Forest vegetation classification and structure measurements are fundamental steps for planning, monitoring, and evaluating large-scale forest changes including restoration treatments. High spatial and spectral resolution remote sensing data are critically needed to classify vegetation and measure their 3-dimensional (3D) canopy structure at the level of individual species. Here we test high-resolution lidar, hyperspectral, and multispectral data collected from unmanned aerial vehicles (UAV) and demonstrate a lidar-hyperspectral image fusion method in treated and control forests with varying tree density and canopy cover as well as in an ecotone environment to represent a gradient of vegetation and topography in northern Arizona, U.S.A. The fusion performs better (88% overall accuracy) than either data type alone, particularly for species with similar spectral signatures, but different canopy sizes. The lidar data provides estimates of individual tree height (R2 = 0.90; RMSE = 2.3 m) and crown diameter (R2 = 0.72; RMSE = 0.71 m) as well as total tree canopy cover (R2 = 0.87; RMSE = 9.5%) and tree density (R2 = 0.77; RMSE = 0.69 trees/cell) in 10 m cells across thin only, burn only, thin-and-burn, and control treatments, where tree cover and density ranged between 22 and 50% and 1–3.5 trees/cell, respectively. The lidar data also produces highly accurate digital elevation model (DEM) (R2 = 0.92; RMSE = 0.75 m). In comparison, 3D data derived from the multispectral data via structure-from-motion produced lower correlations with field-measured variables, especially in dense and structurally complex forests. The lidar, hyperspectral, and multispectral sensors, and the methods demonstrated here can be widely applied across a gradient of vegetation and topography for monitoring landscapes undergoing large-scale changes such as the forests in the southwestern U.S.A.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.04.007","usgsCitation":"Sankey, T.T., Donager, J., McVay, J.L., and Sankey, J.B., 2017, UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA: Remote Sensing of Environment, v. 195, p. 30-43, https://doi.org/10.1016/j.rse.2017.04.007.","productDescription":"14 p.","startPage":"30","endPage":"43","ipdsId":"IP-073648","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":346176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","volume":"195","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ce0a2be4b05fe04cc0210a","contributors":{"authors":[{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":711500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Donager, Jonathon","contributorId":196772,"corporation":false,"usgs":false,"family":"Donager","given":"Jonathon","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":711501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McVay, Jason L.","contributorId":196235,"corporation":false,"usgs":false,"family":"McVay","given":"Jason","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":711502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":711499,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188566,"text":"70188566 - 2017 - The role of paleoecology in restoration and resource management—The past as a guide to future decision-making: Review and example from the Greater Everglades Ecosystem, U.S.A","interactions":[],"lastModifiedDate":"2017-06-15T13:12:17","indexId":"70188566","displayToPublicDate":"2017-06-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"The role of paleoecology in restoration and resource management—The past as a guide to future decision-making: Review and example from the Greater Everglades Ecosystem, U.S.A","docAbstract":"Resource managers around the world are challenged to develop feasible plans for sustainable conservation and/or restoration of the lands, waters, and wildlife they administer—a challenge made greater by anticipated climate change and associated effects over the next century. Increasingly, paleoecologic and geologic archives are being used to extend the period of record of observed data and provide information on centennial to millennial scale responses to long-term drivers of ecosystem change. The development of paleoecology from an emerging field investigating past environments to a highly relevant applied science is reviewed and general examples of the application of paleoecologic research to resource management questions in diverse habitats and regions are provided. Specific examples of the application of paleoecologic research to the restoration of the Greater Everglades Ecosystem of south Florida (U.S.A) are presented. Conducting valuable scientific research that would benefit resource management decisions, however, is not enough. Scientists and resource managers need to be engaged in collaborative discussions from the beginning of the research process to ensure that management questions are being addressed and that the science reaches the people who will benefit from the information. Paleoecology and related disciplines provide an understanding of how ecosystems and individual species function and change over time in response to both natural and anthropogenic drivers. Information on pre-anthropogenic baseline conditions is provided by paleoecologic research, but it is the detection of long-term trends and cycles that allow resource managers to set realistic goals and targets by moving away from the fixed-point baseline concept to one of dynamic landscapes that anticipates and incorporates an expectation of change into decision-making.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2017.00011","usgsCitation":"Wingard, G.L., Bernhardt, C.E., and Wachnicka, A., 2017, The role of paleoecology in restoration and resource management—The past as a guide to future decision-making: Review and example from the Greater Everglades Ecosystem, U.S.A: Frontiers in Ecology and Evolution, v. 5, Article 11: 24 p., https://doi.org/10.3389/fevo.2017.00011.","productDescription":"Article 11: 24 p.","ipdsId":"IP-079801","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":469750,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2017.00011","text":"Publisher Index Page"},{"id":342551,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Greater Everglades Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.5347900390625,\n 24.42214378185897\n ],\n [\n -79.9200439453125,\n 24.42214378185897\n ],\n [\n -79.9200439453125,\n 27.196014383173306\n ],\n [\n -82.5347900390625,\n 27.196014383173306\n ],\n [\n -82.5347900390625,\n 24.42214378185897\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-06","publicationStatus":"PW","scienceBaseUri":"59439c92e4b062508e31a986","contributors":{"authors":[{"text":"Wingard, G. Lynn 0000-0002-3833-5207 lwingard@usgs.gov","orcid":"https://orcid.org/0000-0002-3833-5207","contributorId":605,"corporation":false,"usgs":true,"family":"Wingard","given":"G.","email":"lwingard@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":698366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernhardt, Christopher E. 0000-0003-0082-4731 cbernhardt@usgs.gov","orcid":"https://orcid.org/0000-0003-0082-4731","contributorId":2131,"corporation":false,"usgs":true,"family":"Bernhardt","given":"Christopher","email":"cbernhardt@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":698367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wachnicka, Anna","contributorId":15500,"corporation":false,"usgs":true,"family":"Wachnicka","given":"Anna","email":"","affiliations":[],"preferred":false,"id":698368,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187658,"text":"sir20175048 - 2017 - Groundwater resources of the Devils Postpile National Monument—Current conditions and future vulnerabilities","interactions":[],"lastModifiedDate":"2017-06-16T08:33:04","indexId":"sir20175048","displayToPublicDate":"2017-06-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5048","title":"Groundwater resources of the Devils Postpile National Monument—Current conditions and future vulnerabilities","docAbstract":"This study presents an extensive database on groundwater conditions in and around Devils Postpile National Monument. The database contains chemical analyses of springs and the monument water-supply well, including major-ion chemistry, trace element chemistry, and the first information on a list of organic compounds known as emerging contaminants. Diurnal, seasonal, and annual variations in groundwater discharge and chemistry are evaluated from data collected at five main monitoring sites, where streams carry the aggregate flow from entire groups of springs. These springs drain the Mammoth Mountain area and, during the fall months, contribute a significant fraction of the San Joaquin River flow within the monument. The period of this study, from fall 2012 to fall 2015, includes some of the driest years on record, though the seasonal variability observed in 2013 might have been near normal. The spring-fed streams generally flowed at rates well below those observed during a sequence of wet years in the late 1990s. However, persistence of flow and reasonably stable water chemistry through the recent dry years are indicative of a sizeable groundwater system that should provide a reliable resource during similar droughts in the future. Only a few emerging contaminants were detected at trace levels below 1 microgram per liter (μg/L), suggesting that local human visitation is not degrading groundwater quality. No indication of salt from the ski area on the north side of Mammoth Mountain could be found in any of the groundwaters. Chemical data instead show that natural mineral water, such as that discharged from local soda springs, is the main source of anomalous chloride in the monument supply well and in the San Joaquin River. The results of the study are used to develop a set of recommendations for future monitoring to enable detection of deleterious impacts to groundwater quality and quantity
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175048","usgsCitation":"Evans, W.C., and Bergfeld, D., 2017, Groundwater resources of the Devils Postpile National Monument—Current conditions and future vulnerabilities: U.S. Geological Survey Scientific Investigations Report 2017–5048, 31 p., https://doi.org/10.3133/sir20175048.","productDescription":"Report: v, 31 p.; Table","startPage":"1","endPage":"31","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-079650","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":342512,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5048/coverthb.jpg"},{"id":342513,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5048/sir20175048.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5048"},{"id":342514,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5048/sir20175048_table 2.xlsx","text":"Table 2","size":"62 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5048"}],"country":"United States","state":"California","otherGeospatial":"Devils Postpile National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.41015624999999,\n 37.86618078529668\n ],\n [\n -120.1409912109375,\n 37.56635122499224\n ],\n [\n -119.5806884765625,\n 37.208456662000195\n ],\n [\n -119.4049072265625,\n 37.020098201368114\n ],\n [\n -119.13574218749999,\n 36.59788913307022\n ],\n [\n -119.05883789062501,\n 36.39033486213649\n ],\n [\n -118.97644042968749,\n 36.363798554158635\n ],\n [\n -118.74572753906249,\n 36.37264499608118\n ],\n [\n -118.63037109375,\n 36.36822190085111\n ],\n [\n -118.54248046874999,\n 36.36822190085111\n ],\n [\n -118.3612060546875,\n 36.37706783983682\n ],\n [\n -118.1304931640625,\n 36.42570252039198\n ],\n [\n -117.9876708984375,\n 36.50963615733049\n ],\n [\n -117.75146484375,\n 36.54936246839778\n ],\n [\n -117.586669921875,\n 36.474306755095235\n ],\n [\n -117.2735595703125,\n 36.421282443649496\n ],\n [\n -117.29553222656249,\n 36.84006462037767\n ],\n [\n -117.53173828125,\n 37.08585785263673\n ],\n [\n -117.71301269531249,\n 37.31775185163688\n ],\n [\n -117.93273925781249,\n 37.53150992479082\n ],\n [\n -118.23486328125,\n 37.8271414168374\n ],\n [\n -118.487548828125,\n 38.05674222065296\n ],\n [\n -118.597412109375,\n 38.302869955150044\n ],\n [\n -118.751220703125,\n 38.47939467327645\n ],\n [\n -118.89404296875,\n 38.47509432050245\n ],\n [\n -118.98193359375,\n 38.46649284538942\n ],\n [\n -119.24560546875001,\n 38.453588708941375\n ],\n [\n -119.8553466796875,\n 38.44498466889473\n ],\n [\n -120.16845703125,\n 38.41055825094609\n ],\n [\n -120.5914306640625,\n 38.302869955150044\n ],\n [\n -120.640869140625,\n 38.19502155795575\n ],\n [\n -120.574951171875,\n 38.06106741381201\n ],\n [\n -120.41015624999999,\n 37.86618078529668\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NRP staff
National Research Program
U.S. Geological Survey
345 Middlefield Road, MS-435
Menlo Park, CA 94025
The persistence and geographic expansion of dryland forests in the 21st century will be influenced by how climate change supports the demographic processes associated with tree regeneration. Yet, the way that climate change may alter regeneration is unclear. We developed a quantitative framework that estimates forest regeneration potential (RP) as a function of key environmental conditions for ponderosa pine, a key dryland forest species. We integrated meteorological data and climate projections for 47 ponderosa pine forest sites across the western United States, and evaluated RP using an ecosystem water balance model. Our primary goal was to contrast conditions supporting regeneration among historical, mid-21st century and late-21st century time frames. Future climatic conditions supported 50% higher RP in 2020–2059 relative to 1910–2014. As temperatures increased more substantially in 2060–2099, seedling survival decreased, RP declined by 50%, and the frequency of years with very low RP increased from 25% to 58%. Thus, climate change may initially support higher RP and increase the likelihood of successful regeneration events, yet will ultimately reduce average RP and the frequency of years with moderate climate support of regeneration. Our results suggest that climate change alone may begin to restrict the persistence and expansion of dryland forests by limiting seedling survival in the late 21st century.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.1791","usgsCitation":"Petrie, M., Bradford, J.B., Hubbard, R., Lauenroth, W., Andrews, C.M., and Schlaepfer, D., 2017, Climate change may restrict dryland forest regeneration in the 21st century: Ecology, v. 98, no. 6, p. 1548-1559, https://doi.org/10.1002/ecy.1791.","productDescription":"12 p.","startPage":"1548","endPage":"1559","ipdsId":"IP-081499","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":342558,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -128.408203125,\n 26.27371402440643\n ],\n [\n -96.328125,\n 26.27371402440643\n ],\n [\n -96.328125,\n 51.12421275782688\n ],\n [\n -128.408203125,\n 51.12421275782688\n ],\n [\n -128.408203125,\n 26.27371402440643\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"98","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-30","publicationStatus":"PW","scienceBaseUri":"59439c91e4b062508e31a97e","contributors":{"authors":[{"text":"Petrie, M.D.","contributorId":192983,"corporation":false,"usgs":false,"family":"Petrie","given":"M.D.","email":"","affiliations":[],"preferred":false,"id":698374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698373,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hubbard, R.M.","contributorId":167015,"corporation":false,"usgs":false,"family":"Hubbard","given":"R.M.","email":"","affiliations":[{"id":24595,"text":"USDA Forest Service, Fort Collins CO","active":true,"usgs":false}],"preferred":false,"id":698375,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lauenroth, W.K.","contributorId":192984,"corporation":false,"usgs":false,"family":"Lauenroth","given":"W.K.","email":"","affiliations":[],"preferred":false,"id":698376,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698377,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schlaepfer, D.R.","contributorId":140421,"corporation":false,"usgs":false,"family":"Schlaepfer","given":"D.R.","email":"","affiliations":[{"id":13488,"text":"Dept. of Botany, University of Wyoming, 1000 E. UNIVersity Avenue, Laramie, WY 82070","active":true,"usgs":false}],"preferred":false,"id":698378,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70188610,"text":"70188610 - 2017 - Quantifying drivers of wild pig movement across multiple spatial and temporal scales","interactions":[],"lastModifiedDate":"2017-06-17T11:53:46","indexId":"70188610","displayToPublicDate":"2017-06-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying drivers of wild pig movement across multiple spatial and temporal scales","docAbstract":"Background\nThe movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management.\n\nMethods\nWe obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season.\n\nResults\nWe found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales.\n\nConclusions\nThe analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.","language":"English","publisher":"BioMedCentral","doi":"10.1186/s40462-017-0105-1","usgsCitation":"Kay, S.L., Fischer, J.W., Monaghan, A.J., Beasley, J.C., Boughton, R., Campbell, T.A., Cooper, S.M., Ditchkoff, S.S., Hartley, S.B., Kilgo, J.C., Wisely, S.M., Wyckoff, A.C., Vercauteren, K.C., and Pipen, K.M., 2017, Quantifying drivers of wild pig movement across multiple spatial and temporal scales: Movement Ecology, v. 5, no. 14, p. 1-15, https://doi.org/10.1186/s40462-017-0105-1.","productDescription":" 15 p. ","startPage":"1","endPage":"15","ipdsId":"IP-078294","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469747,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-017-0105-1","text":"Publisher Index Page"},{"id":342619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"14","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-15","publicationStatus":"PW","scienceBaseUri":"59463fa3e4b062508e34408f","contributors":{"authors":[{"text":"Kay, Shannon L.","contributorId":193049,"corporation":false,"usgs":false,"family":"Kay","given":"Shannon","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":698585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fischer, Justin W.","contributorId":171828,"corporation":false,"usgs":false,"family":"Fischer","given":"Justin","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":698586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Monaghan, Andrew J.","contributorId":179216,"corporation":false,"usgs":false,"family":"Monaghan","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":698587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beasley, James C","contributorId":193050,"corporation":false,"usgs":false,"family":"Beasley","given":"James","email":"","middleInitial":"C","affiliations":[],"preferred":false,"id":698588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boughton, Raoul","contributorId":172817,"corporation":false,"usgs":false,"family":"Boughton","given":"Raoul","affiliations":[{"id":27096,"text":"Wildlife Ecology and Conservation, Range Cattle Research and Education Center, University of Florida, 3401 Experiment Station, Ona, Florida 33865 USA","active":true,"usgs":false}],"preferred":false,"id":698589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Campbell, Tyler A","contributorId":193051,"corporation":false,"usgs":false,"family":"Campbell","given":"Tyler","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":698590,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cooper, Susan M","contributorId":193052,"corporation":false,"usgs":false,"family":"Cooper","given":"Susan","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":698591,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ditchkoff, Stephen S.","contributorId":193053,"corporation":false,"usgs":false,"family":"Ditchkoff","given":"Stephen","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":698592,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hartley, Stephen B. 0000-0003-1380-2769 hartleys@usgs.gov","orcid":"https://orcid.org/0000-0003-1380-2769","contributorId":4164,"corporation":false,"usgs":true,"family":"Hartley","given":"Stephen","email":"hartleys@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":698584,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kilgo, John C","contributorId":193054,"corporation":false,"usgs":false,"family":"Kilgo","given":"John","email":"","middleInitial":"C","affiliations":[],"preferred":false,"id":698593,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wisely, Samantha M","contributorId":193055,"corporation":false,"usgs":false,"family":"Wisely","given":"Samantha","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":698594,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wyckoff, A Christy","contributorId":193056,"corporation":false,"usgs":false,"family":"Wyckoff","given":"A","email":"","middleInitial":"Christy","affiliations":[],"preferred":false,"id":698595,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Vercauteren, Kurt C.","contributorId":193057,"corporation":false,"usgs":false,"family":"Vercauteren","given":"Kurt","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":698596,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pipen, Kim M","contributorId":193058,"corporation":false,"usgs":false,"family":"Pipen","given":"Kim","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":698597,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70188578,"text":"70188578 - 2017 - Continuity of the Reelfoot fault across the Cottonwood Grove and Ridgely faults of the New Madrid Seismic Zone","interactions":[],"lastModifiedDate":"2017-06-20T13:28:55","indexId":"70188578","displayToPublicDate":"2017-06-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":960,"text":"BSSA","active":true,"publicationSubtype":{"id":10}},"title":"Continuity of the Reelfoot fault across the Cottonwood Grove and Ridgely faults of the New Madrid Seismic Zone","docAbstract":"Previous investigators have argued that the northwest-striking Reelfoot\nfault of northwest Tennessee and southeastern Missouri is segmented. One segment\nboundary is at the intersection of the northeast-striking Cottonwood Grove and\nRidgely strike-slip faults with the Reelfoot fault. We use seismic reflection and geologic\nmapping to locate and determine the history of the Reelfoot South fault across\nthis boundary zone. One reflection profile revealed a southwest-dipping (81°) Reelfoot\nSouth reverse fault that displaces the top of the Paleozoic 65 m, Cretaceous 40 m,\nPaleocene 31 m, Eocene Wilcox Group 20 m, and Eocene Memphis Sand 16 m. A\nsecond reflection profile reveals a north-dipping (84°) reverse fault 4.3 km south of the\nReelfoot South fault, which defines the southwest margin of the Tiptonville dome.\nA geologic profile of the base of the ∼3:1 Ma Upland complex (Mississippi River\nterrace alluvium) within theMississippi River bluffs reveals ∼6 m of displacement across\nthe Reelfoot South fault. Similarly, Quaternary stream terrace distribution suggests ∼6 m\nof Reelfoot South hanging-wall (Tiptonville dome) uplift that is probably Holocene.\nFault strike trends show the Reelfoot fault and its hanging-wall Tiptonville dome are\nnot laterally offset across the Cottonwood Grove and Ridgely faults. The Reelfoot South\nfault northwest and southeast of the Cottonwood Grove and Ridgely faults has very similar\nvertical displacement on common stratigraphic marker horizons in the upper 900 m.\nThese data indicate the Reelfoot fault/Tiptonville dome has acted as one continuous fault\nzone across the Cottonwood Grove and Ridgely faults since Late Cretaceous.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120150290","usgsCitation":"Greenwood, M., Woolery, E.W., Van Arsdale, R.B., Stephenson, W.J., and Patterson, G.L., 2017, Continuity of the Reelfoot fault across the Cottonwood Grove and Ridgely faults of the New Madrid Seismic Zone: BSSA, v. 106, no. 6, p. 2674-2685, https://doi.org/10.1785/0120150290.","productDescription":"12 p.","startPage":"2674","endPage":"2685","ipdsId":"IP-072716","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":342577,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Tennessee","otherGeospatial":"Reelfoot fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.56878662109375,\n 35.68184060244453\n ],\n [\n -89.1046142578125,\n 36.73668306473141\n ],\n [\n -89.6319580078125,\n 36.78949107451841\n ],\n [\n -90.1153564453125,\n 35.75765724051559\n ],\n [\n -89.56878662109375,\n 35.68184060244453\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-25","publicationStatus":"PW","scienceBaseUri":"59439c90e4b062508e31a972","contributors":{"authors":[{"text":"Greenwood, M.L.","contributorId":192993,"corporation":false,"usgs":false,"family":"Greenwood","given":"M.L.","email":"","affiliations":[],"preferred":false,"id":698414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woolery, Edward W 0000-0003-3398-5830","orcid":"https://orcid.org/0000-0003-3398-5830","contributorId":192994,"corporation":false,"usgs":false,"family":"Woolery","given":"Edward","email":"","middleInitial":"W","affiliations":[],"preferred":false,"id":698415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Arsdale, R. B.","contributorId":121450,"corporation":false,"usgs":true,"family":"Van Arsdale","given":"R.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":698416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Patterson, Gary L. glpatter@usgs.gov","contributorId":519,"corporation":false,"usgs":true,"family":"Patterson","given":"Gary","email":"glpatter@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":698444,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70187693,"text":"sir20175050 - 2017 - Hydrologic characterization of Bushy Park Reservoir, South Carolina, 2013–15","interactions":[],"lastModifiedDate":"2017-06-14T15:42:31","indexId":"sir20175050","displayToPublicDate":"2017-06-14T12:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5050","title":"Hydrologic characterization of Bushy Park Reservoir, South Carolina, 2013–15","docAbstract":"The Bushy Park Reservoir is a relatively shallow impoundment in a semi-tropical climate and is the principal water supply for the 400,000 people of the city of Charleston, South Carolina, and the surrounding areas including the Bushy Park Industrial Complex. Although there is an adequate supply of freshwater in the reservoir, taste-and-odor water-quality issues are a concern. The U.S. Geological Survey conducted an investigation in cooperation with the Charleston Water System to study the hydrology and hydrodynamics of the Bushy Park Reservoir to identify factors affecting water-quality conditions. Specifically, five areas for monitoring and (or) analysis were addressed: (1) hydrologic monitoring of the reservoir to establish a water budget, (2) flow monitoring in the tunnels to compute flow from Bushy Park Reservoir and at critical distribution junctions, (3) water-quality sampling, profiling, and continuous monitoring to identify the causes of taste-and-odor occurrence, (4) technical evaluation of appropriate hydrodynamic and water-quality simulation models for the reservoir, and (5) preliminary evaluation of alternative reservoir operations scenarios.
This report describes the hydrodynamic and hydrologic data collected from 2013 to 2015 to support the application and calibration of a three-dimensional hydrodynamic model and the water-quality monitoring and analysis to gain insight into the principal causes of the Bushy Park Reservoir taste-and-odor episodes. The existing U.S. Geological Survey real-time network on the West Branch of the Cooper River was augmented with a tidal flow gage on Durham Canal Back River, and Foster Creek. The Charleston Water System intake structure was instrumented to collect water-level, water temperature (top and bottom probes), specific conductance (top and bottom probes), wind speed and direction, and photosynthetically active radiation data. In addition to the gages attached to fixed structures, four bottom-mounted velocity profilers were deployed at six locations over different periods. The deployment period for the velocity profiler ranged from 2 weeks to 4 months. During the investigation, tidal cycle (13-hour) streamflow measurements were made at 30-minute intervals at five locations.
The Williams Station is a coal-fired powerplant that withdraws water from Bushy Park Reservoir for cooling purposes. The magnitude of the withdrawal (approximately 550 million gallons per day) is the major factor controlling the circulation in the reservoir. The net flow in Durham Canal to the reservoir is comparable to the withdrawal rates of the powerplant. When the Williams Station is not withdrawing water, the net flow in Durham Canal quickly goes to zero or reverses with a net flow away from the reservoir and to the Cooper River. Plan views of the velocity vectors for the tidal cycle streamflow measurements and rose diagram of the velocity profilers created with the Williams Station withdrawing and not withdrawing water show substantial effects of the distribution of magnitude and direction of the water velocities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175050","collaboration":"Prepared in cooperation with Charleston Water System","usgsCitation":"Conrads, P.A., Petkewich, M.D., Falls, W.F., and Lanier, T.H., 2017, Hydrologic characterization of Bushy Park Reservoir, South Carolina, 2013–15: U.S. Geological Survey Scientific Investigations Report 2017–5050, 83 p., https://doi.org/10.3133/sir20175050.","productDescription":"Report: ix, 83 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077941","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":342449,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GB2274","text":"USGS data release","description":"USGS data release","linkHelpText":"Hydrodynamic data of Bushy Park Reservoir, South Carolina 2013–15"},{"id":342447,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5050/coverthb.jpg"},{"id":342448,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5050/sir20175050.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5050"}],"country":"United States","state":"South Carolina","otherGeospatial":"Santee-Cooper River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.5,\n 32.68\n ],\n [\n -79.7,\n 32.68\n ],\n [\n -79.7,\n 33.56\n ],\n [\n -80.5,\n 33.56\n ],\n [\n -80.5,\n 32.68\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Climate change, sea-level rise, and human development have contributed to the changing geomorphology of the San Francisco Bay - Delta (Bay-Delta) Estuary system. The need to predict scenarios of change led to the development of a new seamless, high-resolution digital elevation model (DEM) of the Bay – Delta that can be used by modelers attempting to understand potential future changes to the estuary system. This report details the three phases of the creation of this DEM. The first phase took a bathymetric-only DEM created in 2005 by the U.S. Geological Survey (USGS), refined it with additional data, and identified areas that would benefit from new surveys. The second phase began a USGS collaboration with the California Department of Water Resources (DWR) that updated a 2012 DWR seamless bathymetric/topographic DEM of the Bay-Delta with input from the USGS and modifications to fit the specific needs of USGS modelers. The third phase took the work from phase 2 and expanded the coverage area in the north to include the Yolo Bypass up to the Fremont Weir, the Sacramento River up to Knights Landing, and the American River up to the Nimbus Dam, and added back in the elevations for interior islands. The constant evolution of the Bay-Delta will require continuous updates to the DEM of the Delta, and there still are areas with older data that would benefit from modern surveys. As a result, DWR plans to continue updating the DEM.
Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Soils develop at the earth's surface via multiple processes that act through time. Precluding burial or disturbance, soil genetic horizons form progressively and reflect the balance among formation processes, surface age, and original substrate composition. Soil morphology provides a key link between process and time (soil age), enabling soils to serve as both relative and numerical dating tools for geomorphic studies and landscape evolution. Five major factors define the contemporary state of all soils: climate, organisms, topography, parent material, and time. Soils developed on similar landforms and parent materials within a given landscape comprise what we term a soil/landform/substrate complex. Soils on such complexes that differ in development as a function of time represent a soil chronosequence. In a soil chronosequence, time constitutes the only independent formation factor; the other factors act through time. Time dictates the variations in soil development or properties (field or laboratory measured) on a soil/landform/substrate complex. Using a dataset within the chronosequence model, we can also formulate various soil development indices based upon one or a combination of soil properties, either for individual soil horizons or for an entire profile. When we evaluate soil data or soil indices mathematically, the resulting equation creates a chronofunction. Chronofunctions help quantify processes and mechanisms involved in soil development, and relate them mathematically to time. These rigorous kinds of comparisons among and within soil/landform complexes constitute an important tool for relative-age dating. After determining one or more absolute ages for a soil/landform complex, we can calculate quantitative soil formation, and or landform-development rates. Multiple dates for several complexes allow rate calculations for soil/landform-chronosequence development and soil-chronofunction calibration.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The International Encyclopedia of Geography","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"John Wiley & Sons, Ltd.","doi":"10.1002/9781118786352.wbieg0437","usgsCitation":"Markewich, H.W., Pavich, M.J., and Wysocki, D., 2017, Soils as relative-age dating tools, chap. of The International Encyclopedia of Geography, 14 p., https://doi.org/10.1002/9781118786352.wbieg0437.","productDescription":"14 p.","ipdsId":"IP-053464","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":342500,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-06","publicationStatus":"PW","scienceBaseUri":"59424b37e4b0764e6c65dc14","contributors":{"authors":[{"text":"Markewich, Helaine W. 0000-0001-9656-3243 helainem@usgs.gov","orcid":"https://orcid.org/0000-0001-9656-3243","contributorId":2008,"corporation":false,"usgs":true,"family":"Markewich","given":"Helaine","email":"helainem@usgs.gov","middleInitial":"W.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":698027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pavich, Milan J. mpavich@usgs.gov","contributorId":2348,"corporation":false,"usgs":true,"family":"Pavich","given":"Milan","email":"mpavich@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":698028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wysocki, Douglas A.","contributorId":61320,"corporation":false,"usgs":true,"family":"Wysocki","given":"Douglas A.","affiliations":[],"preferred":false,"id":698029,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188482,"text":"70188482 - 2017 - Assessment of imperfect detection of blister rust in whitebark pine within the Greater Yellowstone Ecosystem","interactions":[],"lastModifiedDate":"2017-06-14T15:25:03","indexId":"70188482","displayToPublicDate":"2017-06-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"2017/1457","title":"Assessment of imperfect detection of blister rust in whitebark pine within the Greater Yellowstone Ecosystem","docAbstract":"We examined data on white pine blister rust (blister rust) collected during the monitoring of whitebark pine trees in the Greater Yellowstone Ecosystem (from 2004-2015). Summaries of repeat observations performed by multiple independent observers are reviewed and discussed. These summaries show variability among observers and the potential for errors being made in blister rust status. Based on this assessment, we utilized occupancy models to analyze blister rust prevalence while explicitly accounting for imperfect detection. Available covariates were used to model both the probability of a tree being infected with blister rust and the probability of an observer detecting the infection. The fitted model provided strong evidence that the probability of blister rust infection increases as tree diameter increases and decreases as site elevation increases. Most importantly, we found evidence of heterogeneity in detection probabilities related to tree size and average slope of a transect. These results suggested that detecting the presence of blister rust was more difficult in larger trees. Also, there was evidence that blister rust was easier to detect on transects located on steeper slopes.
Our model accounted for potential impacts of observer experience on blister rust detection probabilities and also showed moderate variability among the different observers in their ability to detect blister rust. Based on these model results, we suggest that multiple observer sampling continue in future field seasons in order to allow blister rust prevalence estimates to be corrected for imperfect detection. We suggest that the multiple observer effort be spread out across many transects (instead of concentrated at a few each field season) while retaining the overall proportion of trees with multiple observers around 5-20%. Estimates of prevalence are confounded with detection unless it is explicitly accounted for in an analysis and we demonstrate how an occupancy model can be used to do account for this source of observation error.
","language":"English","publisher":"National Park Service","publisherLocation":"Fort Collins, CO","usgsCitation":"Wright, W.J., and Irvine, K.M., 2017, Assessment of imperfect detection of blister rust in whitebark pine within the Greater Yellowstone Ecosystem: Natural Resource Report 2017/1457, vi, 24 p.","productDescription":"vi, 24 p.","ipdsId":"IP-083274","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":342431,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":342427,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2240718"}],"country":"United States","state":"idaho, Montana, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.5,\n 41.918628865183045\n ],\n [\n -108.446044921875,\n 41.918628865183045\n ],\n [\n -108.446044921875,\n 45.75985868785574\n ],\n [\n -112.5,\n 45.75985868785574\n ],\n [\n -112.5,\n 41.918628865183045\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5940f9b0e4b0764e6c63ea9b","contributors":{"authors":[{"text":"Wright, Wilson J.","contributorId":192867,"corporation":false,"usgs":false,"family":"Wright","given":"Wilson","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":697959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":697958,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188462,"text":"ds1043 - 2017 - Transient electromagnetic soundings in the San Luis Valley, Colorado, near the Great Sand Dunes National Park and Preserve and the Alamosa National Wildlife Refuge (field seasons 2007, 2009, and 2011)","interactions":[],"lastModifiedDate":"2017-06-13T14:30:55","indexId":"ds1043","displayToPublicDate":"2017-06-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1043","title":"Transient electromagnetic soundings in the San Luis Valley, Colorado, near the Great Sand Dunes National Park and Preserve and the Alamosa National Wildlife Refuge (field seasons 2007, 2009, and 2011)","docAbstract":"Transient electromagnetic (TEM) soundings were made in the San Luis Valley, Colorado, to map the location of a blue clay unit as well as to investigate the presence of suspected faults. A total of 147 soundings were made near and in Great Sand Dunes National Park and Preserve, and an additional 6 soundings were made near Hansen Bluff on the eastern edge of the Alamosa National Wildlife Refuge. The blue clay is a significant hydrologic feature in the area that separates an unconfined surface aquifer from a deeper confined aquifer. Knowledge of its location is important to regional hydrological models. Previous analysis of well logs has shown that the blue clay has a resistivity of 10 ohm-meters or less, which is in contrast to the higher resistivity of sand, gravel, and other clay units found in the area, making it a very good target for TEM soundings. The top of the blue clay was found to have considerable relief, suggesting the possibility of deformation of the clay during or after deposition. Because of rift activity, deformation is to be expected. Of the TEM profiles made across faults identified by aeromagnetic data, some showed resistivity variations and (or) subsurface elevation relief of resistivity units, suggestive of faulting. Such patterns were not associated with all suspected faults. The Hansen Bluff profile showed variations in resistivity and depth to conductor that coincide with a scarp between the highlands to the east and the floodplain of the Rio Grande to the west.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1043","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fitterman, D.V., 2017, Transient electromagnetic soundings in the San Luis Valley, Colorado, near the Great Sand Dunes National Park and Preserve and the Alamosa National Wildlife Refuge (field seasons 2007, 2009, and 2011): U.S. Geological Survey Data Series 1043, 39 p., https://doi.org/10.3133/ds1043.","productDescription":"Report: vii; 52 p.","startPage":"1","endPage":"39","numberOfPages":"52","onlineOnly":"Y","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":342428,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7D21VQ5","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Transient Electromagnetic Sounding Data Collected in the San Luis Valley, Colorado near the Great Sand Dunes National Park and Preserve and the Alamosa National Wildlife Refuge (Field Seasons 2007, 2009, and 2011)"},{"id":342407,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1043/coverthb.jpg"},{"id":342408,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1043/ds1043.pdf","text":"Report","size":"3.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1043"}],"country":"United States ","state":"Colorado","otherGeospatial":"San Luis Valley ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.11145019531249,\n 37.42906945530332\n ],\n [\n -105.56488037109375,\n 37.42906945530332\n ],\n [\n -105.56488037109375,\n 37.93444993515032\n ],\n [\n -106.11145019531249,\n 37.93444993515032\n ],\n [\n -106.11145019531249,\n 37.42906945530332\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Crustal Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 964
Denver, CO 80225
The goal of petrochronology is to extract information about the rates and conditions at which rocks and magmas are transported through the Earth’s crust. Garnering this information from the rock record greatly benefits from integrating textural and compositional data with radiometric dating of accessory minerals. Length scales of crystal growth and diffusive transport in accessory minerals under realistic geologic conditions are typically in the range of 1–10’s of μm, and in some cases even substantially smaller, with zircon having among the lowest diffusion coefficients at a given temperature (e.g., Cherniak and Watson 2003). Intrinsic to the compartmentalization of geochemical and geochronologic information from intra-crystal domains is the requirement to determine accessory mineral compositions using techniques that sample at commensurate spatial scales so as to not convolute the geologic signals that are recorded within crystals, as may be the case with single grain or large grain fragment analysis by isotope dilution thermal ionization mass spectrometry (ID-TIMS; e.g., Schaltegger and Davies 2017, this volume; Schoene and Baxter 2017, this volume). Small crystals can also be difficult to extract by mineral separation techniques traditionally used in geochronology, which also lead to a loss of petrographic context. Secondary Ionization Mass Spectrometry, that is SIMS performed with an ion microprobe, is an analytical technique ideally suited to meet the high spatial resolution analysis requirements that are critical for petrochronology (Table 1).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reviews in Mineralogy and Geochemistry","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Mineralogical Society of America","publisherLocation":"Washington, D.C.","usgsCitation":"Schmitt, A.K., and Vazquez, J.A., 2017, Secondary ionization mass spectrometry analysis in petrochronology, chap. 7 of Reviews in Mineralogy and Geochemistry, v. 83, no. 1, p. 199-230.","productDescription":"32 p.","startPage":"199","endPage":"230","ipdsId":"IP-078815","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":342430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":342410,"type":{"id":15,"text":"Index Page"},"url":"https://rimg.geoscienceworld.org/content/83/1/199"}],"volume":"83","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5940f9b1e4b0764e6c63eaa8","contributors":{"authors":[{"text":"Schmitt, Axel K.","contributorId":127614,"corporation":false,"usgs":false,"family":"Schmitt","given":"Axel","email":"","middleInitial":"K.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":697889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":true,"id":697888,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188850,"text":"70188850 - 2017 - A multi-scale evaluation of pack stock effects on subalpine meadow plant communities in the Sierra Nevada","interactions":[],"lastModifiedDate":"2017-06-27T10:52:46","indexId":"70188850","displayToPublicDate":"2017-06-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"A multi-scale evaluation of pack stock effects on subalpine meadow plant communities in the Sierra Nevada","docAbstract":"We evaluated the influence of pack stock (i.e., horse and mule) use on meadow plant communities in Sequoia and Yosemite National Parks in the Sierra Nevada of California. Meadows were sampled to account for inherent variability across multiple scales by: 1) controlling for among-meadow variability by using remotely sensed hydro-climatic and geospatial data to pair stock use meadows with similar non-stock (reference) sites, 2) accounting for within-meadow variation in the local hydrology using in-situ soil moisture readings, and 3) incorporating variation in stock use intensity by sampling across the entire available gradient of pack stock use. Increased cover of bare ground was detected only within “dry” meadow areas at the two most heavily used pack stock meadows (maximum animals per night per hectare). There was no difference in plant community composition for any level of soil moisture or pack stock use. Increased local-scale spatial variability in plant community composition (species dispersion) was detected in “wet” meadow areas at the two most heavily used meadows. These results suggest that at the meadow scale, plant communities are generally resistant to the contemporary levels of recreational pack stock use. However, finer-scale within-meadow responses such as increased bare ground or spatial variability in the plant community can be a function of local-scale hydrological conditions. Wilderness managers can improve monitoring of disturbance in Sierra Nevada meadows by adopting multiple plant community indices while simultaneously considering local moisture regimes.
","language":"English","publisher":"PLoS ONE","doi":"10.1371/journal.pone.0178536","usgsCitation":"Lee, S.R., Berlow, E.L., Ostoja, S., Brooks, M.L., Génin, A., Matchett, J.R., and Hart, S.C., 2017, A multi-scale evaluation of pack stock effects on subalpine meadow plant communities in the Sierra Nevada: PLoS ONE, v. 12, no. 6, p. 1-20, https://doi.org/10.1371/journal.pone.0178536.","productDescription":"20 p. ","startPage":"1","endPage":"20","ipdsId":"IP-080186","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":469756,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0178536","text":"Publisher Index Page"},{"id":342904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Kings Canyon National Park, Sequoia National Park, Yosemite National Park ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.03662109374999,\n 39.977120098439634\n ],\n [\n -121.57470703125,\n 40.98819156349393\n ],\n [\n -122.091064453125,\n 40.763901280945866\n ],\n [\n -121.13525390625,\n 38.58252615935333\n ],\n [\n -119.95971679687499,\n 37.31775185163688\n ],\n [\n -118.927001953125,\n 36.2265501474709\n ],\n [\n -118.76220703125001,\n 35.263561862152095\n ],\n [\n -118.9215087890625,\n 34.95349314197422\n ],\n [\n -118.8006591796875,\n 34.786739162702524\n ],\n [\n -118.641357421875,\n 34.79576153473033\n ],\n [\n -118.114013671875,\n 35.17380831799959\n ],\n [\n -117.8009033203125,\n 35.64390523787731\n ],\n [\n -118.333740234375,\n 37.37015718405753\n ],\n [\n -120.03662109374999,\n 39.977120098439634\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-13","publicationStatus":"PW","scienceBaseUri":"59521d1fe4b062508e3c3657","contributors":{"authors":[{"text":"Lee, Steven R. 0000-0002-4581-3684 srlee@usgs.gov","orcid":"https://orcid.org/0000-0002-4581-3684","contributorId":5630,"corporation":false,"usgs":true,"family":"Lee","given":"Steven","email":"srlee@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":700684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berlow, Eric L.","contributorId":91416,"corporation":false,"usgs":false,"family":"Berlow","given":"Eric","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":700685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ostoja, Steven M.","contributorId":193514,"corporation":false,"usgs":false,"family":"Ostoja","given":"Steven M.","affiliations":[],"preferred":false,"id":700686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":700683,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Génin, Alexandre","contributorId":193515,"corporation":false,"usgs":false,"family":"Génin","given":"Alexandre","affiliations":[],"preferred":false,"id":700687,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Matchett, John R. 0000-0002-2905-6468 jmatchett@usgs.gov","orcid":"https://orcid.org/0000-0002-2905-6468","contributorId":1669,"corporation":false,"usgs":true,"family":"Matchett","given":"John","email":"jmatchett@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":700688,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hart, Stephen C.","contributorId":189074,"corporation":false,"usgs":false,"family":"Hart","given":"Stephen","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":700689,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188461,"text":"70188461 - 2017 - Expanding the North American Breeding Bird Survey analysis to include additional species and regions","interactions":[],"lastModifiedDate":"2017-06-13T09:56:47","indexId":"70188461","displayToPublicDate":"2017-06-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Expanding the North American Breeding Bird Survey analysis to include additional species and regions","docAbstract":"The North American Breeding Bird Survey (BBS) contains data for >700 bird species, but analyses often focus on a core group of ∼420 species. We analyzed data for 122 species of North American birds for which data exist in the North American Breeding Bird Survey (BBS) database but are not routinely analyzed on the BBS Summary and Analysis Website. Many of these species occur in the northern part of the continent, on routes that fall outside the core survey area presently analyzed in the United States and southern Canada. Other species not historically analyzed occur in the core survey area with very limited data but have large portions of their ranges in Mexico and south. A third group of species not historically analyzed included species thought to be poorly surveyed by the BBS, such as rare, coastal, or nocturnal species. For 56 species found primarily in regions north of the core survey area, we expanded the scope of the analysis, using data from 1993 to 2014 during which ≥3 survey routes had been sampled in 6 northern strata (Bird Conservation regions in Alaska, Yukon, and Newfoundland and Labrador) and fitting log-linear hierarchical models for an augmented BBS survey area that included both the new northern strata and the core survey area. We also applied this model to 168 species historically analyzed in the BBS that had data from these additional northern strata. For both groups of species we calculated survey-wide trends for the both core and augmented survey areas from 1993 to 2014; for species that did not occur in the newly defined strata, we computed trends from 1966 to 2014. We evaluated trend estimates in terms of established credibility criteria for BBS results, screening for imprecise trends, small samples, and low relative abundance. Inclusion of data from the northern strata permitted estimation of trend for 56 species not historically analyzed, but only 4 of these were reasonably monitored and an additional 13 were questionably monitored; 39 of these species were likely poorly monitored because of small numbers of samples or very imprecisely estimated trends. Only 4 of 66 “new” species found in the core survey area were reasonably monitored by the BBS; 20 were questionably monitored; and 42 were likely poorly monitored by the BBS because of inefficiency in precision, abundance, or sample size. The hierarchical analyses we present provide a means for reasonable inclusion of the additional species and strata in a common analysis with data from the core area, a critical step in the evolution of the BBS as a continent-scale survey. We recommend that results be presented both 1) from 1993 to the present using the expanded survey area, and 2) from 1966 to the present for the core survey area. Although most of the “new” species we analyzed were poorly monitored by the BBS during 1993–2014, continued expansion of the BBS will improve the quality of information in future analyses for these species and for the many other species presently monitored by the BBS.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/102015-JFWM-109","usgsCitation":"Sauer, J.R., Niven, D., Pardieck, K.L., Ziolkowski, D., and Link, W.A., 2017, Expanding the North American Breeding Bird Survey analysis to include additional species and regions: Journal of Fish and Wildlife Management, v. 8, no. 1, p. 154-172, https://doi.org/10.3996/102015-JFWM-109.","productDescription":"19 p.","startPage":"154","endPage":"172","ipdsId":"IP-069657","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":486817,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/102015-jfwm-109","text":"Publisher Index Page"},{"id":342415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-01","publicationStatus":"PW","scienceBaseUri":"5940f9b2e4b0764e6c63eab0","contributors":{"authors":[{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":697878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Niven, Daniel 0000-0002-9527-0577 dniven@usgs.gov","orcid":"https://orcid.org/0000-0002-9527-0577","contributorId":179148,"corporation":false,"usgs":true,"family":"Niven","given":"Daniel","email":"dniven@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":697879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":697880,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ziolkowski, David Jr. 0000-0002-2500-4417 dziolkowski@usgs.gov","orcid":"https://orcid.org/0000-0002-2500-4417","contributorId":179149,"corporation":false,"usgs":true,"family":"Ziolkowski","given":"David","suffix":"Jr.","email":"dziolkowski@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":697913,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Link, William A. 0000-0002-9913-0256 wlink@usgs.gov","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":146920,"corporation":false,"usgs":true,"family":"Link","given":"William","email":"wlink@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":697882,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70181758,"text":"ofr20171018 - 2017 - Five hydrologic and landscape databases for selected National Wildlife Refuges in the Southeastern United States","interactions":[],"lastModifiedDate":"2017-06-12T10:19:48","indexId":"ofr20171018","displayToPublicDate":"2017-06-12T09:45:00","publicationYear":"2017","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":"2017-1018","title":"Five hydrologic and landscape databases for selected National Wildlife Refuges in the Southeastern United States","docAbstract":"This report serves as metadata and a user guide for five out of six hydrologic and landscape databases developed by the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, to describe data-collection, data-reduction, and data-analysis methods used to construct the databases and provides statistical and graphical descriptions of the databases. Six hydrologic and landscape databases were developed: (1) the Cache River and White River National Wildlife Refuges (NWRs) and contributing watersheds in Arkansas, Missouri, and Oklahoma, (2) the Cahaba River NWR and contributing watersheds in Alabama, (3) the Caloosahatchee and J.N. “Ding” Darling NWRs and contributing watersheds in Florida, (4) the Clarks River NWR and contributing watersheds in Kentucky, Tennessee, and Mississippi, (5) the Lower Suwannee NWR and contributing watersheds in Georgia and Florida, and (6) the Okefenokee NWR and contributing watersheds in Georgia and Florida. Each database is composed of a set of ASCII files, Microsoft Access files, and Microsoft Excel files. The databases were developed as an assessment and evaluation tool for use in examining NWR-specific hydrologic patterns and trends as related to water availability and water quality for NWR ecosystems, habitats, and target species. The databases include hydrologic time-series data, summary statistics on landscape and hydrologic time-series data, and hydroecological metrics that can be used to assess NWR hydrologic conditions and the availability of aquatic and riparian habitat. Landscape data that describe the NWR physiographic setting and the locations of hydrologic data-collection stations were compiled and mapped. Categories of landscape data include land cover, soil hydrologic characteristics, physiographic features, geographic and hydrographic boundaries, hydrographic features, and regional runoff estimates. The geographic extent of each database covers an area within which human activities, climatic variation, and hydrologic processes can potentially affect the hydrologic regime of the NWRs and adjacent areas.
The hydrologic and landscape database for the Cache and White River NWRs and contributing watersheds in Arkansas, Missouri, and Oklahoma has been described and documented in detail (Buell and others, 2012). This report serves as a companion to the Buell and others (2012) report to describe and document the five subsequent hydrologic and landscape databases that were developed: Chapter A—the Cahaba River NWR and contributing watersheds in Alabama, Chapter B—the Caloosahatchee and J.N. “Ding” Darling NWRs and contributing watersheds in Florida, Chapter C—the Clarks River NWR and contributing watersheds in Kentucky, Tennessee, and Mississippi, Chapter D—the Lower Suwannee NWR and contributing watersheds in Georgia and Florida, and Chapter E—the Okefenokee NWR and contributing watersheds in Georgia and Florida.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171018","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Buell, G.R., Gurley, L.N., Calhoun, D.L., and Hunt, A.M., 2017, Five hydrologic and landscape databases for selected National Wildlife Refuges in Southeastern United States: U.S. Geological Survey Open-File Report 2017–1018, 366 p., https://doi.org/10.3133/ofr20171018.","productDescription":"Report: xvi, 386 p. ","startPage":"1","endPage":"366","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-078859","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":342125,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1018/ofr20171018.pdf","text":"Report","size":"62.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1018"},{"id":342122,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1018/coverthb.jpg"},{"id":342126,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7416V4M","text":"USGS data release ","description":"USGS data release ","linkHelpText":"Five Hydrologic and Landscape Databases for Select National Wildlife Refuges in Southeastern United States"}],"country":"United States","state":"Alabama, Arkansas, Florida, Georgia, Kentucky, Missouri, Mississippi, Oklahoma, 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Combining the synoptic mass balance approach with principal components analysis (PCA) can be an effective method for discretising the chemistry of inflows and source areas in watersheds where contamination is diffuse in nature and/or complicated by groundwater interactions. This paper presents a field-scale study in which synoptic sampling and PCA are employed in a mineralized watershed (Lion Creek, Colorado, USA) under low flow conditions to (i) quantify the impacts of mining activity on stream water quality; (ii) quantify the spatial pattern of constituent loading; and (iii) identify inflow sources most responsible for observed changes in stream chemistry and constituent loading. Several of the constituents investigated (Al, Cd, Cu, Fe, Mn, Zn) fail to meet chronic aquatic life standards along most of the study reach. The spatial pattern of constituent loading suggests four primary sources of contamination under low flow conditions. Three of these sources are associated with acidic (pH <3.1) seeps that enter along the left bank of Lion Creek. Investigation of inflow water (trace metal and major ion) chemistry using PCA suggests a hydraulic connection between many of the left bank inflows and mine water in the Minnesota Mine shaft located to the north-east of the river channel. In addition, water chemistry data during a rainfall-runoff event suggests the spatial pattern of constituent loading may be modified during rainfall due to dissolution of efflorescent salts or erosion of streamside tailings. These data point to the complexity of contaminant mobilisation processes and constituent loading in mining-affected watersheds but the combined synoptic sampling and PCA approach enables a conceptual model of contaminant dynamics to be developed to inform remediation.
","language":"English","publisher":"Springer","doi":"10.1007/s11356-017-9038-x","usgsCitation":"Byrne, P., Runkel, R.L., and Walton-Day, K., 2017, Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream: Environmental Science and Pollution Research, v. 24, no. 20, p. 17220-17240, https://doi.org/10.1007/s11356-017-9038-x.","productDescription":"21 p.","startPage":"17220","endPage":"17240","ipdsId":"IP-080091","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":469759,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11356-017-9038-x","text":"Publisher Index Page"},{"id":342403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"20","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-06","publicationStatus":"PW","scienceBaseUri":"593fa830e4b0764e6c627943","contributors":{"authors":[{"text":"Byrne, Patrick","contributorId":192845,"corporation":false,"usgs":false,"family":"Byrne","given":"Patrick","affiliations":[],"preferred":false,"id":697867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697868,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189897,"text":"70189897 - 2017 - A geochemical examination of humidity cell tests","interactions":[],"lastModifiedDate":"2017-11-08T19:26:08","indexId":"70189897","displayToPublicDate":"2017-06-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"A geochemical examination of humidity cell tests","docAbstract":"Humidity cell tests (HCTs) are long-term (20 to >300 weeks) leach tests that are considered by some to be the among the most reliable geochemical characterization methods for estimating the leachate quality of mined materials. A number of modifications have been added to the original HCT method, but the interpretation of test results varies widely. We suggest that the HCTs represent an underutilized source of geochemical data, with a year-long test generating approximately 2500 individual chemical data points. The HCT concentration peaks and valleys can be thought of as a “chromatogram” of reactions that may occur in the field, whereby peaks in concentrations are associated with different geochemical processes, including sulfate salt dissolution, sulfide oxidation, and dissolution of rock-forming minerals, some of which can neutralize acid. Some of these reactions occur simultaneously, some do not, and geochemical modeling can be used to help distinguish the dominant processes. Our detailed examination, including speciation and inverse modeling, of HCTs from three projects with different geology and mineralization shows that rapid sulfide oxidation dominates over a limited period of time that starts between 40 and 200 weeks of testing. The applicability of laboratory tests results to predicting field leachate concentrations, loads, or rates of reaction has not been adequately demonstrated, although early flush releases and rapid sulfide oxidation rates in HCTs should have some relevance to field conditions. Knowledge of possible maximum solute concentrations is needed to design effective treatment and mitigation approaches. Early flush and maximum sulfide oxidation results from HCTs should be retained and used in environmental models. Factors that complicate the use of HCTs include: sample representation, time for microbial oxidizers to grow, sample storage before testing, geochemical reactions that add or remove constituents, and the HCT results chosen for use in modeling the environmental performance at mine sites. Improved guidance is needed for more consistent interpretation and use of HCT results that rely on identifying: the geochemical processes; the mineralogy, including secondary mineralogy; the available surface area for reactions; and the influence of hydrologic processes on leachate concentrations in runoff, streams, and groundwater.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2017.03.016","usgsCitation":"Maest, A., and Nordstrom, D.K., 2017, A geochemical examination of humidity cell tests: Applied Geochemistry, v. 81, p. 109-131, https://doi.org/10.1016/j.apgeochem.2017.03.016.","productDescription":"23 p.","startPage":"109","endPage":"131","ipdsId":"IP-085397","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":469758,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2017.03.016","text":"Publisher Index Page"},{"id":344491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"81","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59819315e4b0e2f5d463b79b","contributors":{"authors":[{"text":"Maest, Ann","contributorId":195266,"corporation":false,"usgs":false,"family":"Maest","given":"Ann","affiliations":[],"preferred":false,"id":706653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":706652,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187444,"text":"sir20175032 - 2017 - Groundwater quality in the Western San Joaquin Valley study unit, 2010: California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2019-12-30T14:45:28","indexId":"sir20175032","displayToPublicDate":"2017-06-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5032","title":"Groundwater quality in the Western San Joaquin Valley study unit, 2010: California GAMA Priority Basin Project","docAbstract":"Water quality in groundwater resources used for public drinking-water supply in the Western San Joaquin Valley (WSJV) was investigated by the USGS in cooperation with the California State Water Resources Control Board (SWRCB) as part of its Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project. The WSJV includes two study areas: the Delta–Mendota and Westside subbasins of the San Joaquin Valley groundwater basin. Study objectives for the WSJV study unit included two assessment types: (1) a status assessment yielding quantitative estimates of the current (2010) status of groundwater quality in the groundwater resources used for public drinking water, and (2) an evaluation of natural and anthropogenic factors that could be affecting the groundwater quality. The assessments characterized the quality of untreated groundwater, not the quality of treated drinking water delivered to consumers by water distributors.
The status assessment was based on data collected from 43 wells sampled by the U.S. Geological Survey for the GAMA Priority Basin Project (USGS-GAMA) in 2010 and data compiled in the SWRCB Division of Drinking Water (SWRCB-DDW) database for 74 additional public-supply wells sampled for regulatory compliance purposes between 2007 and 2010. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency (EPA) and SWRCB-DDW regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a spatially weighted, grid-based method to estimate the proportion of the groundwater resources used for public drinking water that has concentrations for particular constituents or class of constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale within the WSJV study unit, and permits comparison of the two study areas to other areas assessed by the GAMA Priority Basin Project statewide.
Groundwater resources used for public drinking water in the WSJV study unit are among the most saline and most affected by high concentrations of inorganic constituents of all groundwater resources used for public drinking water that have been assessed by the GAMA Priority Basin Project statewide. Among the 82 GAMA Priority Basin Project study areas statewide, the Delta–Mendota study area ranked above the 90th percentile for aquifer-scale proportions of groundwater resources having concentrations of total dissolved solids (TDS), sulfate, chloride, manganese, boron, chromium(VI), selenium, and strontium above benchmarks, and the Westside study area ranked above the 90th percentile for TDS, sulfate, manganese, and boron.
In the WSJV study unit as a whole, one or more inorganic constituents with regulatory or non-regulatory, health-based benchmarks were present at concentrations above benchmarks in about 53 percent of the groundwater resources used for public drinking water, and one or more organic constituents with regulatory health-based benchmarks were detected at concentrations above benchmarks in about 3 percent of the resource. Individual constituents present at concentrations greater than health-based benchmarks in greater than 2 percent of groundwater resources used for public drinking water included: boron (51 percent, SWRCB-DDW notification level), chromium(VI) (25 percent, SWRCB-DDW maximum contaminant level (MCL)), arsenic (10 percent, EPA MCL), strontium (5.1 percent, EPA Lifetime health advisory level (HAL)), nitrate (3.9 percent, EPA MCL), molybdenum (3.8 percent, EPA HAL), selenium (2.6 percent, EPA MCL), and benzene (2.6 percent, SWRCB-DDW MCL). In addition, 50 percent of the resource had TDS concentrations greater than non-regulatory, aesthetic-based SWRCB-DDW upper secondary maximum contaminant level (SMCL), and 44 percent had manganese concentrations greater than the SWRCB-DDW SMCL.
Natural and anthropogenic factors that could affect the groundwater quality were evaluated by using results from statistical testing of associations between constituent concentrations and values of potential explanatory factors, inferences from geochemical and age-dating tracer results, and by considering the water-quality results in the context of the hydrogeologic setting of the WSJV study unit.
Natural factors, particularly the lithologies of the source areas for groundwater recharge and of the aquifers, were the dominant factors affecting groundwater quality in most of the WSJV study unit. However, where groundwater resources used for public supply included groundwater recharged in the modern era, mobilization of constituents by recharge of water used for irrigation also affected groundwater quality. Public-supply wells in the Westside study area had a median depth of 305 m and primarily tapped groundwater recharged hundreds to thousands of years ago, whereas public-supply wells in the Delta–Mendota study area had a median depth of 85 m and primarily tapped either groundwater recharged within the last 60 years or groundwater consisting of mixtures of this modern recharge and older recharge.
Public-supply wells in the WSJV study unit are screened in the Tulare Formation and zones above and below the Corcoran Clay Member are used. The Tulare Formation primarily consists of alluvial sediments derived from the Coast Ranges to the west, except along the valley trough at the eastern margin of the WSJV study unit where the Tulare Formation consists of fluvial sands derived from the Sierra Nevada to the east. Groundwater from wells screened in the Sierra Nevada sands had manganese-reducing or manganese- and iron-reducing oxidation-reduction (redox) conditions. These redox conditions commonly were associated with elevated arsenic or molybdenum concentrations, and the dominance of arsenic(III) in the dissolved arsenic supports reductive dissolution of iron and manganese oxyhydroxides as the mechanism. In addition, groundwater from many wells screened in Sierra Nevada sands contained low concentrations of nitrite or ammonium, indicating reduction of nitrate by denitrification or dissimilatory processes, respectively.
Geology of the Coast Ranges westward of the study unit strongly affects groundwater quality in the WSJV. Elevated concentrations of TDS, sulfate, boron, selenium and strontium in groundwater were primarily associated with aquifer sediments and recharge derived from areas of the Coast Ranges dominated by Cretaceous-to-Miocene age, organic-rich, reduced marine shales, known as the source of selenium in WSJV soils, surface water, and groundwater. Low sulfur-isotopic values (δ34S) of dissolved sulfate indicate that the sulfate was largely derived from oxidation of biogenic pyrite from the shales, and correlations with trace element concentrations, geologic setting, and groundwater geochemical modeling indicated that distributions of sulfate, strontium, and selenium in groundwater were controlled by dissolution of secondary sulfate minerals in soils and sediments.
Elevated concentrations of chromium(VI) were primarily associated with aquifer sediments and recharge derived from areas of the Coast Ranges dominated by the Franciscan Complex and ultramafic rocks. The Franciscan Complex also has boron-rich, sodium-chloride dominated hydrothermal fluids that contribute to elevated concentrations of boron and TDS.
Groundwater from wells screened in Coast Ranges alluvium was primarily oxic and relatively alkaline (median pH value of 7.55) in the Delta–Mendota study area, and primarily nitrate-reducing or suboxic and alkaline (median pH value of 8.4) in the Westside study area. Many groundwater samples from those wells have elevated concentrations of arsenic(V), molybdenum, selenium, or chromium(VI), consistent with desorption of metal oxyanions from mineral surfaces under those geochemical conditions.
High concentrations of benzene were associated with deep wells located in the vicinity of petroleum deposits at the southern end of the Westside study area. Groundwater from these wells had premodern age and anoxic geochemical conditions, and the ratios among concentrations of hydrocarbon constituents were different from ratios found in fuels and combustion products, which is consistent with a geogenic source for the benzene rather than contamination from anthropogenic sources.
Water stable-isotope compositions, groundwater recharge temperatures, and groundwater ages were used to infer four types of groundwater: (1) groundwater derived from natural recharge of water from major rivers draining the Sierra Nevada; (2) groundwater primarily derived from natural recharge of water from Coast Ranges runoff; (3) groundwater derived from recharge of pumped groundwater applied to the land surface for irrigation; and (4) groundwater derived from recharge during a period of much cooler paleoclimate. Water previously used for irrigation was found both above and below the Corcoran Clay, supporting earlier inferences that this clay member is no longer a robust confining unit.
Recharge of water used for irrigation has direct and indirect effects on groundwater quality. Elevated nitrate concentrations and detections of herbicides and fumigants in the Delta–Mendota study area generally were associated with greater agricultural land use near the well and with water recharged during the last 60 years. However, the extent of the groundwater resource affected by agricultural sources of nitrate was limited by groundwater redox conditions sufficient to reduce nitrate. The detection frequency of perchlorate in Delta–Mendota groundwater was greater than expected for natural conditions. Perchlorate, nitrate, selenium, and strontium concentrations were correlated with one another and were greater in groundwater inferred to be recharge of previously pumped groundwater used for irrigation. The source of the perchlorate, selenium, and strontium appears to be salts deposited in the soils and sediments of the arid WSJV that are dissolved and flushed into groundwater by the increased amount of recharge caused by irrigation. In the Delta–Mendota study area, the groundwater with elevated concentrations of selenium was found deeper in the aquifer system than it was reported by a previous study 25 years earlier, suggesting that this transient front of groundwater with elevated concentrations of constituents derived from dissolution of soil salts by irrigation recharge is moving down through the aquifer system and is now reaching the depth zone used for public drinking water supply.
California Water Science Center
California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The use of freshwater diversions (river reintroductions) from the Mississippi River as a restoration tool to rehabilitate Louisiana coastal wetlands has been promoted widely since the first such diversion at Caernarvon became operational in the early 1990s. To date, aside from the Bonnet Carré Spillway (which is designed and operated for flood control), there are only four operational Mississippi River freshwater diversions (two gated structures and two siphons) in coastal Louisiana, and they all target salinity intrusion, shellfish management, and (or) the enhancement of the integrity of marsh habitat. River reintroductions carry small sediment loads for various design reasons, but they can be effective in delivering freshwater to combat saltwater intrusion and increase the delivery of nutrients and suspended fine-grained sediments to receiving wetlands. River reintroductions may be an ideal restoration tool for targeting coastal swamp forest habitat; much of the area of swamp forest habitat in coastal Louisiana is undergoing saltwater intrusion, high rates of submergence, and lack of riverine flow leading to reduced concentrations of important nutrients and suspended sediments, which sustain growth and regeneration, help to aerate swamp soils, and remove toxic compounds from the rhizosphere.
The State of Louisiana Coastal Protection and Restoration Authority (CPRA) has made it a priority to establish a small freshwater river diversion into a coastal swamp forest located between Baton Rouge and New Orleans, Louisiana, to reintroduce Mississippi River water to Maurepas Swamp. While a full understanding of how a coastal swamp forest will respond to new freshwater loading through a Mississippi River reintroduction is unknown, this report provides guidance based on the available literature for establishing performance measures that can be used for evaluating the effectiveness of a Mississippi River reintroduction into the forested wetlands of Maurepas Swamp (project PO-29 of the Coastal Wetlands Planning, Protection and Restoration Act) and aid in adaptive management of the project. PO-29 is a small river reintroduction in scope, and through its operation, it will provide information about the feasibility and reasonable expectations for future river reintroduction projects targeting coastal swamp forests in Louisiana.
Located near Garyville, Louisiana, the Mississippi River reintroduction into Maurepas Swamp project is being designed to deliver a maximum flow of 57 cubic meters per second (m3/s) (or about 2,000 cubic feet per second [ft3/s]) directly from the river, but with a maximum flow through the outflow channel of 42 m3/s (or 1,500 ft3/s) available for at least half of the year. The river reintroduction will divert Mississippi River water through channelized flow and surface water to impact approximately 16,583 hectares (ha) of wetland habitat, much of which is swamp forest and swamp forest transitioning into marsh habitat. The Mississippi River reintroduction into Maurepas Swamp and associated outfall management features collectively should facilitate connectivity of water between the Mississippi River and the entire project area.
At any given location, hydrologic connectivity should occur at intervals between twice yearly and once per decade, and hydrologic management must allow the potential for water drawdowns to foster tree regeneration every 3–13 years. The river reintroduction is also anticipated to maintain salinity in swamp forests dominated by Taxodium distichum (baldcypress) to less than 1.3 practical salinity units (psu) and maintain salinity in mixed baldcypress and Nyssa aquatica (water tupelo) swamp forests to less than 0.8 psu. The river reintroduction should promote soil surface elevation gains of 8–9 millimeters per year (mm/yr) (range, 4.9–12.1 mm/yr) to offset relative sea-level rise and keep total river water nitrate (NO3-) loading into Maurepas Swamp to about 11.25 grams (g) of nitrogen (N) per square meter per year (m-2 yr-1 ) (range, 7.1–15.4 g N m-2 yr-1) to promote near complete uptake of NO3- by the vegetation in the receiving wetlands and reduce impacts to water quality in adjacent and connected water ways (for example, Blind River) and Lake Maurepas. With these performance measures maintained over time, we further expect swamp forest stands to realize improvements in stand density index of as much as 30–45 percent of maximum values for the stand type while maintaining an overstory leaf area index of 2.0–2.9 square meters per square meter or higher as swamp forests recover from decades of low flow, saltwater intrusion, reduced nutrients, and surface elevation deficits associated with isolation from the Mississippi River.
Associated with these performance measures are two major uncertainties: (1) an assumption that we can rely on existing data, literature, and modeling from coastal swamp forests to establish these performance measures and (2) an unknown time frame for evaluating these performance measures. Some performance measures can be assessed quickly, such as those associated with hydrology and nutrient uptake. Some performance measures, such as changes in soil surface elevation and forest structural integrity, could take longer to assess. Once performance measures are assessed across different time scales, however, adjustments to operations of the Mississippi River reintroduction into Maurepas Swamp can be swift. The proposed performance measures are ideal targets, mostly without specific consideration of practical, operational constraints. The measures are intended to be the basis by which adaptive management of the diversion structures can be evaluated. The measures are defined without regard to current conditions so that project success can be evaluated on net outcomes rather than specific change from existing conditions. We expect that the Mississippi River reintroduction into Maurepas Swamp will slow degradation and extend the life of the swamp for decades to centuries.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175036","collaboration":"Prepared in cooperation with the Coastal Protection and Restoration Authority (CPRA) of Louisiana","usgsCitation":"Krauss, K.W., Shaffer, G.P., Keim, R.F., Chambers, J.L., Wood, W.B., and Hartley, S.B., 2017, Performance measures for a Mississippi River reintroduction into the forested wetlands of Maurepas Swamp: U.S. Geological Survey Scientific Investigations Report 2017–5036, 56 p., https://doi.org/10.3133/sir20175036.","productDescription":"vii, 56 p.","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-076437","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research 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]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
Collection of water-quality samples that accurately characterize average particle concentrations and distributions in channels can be complicated by large sources of variability. The U.S. Geological Survey (USGS) developed a fully automated Depth-Integrated Sample Arm (DISA) as a way to reduce bias and improve accuracy in water-quality concentration data. The DISA was designed to integrate with existing autosampler configurations commonly used for the collection of water-quality samples in vertical profile thereby providing a better representation of average suspended sediment and sediment-associated pollutant concentrations and distributions than traditional fixed-point samplers. In controlled laboratory experiments, known concentrations of suspended sediment ranging from 596 to 1,189 mg/L were injected into a 3 foot diameter closed channel (circular pipe) with regulated flows ranging from 1.4 to 27.8 ft3 /s. Median suspended sediment concentrations in water-quality samples collected using the DISA were within 7 percent of the known, injected value compared to 96 percent for traditional fixed-point samplers. Field evaluation of this technology in open channel fluvial systems showed median differences between paired DISA and fixed-point samples to be within 3 percent. The range of particle size measured in the open channel was generally that of clay and silt. Differences between the concentration and distribution measured between the two sampler configurations could potentially be much larger in open channels that transport larger particles, such as sand.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 5th Federal Interagency Hydrologic Modeling Conference and the 10th Federal Interagency Sedimentation Conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Joint Federal Interagency Conference 2015","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, NV","language":"English","publisher":"Department of Interior","publisherLocation":"Reston, VA","usgsCitation":"Selbig, W.R., 2017, Collecting a better water-quality sample: Reducing vertical stratification bias in open and closed channels, in Proceedings of the 5th Federal Interagency Hydrologic Modeling Conference and the 10th Federal Interagency Sedimentation Conference, Reno, NV, April 19-23, 2015, 11 p.","productDescription":"11 p.","ipdsId":"IP-060694","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":342312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":342311,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://acwi.gov/sos/pubs/3rdJFIC/"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593ad6e1e4b0764e6c602147","contributors":{"authors":[{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697626,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70193216,"text":"70193216 - 2017 - Paleozoic and mesozoic GIS data from the Geologic Atlas of the Rocky Mountain Region: Volume 1","interactions":[],"lastModifiedDate":"2017-11-08T10:14:29","indexId":"70193216","displayToPublicDate":"2017-06-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":4,"text":"Book"},"title":"Paleozoic and mesozoic GIS data from the Geologic Atlas of the Rocky Mountain Region: Volume 1","docAbstract":"The Rocky Mountain Association of Geologists (RMAG) is, once again, publishing portions of the 1972 Geologic Atlas of the Rocky Mountain Region (Mallory, ed., 1972) as a geospatial map and data package. Georeferenced tiff (Geo TIFF) images of map figures from this atlas has served as the basis for these data products. Shapefiles and file geodatabase features have been generated and cartographically represented for select pages from the following chapters:
• Phanerozoic Rocks (page 56)
• Cambrian System (page 63)
• Ordovician System (pages 78 and 79)
• Silurian System (pages 87 - 89)
• Devonian System (pages 93, 94, and 96 - 98)
• Mississippian System (pages 102 and 103)
• Pennsylvanian System (pages 114 and 115)
• Permian System (pages 146 and 149 - 154)
• Triassic System (pages 168 and 169)
• Jurassic System (pages 179 and 180)
• Cretaceous System (pages 197 - 201, 207 - 210, 215, - 218, 221, 222, 224, 225, and 227).
The primary purpose of this publication is to provide regional-scale, as well as local-scale, geospatial data of the Rocky Mountain Region for use in geoscience studies. An important aspect of this interactive map product is that it does not require extensive GIS experience or highly specialized software.
","language":"English","publisher":"The Rocky Mountain Association of Geologists","usgsCitation":"2017, Paleozoic and mesozoic GIS data from the Geologic Atlas of the Rocky Mountain Region: Volume 1, e-book.","productDescription":"e-book","ipdsId":"IP-079177","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":348354,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347815,"type":{"id":15,"text":"Index Page"},"url":"https://www.rmag.org/paleozoic---mesozoic-gis-data"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425b7e4b0dc0b45b45359","contributors":{"editors":[{"text":"Graeber, Aimee 0000-0001-5671-3403 agraeber@usgs.gov","orcid":"https://orcid.org/0000-0001-5671-3403","contributorId":169548,"corporation":false,"usgs":true,"family":"Graeber","given":"Aimee","email":"agraeber@usgs.gov","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":720863,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Gunther, Gregory L. 0000-0002-1761-1604 ggunther@usgs.gov","orcid":"https://orcid.org/0000-0002-1761-1604","contributorId":1581,"corporation":false,"usgs":true,"family":"Gunther","given":"Gregory","email":"ggunther@usgs.gov","middleInitial":"L.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":720864,"contributorType":{"id":2,"text":"Editors"},"rank":2}]}} ,{"id":70189162,"text":"70189162 - 2017 - Synthesis centers as critical research infrastructure","interactions":[],"lastModifiedDate":"2018-02-13T14:43:53","indexId":"70189162","displayToPublicDate":"2017-06-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":997,"text":"BioScience","active":true,"publicationSubtype":{"id":10}},"title":"Synthesis centers as critical research infrastructure","docAbstract":"Demand for the opportunity to participate in a synthesis-center activity has increased in the years since the US National Science Foundation (NSF)–funded National Center for Ecological Analysis and Synthesis (NCEAS) opened its doors in 1995 and as more scientists across a diversity of scientific disciplines have become aware of what synthesis centers provide. The NSF has funded four synthesis centers, and more than a dozen new synthesis centers have been established around the world, some following the NSF model and others following different models suited to their national funding environment (http://synthesis-consortium.org).
Scientific synthesis integrates diverse data and knowledge to increase the scope and applicability of results and yield novel insights or explanations within and across disciplines (Pickett et al. 2007, Carpenter et al. 2009). The demand for synthesis comes from the pressing societal need to address grand challenges related to global change and other issues that cut across multiple societal sectors and disciplines and from recognition that substantial added scientific value can be achieved through the synthesis-based analysis of existing data. Demand also comes from groups of scientists who see exciting opportunities to generate new knowledge from interdisciplinary and transdisciplinary collaboration, often capitalizing on the increasingly large volume and variety of available data (Kelling et al. 2009, Bishop et al. 2014, Specht et al. 2015b). The ever-changing nature of societal challenges and the availability of data with which to address them suggest there will be an expanding need for synthesis.
However, we are now entering a phase in which government support for some existing synthesis centers has ended or will be ending soon, forcing those centers to close or develop new operational models, approaches, and funding streams. We argue here that synthesis centers play such a unique role in science that continued long-term public investment to maximize benefits to science and society is justified. In particular, we argue that synthesis centers represent community infrastructure more akin to research vessels than to term-funded centers of science and technology (e.g., NSF Science and Technology Centers). Through our experience running synthesis centers and, in some cases, developing postfederal funding models, we offer our perspective on the purpose and value of synthesis centers. We present case studies of different outcomes of transition plans and argue for a fundamental shift in the conception of synthesis science and the strategic funding of these centers by government funding agencies.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/biosci/bix053","usgsCitation":"Baron, J., Specht, A., Garnier, E., Bishop, P., Campbell, C.A., Davis, F., Fady, B., Field, D., Gross, L.J., Guru, S.M., Halpern, B., Hampton, S.E., Leavitt, P.R., Meagher, T.R., Ometto, J., Parker, J.N., Price, R., Rawson, C.H., Rodrigo, A., Sheble, L.A., and Winter, M., 2017, Synthesis centers as critical research infrastructure: BioScience, v. 67, no. 8, p. 750-759, https://doi.org/10.1093/biosci/bix053.","productDescription":"10 p. 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2017 - Effects of changes in pumping on regional groundwater-flow paths, 2005 and 2010, and areas contributing recharge to discharging wells, 1990–2010, in the vicinity of North Penn Area 7 Superfund site, Montgomery County, Pennsylvania","interactions":[],"lastModifiedDate":"2021-03-16T15:16:14.104532","indexId":"sir20175014","displayToPublicDate":"2017-06-06T09:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5014","title":"Effects of changes in pumping on regional groundwater-flow paths, 2005 and 2010, and areas contributing recharge to discharging wells, 1990–2010, in the vicinity of North Penn Area 7 Superfund site, Montgomery County, Pennsylvania","docAbstract":"A previously developed regional groundwater flow model was used to simulate the effects of changes in pumping rates on groundwater-flow paths and extent of recharge discharging to wells for a contaminated fractured bedrock aquifer in southeastern Pennsylvania. Groundwater in the vicinity of the North Penn Area 7 Superfund site, Montgomery County, Pennsylvania, was found to be contaminated with organic compounds, such as trichloroethylene (TCE), in 1979. At the time contamination was discovered, groundwater from the underlying fractured bedrock (shale) aquifer was the main source of supply for public drinking water and industrial use. As part of technical support to the U.S. Environmental Protection Agency (EPA) during the Remedial Investigation of the North Penn Area 7 Superfund site from 2000 to 2005, the U.S. Geological Survey (USGS) developed a model of regional groundwater flow to describe changes in groundwater flow and contaminant directions as a result of changes in pumping. Subsequently, large decreases in TCE concentrations (as much as 400 micrograms per liter) were measured in groundwater samples collected by the EPA from selected wells in 2010 compared to 2005‒06 concentrations.
To provide insight on the fate of potentially contaminated groundwater during the period of generally decreasing pumping rates from 1990 to 2010, steady-state simulations were run using the previously developed groundwater-flow model for two conditions prior to extensive remediation, 1990 and 2000, two conditions subsequent to some remediation 2005 and 2010, and a No Pumping case, representing pre-development or cessation of pumping conditions. The model was used to (1) quantify the amount of recharge, including potentially contaminated recharge from sources near the land surface, that discharged to wells or streams and (2) delineate the areas contributing recharge that discharged to wells or streams for the five conditions.
In all simulations, groundwater divides differed from surface-water divides, partly because of differences in stream elevations and because of geologic structure and pumping. In the 1990 and 2000 simulations, all recharge in and near the vicinity of North Penn Area 7 discharged to wells, but in the 2005 and 2010 simulations some recharge in this area discharged to streams, indicating possible discharge of contaminated groundwater from North Penn Area 7 sources to streams. As the amount of groundwater withdrawals by wells has declined since 1990, the area contributing recharge to wells in the vicinity of North Penn Area 7 has decreased.
To determine the effect of changes in pumping on flow paths and possible flow-path-related contributions to the observed changes in spatial distribution of contaminants in groundwater from 2005 to 2010, the USGS conducted simulations using the previously developed regional groundwater-flow model using reported pumping and estimated recharge rates for 2005 and 2010. Flow paths from recharge at known contaminant source areas to discharge locations at wells or streams were simulated under steady-state conditions for the two periods. Simulated groundwater-flow paths shifted only slightly from 2005 to 2010 as a result of changes in pumping rates. These slight changes in groundwater-flow paths from known sources of contamination are not coincident with the spatial distribution of observed changes in TCE concentrations from 2005 to 2010, indicating that the decreases of TCE concentrations may be a result of other processes, such as source removal or degradation. Results of the simulations and the absence of increases in TCE-degradation-product concentrations indicate that the decreases of TCE concentrations observed in 2010 may be at least partly related to contaminant-source removal by soil excavation completed in 2005, although additional data would be needed to confirm this preliminary explanation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175014","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Senior, L.A., and Goode, D.J., 2017, Effects of changes in pumping on regional groundwater-flow paths, 2005 and 2010, and areas contributing recharge to discharging wells, 1990–2010, in the vicinity of North Penn Area 7 Superfund site, Montgomery County, Pennsylvania: U.S. Geological Survey Scientific Investigations Report 2017–5014, 36 p., https://doi.org/10.3133/sir20175014.","productDescription":"Report: vi, 36 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077142","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":341375,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7FN14BQ","text":"USGS data release","description":"USGS data release"},{"id":341337,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5014/sir20175014.pdf","text":"Report","size":"4.61 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":341336,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5014/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Montgomery County","otherGeospatial":"North Penn Area 7 Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.304167,\n 40.244444\n ],\n [\n -75.263333,\n 40.244444\n ],\n [\n -75.263333,\n 40.205556\n ],\n [\n -75.304167,\n 40.205556\n ],\n [\n -75.304167,\n 40.244444\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070