Climate change is anticipated to increase the frequency and intensity of droughts, with major impacts to ecosystems globally. Broad-scale assessments of vegetation responses to drought are needed to anticipate, manage, and potentially mitigate climate-change effects on ecosystems. We quantified the drought sensitivity of vegetation in the Pacific Northwest, USA, as the percent reduction in vegetation greenness under droughts relative to baseline moisture conditions. At a regional scale, shrub-steppe ecosystems—with drier climates and lower biomass—showed greater drought sensitivity than conifer forests. However, variability in drought sensitivity was considerable within biomes and within ecosystems and was mediated by landscape topography, climate, and soil characteristics. Drought sensitivity was generally greater in areas with higher elevation, drier climate, and greater soil bulk density. Ecosystems with high drought sensitivity included dry forests along ecotones to shrublands, Rocky Mountain subalpine forests, and cold upland sagebrush communities. In forests, valley bottoms and areas with low soil bulk density and high soil available water capacity showed reduced drought sensitivity, suggesting their potential as drought refugia. These regional-scale drought-sensitivity patterns discerned from remote sensing can complement plot-scale studies of plant physiological responses to drought to help inform climate-adaptation planning as drought conditions intensify.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-020-75273-5","usgsCitation":"Cartwright, J.M., Littlefield, C.E., Michalak, J., Lawler, J.J., and Dobrowski, S., 2020, Topographic, soil, and climate drivers of drought sensitivity in forests and shrublands of the Pacific Northwest, USA: Scientific Reports, v. 10, 18486, 13 p., https://doi.org/10.1038/s41598-020-75273-5.","productDescription":"18486, 13 p.","ipdsId":"IP-105631","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":454954,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-75273-5","text":"Publisher Index Page"},{"id":436741,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UNYG2R","text":"USGS data release","linkHelpText":"Analysis of drought sensitivity in the Pacific Northwest (Washington, Oregon, and Idaho) from 2000 through 2016"},{"id":436740,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UNYG2R","text":"USGS data release","linkHelpText":"Analysis of drought sensitivity in the Pacific Northwest (Washington, Oregon, and Idaho) from 2000 through 2016"},{"id":380119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington, Oregon, Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.62890625,\n 48.45835188280866\n ],\n [\n -124.76074218749999,\n 41.86956082699455\n ],\n [\n -110.91796875,\n 41.83682786072714\n ],\n [\n -111.09374999999999,\n 45.089035564831036\n ],\n [\n -113.37890625,\n 44.87144275016589\n ],\n [\n -116.27929687499999,\n 49.06666839558117\n ],\n [\n -123.3984375,\n 49.009050809382046\n ],\n [\n -124.62890625,\n 48.45835188280866\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803898,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Littlefield, Caitlin E. 0000-0003-3771-7956","orcid":"https://orcid.org/0000-0003-3771-7956","contributorId":220623,"corporation":false,"usgs":false,"family":"Littlefield","given":"Caitlin","email":"","middleInitial":"E.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":803899,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michalak, Julia 0000-0002-2524-8390","orcid":"https://orcid.org/0000-0002-2524-8390","contributorId":210589,"corporation":false,"usgs":false,"family":"Michalak","given":"Julia","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":803900,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawler, Joshua J.","contributorId":73327,"corporation":false,"usgs":false,"family":"Lawler","given":"Joshua","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":803901,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dobrowski, Solomon","contributorId":229621,"corporation":false,"usgs":false,"family":"Dobrowski","given":"Solomon","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":803902,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217193,"text":"70217193 - 2020 - Restoration of rapids habitat in a Great Lakes connecting channel, the St. Marys River, Michigan","interactions":[],"lastModifiedDate":"2021-01-12T13:21:25.45235","indexId":"70217193","displayToPublicDate":"2020-10-28T07:19:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Restoration of rapids habitat in a Great Lakes connecting channel, the St. Marys River, Michigan","docAbstract":"Aquatic habitat has been extensively altered throughout the Laurentian Great Lakes to increase navigation connectivity. In particular, the St. Marys River, a Great Lakes connecting channel, lost >50% of its historic rapids habitat over the past century. In 2016, the natural flow was restored to the Little Rapids area of the St. Marys River. The goal of our study was to evaluate physical and ecological responses to the restoration of the Little Rapids area. Extensive habitat and biological data were collected prior to restoration (2013 and 2014), and after restoration (2017 and 2018). Measured parameters included total suspended solids, current velocity, benthic macroinvertebrates, and larval, juvenile, and adult fishes. Total suspended solids stayed low (<4 mg/L) following restoration, with the exception of a single construction‐related event. Pre‐restoration data indicated that all measured velocities were below the target flow rate of 0.24 m/s, whereas 70% of the measured habitat was above the target flow post‐restoration. Abundance and richness of benthic macroinvertebrates were reduced following restoration (>90% reduction). We observed a 45% increase in richness of larval fish 2 years after restoration and a 131% increase in catch per unit effort. For adult fishes, the proportion of individuals with a preference for fast‐moving waters increased from 1.5 to 45% in the restored area, and from 7 to 15% upstream of the restored area; a similar response was observed for lithophilic spawners. The physical and biological conditions of the Little Rapids improved and resembled conditions typical of rapids habitat extent in other areas of the river and other systems.
Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12213524","usgsCitation":"Gao, F., Anderson, M., and Hively, W.D., 2020, Detecting cover crop end-of-season using VENµS and sentinel-2 satellite imagery: Remote Sensing, v. 12, no. 21, 22 p., https://doi.org/10.3390/rs12213524.","productDescription":"22 p.","ipdsId":"IP-123386","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":454959,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12213524","text":"Publisher Index Page"},{"id":380903,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"21","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Gao, Feng 0000-0002-1865-2846","orcid":"https://orcid.org/0000-0002-1865-2846","contributorId":70671,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":805911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Martha","contributorId":210925,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","affiliations":[],"preferred":false,"id":805912,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":805913,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216661,"text":"70216661 - 2020 - Virome of bat guano from nine northern California roosts","interactions":[],"lastModifiedDate":"2021-02-04T00:06:57.522623","indexId":"70216661","displayToPublicDate":"2020-10-28T07:05:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2497,"text":"Journal of Virology","active":true,"publicationSubtype":{"id":10}},"title":"Virome of bat guano from nine northern California roosts","docAbstract":"Bats are hosts to a large variety of viruses, including many capable of cross species transmissions to other mammals or humans. We characterized the virome in guano from five common bat species in 9 Northern California roosts and a pool of 5 individual bats. Genomes belonging to 14 viral families known to infect mammals and 17 viral families infecting insects or of unknown tropism were detected. Near or complete genomes of a novel parvovirus, astrovirus, nodavirus, CRESS-DNA viruses and densoviruses and more partial genomes of a novel alphacoronavirus, and bunyavirus were characterized. Lower numbers of reads with >90% amino acid identity to previously described calicivirus, circovirus, adenoviruses, hepatovirus, bocaparvoviruses, and polyomavirus in other bat species were also found likely reflecting their wide distribution among different bats. Unexpectedly a few sequence reads of canine parvovirus 2 and the recently described mouse kidney parvovirus were also detected and their presence confirmed by PCR possibly originating from guano contamination by carnivores and rodents. The majority of eukaryotic viral reads were highly divergent indicating that numerous viruses still remain to be characterized even from such a heavily investigated order as Chiroptera.
","language":"English","publisher":"American Society for Microbiology","doi":"10.1128/JVI.01713-20","usgsCitation":"Li, Y., Altan, E., Reyes, G., Halstead, B., Deng, X., and Delwart, E., 2020, Virome of bat guano from nine northern California roosts: Journal of Virology, v. 95, no. 3, p. e01713-e01720, https://doi.org/10.1128/JVI.01713-20.","productDescription":"8 p.","startPage":"e01713","endPage":"e01720","ipdsId":"IP-122602","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":454960,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7925108","text":"External Repository"},{"id":380833,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Northern California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.25537109375,\n 37.47485808497102\n ],\n [\n -119.72900390625001,\n 37.47485808497102\n ],\n [\n -119.72900390625001,\n 41.96765920367816\n ],\n [\n -124.25537109375,\n 41.96765920367816\n ],\n [\n -124.25537109375,\n 37.47485808497102\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"95","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Yanpeng","contributorId":245294,"corporation":false,"usgs":false,"family":"Li","given":"Yanpeng","email":"","affiliations":[{"id":49143,"text":"Vitalant Research Institute, San Francisco, California, USA","active":true,"usgs":false}],"preferred":false,"id":805783,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Altan, Eda","contributorId":245295,"corporation":false,"usgs":false,"family":"Altan","given":"Eda","email":"","affiliations":[{"id":49143,"text":"Vitalant Research Institute, San Francisco, California, USA","active":true,"usgs":false}],"preferred":false,"id":805784,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reyes, Gabriel 0000-0001-9281-5300 greyes@usgs.gov","orcid":"https://orcid.org/0000-0001-9281-5300","contributorId":199338,"corporation":false,"usgs":true,"family":"Reyes","given":"Gabriel","email":"greyes@usgs.gov","affiliations":[],"preferred":true,"id":805785,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805786,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Deng, Xutao","contributorId":245296,"corporation":false,"usgs":false,"family":"Deng","given":"Xutao","email":"","affiliations":[{"id":49143,"text":"Vitalant Research Institute, San Francisco, California, USA","active":true,"usgs":false}],"preferred":false,"id":805787,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delwart, Eric","contributorId":179329,"corporation":false,"usgs":false,"family":"Delwart","given":"Eric","email":"","affiliations":[],"preferred":false,"id":805788,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267767,"text":"70267767 - 2020 - Ontogenetic shifts in mesohabitat use of young-of-year Rio Grande blue sucker in the Big Bend region of the Rio Grande","interactions":[],"lastModifiedDate":"2025-05-30T16:03:05.271414","indexId":"70267767","displayToPublicDate":"2020-10-28T00:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Ontogenetic shifts in mesohabitat use of young-of-year Rio Grande blue sucker in the Big Bend region of the Rio Grande","docAbstract":"Alteration of flow regimes by anthropogenic activities is one of the primary environmental problems in riverine systems. Understanding how hydrologic conditions can affect ontogenetic habitat shifts of imperiled fishes is important in order to develop conservation and management strategies for each life-history stage. We examined relationships between the abundance of young-of-the-year (YOY) Rio Grande Blue Sucker and various abiotic variables in the Trans-Pecos region of the Rio Grande in Texas, USA. We used open N-mixture modeling to better understand the factors affecting ontogenetic habitat shifts of the imperiled aridland river fish. In addition, we examined differences in Rio Grande Blue Sucker total length among three mesohabitat types (pool, riffle, and run). The results of open N-mixture modeling suggested that as pool area increased, the abundance of YOY Rio Grande Blue Sucker increased. Total length of YOY Rio Grande Blue Sucker also significantly differed among the three mesohabitat types. The total lengths of YOY Rio Grande Blue Sucker in pool habitats were lower than in other mesohabitats, suggesting that YOY Rio Grande Blue Sucker undergo ontogenetic habitat shifts into greater current velocity habitats as they grow. The habitat associations we documented support the growing body of research emphasizing the importance of maintaining sufficient and appropriately timed flows to avoid prolonged low flows that limit habitat availability for native fish species during sensitive life stages in the Rio Grande and other aridland rivers.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10641-020-01038-8","usgsCitation":"Miyazono, S., Pease, A., Fritts, S., and Grabowski, T.B., 2020, Ontogenetic shifts in mesohabitat use of young-of-year Rio Grande blue sucker in the Big Bend region of the Rio Grande: Environmental Biology of Fishes, v. 103, p. 1471-1480, https://doi.org/10.1007/s10641-020-01038-8.","productDescription":"10 p.","startPage":"1471","endPage":"1480","ipdsId":"IP-118286","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Rio Grande in the Big Bend region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.91886105019951,\n 29.728911907450694\n ],\n [\n -103.91886105019951,\n 28.965625411076672\n ],\n [\n -102.76942901752302,\n 28.965625411076672\n ],\n [\n -102.76942901752302,\n 29.728911907450694\n ],\n [\n -103.91886105019951,\n 29.728911907450694\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"103","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Miyazono, Seiji","contributorId":356122,"corporation":false,"usgs":false,"family":"Miyazono","given":"Seiji","affiliations":[{"id":37463,"text":"TTU","active":true,"usgs":false}],"preferred":false,"id":938781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pease, Allison A.","contributorId":356124,"corporation":false,"usgs":false,"family":"Pease","given":"Allison A.","affiliations":[{"id":37463,"text":"TTU","active":true,"usgs":false}],"preferred":false,"id":938782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fritts, Sarah","contributorId":356126,"corporation":false,"usgs":false,"family":"Fritts","given":"Sarah","affiliations":[{"id":84915,"text":"tsu","active":true,"usgs":false}],"preferred":false,"id":938783,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grabowski, Timothy B. 0000-0001-9763-8948 tgrabowski@usgs.gov","orcid":"https://orcid.org/0000-0001-9763-8948","contributorId":4178,"corporation":false,"usgs":true,"family":"Grabowski","given":"Timothy","email":"tgrabowski@usgs.gov","middleInitial":"B.","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":938780,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217538,"text":"70217538 - 2020 - Cenozoic tectonic evolution of the Ecemiş fault zone and adjacent basins, central Anatolia, Turkey during the transition from Arabia - Eurasia collision to escape tectonics","interactions":[],"lastModifiedDate":"2021-01-21T21:34:51.473223","indexId":"70217538","displayToPublicDate":"2020-10-27T15:31:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Cenozoic tectonic evolution of the Ecemiş fault zone and adjacent basins, central Anatolia, Turkey during the transition from Arabia - Eurasia collision to escape tectonics","docAbstract":"The effects of Arabia-Eurasia collision are recorded in faults, basins, and exhumed metamorphic massifs across eastern and central Anatolia. These faults and basins also preserve evidence of major changes in deformation and associated sedimentary processes along major suture zones including the Inner Tauride suture where it lies along the southern (Ecemiş) segment of the Central Anatolian fault zone. Stratigraphic and structural data from the Ecemiş fault zone, adjacent NE Ulukışla basin, and metamorphic dome (Niğde Massif) record two fundamentally different stages in the Cenozoic tectonic evolution of this part of central Anatolia. The Paleogene sedimentary and volcanic strata of the NE Ulukışla basin (Ecemiş corridor) were deposited in marginal marine to marine environments on the exhuming Niğde Massif and east of it. A late Eocene–Oligocene transpressional stage of deformation involved oblique northward thrusting of older Paleogene strata onto the eastern Niğde Massif and of the eastern massif onto the rest of the massif, reburying the entire massif to >10 km depth and accompanied by left-lateral motion on the Ecemiş fault zone. A profound change in the tectonic setting at the end of the Oligocene produced widespread transtensional deformation across the area west of the Ecemiş fault zone in the Miocene. In this stage, the Ecemiş fault zone had at least 25 km of left-lateral offset. Before and during this faulting episode, the central Tauride Mountains to the east became a source of sediments that were deposited in small Miocene transtensional basins formed on the Eocene–Oligocene thrust belt between the Ecemiş fault zone and the Niğde Massif. Normal faults compatible with SW-directed extension cut across the Niğde Massif and are associated with a second (Miocene) re-exhumation of the Massif. Geochronology and thermochronology indicate that the transtensional stage started at ca. 23–22 Ma, coeval with large and diverse geological and tectonic changes across Anatolia.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02255.1","usgsCitation":"Umhoefer, P.J., Thompson, S., Lefebre, C., Cosca, M., Teyssier, C., and Whitney, D.L., 2020, Cenozoic tectonic evolution of the Ecemiş fault zone and adjacent basins, central Anatolia, Turkey during the transition from Arabia - Eurasia collision to escape tectonics: Geosphere, v. 16, no. 6, p. 1358-1384, https://doi.org/10.1130/GES02255.1.","productDescription":"27 p.","startPage":"1358","endPage":"1384","ipdsId":"IP-120164","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":454961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02255.1","text":"Publisher Index Page"},{"id":382449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 31.420898437499996,\n 36.932330061503144\n ],\n [\n 36.309814453125,\n 36.932330061503144\n ],\n [\n 36.309814453125,\n 39.93501296038254\n ],\n [\n 31.420898437499996,\n 39.93501296038254\n ],\n [\n 31.420898437499996,\n 36.932330061503144\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-10-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Umhoefer, Paul J.","contributorId":200335,"corporation":false,"usgs":false,"family":"Umhoefer","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":808611,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, Stuart","contributorId":248207,"corporation":false,"usgs":false,"family":"Thompson","given":"Stuart","email":"","affiliations":[{"id":27205,"text":"U. Arizona","active":true,"usgs":false}],"preferred":false,"id":808612,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lefebre, Come","contributorId":248208,"corporation":false,"usgs":false,"family":"Lefebre","given":"Come","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":808613,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cosca, Michael 0000-0002-0600-7663","orcid":"https://orcid.org/0000-0002-0600-7663","contributorId":33043,"corporation":false,"usgs":true,"family":"Cosca","given":"Michael","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":808614,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Teyssier, Christian","contributorId":248209,"corporation":false,"usgs":false,"family":"Teyssier","given":"Christian","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":808615,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whitney, Donna L.","contributorId":187715,"corporation":false,"usgs":false,"family":"Whitney","given":"Donna","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":808616,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201829,"text":"tm4A3 - 2020 - Statistical methods in water resources","interactions":[{"subject":{"id":47512,"text":"twri04A3 - 2002 - Statistical methods in water resources","indexId":"twri04A3","publicationYear":"2002","noYear":false,"displayTitle":"Statistical Methods in Water Resources","title":"Statistical methods in water resources"},"predicate":"SUPERSEDED_BY","object":{"id":70201829,"text":"tm4A3 - 2020 - Statistical methods in water resources","indexId":"tm4A3","publicationYear":"2020","noYear":false,"title":"Statistical methods in water resources"},"id":1}],"lastModifiedDate":"2024-08-13T14:02:36.434133","indexId":"tm4A3","displayToPublicDate":"2020-10-27T09:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-A3","displayTitle":"Statistical Methods in Water Resources","title":"Statistical methods in water resources","docAbstract":"This text began as a collection of class notes for a course on applied statistical methods for hydrologists taught at the U.S. Geological Survey (USGS) National Training Center. Course material was formalized and organized into a textbook, first published in 1992 by Elsevier as part of their Studies in Environmental Science series. In 2002, the work was made available online as a USGS report.
The text has now been updated as a USGS Techniques and Methods Report. It is intended to be a text in applied statistics for hydrology, environmental science, environmental engineering, geology, or biology that addresses distinctive features of environmental data. For example, water resources data tend to have many variables with a lower bound of zero, tend to be more skewed than data from many other disciplines, commonly contain censored data (less than values), and assumptions that the data are normally distributed are not appropriate. Computer-intensive methods (bootstrapping and permutation tests) now improve upon and replace the dependence on t-intervals, t-tests, and analysis of variance. A new chapter on sampling design addresses questions such as “How many observations do I need?” The chapter also presents distribution-free methods to help plan sampling efforts. The trends chapter has been updated to include the WRTDS (Weighted Regressions on Time, Discharge, and Season) method for analysis of water-quality data. This new version contains updated graphics and updated guidance on the use of statistical techniques. The text utilizes R, a programming language and open-source software environment, for all exercises and most graphics, and the R code used to generate figures and examples is provided for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4A3","usgsCitation":"Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3, 458 p., https://doi.org/10.3133/tm4a3. [Supersedes USGS Techniques of Water-Resources Investigations, book 4, chap. A3, version 1.1.]","productDescription":"Report: xxii, 458 p.; Data Release","numberOfPages":"484","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-089727","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":418371,"rank":5,"type":{"id":12,"text":"Errata"},"url":"https://pubs.usgs.gov/tm/04/a03/Errata_Sheet.pdf","text":"Errata Sheet","size":"136 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Errata Sheet"},{"id":379731,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://store.usgs.gov/product/533012","text":"Print Version Available"},{"id":374999,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JWL6XR","text":"USGS data release","linkHelpText":"Statistical Methods in Water Resources - Supporting Materials"},{"id":375013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/a03/tm4a3.pdf","text":"Report","size":"9.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-A3"},{"id":375000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/a03/coverthb.jpg"}],"publicComments":"Techniques and Methods 4-A3 supersedes Techniques of Water-Resources Investigations, book 4, chapter A3, version 1.1.","contact":"Chief, Analysis and Prediction Branch
Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Dr., Mail Stop 415
Reston, VA 20192
This report provides an overview of existing hydrologic information describing the quality, quantity, and extent of the major surface-water and groundwater resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma. Hydrologic information is provided for five major river systems (Cimarron River, North Canadian River, Canadian River, Washita River, and North Fork Red River), two reservoirs (Foss Reservoir and Canton Lake), and eight aquifers consisting of the alluvial aquifers associated with each of the five major river systems and three major bedrock aquifers (Ogallala aquifer, Elk City aquifer, and Rush Springs aquifer).
Types of information provided about rivers and reservoirs for the Cheyenne and Arapaho Tribal jurisdictional area include diversion sites and amounts of water allocated and diverted for permitted uses in 2015; treated wastewater discharge sites and amounts discharged in 2015; and characteristics describing water-quality field properties, major ions, nutrients, and selected trace elements. Major ions, nutrients, and selected trace elements are compared to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water. Additionally, statistics are provided describing daily, monthly, and annual streamflow characteristics at 12 U.S. Geological Survey streamgages. Streamflow statistics include the magnitudes and frequencies of floods, base-flow characteristics, and long-term streamflow trends.
Types of information provided about the aquifers include amounts of water allocated and pumped for permitted uses in 2015; characteristics of groundwater describing water-quality field properties, major ions, nitrate (measured as nitrogen), and selected trace elements with comparisons to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water; groundwater levels and long-term changes in water levels; and ranges of hydraulic conductivity, aquifer recharge, specific yield, transmissivity, and well yields from reports and groundwater-flow models.
Surface water is used primarily for irrigation and mining and other nonconsumptive uses in the Cheyenne and Arapaho Tribal jurisdictional area, except from the Washita and North Fork Red Rivers, where water is treated for use as a public-water supply. Large concentrations of dissolved solids are the primary limiting factor affecting the use of surface water. Median concentrations of dissolved solids in surface water range from less than 1,000 milligrams per liter (mg/L) in samples from the North Canadian River to greater than 9,000 mg/L in samples from the Cimarron River. Large dissolved solids concentrations are correlated with hard water. Median hardness as calcium carbonate concentrations in surface water ranges from 427 mg/L in samples from Canton Lake to 1,000 mg/L in samples from the Washita River.
In 2015, groundwater was used at more than twice the rate of surface water in the Cheyenne and Arapaho Tribal jurisdictional area. Although the alluvial aquifers are considered reliably good sources of water in the Cheyenne and Arapaho Tribal jurisdictional area, concentrations of nitrate (measured as nitrogen) exceed the maximum contaminant level of 10 mg/L established by the U.S. Environmental Protection Agency for finished drinking water in parts of all of the alluvial aquifers. Water from the three major bedrock aquifers is used for irrigation, mining, public-water supply, and other uses; however, large concentrations of dissolved solids, nitrate (measured as nitrogen), and naturally occurring trace elements such as arsenic and uranium may limit the use of groundwater as a source of public-water supply in some areas. As of 2015, the depletion of groundwater from the major aquifers in west-central Oklahoma is a minor concern to the Oklahoma Water Resources Board. Groundwater levels and other hydrologic information show that recharge rates exceed the rates of water pumped from aquifers, except in areas that may be affected locally by groundwater depletions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205105","collaboration":"Prepared in cooperation with the Cheyenne and Arapaho Tribes of Oklahoma and the Bureau of Indian Affairs","usgsCitation":"Becker, C.J., and Varonka, M.S., 2020, Water resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma, with an analysis of data gaps through 2015 (ver. 1.1, January 2021): U.S. Geological Survey Scientific Investigations Report 2020–5105, 158 p., 1 app., https://doi.org/10.3133/sir20205105..","productDescription":"xi, 158 p.","numberOfPages":"175","onlineOnly":"Y","ipdsId":"IP-109610","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":382059,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5105/versionHist.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5105 Version History"},{"id":379749,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5105/sir20205105.pdf","text":"Report","size":"36.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5105"},{"id":379748,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5105/coverthb2.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Cheyenne and Arapaho Tribal Jurisdictional Area","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.3595,35.1163],[-99.4067,35.1161],[-99.409,35.1148],[-99.4129,35.1134],[-99.4163,35.1134],[-99.4196,35.1143],[-99.4219,35.1138],[-99.4258,35.1129],[-99.4281,35.1111],[-99.4337,35.1097],[-99.4393,35.111],[-99.4432,35.1124],[-99.4449,35.116],[-99.7833,35.1161],[-99.7839,35.1151],[-99.7852,35.028],[-99.8897,35.0277],[-100.0008,35.0295],[-100.0014,35.1818],[-100.0015,35.2034],[-100.0013,35.2689],[-100.0012,35.293],[-100.0009,35.4223],[-100.0014,35.4558],[-100.0011,35.6197],[-100.001,35.64],[-100.0015,35.8008],[-100.0015,35.8782],[-100.0015,35.9478],[-100.002,36.0539],[-100.0025,36.1891],[-100.003,36.3134],[-100.0031,36.3348],[-100.0038,36.4998],[-100.0044,36.5849],[-100.0045,36.5917],[-99.6193,36.5916],[-99.605,36.5917],[-99.6043,36.506],[-99.6038,36.3051],[-99.6034,36.2457],[-99.5954,36.2457],[-99.5976,36.1639],[-99.382,36.1645],[-98.9565,36.1587],[-98.7878,36.1613],[-98.7428,36.1625],[-98.7251,36.1634],[-98.6362,36.1636],[-98.634,36.1636],[-98.3177,36.1645],[-98.2122,36.1656],[-98.1057,36.1658],[-97.6759,36.1663],[-97.6765,36.0715],[-97.6763,35.984],[-97.6761,35.8973],[-97.6757,35.7253],[-97.6753,35.5506],[-97.6719,35.5506],[-97.6729,35.4639],[-97.6733,35.3763],[-97.6729,35.335],[-97.6836,35.3351],[-97.6898,35.3338],[-97.6948,35.3339],[-97.7016,35.3353],[-97.7033,35.3353],[-97.7073,35.334],[-97.7118,35.3313],[-97.7181,35.3287],[-97.7231,35.3278],[-97.7288,35.3274],[-97.7367,35.328],[-97.7423,35.3298],[-97.749,35.3326],[-97.7546,35.3354],[-97.7635,35.3414],[-97.7759,35.3434],[-97.7917,35.3408],[-97.829,35.3348],[-97.838,35.3354],[-97.8464,35.3368],[-97.8565,35.341],[-97.8582,35.3428],[-97.8614,35.3551],[-97.8636,35.3588],[-97.8669,35.3615],[-97.8703,35.3629],[-97.8754,35.3625],[-97.8811,35.3608],[-97.8845,35.3572],[-97.8885,35.3545],[-97.8936,35.3513],[-97.9009,35.3514],[-97.9082,35.3528],[-97.9105,35.3533],[-97.915,35.3556],[-97.925,35.3612],[-97.9351,35.3649],[-97.9368,35.3672],[-97.9378,35.3744],[-97.9395,35.3763],[-97.9429,35.3772],[-97.9474,35.3777],[-97.9491,35.3764],[-97.9502,35.3745],[-97.9492,35.3668],[-97.951,35.3596],[-97.9527,35.3555],[-97.955,35.3528],[-97.9562,35.3519],[-97.9663,35.3525],[-97.9697,35.3529],[-97.9736,35.3543],[-97.9843,35.3599],[-97.9994,35.365],[-98.0184,35.3765],[-98.0985,35.3767],[-98.305,35.3744],[-98.3051,35.5437],[-98.3085,35.541],[-98.3119,35.5401],[-98.3142,35.5406],[-98.3193,35.5429],[-98.3193,35.5438],[-98.3187,35.5479],[-98.3181,35.5502],[-98.6199,35.552],[-98.6209,35.4639],[-98.6216,35.2038],[-98.616,35.2038],[-98.6177,35.0994],[-98.621,35.0981],[-98.6255,35.1035],[-98.6294,35.1167],[-98.6355,35.1231],[-98.6406,35.1231],[-98.6429,35.1145],[-98.6451,35.1113],[-98.6496,35.1141],[-98.6513,35.1177],[-98.649,35.1195],[-98.6485,35.1213],[-98.6485,35.1231],[-98.6513,35.125],[-98.6575,35.1236],[-98.6665,35.1209],[-98.6738,35.1187],[-98.6778,35.1082],[-98.6822,35.1101],[-98.6856,35.1078],[-98.6985,35.1115],[-98.7042,35.111],[-98.7109,35.1065],[-98.7132,35.1065],[-98.721,35.1138],[-98.7255,35.1115],[-98.7317,35.1129],[-98.7351,35.1029],[-98.7379,35.102],[-98.7401,35.107],[-98.748,35.1166],[-98.8244,35.1176],[-98.9312,35.1168],[-98.9807,35.1173],[-99.0425,35.1168],[-99.2555,35.1161],[-99.3595,35.1163]]]},\"properties\":{\"name\":\"Beckham\",\"state\":\"OK\"}}]}","edition":"Version 1.0: October 27, 2020; Version 1.1: January 11, 2021","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The utilization, preservation, and conservation of the Nation’s resources requires well-informed management decisions. The North Atlantic and Appalachian Region (NAAR) of the U.S. Geological Survey (USGS) supports science-based decision making for Federal, State, and local policymakers to meet the challenges of today and into the future. The science centers in the NAAR have well-deserved reputations as world leaders in delivering unbiased science. We help protect the lives and property of our families, friends, neighbors, and the Nation by providing the data and scientific interpretation that decision makers need to make informed choices on a myriad of topics. Many of our jobs include inherent risk. When others are moving themselves and their families to higher ground during storms, NAAR employees can be found heading toward high water to ensure that accurate streamflow and storm-tide data continue to be collected and delivered to the public and first responders.
In March 2020, the world changed, and the NAAR staff adapted to it. Despite the challenges, the NAAR has had an incredibly productive year. I am not just citing publications (with our labs and field offices closed in the spring, centers increased annual publications by 10 to 40 percent compared with 2019) or partnerships (new science initiatives and partnerships are up significantly as well). Leaders at the center level created the right environments for their teams to be safe but still meet and exceed their program goals. Our vast data collection networks were maintained and enhanced. Our laboratories met holding times and quality-control objectives. When folks asked for help, our staff provided. Some solutions were not perfect at first, but they just kept trying. What started as a short-term inconvenience may now have become the new normal, but in quickly adapting, the NAAR staff showed dedication and wisdom, made the region a little safer, and just might change the world. This general information product highlights just a few of the many accomplishments of the NAAR staff during these challenging times and offers a taste of all the great work being done by the USGS community.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip207","isbn":"978-1-4113-4381-8","usgsCitation":"U.S. Geological Survey, 2020, Meeting the challenge—U.S. Geological Survey North Atlantic and Appalachian Region fiscal year 2020 in review: U.S. Geological Survey General Information Product 207, 20 p., https://doi.org/10.3133/gip207.","productDescription":"20 p.","numberOfPages":"20","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123417","costCenters":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"links":[{"id":379535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/207/gip207.pdf","text":"Report","size":"5.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 207"},{"id":379534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/207/coverthb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Kentucky, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia","otherGeospatial":"Appalachian region, North Atlantic region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.76171875,\n 36.56260003738545\n ],\n [\n -73.7841796875,\n 40.34654412118006\n ],\n [\n -70.6640625,\n 41.178653972331674\n ],\n [\n -69.873046875,\n 41.73852846935917\n ],\n [\n -70.7080078125,\n 42.68243539838623\n ],\n [\n -69.3896484375,\n 43.929549935614595\n ],\n [\n -67.5,\n 44.465151013519616\n ],\n [\n -66.70898437499999,\n 45.1510532655634\n ],\n [\n -67.67578124999999,\n 45.85941212790755\n ],\n [\n -67.939453125,\n 47.21956811231547\n ],\n [\n -69.2578125,\n 47.54687159892238\n ],\n [\n -70.57617187499999,\n 45.583289756006316\n ],\n [\n -71.71875,\n 45.058001435398275\n ],\n [\n -74.970703125,\n 45.058001435398275\n ],\n [\n -76.201171875,\n 44.213709909702054\n ],\n [\n -76.9482421875,\n 43.35713822211053\n ],\n [\n -78.31054687499999,\n 43.45291889355465\n ],\n [\n -78.837890625,\n 43.197167282501276\n ],\n [\n -78.8818359375,\n 42.71473218539458\n ],\n [\n -80.5078125,\n 41.86956082699455\n ],\n [\n -80.419921875,\n 40.17887331434696\n ],\n [\n -82.44140625,\n 38.44498466889473\n ],\n [\n -82.9248046875,\n 38.61687046392973\n ],\n [\n -84.6826171875,\n 39.13006024213511\n ],\n [\n -86.17675781249999,\n 38.03078569382294\n ],\n [\n -87.8466796875,\n 37.92686760148135\n ],\n [\n -88.3740234375,\n 37.54457732085582\n ],\n [\n -89.1650390625,\n 37.33522435930639\n ],\n [\n -89.4287109375,\n 36.59788913307022\n ],\n [\n -75.76171875,\n 36.56260003738545\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Regional Director
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12201 Sunrise Valley Drive
Reston, VA 20192
The city of Summerset is a growing community in west South Dakota. The Sun Valley Estates subdivision in the north part of the city was developed on unconsolidated deposits surrounded by steep terrain. During years with greater than normal precipitation, particularly in 2019, groundwater levels increased in the unconsolidated deposits and caused damage to stormwater systems, sewer infrastructure, and houses with basements. The U.S. Geological Survey, in cooperation with the City of Summerset, completed a study of the hydrogeology and groundwater flow in the alluvial aquifer part of the unconsolidated deposits in north Summerset to understand the groundwater system in the area and to provide hydrogeologic information in support of future development planning.
The study area included most of the Sun Valley Estates subdivision in the north part of the city of Summerset in the east Black Hills of west South Dakota. About 0.7 square mile of water-bearing alluvial deposits is included in the study area. Precipitation in the study area from 2017 to 2019 was compared to the monthly normal values at a nearby climate site. The largest departure from normal was in May 2019 with precipitation exceeding the monthly normal by about 5 inches (in.). All months in 2019, except March, exceeded the monthly normal precipitation. Cumulative departure from normal precipitation in 2019 increased from about 4 in. greater than normal in January to about 18 in. greater than normal in December.
The geologic setting of the study area is characterized by the surrounding Black Hills. Unconsolidated Quaternary-age deposits overlie consolidated to partially consolidated Mesozoic-age and Paleozoic-age shales, sandstones, and limestones. Surficial deposits of alluvium and other unconsolidated deposits are the primary surficial geologic units in the study area and form the components of the alluvium hydrogeologic unit of the study area. Results from previous studies of alluvium along nearby Rapid Creek estimated hydraulic conductivity to range from 89 to 2,292 feet per day (ft/d), transmissivity to range from 1,001 to 32,083 feet squared per day, and storage coefficients to range from 0.0002 to 0.16. Hydraulic conductivity and transmissivity generally decreased downstream along Rapid Creek (west to east). Slug tests were completed August 16, 2019, at two observation wells completed in the alluvial aquifer in the Sun Valley Estates subdivision to determine hydraulic conductivity. Hydraulic conductivity estimated from AQTESOLV curve-fitting analysis using the Bouwer-Rice method for all slug-in and slug-out trials from two observation wells in the study ranged from 0.20 to 0.26 ft/d for well 441318103220001 (SunValley1 well) and from 0.54 to 14 ft/d for well 441319103215701 (SunValley2 well). The mean, median, and standard deviation of all trials at both wells were 4.3 ft/d, 0.8 ft/d, and 5.6 ft/d, respectively.
The extent of the alluvial aquifer was determined by geologic maps and lithologic logs. Alluvial deposits in the study area extend to about 1 mile in the north–south direction and about 1.5 miles in the southeast–northwest direction. The direction of groundwater flow was estimated using water-level records and topographic maps. The resulting potentiometric map indicated that groundwater in the alluvial aquifer under the Sun Valley Estates subdivision originates from higher elevations of the west part of the area of interest and from streams in the southeast part. Recharge and evapotranspiration estimates were results from a Soil-Water Balance model that calculated a matrix of recharge for 2019 with values ranging from 0 to 11.4 in. and an annual mean value of 5.1 in. across the study area. Soil-Water Balance-estimated potential evapotranspiration for 2019 ranged from 28.90 to 28.75 in. and the estimated annual mean was 28.86 in. across the study area. Estimated groundwater budget components for the alluvial aquifer in the area of interest included inflows and outflows. Total estimated groundwater budget components for inflows for 2019 were about 66 percent from recharge, 33 percent from streamflow, and 1 percent from inflow from adjacent aquifers. Total estimated outflows were about 99-percent evapotranspiration and less than 1-percent outflow to adjacent aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205097","issn":"2328-0328","usgsCitation":"Eldridge, W.G., and Anderson, T.M., 2020, Hydrogeology and groundwater flow in alluvial deposits, north Summerset, South Dakota: U.S. Geological Survey Scientific Investigations Report 2020–5097, 31 p., https://doi.org/10.3133/sir20205097.","productDescription":"Report: vii, 31 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-116994","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":379700,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5097/coverthb.jpg"},{"id":379703,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","description":"USGS data release","linkHelpText":"USGS Water Data for the Nation"},{"id":379702,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TKVMXU","text":"USGS data release","description":"USGS data release","linkHelpText":"Soil-Water Balance model for alluvial deposits in Summerset, South Dakota"},{"id":379701,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5097/sir20205097.pdf","text":"Report","size":"5.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5097"}],"country":"United States","state":"South Dakota","city":"Sommerset","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.37310791015625,\n 44.15856343854312\n ],\n [\n -103.28109741210938,\n 44.15856343854312\n ],\n [\n -103.28109741210938,\n 44.203866109361435\n ],\n [\n -103.37310791015625,\n 44.203866109361435\n ],\n [\n -103.37310791015625,\n 44.15856343854312\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Excessive nutrient inputs from tributary streams and rivers contribute to harmful algal blooms and coastal ecosystem degradation worldwide. However, the role that small tributaries play in coastal nutrient dynamics remains unknown because most monitoring and regulatory efforts focus only on the largest tributaries. We combined a 6-d sampling effort with discharge modeling to characterize nutrient inputs from nearly all watersheds draining to the world’s fifth largest lake. We found that streams are particularly likely to promote eutrophication in coastal ecosystems because they deliver water with higher concentrations of nutrients that are readily available to algae. Thus, our findings indicate that efforts to control nutrient loading could be enhanced by looking beyond the largest tributaries to include smaller streams.
No abstract available.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13954","usgsCitation":"Nimmo, J.R., 2020, Imperatives for predicting preferential and diffuse flow in the unsaturated zone: 1. Equal emphasis: Hydrological Processes, v. 34, no. 26, p. 5690-5693, https://doi.org/10.1002/hyp.13954.","productDescription":"4 p.","startPage":"5690","endPage":"5693","ipdsId":"IP-123789","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":405457,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"26","noUsgsAuthors":false,"publicationDate":"2020-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":849509,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70235840,"text":"70235840 - 2020 - Imperatives for predicting preferential and diffuse flow in the unsaturated zone: 2. Disparate formulation","interactions":[],"lastModifiedDate":"2022-08-23T13:55:39.99211","indexId":"70235840","displayToPublicDate":"2020-10-26T08:53:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Imperatives for predicting preferential and diffuse flow in the unsaturated zone: 2. Disparate formulation","docAbstract":"No abstract available.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13957","usgsCitation":"Nimmo, J.R., 2020, Imperatives for predicting preferential and diffuse flow in the unsaturated zone: 2. Disparate formulation: Hydrological Processes, v. 34, no. 26, p. 5694-5698, https://doi.org/10.1002/hyp.13957.","productDescription":"5 p.","startPage":"5694","endPage":"5698","ipdsId":"IP-123788","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":405455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"26","noUsgsAuthors":false,"publicationDate":"2020-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":849508,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70217374,"text":"70217374 - 2020 - Integrated geophysical analysis provides an alternate interpretation of the northern margin of the North American Midcontinent Rift System, Central Lake Superior","interactions":[],"lastModifiedDate":"2021-01-20T14:21:06.01387","indexId":"70217374","displayToPublicDate":"2020-10-26T08:18:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3906,"text":"Interpretation","active":true,"publicationSubtype":{"id":10}},"title":"Integrated geophysical analysis provides an alternate interpretation of the northern margin of the North American Midcontinent Rift System, Central Lake Superior","docAbstract":"The Midcontinent Rift System (MRS) is a 1.1 Ga sequence of voluminous basaltic eruptions and multiple intrusions followed by widespread sedimentation that extends across the Midcontinent and northern Great Lakes region of North America. Previous workers have commonly used seismic-reflection data (Great Lakes International Multidisciplinary Program on Crustal Evolution [GLIMPCE] line A) to demonstrate that the northern rift margin in central Lake Superior developed as a normal growth fault that was structurally inverted to a reverse fault during a compressional event after rifting had ended. A prominent, curvilinear aeromagnetic anomaly that extends from Isle Royale, Michigan, to Superior Shoal in central Lake Superior, Ontario (the IR-SS anomaly), is commonly presented as a manifestation of this reverse fault. We have integrated multidisciplinary geophysical analyses (seismic-reflection, seismic-refraction, aeromagnetic, and gravity), physical-property information (density, magnetic susceptibility and remanence, and compressional-wave velocity), and geologic concepts to develop an alternate interpretation of the rift margin along GLIMPCE line A, where it intersects the IR-SS anomaly. Our new model indicates that a normal fault is the dominant structure at the northern rift margin along line A, contrary to the original rift-margin paradigm, which asserts that compressional structures are the dominant features preserved today. Integral to this alternate model is a newly interpreted, prerift sedimentary basin intruded by sills in northern Lake Superior. Our alternate model of the northern rift margin has implications for interpreting the style, scale, and timing of extension, rift-related intrusion, and compression during development of the MRS.
Lava that erupted during the 2014–2015 Holuhraun eruption in Iceland flowed into a proglacial river system, resulting in aqueous cooling of the lava and an ephemeral hydrothermal system. We carried out a monitoring study of this system from 2015 to 2018 to document the cooling of the lava over this time, using thermocouple measurements and data-logging sensors. The heat loss rate from advection through this hydrothermal system in August 2015 was ~5.5 × 108 W; since eruption, aqueous cooling likely accounted for ~1% of the total heat loss from the lava. This estimate excludes steam losses from fumaroles as well as any groundwater that was not released to the surface, and thus is a lower bound. Near the terminus of the flow, advection of heat by flowing water may have locally accounted for tens of percent of the total cooling of that part of the flow. Our data quantify the importance of water cooling for this lava flow and can be compared with models to better understand lava–water interactions more generally. We also provide detailed methods for simple, low-cost monitoring of similar instances in the future.
As climate warming and precipitation increase at high latitudes, permafrost terrains across the circumpolar north are poised for intensified geomorphic activity and sediment mobilization that are expected to persist for millennia. In previously glaciated permafrost terrain, ice-rich deposits are associated with large stores of reactive mineral substrate. Over geological timescales, chemical weathering moderates atmospheric CO2 levels, raising the prospect that mass wasting driven by terrain consolidation following thaw (thermokarst) may enhance weathering of permafrost sediments and thus climate feedbacks. The nature of these feedbacks depends upon the mineral composition of sediments (weathering sources) and the balance between atmospheric exchange of CO2 vs. fluvial export of carbonate alkalinity (Σ[
Fault creep reduces seismic hazard and serves as a window into plate boundary processes; however, creep rates are typically constrained with sparse measurements. We use differential lidar topography (11–13 year time span) to measure a spatially dense surface deformation field along a 150 km section of the Central San Andreas and Calaveras faults. We use an optimized windowed‐iterative‐closest‐point approach to resolve independent creep rates every 400 m at 1–2 km apertures. Rates vary from <10 mm/year along the creeping fault ends to over 30 mm/year along much of the central 100 km of the fault. Creep rates are 3–8 mm/year higher than most rates from alignment arrays and creepmeters, likely due to the larger aperture of the topographic differencing. Creep is often focused along discrete fault traces, but strain is sometimes distributed in areas of complex fault geometry, such as Mustang Ridge. Our observations constrain shallow seismic moment accumulation and the location of the creeping fault trace.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL090628","usgsCitation":"Scott, C.P., DeLong, S.B., and Arrosmith, J.R., 2020, Distribution of aseismic deformation along the central San Andreas and Calaveras Faults from differencing repeat airborne lidar: Geophysical Research Letters, v. 47, no. 22, e2020GL090628, 10 p., https://doi.org/10.1029/2020GL090628.","productDescription":"e2020GL090628, 10 p.","ipdsId":"IP-120619","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":454973,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl090628","text":"Publisher Index Page"},{"id":436743,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76W9896","text":"USGS data release","linkHelpText":"Data from Theodolite Measurements of Creep Rates on San Francisco Bay Region Faults, California (ver. 2.2, July 2023)"},{"id":383141,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"central San Andreas fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.86035156249999,\n 36.155617833818525\n ],\n [\n -120.65185546875,\n 36.155617833818525\n ],\n [\n -120.65185546875,\n 36.87962060502676\n ],\n [\n -121.86035156249999,\n 36.87962060502676\n ],\n [\n -121.86035156249999,\n 36.155617833818525\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"22","noUsgsAuthors":false,"publicationDate":"2020-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Chelsea P 0000-0002-3884-4693","orcid":"https://orcid.org/0000-0002-3884-4693","contributorId":248847,"corporation":false,"usgs":false,"family":"Scott","given":"Chelsea","email":"","middleInitial":"P","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":810064,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeLong, Stephen B. 0000-0002-0945-2172 sdelong@usgs.gov","orcid":"https://orcid.org/0000-0002-0945-2172","contributorId":5240,"corporation":false,"usgs":true,"family":"DeLong","given":"Stephen","email":"sdelong@usgs.gov","middleInitial":"B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":810065,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arrosmith, J Ramon 0000-0003-1756-3697","orcid":"https://orcid.org/0000-0003-1756-3697","contributorId":248848,"corporation":false,"usgs":false,"family":"Arrosmith","given":"J","email":"","middleInitial":"Ramon","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":810066,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216891,"text":"70216891 - 2020 - Isolation and characterization of microsatellite loci in merlins (Falco columbarius) and cross-species amplification in gyrfalcons (F. rusticolus) and peregrine falcons (F. peregrinus)","interactions":[],"lastModifiedDate":"2020-12-14T15:09:13.182404","indexId":"70216891","displayToPublicDate":"2020-10-24T09:04:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7467,"text":"Molecular Biology Reports","onlineIssn":"1573-4978","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Isolation and characterization of microsatellite loci in merlins (Falco columbarius) and cross-species amplification in gyrfalcons (F. rusticolus) and peregrine falcons (F. peregrinus)","title":"Isolation and characterization of microsatellite loci in merlins (Falco columbarius) and cross-species amplification in gyrfalcons (F. rusticolus) and peregrine falcons (F. peregrinus)","docAbstract":"I. Background: Merlins, Falco columbarius, breed throughout temperate and high latitude habitats in Asia, Europe, and North America. Like peregrine falcons, F. peregrinus, merlins underwent population declines during the mid-to-late 20th century, due to organochlorine-based contamination, and have subsequently recovered, at least in North American populations. \nII. Methods and Results: To better understand levels of genetic diversity and population structuring in contemporary populations and to assess the impact of the 20th century decline, we used genomic data archived in public databases and constructed genomic libraries to isolate and characterize a suite of 17 microsatellite markers for use in merlins. We also conducted cross-amplification experiments to determine the markers’ utility in peregrine falcons and gyrfalcons, F. rusticolus. \nIII. Conclusions: These markers provide a valuable addition to marker suites that can be used to determine individual identity and conduct genetic analyses on merlins and congeners.","language":"English","publisher":"Springer","doi":"10.1007/s11033-020-05842-4","usgsCitation":"Hull, J.M., Sage, G.K., Sonsthagen, S.A., Gravley, M.C., Martinico, B.L., Booms, T.L., Swem, T., and Talbot, S.L., 2020, Isolation and characterization of microsatellite loci in merlins (Falco columbarius) and cross-species amplification in gyrfalcons (F. rusticolus) and peregrine falcons (F. peregrinus): Molecular Biology Reports, v. 47, p. 8377-8383, https://doi.org/10.1007/s11033-020-05842-4.","productDescription":"7 p.","startPage":"8377","endPage":"8383","ipdsId":"IP-108647","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":436744,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BOU6CP","text":"USGS data release","linkHelpText":"Genetic Data for Merlin (Falco columbarius) and Cross-Species Microsatellite Amplification in Select Falco Species, North America"},{"id":381253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","noUsgsAuthors":false,"publicationDate":"2020-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Hull, Joshua M.","contributorId":127686,"corporation":false,"usgs":false,"family":"Hull","given":"Joshua","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":806750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sage, George K. 0000-0003-1431-2286 ksage@usgs.gov","orcid":"https://orcid.org/0000-0003-1431-2286","contributorId":87833,"corporation":false,"usgs":true,"family":"Sage","given":"George","email":"ksage@usgs.gov","middleInitial":"K.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":806751,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":806752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gravley, Megan C. 0000-0002-4947-0236 mgravley@usgs.gov","orcid":"https://orcid.org/0000-0002-4947-0236","contributorId":202812,"corporation":false,"usgs":true,"family":"Gravley","given":"Megan","email":"mgravley@usgs.gov","middleInitial":"C.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":806753,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martinico, Breanna L.","contributorId":215572,"corporation":false,"usgs":false,"family":"Martinico","given":"Breanna","email":"","middleInitial":"L.","affiliations":[{"id":39284,"text":"U. of California, Davis","active":true,"usgs":false}],"preferred":false,"id":806754,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Booms, Travis L.","contributorId":199285,"corporation":false,"usgs":false,"family":"Booms","given":"Travis","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":806755,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Swem, Ted","contributorId":200583,"corporation":false,"usgs":false,"family":"Swem","given":"Ted","affiliations":[],"preferred":false,"id":806756,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":806757,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70216694,"text":"70216694 - 2020 - Characterization of acoustic detection efficiency using a gliding robotic fish as a mobile receiver platform","interactions":[],"lastModifiedDate":"2020-12-01T13:03:58.668537","indexId":"70216694","displayToPublicDate":"2020-10-24T06:56:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of acoustic detection efficiency using a gliding robotic fish as a mobile receiver platform","docAbstract":"Autonomous underwater vehicles (AUVs) and animal telemetry have become important tools for understanding the relationships between aquatic organisms and their environment, but more information is needed to guide the development and use of AUVs as effective animal tracking platforms. A forward-facing acoustic telemetry receiver (VR2Tx 69 kHz; VEMCO, Bedford, Nova Scotia) attached to a novel AUV (gliding robotic fish) was tested in a freshwater lake to (1) compare its detection efficiency (i.e., the probability of detecting an acoustic signal emitted by a tag) of acoustic tags (VEMCO model V8-4H 69 kHz) to stationary receivers and (2) determine if detection efficiency was related to distance between tag and receiver, direction of movement (toward or away from transmitter), depth, or pitch.
Detection efficiency for mobile (robot-mounted) and stationary receivers were similar at ranges less than 300 m, on average across all tests, but detection efficiency for the mobile receiver decreased faster than for stationary receivers at distances greater than 300 m. Detection efficiency was higher when the robot was moving toward the transmitter than when moving away from the transmitter. Detection efficiency decreased with depth (surface to 4 m) when the robot was moving away from the transmitter, but depth had no significant effect on detection efficiency when the robot was moving toward the transmitter. Detection efficiency was higher when the robot was descending (pitched downward) than ascending (pitched upward) when moving toward the transmitter, but pitch had no significant effect when moving away from the transmitter.
Results suggested that much of the observed variation in detection efficiency is related to shielding of the acoustic signal by the robot body depending on the positions and orientation of the hydrophone relative to the transmitter. Results are expected to inform hardware, software, and operational changes to gliding robotic fish that will improve detection efficiency. Regardless, data on the size and shape of detection efficiency curves for gliding robotic fish will be useful for planning future missions and should be relevant to other AUVs for telemetry. With refinements, gliding robotic fish could be a useful platform for active tracking of acoustic tags in certain environments.
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2020 - Evaluation of the U.S. Geological Survey streamgage network in South Carolina, 2017","interactions":[],"lastModifiedDate":"2020-10-25T17:23:47.879673","indexId":"ofr20201104","displayToPublicDate":"2020-10-23T12:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1104","displayTitle":"Evaluation of the U.S. Geological Survey Streamgage Network in South Carolina, 2017","title":"Evaluation of the U.S. Geological Survey streamgage network in South Carolina, 2017","docAbstract":"The U.S. Geological Survey (USGS) has been monitoring streamflow in South Carolina since the late 1800s. From the beginning, the USGS streamgage network in South Carolina has been dynamic, with streamgages being added or removed depending on their purpose and the availability of funding from Federal, State, and local partners. Streamflow monitoring is important for acquiring real-time data during flood events, but also for collecting long-term data that can be used to compute the magnitude and frequency of floods and to frame flood events in a historical perspective. These data are also critical for being able to develop regional regression equations that can be used to estimate flood characteristics at ungaged locations, which is important for infrastructure planning and design. The historical flooding that occurred in South Carolina in 2015, 2016, and 2018 highlighted the importance of collecting these data. Therefore, the USGS, in cooperation with the South Carolina Department of Transportation, evaluated the USGS streamgage network in South Carolina for the purpose of helping guide decisions concerning future streamgage location selection, both spatially and in terms of the range of drainage basin characteristics that are typically important in flood-frequency analyses. The results of this evaluation are presented in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201104","collaboration":"Prepared in cooperation with the South Carolina Department of Transportation","usgsCitation":"Feaster, T.D., and Kolb, K.R., 2020, Evaluation of the U.S. Geological Survey streamgage network in South Carolina, 2017: U.S. Geological Survey Open-File Report 2020–1104, 15 p., https://doi.org/10.3133/ofr20201104.","productDescription":"Report: vii, 15 p.; 1 Plate: 40.00 x 40.00 inches; Appendixes 1-3; Data Release","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-116207","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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U.S. Geological Survey
1770 Corporate Drive
Suite 500
Norcross, GA 30093
The “National Field Manual for the Collection of Water-Quality Data” (NFM) provides guidelines and procedures for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation’s surface water and groundwater resources. This chapter, NFM A6.2, provides guidance and protocols for the measurement of dissolved oxygen, which include the scientific basis of the measurement, selection and maintenance of equipment, calibration, troubleshooting, and procedures for measurement and reporting. It updates and supersedes USGS Techniques of Water-Resources Investigations, book 9, chapter A6.2, version 3.0, by Stewart A, Rounds, Franceska D. Wilde, and George F. Ritz. Dissolved oxygen is routinely measured when water samples are collected, is often continually measured at USGS streamgages, and is a parameter regularly measured during laboratory and field experiments. The field method for measuring dissolved oxygen described in this chapter is applicable to most natural waters.
Before 2017, the USGS NFM chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0—U.S. Geological Survey, 2018) at https://doi.org/10.3133/tm9A0. The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.er.usgs.gov. Comments, questions, and suggestions related to the NFM can be addressed to nfm@usgs.gov.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: National field manual for the collection of water-quality data in Book 9: Handbooks for water-resources investigations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm9A6.2","usgsCitation":"U.S. Geological Survey, 2020, Chapter A6.2. Dissolved oxygen: U.S. Geological Survey Techniques and Methods 9-A6.2, vi, 33 p., https://doi.org/10.3133/tm9A6.2.","productDescription":"vi, 33 p.","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112251","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":379603,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm9A0","text":"Techniques and Methods 9-A0","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- General introduction for the “National Field Manual for the Collection of Water-Quality Data”"},{"id":379506,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/09/a6.2/coverthb.jpg"},{"id":379509,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/09/a6.2/versionhistory.txt","text":"Version history","size":"2.54 KB","linkFileType":{"id":2,"text":"txt"}},{"id":379507,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/09/a6.2/tm9a6.2.pdf","text":"Report","size":"1.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM9-A6.2"}],"contact":"Director, Observing Systems Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 432
Reston, VA 20192
Insecticides in streams are increasingly a global concern, yet information on safe concentrations for aquatic ecosystems is sparse. In a 30-day mesocosm experiment exposing native benthic aquatic invertebrates to the common insecticide fipronil and four degradates, fipronil compounds caused altered emergence and trophic cascades. Effect concentrations eliciting a 50% response (EC50) were developed for fipronil and its sulfide, sulfone, and desulfinyl degradates; taxa were insensitive to fipronil amide. Hazard concentrations for 5% of affected species derived from up to 15 mesocosm EC50 values were used to convert fipronil compound concentrations in field samples to the sum of toxic units (∑TUFipronils). Mean ∑TUFipronils exceeded 1 (indicating toxicity) in 16% of streams sampled from five regional studies. The Species at Risk invertebrate metric was negatively associated with ∑TUFipronils in four of five regions sampled. This ecological risk assessment indicates that low concentrations of fipronil compounds degrade stream communities in multiple regions of the United States.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.abc1299","usgsCitation":"Miller, J., Schmidt, T., Van Metre, P.C., Mahler, B., Sandstrom, M.W., Nowell, L.H., Carlisle, D.M., and Moran, P.W., 2020, Common insecticide disrupts aquatic communities: A mesocosm-to-field ecological risk assessment of fipronil and its degradates in U.S. streams: Science Advances, v. 6, no. 43, https://doi.org/10.1126/sciadv.abc1299.","productDescription":"eabc1299, 12 p.","startPage":"eabc1299","ipdsId":"IP-114600","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":454979,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.abc1299","text":"Publisher Index Page"},{"id":436746,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XR80GW","text":"USGS data release","linkHelpText":"Data set for an ecological risk assessment of Firpronil compounds in US streams"},{"id":414623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"43","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Janet L.","contributorId":239985,"corporation":false,"usgs":false,"family":"Miller","given":"Janet L.","affiliations":[{"id":48080,"text":"Colorado State University, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":867352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":867353,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Metre, Peter C. 0000-0001-7564-9814","orcid":"https://orcid.org/0000-0001-7564-9814","contributorId":211144,"corporation":false,"usgs":true,"family":"Van Metre","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":867354,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahler, Barbara 0000-0002-9150-9552 bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":867355,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true}],"preferred":true,"id":867356,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nowell, Lisa H. 0000-0001-5417-7264 lhnowell@usgs.gov","orcid":"https://orcid.org/0000-0001-5417-7264","contributorId":490,"corporation":false,"usgs":true,"family":"Nowell","given":"Lisa","email":"lhnowell@usgs.gov","middleInitial":"H.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867357,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":867358,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moran, Patrick W. 0000-0002-2002-3539 pwmoran@usgs.gov","orcid":"https://orcid.org/0000-0002-2002-3539","contributorId":489,"corporation":false,"usgs":true,"family":"Moran","given":"Patrick","email":"pwmoran@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867359,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70216563,"text":"70216563 - 2020 - A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species","interactions":[],"lastModifiedDate":"2020-11-25T15:25:24.210332","indexId":"70216563","displayToPublicDate":"2020-10-23T09:22:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species","docAbstract":"Predictive models of post-fire tree and stem mortality are vital for management planning and understanding fire effects. Post-fire tree and stem mortality have been traditionally modeled as a simple empirical function of tree defenses (e.g., bark thickness) and fire injury (e.g., crown scorch). We used the Fire and Tree Mortality database (FTM)—which includes observations of tree mortality in obligate seeders and stem mortality in basal resprouting species from across the USA—to evaluate the accuracy of post-fire mortality models used in the First Order Fire Effects Model (FOFEM) software system. The basic model in FOFEM, the Ryan and Amman (R-A) model, uses bark thickness and percentage of crown volume scorched to predict post-fire mortality and can be applied to any species for which bark thickness can be calculated (184 species-level coefficients are included in the program). FOFEM (v6.7) also includes 38 species-specific tree mortality models (26 for gymnosperms, 12 for angiosperms), with unique predictors and coefficients. We assessed accuracy of the R-A model for 44 tree species and accuracy of 24 species-specific models for 13 species, using data from 93 438 tree-level observations and 351 fires that occurred from 1981 to 2016.
For each model, we calculated performance statistics and provided an assessment of the representativeness of the evaluation data. We identified probability thresholds for which the model performed best, and the best thresholds with either ≥80% sensitivity or specificity. Of the 68 models evaluated, 43 had Area Under the Receiver Operating Characteristic Curve (AUC) values ≥0.80, indicating excellent performance, and 14 had AUCs <0.7, indicating poor performance. The R-A model often over-predicted mortality for angiosperms; 5 of 11 angiosperms had AUCs <0.7. For conifers, R-A over-predicted mortality for thin-barked species and for small diameter trees. The species-specific models had significantly higher AUCs than the R-A models for 10 of the 22 models, and five additional species-specific models had more balanced errors than R-A models, even though their AUCs were not significantly different or were significantly lower.
Approximately 75% of models tested had acceptable, excellent, or outstanding predictive ability. The models that performed poorly were primarily models predicting stem mortality of angiosperms or tree mortality of thin-barked conifers. This suggests that different approaches—such as different model forms, better estimates of bark thickness, and additional predictors—may be warranted for these taxa. Future data collection and research should target the geographical and taxonomic data gaps and poorly performing models identified in this study. Our evaluation of post-fire tree mortality models is the most comprehensive effort to date and allows users to have a clear understanding of the expected accuracy in predicting tree death from fire for 44 species.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s42408-020-00082-0","usgsCitation":"Cansler, C., Hood, S.M., van Mantgem, P., and Varner, J.M., 2020, A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species: Fire Ecology, v. 16, 25, 37 p., https://doi.org/10.1186/s42408-020-00082-0.","productDescription":"25, 37 p.","ipdsId":"IP-115072","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":454980,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-020-00082-0","text":"Publisher Index Page"},{"id":380782,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","noUsgsAuthors":false,"publicationDate":"2020-10-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Cansler, C. Alina","contributorId":245203,"corporation":false,"usgs":false,"family":"Cansler","given":"C. Alina","affiliations":[{"id":49115,"text":"USDA Forest Service, Rocky Mountain Research Station, Fire, Fuel, and Smoke Science Program, 5775 US Highway 10 W, Missoula, Montana, 59808, USA","active":true,"usgs":false}],"preferred":false,"id":805617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hood, Sharon M.","contributorId":221183,"corporation":false,"usgs":false,"family":"Hood","given":"Sharon","email":"","middleInitial":"M.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":805618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Varner, J. Morgan 0000-0003-3781-5839","orcid":"https://orcid.org/0000-0003-3781-5839","contributorId":244802,"corporation":false,"usgs":false,"family":"Varner","given":"J.","email":"","middleInitial":"Morgan","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":805620,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215983,"text":"70215983 - 2020 - Double exposure and dynamic vulnerability: Assessing economic well-being, ecological change and the development of the oil and gas industry in coastal Louisiana","interactions":[],"lastModifiedDate":"2020-11-02T14:02:27.322344","indexId":"70215983","displayToPublicDate":"2020-10-23T07:58:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3385,"text":"Shore & Beach","printIssn":"0037-4237","active":true,"publicationSubtype":{"id":10}},"title":"Double exposure and dynamic vulnerability: Assessing economic well-being, ecological change and the development of the oil and gas industry in coastal Louisiana","docAbstract":"The oil and gas industry has been a powerful driver of economic change in coastal Louisiana for the latter half of the 20th century and into the 21st. Yet, the overall impact of the industry on the economic well-being of host communities is varied, both spatially and temporally. While the majority of Louisiana’s oil and gas production now occurs offshore, processing the extracted product is an energy-intensive undertaking requiring an expansive network of land-based infrastructure. Despite the positive economic aspects of this development, there are also potential negatives posed to coastal ecosystems and to communities located adjacent to oil and gas infrastructure. This research utilizes a double exposure framework to explore the relationship between oil and gas infrastructure development, fish and shellfish habitat, and economic well-being in Louisiana’s coastal zone from 1950 to 2010. The approach followed four main steps: (1) Developing a hazardousness of place model to identify areas of magnified risk due to the combined hazards of multiple potential exposure sites related to the extraction and processing of crude oil and natural gas; (2) developing a model of ecological functioning to measure the ability of aquatic habitat to support key fish and shellfish species; (3) utilizing an integrated community economic well-being index to assess change on a decadal timescale; and (4) analyzing selected oil-dependent communities to illustrate how change processes occurring in different energy sectors result in differential outcomes. The results suggest that, for many communities, the dependence on the oil and gas industry has increased economic well-being but also increased sensitivity to natural and human-induced changes, including fluctuating economic conditions, environmental stress, coastal habitat destruction, and increasing social and economic pressures.","language":"English","publisher":"American Shore and Beach Preservation Association (ASBPA)","doi":"10.34237/1008819","usgsCitation":"Hemmerling, S., Carruthers, T., Hijuelos, A., and Bienn, H.C., 2020, Double exposure and dynamic vulnerability: Assessing economic well-being, ecological change and the development of the oil and gas industry in coastal Louisiana: Shore & Beach, v. 88, no. 1, p. 72-82, https://doi.org/10.34237/1008819.","productDescription":"11 p.","startPage":"72","endPage":"82","ipdsId":"IP-112627","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":380018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.779296875,\n 28.43971381702788\n ],\n [\n -89.05517578125,\n 28.43971381702788\n ],\n [\n -89.05517578125,\n 30.543338954230222\n ],\n [\n -93.779296875,\n 30.543338954230222\n ],\n [\n -93.779296875,\n 28.43971381702788\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"88","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Hemmerling, Scott","contributorId":221274,"corporation":false,"usgs":false,"family":"Hemmerling","given":"Scott","affiliations":[],"preferred":false,"id":803667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carruthers, Tim J. B.","contributorId":140566,"corporation":false,"usgs":false,"family":"Carruthers","given":"Tim J. B.","affiliations":[],"preferred":false,"id":803668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hijuelos, Ann 0000-0003-0922-6754","orcid":"https://orcid.org/0000-0003-0922-6754","contributorId":201525,"corporation":false,"usgs":true,"family":"Hijuelos","given":"Ann","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":803669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bienn, Harris C.","contributorId":244280,"corporation":false,"usgs":false,"family":"Bienn","given":"Harris","email":"","middleInitial":"C.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":803670,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}