lastModifiedDate":"2023-01-10T13:25:00.980147","indexId":"70239342","displayToPublicDate":"2022-12-22T07:22:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of per capita contributions from a spatial model provides strategies for controlling spread of invasive carp","docAbstract":"Metapopulation models may be applied to inform natural resource management to guide actions targeted at location-specific subpopulations. Model insights frequently help to understand which subpopulations to target and highlight the importance of connections among subpopulations. For example, managers often treat aquatic invasive species populations as discrete populations due to hydrological (e.g., lakes, pools formed by dams) or jurisdictional boundaries (e.g., river segments by country or jurisdictional units such as states or provinces). However, aquatic invasive species often have high rates of dispersion and migration among heterogenous locations, which complicates traditional metapopulation models and may not conform to management boundaries. Controlling invasive species requires consideration of spatial dynamics because local management activities (e.g., harvest, movement deterrents) may have important impacts on connected subpopulations. We expand upon previous work to create a spatial linear matrix model for an aquatic invasive species, Bighead Carp, in the Illinois River, USA, to examine the per capita contributions of specific subpopulations and impacts of different management scenarios on these subpopulations. Managers currently seek to prevent Bighead Carp from invading the Great Lakes via a connection between the Illinois Waterway and Lake Michigan by allocating management actions across a series of river pools. We applied the model to highlight how spatial variation in movement rates and recruitment can affect decisions about where management activities might occur. We found that where the model suggested management actions should occur depend crucially on the specific management goal (i.e., limiting the growth rate of the metapopulation vs. limiting the growth rate of the invasion front) and the per capita recruitment rate in downstream pools. Our findings illustrate the importance of linking metapopulation dynamics to management goals for invasive species control.
Cross-correlation waveforms of seismic noise in the Seattle basin, Washington, were analyzed to determine the group velocities of surface waves and constrain the shear-wave velocity (VS) for depths less than about 2 kilometers (km). Twenty broadband seismometers were deployed for about 3 weeks in three dense arrays separated by about 5 km, with minimum intra-array station spacing of about 0.5 km. Cross correlations of only 9 days of noise recordings produced Green’s functions at periods of 2 to 6 seconds (s) for sites about 5 km apart. Usable noise correlations for shorter periods of 0.5 to 1.0 s were found for sites within the arrays separated by 1 to 2 km. We bandpass filtered the inter- and intra-array cross-correlation waveforms to determine Love-wave group velocities at periods of 0.5 to 6 s for paths within the Seattle basin and at 3 to 5 s for paths crossing the southern edge of the basin. We developed a non-linear inversion program to determine VS profiles that fit the observed group velocities for paths in the basin. We found that these group velocities are well fit by a variety of VS profiles, each with a distinct jump in VS at depths ranging from 0.9 to 1.3 km. This jump in VS is inferred to represent the top of bedrock. The observed group velocities are not matched by models with the top of bedrock at 0.7-km depth or shallower. The group velocities are also fit by a model with no large jumps in VS in depths less than 2.4 km. The VS profile for the middle of the basin from Stephenson and others (2017), with a depth to bedrock of 0.9 km, also adequately fits the group velocity observations, if a velocity gradient is added from 0.05- to 0.1-km depth. The results indicate that short (3-week) deployments of seismometers to record seismic noise may provide useful constraints on the VS of sedimentary basins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221108","collaboration":"Prepared in cooperation with the University of Washington","usgsCitation":"Frankel, A., and Bodin, P., 2022, Using seismic noise correlation to determine the shallow velocity structure of the Seattle basin, Washington: U.S. Geological Survey Open-File Report 2022–1108, 13 p., https://doi.org/10.3133/ofr20221108.","productDescription":"vi, 12 p.","onlineOnly":"Y","ipdsId":"IP-140830","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":501842,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114001.htm","linkFileType":{"id":5,"text":"html"}},{"id":410660,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1108/ofr20221108.XML"},{"id":410656,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1108/coverthb.jpg"},{"id":410657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1108/ofr20221108.pdf","text":"Report","description":"OFR 2022-1108"},{"id":410658,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221108/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1108"},{"id":410659,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1108/images"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Seattle Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.45036951581977,\n 47.693059199440825\n ],\n [\n -122.45036951581977,\n 47.51906296781365\n ],\n [\n -122.22524539503297,\n 47.51906296781365\n ],\n [\n -122.22524539503297,\n 47.693059199440825\n ],\n [\n -122.45036951581977,\n 47.693059199440825\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, California 94025
The U.S. Geological Survey’s Grand Canyon Monitoring and Research Center, in coordination with the Glen Canyon Dam Adaptive Management Program, has monitored the geomorphic condition of select archaeological sites along the Colorado River in Grand Canyon using high-resolution terrestrial light detection and ranging (lidar) topographic surveys. Many of these sites are vulnerable to degradation by natural erosional processes. Regulation of the Colorado River by some operations of Glen Canyon Dam has been shown to affect archaeological resources by directly or indirectly causing degradation of site condition. Conversely, some specific operations of Glen Canyon Dam, such as controlled flood releases (termed high flow experiments), can potentially be used to slow or stop erosion at some degraded archaeological sites. Results of monitoring conducted with terrestrial lidar surveys from 2006 to 2010 have been synthesized in previous reports and publications. Here, we present and summarize results of monitoring conducted at 30 archaeological sites within 23 monitoring locations from 2010 to 2020. This report presents a sample of a much larger population of Colorado River archaeological sites in Grand Canyon that are being qualitatively monitored by the National Park Service (NPS). To ensure relevance to the NPS monitoring program, the quantitative high-resolution topographic monitoring presented in this report focused on sites binned by geomorphic context, using two previously published geomorphic classification frameworks to identify important changes in geomorphic condition within archaeological sites that can be related to operations of Glen Canyon Dam. We found that 22 archaeological sites changed within one or both of the previously determined geomorphic classifications, and changes at 21 of those 22 sites were interpreted as a transition to a more degraded geomorphic condition. The monitoring records contained within this report represent the foundation for future monitoring of these and other archaeological sites with high-resolution topographic surveys and change detection. These monitoring results provide benchmarks for managers of cultural resources along the Colorado River in Grand Canyon to assess significant changes to cultural resource integrity, aid in future risk management at these locations, and illustrate methods relevant for assessing geomorphic condition changes within other river valleys.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221097","usgsCitation":"Caster, J., Sankey, J.B., Fairley, H., and Kasprak, A., 2022, Terrestrial lidar monitoring of the effects of Glen Canyon Dam operations on the geomorphic condition of archaeological sites in Grand Canyon National Park, 2010–2020: U.S. Geological Survey Open-File Report 2022–1097, 100 p., https://doi.org/10.3133/ofr20221097.","productDescription":"xii, 100 p.","onlineOnly":"Y","ipdsId":"IP-112281","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":501838,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114000.htm","linkFileType":{"id":5,"text":"html"}},{"id":410864,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1097/ofr20221097.pdf","text":"Report","size":"60.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1097"},{"id":410863,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1097/coverthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.94729427966845,\n 36.935208423901784\n ],\n [\n -113.47307709825252,\n 36.935208423901784\n ],\n [\n -113.47307709825252,\n 35.500002816586004\n ],\n [\n -110.94729427966845,\n 35.500002816586004\n ],\n [\n -110.94729427966845,\n 36.935208423901784\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Historical trends in the direction and magnitude of ocean surface wave height, period, or direction are debated due to diverse data, time-periods, or methodologies. Using a consistent community-driven ensemble of global wave products, we quantify and establish regions with robust trends in global multivariate wave fields between 1980 and 2014. We find that about 30–40% of the global ocean experienced robust seasonal trends in mean and extreme wave height, period, and direction. Most of the Southern Hemisphere exhibited strong upward-trending wave heights (1–2 cm per year) and periods during winter and summer. Ocean basins with robust positive trends are far larger than those with negative trends. Historical trends calculated over shorter periods generally agree with satellite records but vary from product to product, with some showing a consistently negative bias. Variability in trends across products and time-periods highlights the importance of considering multiple sources when seeking robust change analyses.
The U.S. Geological Survey (USGS) completed an assessment in 2007 of the undiscovered, technically recoverable, continuous gas potential of Cretaceous–Tertiary coal beds of the onshore areas and State waters of the northern Gulf of Mexico Coastal Plain. The assessment was based on geologic elements including hydrocarbon source rocks, availability of suitable reservoir rocks, and hydrocarbon accumulations in three coalbed gas total petroleum systems (TPSs) identified in the region: (1) the Olmos Coalbed Gas TPS (Upper Cretaceous), (2) the Wilcox Coalbed Gas TPS (Paleocene–Eocene), and (3) the Cretaceous-Tertiary Coalbed Gas TPS. Four continuous assessment units (AUs) were defined within these three TPSs: (1) the Cretaceous Olmos Coalbed Gas AU, (2) the Rio Escondido Basin Olmos Coalbed Gas AU, (3) the Wilcox Coalbed Gas AU, and (4) the Cretaceous-Tertiary Coalbed Gas AU, which was not quantitatively assessed and which includes all other Cretaceous and Tertiary coal beds that are not included in the other AUs.
This USGS assessment estimated a mean of 4.06 trillion cubic feet of undiscovered, technically recoverable, continuous coalbed gas resources in the four AUs that were assessed. Nearly all of the undiscovered continuous gas resources that were estimated (95 percent, or 3.86 trillion cubic feet of gas [TCFG]) were in the Wilcox Coalbed Gas AU. The continuous gas resources resided in coalbed reservoirs. Gas sourced from these coal beds may also occur as conventional accumulations in adjacent or interlayered sandstones that were not included in this assessment of continuous resources. The assessment was conducted via the established USGS methodology for continuous petroleum accumulations and reflects estimates of undiscovered resources based on vertical (nonhorizontal) drilling technology.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171167","usgsCitation":"Warwick, P.D., 2022, Geologic assessment of undiscovered gas resources in Cretaceous–Tertiary coal beds of the U.S. Gulf of Mexico Coastal Plain: U.S. Geological Survey Open-File Report 2017–1167, 52 p., https://doi.org/10.3133/ofr20171167.","productDescription":"Report: vi, 52 p.; 3 Appendixes","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-017257","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":501520,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113996.htm","linkFileType":{"id":5,"text":"html"}},{"id":410558,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/ofr20171111","text":"Open-File Report 2017–1111","linkHelpText":"- Geologic assessment of undiscovered conventional oil and gas resources in the Lower Paleogene Midway and Wilcox Groups, and the Carrizo Sand of the Claiborne Group, of the Northern Gulf coast region"},{"id":410555,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1167/ofr20171167_appendix1.pdf","text":"Appendix 1","size":"165 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Input Data Form for the Cretaceous Olmos Coalbed Gas Assessment Unit (50470281)"},{"id":410985,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20171167/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2017-1167"},{"id":410553,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1167/ofr20171167.pdf","text":"Report","size":"15.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1167"},{"id":410552,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1167/coverthb.jpg"},{"id":409355,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2017/1167/ofr20171167.XML"},{"id":409356,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2017/1167/images/"},{"id":410556,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1167/ofr20171167_appendix2.pdf","text":"Appendix 2","size":"161 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Input Data Form for the Rio Escondido Basin Olmos Coalbed Gas Assessment Unit (53000281)"},{"id":410557,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1167/ofr20171167_appendix3.pdf","text":"Appendix 3","size":"168 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Input Data Form for the Wilcox Coalbed Gas Assessment Unit (50470381)"}],"country":"United States","otherGeospatial":"U.S. Gulf of Mexico Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.32775336731807,\n 26.212616580411137\n ],\n [\n -81.93279691237665,\n 26.212616580411137\n ],\n [\n -81.3178237043738,\n 38.82626189520937\n ],\n [\n -99.67916662903421,\n 38.491360932976605\n ],\n [\n -102.44654606504763,\n 38.43753817164347\n ],\n [\n -102.40261940733356,\n 36.63809827557699\n ],\n [\n -102.4904727227622,\n 31.785348237738653\n ],\n [\n -99.32775336731807,\n 26.212616580411137\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Program Coordinator, Energy Resources Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Telephone: 703–648–6470
AskEnergyProgram@usgs.gov
This document is the first of three standard operating procedures (SOPs) providing instructions and considerations for conducting mobile acoustic surveys along road transects to collect bat acoustic data following the North American Bat Monitoring Program (NABat) protocol and sample design. This SOP focuses specifically on selecting NABat grid cells and establishing mobile transect survey routes using online tools available through the NABat Partner Portal. Intended audiences for this document include those in charge of facilitating surveys within their region (for example, State or provincial managers, and NABat regional hub coordinators), and project leaders or survey coordinators responsible for setting up and organizing NABat mobile transect monitoring for their organization or area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2C1","usgsCitation":"Martin, J., Smith, D., Li, H., Hall, M., Ferrall, E., Rae, J., Straw, B., and Reichert, B., 2022, North American Bat Monitoring Program (NABat) mobile acoustic transect surveys standard operating procedure 1—Locating and establishing mobile transect routes: U.S. Geological Survey Techniques and Methods 2–C1, 7 p., https://doi.org/10.3133/tm2C1.","productDescription":"vi, 7 p.","onlineOnly":"Y","ipdsId":"IP-129323","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":411436,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/tm2C1/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T and M 2-C1"},{"id":411159,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/c1/tm2c1.xml"},{"id":411158,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/c1/images"},{"id":410000,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C3","text":"USGS Techniques and Methods 2-C3—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 3—Conducting Mobile Transect Surveys"},{"id":409927,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/c1/coverthb.jpg"},{"id":410375,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C2","text":"USGS Techniques and Methods 2-C2—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 2—Field Season and Survey Preparation"},{"id":409928,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/c1/tm2c1.pdf","text":"Report","size":"756 kB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2-C1"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
This standard operating procedure (SOP) provides instructions and considerations for conducting mobile acoustic surveys along road transects to collect bat acoustic data following the North American Bat Monitoring Program (NABat) protocol and sample design. This report discusses measures for ensuring the safety of surveyors and efficiency of mobile transect surveys. This guidance is intended to aid surveyors in equipment setup and operation, including how to mount and orient equipment. Finally, this document provides guidance on driving the route, performing end of survey procedures, and steps for data management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2C3","usgsCitation":"Martin, J., Hall, M., Ferrall, E., Li, H., Rae, J., Straw, B., and Reichert, B., 2022, North American Bat Monitoring Program (NABat) mobile acoustic transect surveys standard operating procedure 3—Conducting mobile transect surveys: U.S. Geological Survey Techniques and Methods 2–C3, 6 p., https://doi.org/10.3133/tm2C3.","productDescription":"vi, 6 p.","onlineOnly":"Y","ipdsId":"IP-129332","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":410860,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/c3/images"},{"id":410378,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C2","text":"USGS Techniques and Methods 2-C2—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 2—Field Season and Survey Preparation"},{"id":410377,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C1","text":"USGS Techniques and Methods 2-C1—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 1—Locating and Establishing Mobile Transect Routes"},{"id":409930,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/c3/tm2c3.pdf","text":"Report","size":"1.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2-C3"},{"id":411438,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/tm2C3/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T and M 2-C3"},{"id":410861,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/c3/tm2c3.xml"},{"id":409929,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/c3/coverthb.jpg"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
This document is the second of three standard operating procedures providing instructions and considerations for conducting mobile acoustic surveys along road transects to collect bat acoustic data following the North American Bat Monitoring Program (NABat) protocol and sample design. This standard operating procedure focuses specifically on considerations for establishing the field survey season and preparing to conduct mobile acoustic transect surveys. Intended audiences for this document include those in charge of facilitating surveys within their region (for example, state or provincial mangers and NABat regional hub coordinators), project leaders or survey coordinators responsible for setting up and organizing NABat mobile transect monitoring for their organization or area, and field staff preparing to conduct surveys within the field.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2C2","usgsCitation":"Martin, J., Rae, J., Hall, M., Ferrall, E., Li, H., Straw, B., and Reichert, B., 2022, North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 2—Field Season and Survey Preparation: U.S. Geological Survey Techniques and Methods 2–C2, 9 p., https://doi.org/10.3133/tm2C2.","productDescription":"vi, 9 p.","onlineOnly":"Y","ipdsId":"IP-129325","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":411437,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/tm2C2/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T and M 2-C2"},{"id":410859,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/c2/tm2c2.xml"},{"id":410858,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/c2/images"},{"id":410374,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C3","text":"USGS Techniques and Methods 2-C3—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 3—Conducting Mobile Transect Surveys"},{"id":410373,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C1","text":"USGS Techniques and Methods 2-C1—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 1—Locating and Establishing Mobile Transect Routes"},{"id":410372,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/c2/tm2c2.pdf","text":"Report","size":"928 kB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2-C2"},{"id":410371,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/c2/coverthb.jpg"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
The Landsat Collection 2 Level-3 Fractional Snow Covered Area science product indicates the percentage of pixels covered by snow for Landsat 4–9 imagery. Landsat’s spatial resolution offers the capability to map snow cover patterns across topographically complex mountainous regions. Snow cover is spatially and temporally variable and is often concentrated in remote or inaccessible land regions, making spaceborne remote sensing the most feasible approach to measure and monitor snow cover change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223086","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Fractional Snow Covered Area science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3086, 2 p., https://doi.org/10.3133/fs20223086.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139626","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":418251,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3086/fs20223086.pdf","text":"Report","size":"1.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3086"},{"id":410799,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418250,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3086/coverthb2.jpg"},{"id":418252,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3086/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: December 20, 2022; Version 1.1: June 21, 2023","contact":"Customer Services, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
A significant number of the world’s approximately 1,400 subaerial volcanoes with Holocene eruptions are unmonitored by ground-based sensors yet constitute a potential hazard to nearby residents and infrastructure, as well as air travel and global commerce. Data from an international constellation of more than 60 current satellite instruments provide a cost-effective means of tracking activity and potentially forecasting hazards at volcanoes around the world. These data span the electromagnetic spectrum: ultraviolet, optical, infrared, and microwave (synthetic aperture radar). They can measure volcanic thermal and gas emissions, ground displacement, and surface and topographic change, providing information that addresses one of the grand challenges in volcanology—to overcome our incomplete understanding of the relation between volcanic unrest and eruption, which is currently based on only a few well-studied volcanoes.
Although the potential of volcano remote sensing has been recognized for decades, there are many hurdles to clear before remote sensing data can be used fully by all volcano observatories. These include: (1) the limited temporal and spatial coverage of active volcanoes by satellites and the delayed distribution of those data; (2) the lack of background data acquired at all volcanoes; and (3) limited access to, and utilization of, remote sensing data in some areas owing to a lack of expertise, licensing, user-friendly formats, data access portals, or computational infrastructure.
While remote sensing data will never replace ground-based monitoring, a joint observation strategy provides a powerful means of assessing volcanic activity before, during, and after hazardous eruptions, especially given the unique spatial, temporal, and spectral perspective provided by remote measurements. A coordinated international remote sensing observation strategy for volcanoes—similar to one used by the cryosphere community—along with a volcano space task group to maximize the utility of satellite data for volcano monitoring would be highly beneficial. Such a vision could facilitate (1) global coordination of satellite observations (as done for polar regions) for background monitoring and eruption response, (2) open data that can be rapidly distributed during crises, (3) communication tools and forums for discussion of satellite data, (4) integrated ground and satellite databases of unrest, and (5) global capacity building.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225116","usgsCitation":"Pritchard, M.E., Poland, M., Reath, K., Andrews, B., Bagnardi, M., Biggs, J., Carn, S., Coppola, D., Ebmeier, S.K., Furtney, M.A., Girona, T., Griswold, J., Lopez, T., Lundgren, P., Ogburn, S., Pavolonis, M., Rumpf, E., Vaughan, G., Wauthier, C., Wessels, R., Wright, R., Anderson, K.R., Bato, M.G., and Roman, A., 2022, Optimizing satellite resources for the global assessment and mitigation of volcanic hazards—Suggestions from the USGS Powell Center Volcano Remote Sensing Working Group: U.S. Geological Survey Scientific Investigations Report 2022–5116, 69 p., https://doi.org/10.3133/sir20225116.","productDescription":"Report: ix, 69 p.; 1 Table","onlineOnly":"Y","ipdsId":"IP-110516","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":410734,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5116/sir20225116_appendix1_table1.1.xlsx","text":"Table 1.1","size":"243 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5116 Table 1.1"},{"id":410733,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5116/sir20225116.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5116"},{"id":410732,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5116/coverthb2.jpg"}],"contact":"Director, Volcano Science Center
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA 38683
Jewel Cave National Monument is in the western Black Hills of South Dakota and contains an extensive cave network, including various subterranean water bodies (cave lakes) that are believed to represent the regionally important Madison aquifer. Recent investigations have sought to improve understanding of hydrogeologic characteristics of cave lakes in Jewel Cave. The U.S. Geological Survey, in cooperation with the National Park Service, collected water-level and water-chemistry data within and near Jewel Cave to better understand groundwater interactions in Jewel Cave and to evaluate recharge characteristics of cave lakes. Continuous water-level data were collected at two cave lakes (Hourglass and New Years Lakes) from 2018 to 2021, and discrete measurements were collected by National Park Service staff from 2015 to 2021. Water samples were collected from one stream, one rain collector, three springs, and two cave lakes. The approach for this study included comparing water-level data collected from two cave lakes to historical climate data and using multivariate statistical analyses to evaluate water samples collected during this study and from previous investigations. This study builds on interpretations from previous investigations that collected similar datasets and performed similar analyses.
Hydrographs of Hourglass and News Years Lakes from 2015 to 2021 demonstrated the variability of groundwater levels in Jewel Cave in response to dry and wet climate conditions. Hourglass Lake displayed small (up to 4.8 feet), gradual water-level changes, whereas New Years Lake displayed relatively large (up to at least 27.5 feet) and rapid water-level changes. Hourglass and New Years Lakes are about 0.4 mile apart at the land surface, and the water-level elevation between the lakes varied from 61 to 93.5 feet from 2016 to 2021. The proximity and relatively small elevation difference of Hourglass and New Years Lakes indicated different recharge sources and (or) mechanisms were responsible for hydrograph dissimilarities. Water-level changes at Hourglass Lake were similar to water-level changes at a well completed in the Madison aquifer about 9 miles south of Jewel Cave National Monument, which indicated Hourglass Lake may be recharged similar to the regional Madison aquifer along outcrops north of Jewel Cave. New Years Lake displayed almost no similarities to the well completed in the Madison aquifer—indicating a more direct connection to local recharge rather than solely from outcrops recharging the regional Madison aquifer.
Results from multivariate statistical analyses of water-chemistry data were used to evaluate recharge observations from water-level data. The water chemistry of Hourglass Lake indicated its water was chemically more similar to precipitation than other groundwater sites sampled. A conceptual karst recharge model indicated that the dominant recharge source to Hourglass Lake was diffuse allogenic recharge from vertical movement of infiltrated precipitation through vertical or near-vertical fractures that extend through the Minnelusa Formation and unsaturated zone of the Madison Limestone. The water chemistry of New Years Lake was chemically similar to Hell Canyon Creek about 0.2 mile from New Years Lake at the land surface. Streamflow loss zones (concentrated allogenic recharge) along Hell Canyon Creek have not been mapped, but their presence in the Jewel Cave area has been speculated by previous investigations. A fault observed in the cave ceiling above New Years Lake by National Park Service staff could provide a natural conduit for direct recharge from Hell Canyon Creek to New Years Lake if the fault is extensive. Additional water-chemistry and water-level data, as well as streamflow data upstream and downstream of the potential streamflow loss zone along Hell Canyon Creek, are needed to prove the presence of this loss zone and discern further correlations between streamflow and water levels in New Years Lake. Observations from previous investigations and this study indicated recharge to Jewel Cave is complex and occurs on various timescales that are affected temporally by precipitation patterns and spatially by hydrologic connection with the overlying Minnelusa aquifer of the Minnelusa Formation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225108","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Medler, C.J., 2022, Hydrogeologic characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from water-level and water-chemistry data, 2015–21: U.S. Geological Survey Scientific Investigations Report 2022–5108, 47 p., https://doi.org/10.3133/sir20225108.","productDescription":"Report: viii, 47 p.; Dataset","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-137086","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":410716,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.XML"},{"id":410714,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5108/coverthb.jpg"},{"id":410715,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.pdf","text":"Report","size":"8.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5108"},{"id":410717,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5108/images"},{"id":410718,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":410721,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225108/full","text":"Report"}],"country":"United States","state":"South Dakota","otherGeospatial":"Jewel Cave National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.2,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.738808274610875\n ]\n ]\n ],\n \"type\": \"Polygon\"\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
Dry mixed-conifer forests of the southwestern United States are experiencing rapid, anthropogenically driven fire regime change. Prior to the Euro-American settlement, most of these forests experienced frequent surface fires but are now vulnerable to uncharacteristically large, high-severity fires. Fire directly influences the structure and composition of these forests and, in turn, the wildlife that inhabit them. Changing fire regimes result in a certain decline of some species and uncertain consequences for others. The Mexican spotted owl (Strix occidentalis lucida) is a federally listed threatened species of particular note in southwestern mixed-conifer forests. High-severity fire is cited as the owl’s primary threat in the revised species recovery plan, but uncertainties surround the impacts of high-severity fire on the habitat of the threatened owl, particularly across a timeframe longer than a few years. Our objective was to explore the long-term (100-year) effects of fire severity on elements of forest structure vital for Mexican spotted owl nesting. We quantified structural attributes for nest/roost habitat across mixed-conifer forests that burned at varying severity levels and time periods in the last century. We then examined the drivers of structural attributes by detecting statistical differences between severity classes and time periods through permutational multivariate analysis of variance.
High-severity fire has the strongest deleterious impact on elements of forest structure (total basal area, percent medium tree basal area, percent large tree basal area, large tree density, and canopy cover) vital to Mexican spotted owl nesting, and although the structural differences between severity classes diminish with time, it took ≥ 80–100 years to reach the structural conditions desired for Mexican spotted owl nesting after stand-replacing fires. The most important attribute measured, canopy cover, required 90–100 years after high-severity fires to reach levels most suitable for Mexican spotted owls in the Lincoln National Forest.
As fires increase in frequency, severity, and size compared to the last century, the Lincoln National Forest is projected to face an overall decrease in the structural conditions needed for Mexican spotted owl nesting habitat in this region. Short intervals between uncharacteristically high-severity fires in particular pose an imminent threat to nesting habitat.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-022-00158-z","usgsCitation":"Durboraw, T.D., Boal, C.W., Fleck, M.S., and Gill, N., 2022, Long-term recovery of Mexican spotted owl nesting habitat after fire in the Lincoln National Forest, New Mexico: Fire Ecology, v. 18, 31, 20 p., https://doi.org/10.1186/s42408-022-00158-z.","productDescription":"31, 20 p.","ipdsId":"IP-140844","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":445646,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-022-00158-z","text":"Publisher Index Page"},{"id":433162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Lincoln National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106,\n 34\n ],\n [\n -106,\n 32.5\n ],\n [\n -105,\n 32.5\n ],\n [\n -105,\n 34\n ],\n [\n -106,\n 34\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2022-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Durboraw, Tara D.","contributorId":341366,"corporation":false,"usgs":false,"family":"Durboraw","given":"Tara","email":"","middleInitial":"D.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleck, Mary S.","contributorId":341367,"corporation":false,"usgs":false,"family":"Fleck","given":"Mary","email":"","middleInitial":"S.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gill, Nathan S.","contributorId":341368,"corporation":false,"usgs":false,"family":"Gill","given":"Nathan S.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908312,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238955,"text":"70238955 - 2022 - Indigenous fire management and cross-scale fire-climate relationships in the Southwest United States from 1500 to 1900 CE","interactions":[],"lastModifiedDate":"2022-12-19T14:54:54.348462","indexId":"70238955","displayToPublicDate":"2022-12-19T08:38:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Indigenous fire management and cross-scale fire-climate relationships in the Southwest United States from 1500 to 1900 CE","docAbstract":"Prior research suggests that Indigenous fire management buffers climate influences on wildfires, but it is unclear whether these benefits accrue across geographic scales. We use a network of 4824 fire-scarred trees in Southwest United States dry forests to analyze up to 400 years of fire-climate relationships at local, landscape, and regional scales for traditional territories of three different Indigenous cultures. Comparison of fire-year and prior climate conditions for periods of intensive cultural use and less-intensive use indicates that Indigenous fire management weakened fire-climate relationships at local and landscape scales. This effect did not scale up across the entire region because land use was spatially and temporally heterogeneous at that scale. Restoring or emulating Indigenous fire practices could buffer climate impacts at local scales but would need to be repeatedly implemented at broad scales for broader regional benefits.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.abq3221","usgsCitation":"Roos, C., Guiterman, C.H., Margolis, E.Q., Swetnam, T., Laluk, N.C., Thompson, K.F., Toya, C., Farris, C.A., Fule, P.Z., Iniguez, J.M., Kaib, J.M., O’Connor, C.D., and Whitehair, L., 2022, Indigenous fire management and cross-scale fire-climate relationships in the Southwest United States from 1500 to 1900 CE: Science Advances, v. 8, no. 49, eabq3221, 12 p., https://doi.org/10.1126/sciadv.abq3221.","productDescription":"eabq3221, 12 p.","ipdsId":"IP-140760","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":445649,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.abq3221","text":"Publisher Index Page"},{"id":410707,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, New Mexico","otherGeospatial":"Chuska Mountains, Chiricahua Mountains, Defiance Plateau, Jemez Mountains, Lukachukai Mountains, Pinaleño Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.41484962647522,\n 36.560720206412725\n ],\n [\n -109.63457618897512,\n 36.144856877679004\n ],\n [\n -109.55767189210022,\n 35.44086420737669\n ],\n [\n -109.16216407960029,\n 35.22576055493633\n ],\n [\n -108.60186134522512,\n 35.904953643186545\n ],\n [\n -108.87651954835032,\n 36.560720206412725\n ],\n [\n -109.29400001710015,\n 36.640099917000086\n ],\n [\n -109.41484962647522,\n 36.560720206412725\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.99786729122823,\n 36.22998127242933\n ],\n [\n -107.02533311154072,\n 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]\n}","volume":"8","issue":"49","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roos, Chris I.","contributorId":300058,"corporation":false,"usgs":false,"family":"Roos","given":"Chris I.","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":859351,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guiterman, Christopher H.","contributorId":190553,"corporation":false,"usgs":false,"family":"Guiterman","given":"Christopher","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":859352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":859353,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swetnam, Thomas W.","contributorId":90455,"corporation":false,"usgs":false,"family":"Swetnam","given":"Thomas W.","affiliations":[],"preferred":false,"id":859354,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Laluk, Nicholas C.","contributorId":300059,"corporation":false,"usgs":false,"family":"Laluk","given":"Nicholas","email":"","middleInitial":"C.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":859355,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thompson, Kerry F.","contributorId":300060,"corporation":false,"usgs":false,"family":"Thompson","given":"Kerry","email":"","middleInitial":"F.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":859356,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Toya, Chris","contributorId":300061,"corporation":false,"usgs":false,"family":"Toya","given":"Chris","email":"","affiliations":[{"id":65008,"text":"Pueblo of Jemez","active":true,"usgs":false}],"preferred":false,"id":859357,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Farris, Calvin A.","contributorId":292802,"corporation":false,"usgs":false,"family":"Farris","given":"Calvin","email":"","middleInitial":"A.","affiliations":[{"id":63015,"text":"National Park Service, Division of Fire and Aviation Management, P.O. Box 1713, Klamath Falls, OR 97601, USA","active":true,"usgs":false}],"preferred":false,"id":859358,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fule, Peter Z.","contributorId":298160,"corporation":false,"usgs":false,"family":"Fule","given":"Peter","email":"","middleInitial":"Z.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":859359,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Iniguez, Jose M.","contributorId":300062,"corporation":false,"usgs":false,"family":"Iniguez","given":"Jose","email":"","middleInitial":"M.","affiliations":[{"id":34678,"text":"US Forest Service Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":859360,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kaib, J. Mark","contributorId":300063,"corporation":false,"usgs":false,"family":"Kaib","given":"J.","email":"","middleInitial":"Mark","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":859361,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"O’Connor, Christopher D.","contributorId":300064,"corporation":false,"usgs":false,"family":"O’Connor","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":34678,"text":"US Forest Service Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":859362,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Whitehair, Lionel","contributorId":300065,"corporation":false,"usgs":false,"family":"Whitehair","given":"Lionel","email":"","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":859363,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70240649,"text":"70240649 - 2022 - Ecological Coastal Units – Standardized global shoreline characteristics","interactions":[],"lastModifiedDate":"2023-02-10T14:26:17.604346","indexId":"70240649","displayToPublicDate":"2022-12-19T08:19:59","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Ecological Coastal Units – Standardized global shoreline characteristics","docAbstract":"A new set of resources is now available that describe global shoreline characteristics. High resolution (30 m), globally comprehensive Coastal Segment Units (CSUs) and Ecological Coastal Units (ECUs) were developed in a collaboration between the U.S. Geological Survey (USGS), Esri, and the Marine Biodiversity Observation Network (MBON). The data were produced from a segmentation and characterization of a global shoreline vector extracted from year 2014 Landsat imagery. A total of 4 million 1 km shoreline segments were attributed with values from ten variables which describe the ecological settings in which the coastline occurs, including water-side properties, landside properties, and properties of the coastline itself. These data were developed as part of a Group on Earth Observations (GEO) global ecosystem mapping initiative called GEO Ecosystems (GEO ECO). The development of the resource and its intended utility are reviewed herein.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Oceans 2022, Hampton Roads","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Oceans 2022, Hampton Roads","conferenceDate":"Oct 17-20, 2022","conferenceLocation":"Hampton Roads, VA","language":"English","publisher":"IEEE","doi":"10.1109/OCEANS47191.2022.9977390","usgsCitation":"Sayre, R., Butler, K., Van Graafeiland, K., Breyer, S., and Wright, D., 2022, Ecological Coastal Units – Standardized global shoreline characteristics, in Oceans 2022, Hampton Roads, Hampton Roads, VA, Oct 17-20, 2022, 4 p., https://doi.org/10.1109/OCEANS47191.2022.9977390.","productDescription":"4 p.","ipdsId":"IP-146446","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":412943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sayre, Roger 0000-0001-6703-7105","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":302356,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":864110,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Butler, Kevin","contributorId":267714,"corporation":false,"usgs":false,"family":"Butler","given":"Kevin","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":864111,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Graafeiland, Keith","contributorId":245012,"corporation":false,"usgs":false,"family":"Van Graafeiland","given":"Keith","affiliations":[],"preferred":false,"id":864112,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breyer, Sean","contributorId":267716,"corporation":false,"usgs":false,"family":"Breyer","given":"Sean","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":864113,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Dawn","contributorId":200268,"corporation":false,"usgs":false,"family":"Wright","given":"Dawn","affiliations":[],"preferred":false,"id":864114,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238970,"text":"70238970 - 2022 - Seismic multi-hazard and impact estimation via causal inference from satellite imagery","interactions":[],"lastModifiedDate":"2022-12-19T14:11:08.881758","indexId":"70238970","displayToPublicDate":"2022-12-19T08:07:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Seismic multi-hazard and impact estimation via causal inference from satellite imagery","docAbstract":"Rapid post-earthquake reconnaissance is important for emergency responses and rehabilitation by providing accurate and timely information about secondary hazards and impacts, including landslide, liquefaction, and building damage. Despite the extensive collection of geospatial data and satellite images, existing physics-based and data-driven methods suffer from low estimation performance due to the complex and event-specific causal dependencies underlying the cascading processes of earthquake-triggered hazards and impacts. Herein, we present a rapid seismic multi-hazard and impact estimation system that leverages advanced statistical causal inference and remote sensing techniques. The unique feature of this system is that it provides accurate and high-resolution estimations on a regional scale by jointly inferring multiple hazards and building damage from satellite images through modeling their causal dependencies. We evaluate our system on multiple seismic events from diverse countries around the globe. Our results corroborate that incorporating causal dependencies significantly improves large-scale estimation accuracy for multiple hazards and impacts compared to existing systems. The results also reveal quantitative causal mechanisms among earthquake-triggered multi-hazard and impact for multiple seismic events. Our system establishes a new way to extract and utilize the complex interactions of multiple hazards and impacts for effective disaster responses and advancing understanding of seismic geological processes.
","language":"English","publisher":"Springer","doi":"10.1038/s41467-022-35418-8","usgsCitation":"Xu, S., Dimasaka, J., Wald, D.J., and Noh, H.Y., 2022, Seismic multi-hazard and impact estimation via causal inference from satellite imagery: Nature Communications, v. 13, 7793, 13 p., https://doi.org/10.1038/s41467-022-35418-8.","productDescription":"7793, 13 p.","ipdsId":"IP-131046","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":445655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-022-35418-8","text":"Publisher Index Page"},{"id":410700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2022-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Susu","contributorId":300127,"corporation":false,"usgs":false,"family":"Xu","given":"Susu","email":"","affiliations":[{"id":65025,"text":"Stony Brook University, NY, USA","active":true,"usgs":false}],"preferred":false,"id":859455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dimasaka, Joshua","contributorId":300128,"corporation":false,"usgs":false,"family":"Dimasaka","given":"Joshua","email":"","affiliations":[{"id":65026,"text":"Stanford University, CA, USA","active":true,"usgs":false}],"preferred":false,"id":859456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":859457,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noh, Hae Young","contributorId":265961,"corporation":false,"usgs":false,"family":"Noh","given":"Hae","email":"","middleInitial":"Young","affiliations":[{"id":54844,"text":"Carnegie Mellon University (now at Stanford University)","active":true,"usgs":false}],"preferred":false,"id":859458,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238906,"text":"sim3497 - 2022 - Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021","interactions":[],"lastModifiedDate":"2026-04-01T15:30:56.71177","indexId":"sim3497","displayToPublicDate":"2022-12-19T07:51:11","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3497","displayTitle":"Delineating the Pierre Shale from Geophysical Surveys East and Southeast of Ellsworth Air Force Base, South Dakota, 2021","title":"Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the U.S. Air Force Civil Engineer Center, used surface-geophysical methods to delineate the top of Cretaceous Pierre Shale along survey transects in selected areas east and southeast of Ellsworth Air Force Base, South Dakota, from April to September 2021. Two complementary geophysical methods—electrical resistivity and passive seismic—were used along 21 colocated transect surveys east and southeast of Ellsworth Air Force Base for a total of 24.7 line-kilometers. Electrical resistivity results were analyzed using EarthImager2D electrical resistivity tomography processing and inversion software. Two-dimensional earth models showing the electrical properties of the subsurface were evaluated by directly comparing the high and low subsurface resistivity values to a surficial-geologic map and nearby wells with drillers logs. Passive seismic data were analyzed using the horizontal-to-vertical spectral ratio method to determine the depth to the Cretaceous Pierre Shale at each survey point. The depth to the Pierre Shale along the transects ranged from 0.0 to about 19.8 meters, and the mean and median depths were about 6.1 and 5.6 meters, respectively. The elevation of the Pierre Shale and thickness of unconsolidated deposits generally increased with land-surface elevation from south to north; however, some transects displayed topographically high and low areas that did not correlate with land-surface topography.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3497","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineer Center","usgsCitation":"Medler, C.J., 2022, Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021: U.S. Geological Survey Scientific Investigations Map 3497, 3 sheets, 15-p. pamphlet, https://doi.org/10.3133/sim3497.","productDescription":"Report: vi, 15 p.; 3 Sheets: 64.00 × 53.33 inches or smaller; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-137098","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":501938,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113997.htm","linkFileType":{"id":5,"text":"html"}},{"id":410625,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet03.pdf","text":"Sheet 3","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 3","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 4A, 4B, 4D, 4E, 4FD3, 4FD4, 4FD5, 4G, 4H, and 5, Ellsworth Air Force Base, South Dakota"},{"id":410609,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet02.pdf","text":"Sheet 2","size":"14.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 2","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 2, 3A, 3B, 3D, 3E, and 3F, Ellsworth Air Force Base, South Dakota"},{"id":410608,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet01.pdf","text":"Sheet 1","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 1","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 1A, 1C, 1D, 4F Alternate 1, and 4F Alternate 2, Ellsworth Air Force Base, South Dakota"},{"id":410607,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3497/images"},{"id":410606,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3497/sim3497.XML"},{"id":410605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3497/sim3497.pdf","text":"Report","size":"8.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497"},{"id":410604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3497/coverthb.jpg"},{"id":410698,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3497/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":410626,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X57BS0","text":"USGS data release","linkHelpText":"Electrical resistivity tomography (ERT) and horizontal-to-vertical spectral ratio (HVSR) data collected East and Southeast of Ellsworth Air Force Base, South Dakota, in 2021"}],"country":"United States","state":"South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.08,\n 44.06\n ],\n [\n -102.84286176348763,\n 44.06\n ],\n [\n -102.84286176348763,\n 44.2\n ],\n [\n -103.08,\n 44.2\n ],\n [\n -103.08,\n 44.06\n ]\n ]\n ],\n \"type\": \"Polygon\"\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
Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg / L, we found that (1) logistic regression models were ∼80 % accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.
","language":"English","publisher":"SPIE Digital Library","doi":"10.1117/1.JRS.16.044522","usgsCitation":"King, T.V., Hundt, S., Hafen, K., Stengel, V.G., and Ducar, S.D., 2022, Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data: Journal of Applied Remote Sensing, v. 16, no. 4, 044522, 22 p., https://doi.org/10.1117/1.JRS.16.044522.","productDescription":"044522, 22 p.","ipdsId":"IP-127684","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":445656,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1117/1.jrs.16.044522","text":"Publisher Index Page"},{"id":435594,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GF0CBG","text":"USGS data release","linkHelpText":"Chlorophyll-a concentrations and algal bloom condition paired with Sentinel-2 aquatic reflectance values collected for Brownlee Reservoir, ID from 2015 through 2020"},{"id":410786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.6105415720396,\n 45.15126198574853\n ],\n [\n -117.6105415720396,\n 43.793442297404255\n ],\n [\n -116.58806901557723,\n 43.793442297404255\n ],\n [\n -116.58806901557723,\n 45.15126198574853\n ],\n [\n -117.6105415720396,\n 45.15126198574853\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"King, Tyler V. 0000-0002-5785-3077","orcid":"https://orcid.org/0000-0002-5785-3077","contributorId":292424,"corporation":false,"usgs":true,"family":"King","given":"Tyler","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hundt, Stephen A. 0000-0002-6484-0637","orcid":"https://orcid.org/0000-0002-6484-0637","contributorId":204678,"corporation":false,"usgs":true,"family":"Hundt","given":"Stephen","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hafen, Konrad 0000-0002-1451-362X","orcid":"https://orcid.org/0000-0002-1451-362X","contributorId":215959,"corporation":false,"usgs":true,"family":"Hafen","given":"Konrad","email":"","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stengel, Victoria G. 0000-0003-0481-3159 vstengel@usgs.gov","orcid":"https://orcid.org/0000-0003-0481-3159","contributorId":5932,"corporation":false,"usgs":true,"family":"Stengel","given":"Victoria","email":"vstengel@usgs.gov","middleInitial":"G.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ducar, Scott D. 0000-0003-0781-5598","orcid":"https://orcid.org/0000-0003-0781-5598","contributorId":297547,"corporation":false,"usgs":true,"family":"Ducar","given":"Scott","email":"","middleInitial":"D.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859502,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238904,"text":"sir20225094 - 2022 - Groundwater quality and geochemistry of the western wet gas part of the Marcellus Shale Oil and Gas Play in West Virginia","interactions":[],"lastModifiedDate":"2022-12-19T12:01:47.434879","indexId":"sir20225094","displayToPublicDate":"2022-12-16T19:15:00","publicationYear":"2022","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":"2022-5094","displayTitle":"Groundwater Quality and Geochemistry of the Western Wet Gas Part of the Marcellus Shale Oil and Gas Play in West Virginia","title":"Groundwater quality and geochemistry of the western wet gas part of the Marcellus Shale Oil and Gas Play in West Virginia","docAbstract":"Thirty rural residential water wells in the wet gas region of the Marcellus Shale oil and gas play in northwestern West Virginia were sampled by the U.S. Geological Survey (USGS) in 2018, in cooperation with West Virginia State agencies, to analyze for a range of water-quality constituents, including major ions, trace metals, radionuclides, bacteria, and methane and other dissolved hydrocarbon gases. The groundwater-quality data collected for this study were used to assess the overall quality of groundwater in the study area in relation to public drinking-water standards. The groundwater-quality data were also evaluated with respect to geology, well depth, topographic setting, and proximity to oil and gas wells to identify possible relations to these factors.
The presence of total coliform bacteria in groundwater is a potential indicator of surface contamination. The presence of Escherichia coli bacteria is indicative of fecal contamination of groundwater from either human or animal sources and may be considered an indicator of other related pathogens such as viruses. Total coliforms were detected in 26 of the 30 (87 percent) wells sampled. Eleven of the 30 (37 percent) wells sampled had detections of Escherichia coli bacteria.
Sodium concentrations in 24 of 30 (80 percent) samples exceeded the U.S. Environmental Protection Agency (EPA) 20-milligram per Liter (mg/L) health-based value (HBV). Manganese, aluminum, and iron concentrations exceeded the EPA 50, 2.0, and 300 micrograms per liter (μg/L) secondary maximum contaminant level (SMCL) drinking-water standards at 14 (47 percent), 7 (23 percent), and 5 (17 percent) of the 30 wells sampled. Two of the 30 (7 percent) wells sampled had concentrations of manganese that exceeded the 300-μg/L USGS health-based screening level (HBSL). Arsenic concentrations at 7 of 30 (23 percent) wells sampled exceeded the 10-μg/L EPA maximum contaminant level (MCL) health-based drinking water standard. The EPA maximum contaminant level goal (MCLG) for arsenic is 0 μg/L and 29 of 30 wells sampled contained detectable concentrations of arsenic.
None of the 30 wells sampled exceeded the U.S. Office of Surface Mining Reclamation and Enforcement (OSMRE) 28-mg/L immediate action level (IAL) for methane in groundwater and only 1 of 30 (3 percent) sites exceeded the 10-mg/L OSMRE level of concern (LOC) for methane in groundwater. Of the 28 wells sampled for radon-222 all 28 (100 percent) exceeded the EPA proposed 300-picocuries per liter (pCi/L) MCL for radon. None of the samples exceeded the 4,000-pCi/L alternate maximum contaminant level (AMCL) which is applicable to public drinking water systems that have adopted radon mitigation programs.
Wilcoxon Signed Rank Tests indicated statistically significant differences at a 95 percent confidence interval (p less than 0.05) in radium-226, barium, and ethane groundwater concentrations with respect to the density of oil and gas wells present within a 500-meter (m) radius around the rural residential wells sampled for the study. Samples from residential wells that had four or fewer oil and gas wells in the surrounding 500-m radius had statistically lower concentrations of radium-226, bromide, and ethane than samples from residential wells sampled that had five or more oil and gas wells in the surrounding 500-m radius. Given the available data, the relationship between concentrations of radium-226, bromide, and ethane for wells sampled in this study and oil and gas development or natural geochemical processes is not clear.
Groundwater-age tracers (chlorofluorocarbons, tritium, and sulfur hexafluoride) were sampled at 17 of the 30 wells. All 17 samples contained a fraction of young, post-1950s groundwater. Many of the groundwater samples collected for this study have high calcium to sodium ratios and low total dissolved solids concentrations, indicating they are dominated by recently recharged water. A subset of samples had chloride to bromide mass ratios between 70 and 200, indicating that deep Appalachian basin brines mixed with the shallow groundwater. For most of the samples in this study, the C1 through C6 hydrocarbons have characteristics that reflect a biogenic gas signature that has, to varying degrees, undergone oxidation processes during transport. None of the samples show a characteristic thermogenic cracking pattern among the hydrocarbon ratios.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225094","isbn":"978-1-4113-4489-1","collaboration":"Prepared in cooperation with the West Virginia Department of Environmental Protection Division of Water and Waste Management and the West Virginia Department of Health and Human Resources Office of Environmental Health Services","usgsCitation":"Kozar, M.D., McAdoo, M.A., and Haase, K.B., 2022, Groundwater quality and geochemistry of the western wet gas part of the Marcellus Shale Oil and Gas Play in West Virginia: U.S. Geological Survey Scientific Investigations Report 2022–5094, 88 p., https://doi.org/10.3133/sir20225094.","productDescription":"Report: xiv, 88 p.; Data Release; Appendix","numberOfPages":"88","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-139572","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center 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1730 East Parham Road
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Streamflow in rivers can be separated into a relatively steady component, or baseflow, that represents reliably available surface water and more dynamic components of runoff that typically represent a large fraction of total streamflow. A spatially aggregated numerical time-series model was developed to separate the baseflow component of a streamflow time-series using a state-space framework in which baseflow is a non-linear function of upstream storage, an unmeasured state variable. The state-space framework allows forecasting of baseflow for periods with no rainfall or snowmelt and estimation of residence times in contrast to other hydrograph separation models. The use of a non-linear relation between baseflow and storage maintains model performance over a wide range of time scales but will only provide reliable predictions for periods when the rate of streamflow recession as a fraction of streamflow decreases over time.
The baseflow separation model, BFS, is implemented as set of functions in the statistical computing language R. BFS is run using the main function, bf_sep, which reads model input (a time series of streamflow), calculates the baseflow component of streamflow, writes model output to a file, and returns an error to the user to facilitate automated calibration. The function, bf_sep, has six arguments, which a user must enter: a numerical vector with the time series of measured streamflow volume for each time step; a character string, timestep, that has a value of either “daily” or “hourly” indicating the time step; a character string, error_basis, indicating which simulated streamflow components are used for error calculations; a six-element numeric vector, flow, with parameters characterizing streamflow; a six-element vector, basin_char, with parameters characterizing the geometry of stream basin and reservoirs; and a six-element vector, gw_hyd, with hydraulic parameters. The function bf_sep calls a series of other functions to calculate surface and base reservoir storage and fluxes.
Calibration of a non-linear model for baseflow recession must confront three issues. First, baseflow is a component of streamflow, so it is always less than or equal to streamflow but there is no independent standard for the baseflow component of streamflow. Second, optimization routines can converge on a set of model parameters that result in relatively steady but minimal baseflow that does not exceed streamflow, Q, but has a limited dynamic range. Third, the power function used to generate non-linear first-order baseflow recession (dQ/dt)/Q ≠ constant) may only be sensitive to parameters over a limited range of values, which may not be found by optimization routines.
To address these issues, BFS calculates error as the mean of weighted differences between measured streamflow and either simulated baseflow or the sum of simulated baseflow and surface flow as a fraction of measured streamflow. The difference for each time step is weighted by an exponential function of the length of recession for each time step ranging from 0 for periods when streamflow increases and approaching 1 for long recessional periods. The weight is set to 1 for any time step when simulated streamflow exceeds measured streamflow. Error calculation incorporates limited precision of streamflow measurements.
A four-step calibration process was developed to find a set of viable parameters that maximize the baseflow component within the constraints of the conceptual model (a first-order recession rate that decreases during dry periods). BFS was calibrated at 13,208 U.S. Geological Survey streamgages with available daily streamflow records for at least 300 days from water years 1981 to 2020. The total simulated baseflow component as a fraction of streamflow (BFF) was generally less than the baseflow index (BFI) for 8,368 streamgages where BFF and BFI were available. The median difference was BFF–BFI = 0.11. Large differences were most common in the Interior West where streamflow in many rivers is regulated and is generated predominantly by snowmelt. The baseflow separation model generally allocates less streamflow to baseflow than graphical hydrograph separation in snowmelt rivers.
BFS can be used to forecast streamflow during dry periods by using a time series of real-time streamflow with values of Not Available (NA), appended to the time-series to represent missing (future) streamflow values. The forecast skill of BFS was evaluated in terms of difference between simulated baseflow and measured streamflow as a fraction of measured streamflow on the days of the annual maximum recession period at 5,916 of the sites with at least 10 years of record. The median annual error was less than 50 percent at one-half of the sites and generally improved for drier years with longer recession periods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225114","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the Washington State Department of Ecology","usgsCitation":"Konrad, C.P., 2022, BFS—A non-linear, state-space model for baseflow separation and prediction: U.S. Geological Survey Scientific Investigations Report 2022–5114, 24 p., https://doi.org/10.3133/sir20225114.","productDescription":"Report: vii, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-122969","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":410595,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5114/coverthb.jpg"},{"id":410596,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5114/sir20225114.pdf","text":"Report","size":"18.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5114"},{"id":410598,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AIPHEP","text":"USGS data release","description":"USGS data release","linkHelpText":"Non-linear baseflow separation model with parameters and results (ver. 2.0, October 2022)"},{"id":410599,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5114/images"},{"id":410600,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5114/sir20225114.XML"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The US National Park Service mission includes conserving native species and historical landscapes ‘unimpaired for the enjoyment of future generations’. However, humans have increased the introduction of non-native species that can become invasive and which have harmful impacts on native species and landscapes. We revisit two previous papers, ‘Alien Species in National Parks: Drawing Lines in Space and Time’, published in 1995 by D.B. Houston and E.G. Schreiner, and ‘Climate Change and “Alien Species in National Parks”: Revisited’, published in 2014 by T.J. Stohlgren, J.R. Resnik and G.E. Plumb, to demonstrate the organizational progress that has been made in reducing impacts of invasive species despite the increasing pressure of increasing numbers of non-native species. The National Park Service has continued efforts on invasive plant management, established an Invasive Animal Program in 2018 and developed a Pest & Invasive Species Project Kit to compile information to inform management regardless of taxonomic group. Additionally, the Park Service has expanded their toolset to make decisions related to invasive species and climate change to focus on achievable goals. Since the 1995 publication, the scale of invasion has increased, and impacts of climate change are more noticeable since the 2014 publication, increasing the complexity in trying to achieve the National Park Service mission.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Invasive species and global climate change","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CAB International","usgsCitation":"Jarnevich, C.S., Hogan, T., Sieracki, J., Lipsky, C., and Wullschleger, J., 2022, Climate change and ‘alien species in National Parks’: Revisited, chap. of Invasive species and global climate change, p. 188-202.","productDescription":"15 p.","startPage":"188","endPage":"202","ipdsId":"IP-137011","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":420477,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cabidigitallibrary.org/doi/10.1079/9781800621459.0010","linkFileType":{"id":5,"text":"html"}},{"id":420479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Ziska, Lewis H.","contributorId":292083,"corporation":false,"usgs":false,"family":"Ziska","given":"Lewis","email":"","middleInitial":"H.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":882065,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881882,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogan, Terri","contributorId":240929,"corporation":false,"usgs":false,"family":"Hogan","given":"Terri","email":"","affiliations":[{"id":48162,"text":"National Park Service, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":881883,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sieracki, Jennifer","contributorId":236914,"corporation":false,"usgs":false,"family":"Sieracki","given":"Jennifer","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":true,"id":881884,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lipsky, Christine","contributorId":328978,"corporation":false,"usgs":false,"family":"Lipsky","given":"Christine","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":881885,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wullschleger, John","contributorId":328980,"corporation":false,"usgs":false,"family":"Wullschleger","given":"John","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":881886,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}