This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions.
The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The skew coefficient values for each streamgage were then computed as the variance-weighted average of at-site and regional skew coefficients. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter.
This report also provides the following: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant.
The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted annual maximum peak discharge records by streamgage are provided at https://doi.org/10.3133/sir20165050 for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165050","collaboration":"Prepared in cooperation with the Illinois Center for Transportation, the Illinois Department of Transportation, and the Federal Highway Administration","usgsCitation":"Over, T.M., Saito, R.J., Veilleux, A.G., O’Shea, P.S., Sharpe, J.B., Soong, D.T., and Ishii, A.L., 2016, Estimation of peak discharge quantiles for selected annual exceedance probabilities in northeastern Illinois (ver. 3.0, June 2021): U.S. Geological Survey Scientific Investigations Report 2016–5050, 50 p. with appendix, https://doi.org/10.3133/sir20165050.","productDescription":"Report: x, 51 p.; Tables; Companion Files; Version History","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072125","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":386876,"rank":13,"type":{"id":25,"text":"Version 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probability"},{"id":386856,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_04.csv","text":"Table 4","size":"9.96 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 4","linkHelpText":"— Segment information for 181 U.S. Geological Survey streamgages used in this study, northeastern Illinois and adjacent states"},{"id":386855,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_03.csv","text":"Table 3","size":"7.02 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 3","linkHelpText":"— Spatially averaged basin characteristics considered for developing spatial regression equations in this study in northeastern Illinois"},{"id":386854,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_02.csv","text":"Table 2","size":"104 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 2","linkHelpText":"— Estimated peak discharge quantiles for 181 streamgages in northeastern Illinois and adjacent states, at selected exceedance probabilities"},{"id":386853,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_01.csv","text":"Table 1","size":"29.2 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 1","linkHelpText":"— U.S. Geological Survey streamgages used in this study in northeastern Illinois and adjacent states"},{"id":386852,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050.pdf","text":"Report","size":"6.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5050"},{"id":324328,"rank":1,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_Links_To_Files.html","text":"Annual maximum peak discharge and associated urban fraction and precipitation values by streamgage","size":"29 kB","linkFileType":{"id":5,"text":"html"},"description":"SIR 2016–5050 Supplemental Graphs and Tables"},{"id":386861,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_tables.xlsx","text":"Tables 1 through 4, 6, 8 and 13 and Table 1–1","size":"673 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5050 Tables"},{"id":386860,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_appendix_table_1.1.csv","text":"Table 1.1","size":"7.58 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 1.1","linkHelpText":"— Skew statistics at streamgages used in the development of the regional skew model in this study in northeastern Illinois"},{"id":386857,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_06.csv","text":"Table 6","size":"345 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 6","linkHelpText":"— Observed and urban-adjusted annual maximum peak discharges and associated urbanization and precipitation values at 181 streamgages in northeastern Illinois and adjacent states"},{"id":349173,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5050/coverthb3.jpg"}],"country":"United States","state":"Illinois","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.14056396484375,\n 42.282389042899574\n ],\n [\n -88.03619384765625,\n 42.28340504748079\n ],\n [\n -87.9730224609375,\n 42.28289704723818\n ],\n [\n -87.95310974121094,\n 42.28315104787148\n ],\n [\n -87.93319702148438,\n 42.28340504748079\n ],\n [\n -87.89337158203125,\n 42.28264304558087\n ],\n [\n -87.87277221679688,\n 42.28340504748079\n ],\n [\n -87.835693359375,\n 42.28442103567816\n ],\n [\n -87.7659,\n 42.155\n ],\n [\n -87.7572,\n 42.1548\n 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U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801
Much remains unknown about the genetic status and population connectivity of high-elevation and high-latitude freshwater invertebrates, which often persist near snow and ice masses that are disappearing due to climate change. Here we report on the conservation genetics of the meltwater stonefly Lednia tumana (Ricker) of Montana, USA, a cold-water obligate species. We sequenced 1530 bp of mtDNA from 116 L. tumana individuals representing “historic” (>10 yr old) and 2010 populations. The dominant haplotype was common in both time periods, while the second-most-common haplotype was found only in historic samples, having been lost in the interim. The 2010 populations also showed reduced gene and nucleotide diversity and increased genetic isolation. We found lower genetic diversity in L. tumana compared to two other North American stonefly species, Amphinemura linda (Ricker) and Pteronarcys californica Newport. Our results imply small effective sizes, increased fragmentation, limited gene flow, and loss of genetic variation among contemporary L. tumana populations, which can lead to reduced adaptive capacity and increased extinction risk. This study reinforces concerns that ongoing glacier loss threatens the persistence of L. tumana, and provides baseline data and analysis of how future environmental change could impact populations of similar organisms.
","language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0157386","usgsCitation":"Jordan, S., Giersch, J., Muhlfeld, C.C., Hotalling, S., Fanning, L., Tappenbeck, T.H., and Luikart, G., 2016, Loss of genetic diversity and increased subdivision in an endemic Alpine Stonefly threatened by climate change: PLoS ONE, v. 11, no. 6, e0157386; 12 p., https://doi.org/10.1371/journal.pone.0157386.","productDescription":"e0157386; 12 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069801","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":470827,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0157386","text":"Publisher Index Page"},{"id":324527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.01562499999999,\n 49.009050809382046\n ],\n [\n -108.19335937499999,\n 48.99463598353405\n ],\n [\n -104.0625,\n 49.009050809382046\n ],\n [\n -104.0625,\n 44.99588261816546\n ],\n [\n -111.07177734375,\n 45.02695045318546\n ],\n [\n -111.07177734375,\n 44.49650533109345\n ],\n [\n -111.4013671875,\n 44.74673324024678\n ],\n [\n -111.46728515624999,\n 44.63739123445585\n ],\n [\n -111.55517578125,\n 44.55916341529182\n ],\n [\n -111.73095703125,\n 44.5278427984555\n ],\n [\n -112.0166015625,\n 44.5278427984555\n ],\n [\n -112.21435546875,\n 44.5435052132082\n ],\n [\n -112.39013671875,\n 44.449467536006935\n ],\n [\n -112.65380859375,\n 44.465151013519616\n ],\n [\n -112.8955078125,\n 44.37098696297173\n ],\n [\n -113.00537109375,\n 44.49650533109345\n ],\n [\n -113.09326171875,\n 44.6061127451739\n ],\n [\n -113.18115234375,\n 44.762336674810996\n ],\n [\n -113.48876953125,\n 44.809121700077355\n ],\n [\n -113.51074218749999,\n 44.99588261816546\n ],\n [\n -113.62060546875,\n 45.19752230305682\n ],\n [\n -113.73046875,\n 45.30580259943578\n ],\n [\n -113.7744140625,\n 45.506346901083425\n ],\n [\n -113.88427734374999,\n 45.61403741135093\n ],\n [\n -114.01611328125,\n 45.67548217560647\n ],\n [\n -114.12597656249999,\n 45.598665689820635\n ],\n [\n -114.2578125,\n 45.55252525134013\n ],\n [\n -114.3017578125,\n 45.49094569262732\n ],\n [\n -114.45556640625,\n 45.55252525134013\n ],\n [\n -114.54345703125,\n 45.644768217751924\n ],\n [\n -114.54345703125,\n 45.78284835197676\n ],\n [\n -114.45556640625,\n 45.81348649679971\n ],\n [\n -114.43359375,\n 45.920587344733654\n ],\n [\n -114.49951171875,\n 46.027481852486645\n ],\n [\n -114.49951171875,\n 46.164614496897094\n ],\n [\n -114.36767578124999,\n 46.27103747280261\n ],\n [\n -114.36767578124999,\n 46.37725420510028\n ],\n [\n -114.45556640625,\n 46.52863469527167\n ],\n [\n -114.47753906249999,\n 46.649436163350245\n ],\n [\n -114.63134765625001,\n 46.73986059969267\n ],\n [\n -114.7412109375,\n 46.73986059969267\n ],\n [\n -114.80712890625,\n 46.86019101567027\n ],\n [\n -114.9609375,\n 46.965259400349275\n ],\n [\n -115.24658203125,\n 47.15984001304432\n ],\n [\n -115.3564453125,\n 47.2195681123155\n ],\n [\n -115.48828125000001,\n 47.30903424774781\n ],\n [\n -115.7080078125,\n 47.44294999517949\n ],\n [\n -115.7080078125,\n 47.54687159892238\n ],\n [\n -115.77392578125,\n 47.65058757118734\n ],\n [\n -115.90576171874999,\n 47.79839667295524\n ],\n [\n -116.05957031249999,\n 47.945786463687185\n ],\n [\n -116.01562499999999,\n 49.009050809382046\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-27","publicationStatus":"PW","scienceBaseUri":"577391a6e4b07657d1a88bd4","contributors":{"authors":[{"text":"Jordan, Steve","contributorId":168297,"corporation":false,"usgs":false,"family":"Jordan","given":"Steve","email":"","affiliations":[{"id":25242,"text":"Department of Biology, Bucknell University, Lewisburg, Pennsylvania 17837, USA","active":true,"usgs":false}],"preferred":false,"id":641058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Giersch, J. Joseph 0000-0001-7818-3941 jgiersch@usgs.gov","orcid":"https://orcid.org/0000-0001-7818-3941","contributorId":4022,"corporation":false,"usgs":true,"family":"Giersch","given":"J. Joseph","email":"jgiersch@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":false,"id":641059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":641060,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hotalling, Scott","contributorId":172501,"corporation":false,"usgs":false,"family":"Hotalling","given":"Scott","email":"","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":641061,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fanning, Liz","contributorId":172502,"corporation":false,"usgs":false,"family":"Fanning","given":"Liz","email":"","affiliations":[{"id":16651,"text":"Bucknell University","active":true,"usgs":false}],"preferred":false,"id":641062,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tappenbeck, Tyler H.","contributorId":176876,"corporation":false,"usgs":false,"family":"Tappenbeck","given":"Tyler","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":653866,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Luikart, Gordon","contributorId":97409,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","affiliations":[{"id":6580,"text":"University of Montana, Flathead Lake Biological Station, Polson, Montana 59860, USA","active":true,"usgs":false}],"preferred":false,"id":641063,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70174060,"text":"ofr20161109 - 2016 - Jaguar taxonomy and genetic diversity for southern Arizona, United States, and Sonora, Mexico","interactions":[],"lastModifiedDate":"2016-06-29T09:34:58","indexId":"ofr20161109","displayToPublicDate":"2016-06-28T00:00:00","publicationYear":"2016","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":"2016-1109","title":"Jaguar taxonomy and genetic diversity for southern Arizona, United States, and Sonora, Mexico","docAbstract":"The jaguar is the largest Neotropical felid and the only extant representative of the genus Panthera in the Americas. In recorded history, the jaguars range has extended from the Southern United States, throughout Mexico, to Central and South America, and they occupy a wide variety of habitats. A previous jaguar genetic study found high historical levels of gene flow among jaguar populations over broad areas but did not include any samples of jaguar from the States of Arizona, United States, or Sonora, Mexico. Arizona and Sonora have been part of the historical distribution of jaguars; however, poaching and habitat fragmentation have limited their distribution until they were declared extinct in the United States and endangered in Sonora. Therefore, a need was apparent to have this northernmost (Arizona/Sonora) jaguar population included in an overall jaguar molecular taxonomy and genetic diversity analyses. In this study, we used molecular genetic markers to examine diversity and taxonomy for jaguars in the Northwestern Jaguar Recovery Unit (NJRU; Sonora, Sinaloa, and Jalisco, Mexico; and southern Arizona and New Mexico, United States) relative to jaguars in other parts of the jaguar range (Central and South America). The objectives of this study were to:
Leader, Arizona Cooperative Fish and Wildlife Research Unit
U.S. Geological Survey
325 Biosciences East
Tucson, Arizona 85721
http://www.coopunits.org/Arizona/
A three-dimensional numerical model of groundwater flow was developed for the Wood River Valley (WRV) aquifer system, Idaho, to evaluate groundwater and surface-water availability at the regional scale. This mountain valley is located in Blaine County and has a drainage area of about 2,300 square kilometers (888 square miles). The model described in this report can serve as a tool for water-rights administration and water-resource management and planning. The model was completed with support from the Idaho Department of Water Resources, and is part of an ongoing U.S. Geological Survey effort to characterize the groundwater resources of the WRV. A highly reproducible approach was taken for constructing the WRV groundwater-flow model. The collection of datasets, source code, and processing instructions used to construct and analyze the model was distributed as an R statistical-computing and graphics package.
\nFlow in the WRV aquifer was simulated using the MODFLOW-USG groundwater flow model. The transient flow model simulates groundwater flow between 1995 and 2010. The model uses a 100-meter (328-feet) uniform grid spacing with 54,922 active model cells distributed over three model layers. A confining unit in the south-central part of the Bellevue fan necessitated the use of a multi-layer model. Specified-flow boundaries were used to simulate the groundwater inflows from each of the major tributary basins (also known as tributary basin underflow) and the areal recharge of precipitation and applied irrigation. Head‑dependent flow boundaries were used to simulate the stream-aquifer flow exchange in river reaches and the groundwater discharge at the outlet boundaries of Stanton Crossing and Silver Creek. The model was calibrated by adjusting aquifer hydraulic properties to match simulated and measured water levels and stream-aquifer flow exchange, using the parameter-estimation program PEST. The model reasonably simulated the measured water-table elevation, orientation, and gradients. Stream-aquifer flow exchange along river reaches also was reasonably simulated by the model.
\nInflow into the WRV aquifer system originates from three sources (from largest to smallest):
\nOutflow from the WRV aquifer system originates from five sources (from largest to smallest):
\nTemporal changes in aquifer storage are most affected by areal recharge and groundwater pumping, and also contribute to changes in streamflow gains.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165080","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Fisher, J.C., Bartolino, J.R., Wylie, A.H., Sukow, Jennifer, and McVay, Michael, 2016, Groundwater-flow model of the Wood River Valley aquifer system, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2016–5080, 71 p., https://dx.doi.org/10.3133/sir20165080.","productDescription":"Report: viii, 71 p.; Appendixes A-H; Model Archive; Data Repository","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-039541","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":324425,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixE.pdf","text":"Appendix E","size":"6.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix E","linkHelpText":"Tributary Basin Underflow into the Wood River Valley Aquifer System, South-Central Idaho"},{"id":324424,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixD.pdf","text":"Appendix D","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix D","linkHelpText":"Uncalibrated Groundwater-Flow Model for the Wood River Valley Aquifer System, South-Central Idaho"},{"id":324426,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixF.pdf","text":"Appendix F","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix F","linkHelpText":"Natural Groundwater Recharge and Discharge in the Wood River Valley Aquifer System, South-Central Idaho"},{"id":324428,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixH.pdf","text":"Appendix H","size":"9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix H","linkHelpText":"Calibration of the Wood River Valley Groundwater Flow Model"},{"id":324427,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixG.pdf","text":"Appendix G","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix G","linkHelpText":"Incidental Groundwater Recharge and Pumping Demand in the Wood River Valley Aquifer System, South-Central Idaho"},{"id":324430,"rank":12,"type":{"id":7,"text":"Companion Files"},"url":"https://github.com/USGS-R/wrv","text":"R-package repository"},{"id":324423,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixC.pdf","text":"Appendix C","size":"6.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix C","linkHelpText":"Creating Datasets for the R-Package ‘wrv’"},{"id":324429,"rank":11,"type":{"id":7,"text":"Companion Files"},"url":"https://dx.doi.org/10.5066/F7C827DT","text":"Model Archive"},{"id":324419,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5080/coverthb.jpg"},{"id":324420,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Report PDF"},{"id":324421,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixA.pdf","text":"Appendix A","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix A","linkHelpText":"An Introduction to the R-Package ‘wrv’"},{"id":324422,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixB.pdf","text":"Appendix B","size":"525 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix B","linkHelpText":"Manual for Functions and Datasets in the R-Package ‘wrv’"}],"country":"United States","state":"Idaho","otherGeospatial":"Wood River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.47753906249999,\n 43.30119623257966\n ],\n [\n -114.47753906249999,\n 43.82065657651685\n ],\n [\n -114.04083251953124,\n 43.82065657651685\n ],\n [\n -114.04083251953124,\n 43.30119623257966\n ],\n [\n -114.47753906249999,\n 43.30119623257966\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
http://id.water.usgs.gov
We calibrated the watershed model SPARROW (Spatially Referenced Regressions on Watershed attributes) to give estimates of suspended-sediment loads for western Oregon and parts of northwestern California. Estimates of suspended-sediment loads were derived from a nonlinear least squares regression that related explanatory variables representing landscape and transport conditions to measured suspended-sediment loads at 68 measurement stations. The model gives estimates of model coefficients and their uncertainty within a spatial framework defined by the National Hydrography Dataset Plus hydrologic network. The resulting model explained 64 percent of the variability in suspended-sediment yield and had a root mean squared error value of 0.737. The predictor variables selected for the final model were (1) generalized lithologic province, (2) mean annual precipitation, and (3) burned area (by recent wildfire). Other landscape characteristics also were considered, but they were not significant predictors of sediment transport, were strongly correlated with another predictor variable, or were not as significant as the predictors selected for the final model.
\nThe northern Oregon coastal drainages had the highest predicted suspended sediment yields (median yield 475 kilograms per hectare per year) and the Klamath River Basin had the lowest (median yield 53 kilograms per hectare per year). Quaternary deposits were, on average, the largest contributor to incremental suspended-sediment yield even though this lithologic province only makes up 17 percent of the modeling domain. Coast Range sedimentary rocks and Coast Range volcanic rocks had high suspended-sediment yields whereas, in addition to the Klamath terrane, the Western Cascade and High Cascade lithologic provinces had low suspended-sediment yields. Precipitation and the area affected by recent wildfire both positively correlated with suspended-sediment load.
\nSuspended-sediment transport rates predicted by this SPARROW model are less than historical (1956–73) and long‑term (thousands of years) geological rates. This difference likely results, in part, from biases in the data underlying the SPARROW model, probably resulting in predicted suspended-sediment estimates that underestimate actual transport rates. However, the differences also likely owe to natural and human-caused variation in suspended-sediment yields as they respond to changes in climate, vegetation, fire frequency, and land use. In particular, decreases in mean annual suspended-sediment yields within the Umpqua River Basin since 1956–73 may owe to less intense forest harvest, passage of the Oregon Forest Practices Act of 1971, and increased emphasis in habitat protection in recent decades. Such sensitivity may have implications for the spatial and temporal distributions of aquatic and riparian habitats.
\nKnowledge of the regionally important patterns and factors in suspended-sediment sources and transport could support broad-scale, water-quality management objectives and priorities. Because of biases and limitations of this model, however, these results are most applicable for general comparisons and for broad areas such as large watersheds. For example, despite having similar area, precipitation, and land-use, the Umpqua River Basin generates 68 percent more suspended sediment than the Rogue River Basin, chiefly because of the large area of Coast Range sedimentary province in the Umpqua River Basin. By contrast, the Rogue River Basin contains a much larger area of Klamath terrane rocks, which produce significantly less suspended load, although recent fire disturbance (in 2002) has apparently elevated suspended sediment yields in the tributary Illinois River watershed. Fine-scaled analysis, however, will require more intensive, locally focused measurements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165079","usgsCitation":"Wise, D.R., and O’Connor, J.E., 2016, A spatially explicit suspended-sediment load model for western Oregon: U.S. Geological Survey Scientific Investigations Report 2016–5079, 25 p., https://dx.doi.org/10.3133/sir20165079.","productDescription":"Report: v, 25 p.; Appendix A; Companion File","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064150","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":324455,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5079/coverthb.jpg"},{"id":324458,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2016/5079/sir20165079_NHDV2_predict_data.txt","text":"Mean annual suspended loads estimated by the SPARROW model","size":"1 MB","linkFileType":{"id":2,"text":"txt"}},{"id":324456,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5079/sir20165079.pdf","text":"Report","size":"18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5079 Report PDF"},{"id":324457,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5079/sir20165079_appendixa.xlsx","text":"Appendix A ","size":"23 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5079 Appendix A","linkHelpText":"Summary of Calibration Data for the Suspended Sediment Sparrow Model Developed for Western Oregon and Northwestern California"}],"contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
The U.S. Geological Survey, in cooperation with the Idaho Transportation Department, updated regional regression equations to estimate peak-flow statistics at ungaged sites on Idaho streams using recent streamflow (flow) data and new statistical techniques. Peak-flow statistics with 80-, 67-, 50-, 43-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (1.25-, 1.50-, 2.00-, 2.33-, 5.00-, 10.0-, 25.0-, 50.0-, 100-, 200-, and 500-year recurrence intervals, respectively) were estimated for 192 streamgages in Idaho and bordering States with at least 10 years of annual peak-flow record through water year 2013. The streamgages were selected from drainage basins with little or no flow diversion or regulation. The peak-flow statistics were estimated by fitting a log-Pearson type III distribution to records of annual peak flows and applying two additional statistical methods: (1) the Expected Moments Algorithm to help describe uncertainty in annual peak flows and to better represent missing and historical record; and (2) the generalized Multiple Grubbs Beck Test to screen out potentially influential low outliers and to better fit the upper end of the peak-flow distribution. Additionally, a new regional skew was estimated for the Pacific Northwest and used to weight at-station skew at most streamgages. The streamgages were grouped into six regions (numbered 1_2, 3, 4, 5, 6_8, and 7, to maintain consistency in region numbering with a previous study), and the estimated peak-flow statistics were related to basin and climatic characteristics to develop regional regression equations using a generalized least squares procedure. Four out of 24 evaluated basin and climatic characteristics were selected for use in the final regional peak-flow regression equations.
Overall, the standard error of prediction for the regional peak-flow regression equations ranged from 22 to 132 percent. Among all regions, regression model fit was best for region 4 in west-central Idaho (average standard error of prediction=46.4 percent; pseudo-R2>92 percent) and region 5 in central Idaho (average standard error of prediction=30.3 percent; pseudo-R2>95 percent). Regression model fit was poor for region 7 in southern Idaho (average standard error of prediction=103 percent; pseudo-R2<78 percent) compared to other regions because few streamgages in region 7 met the criteria for inclusion in the study, and the region’s semi-arid climate and associated variability in precipitation patterns causes substantial variability in peak flows.
A drainage area ratio-adjustment method, using ratio exponents estimated using generalized least-squares regression, was presented as an alternative to the regional regression equations if peak-flow estimates are desired at an ungaged site that is close to a streamgage selected for inclusion in this study. The alternative drainage area ratio-adjustment method is appropriate for use when the drainage area ratio between the ungaged and gaged sites is between 0.5 and 1.5.
The updated regional peak-flow regression equations had lower total error (standard error of prediction) than all regression equations presented in a 1982 study and in four of six regions presented in 2002 and 2003 studies in Idaho. A more extensive streamgage screening process used in the current study resulted in fewer streamgages used in the current study than in the 1982, 2002, and 2003 studies. Fewer streamgages used and the selection of different explanatory variables were likely causes of increased error in some regions compared to previous studies, but overall, regional peak‑flow regression model fit was generally improved for Idaho. The revised statistical procedures and increased streamgage screening applied in the current study most likely resulted in a more accurate representation of natural peak-flow conditions.
The updated, regional peak-flow regression equations will be integrated in the U.S. Geological Survey StreamStats program to allow users to estimate basin and climatic characteristics and peak-flow statistics at ungaged locations of interest. StreamStats estimates peak-flow statistics with quantifiable certainty only when used at sites with basin and climatic characteristics within the range of input variables used to develop the regional regression equations. Both the regional regression equations and StreamStats should be used to estimate peak-flow statistics only in naturally flowing, relatively unregulated streams without substantial local influences to flow, such as large seeps, springs, or other groundwater-surface water interactions that are not widespread or characteristic of the respective region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165083","collaboration":"Prepared in cooperation with Idaho Transportation Department","usgsCitation":"Wood, M.S., Fosness, R.L., Skinner, K.D., and Veilleux, A.G., 2016, Estimating peak-flow frequency statistics for selected gaged and ungaged sites in naturally flowing streams and rivers in Idaho (ver. 1.1, April 2017): U.S. Geological Survey Scientific Investigations Report 2016–5083, 56 p., https://doi.org/10.3133/sir20165083.","productDescription":"Report: vi, 56 p.; Appendix A","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-046287","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":324444,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5083/sir20165083.pdf","text":"Report","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5083 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\"}}]}","edition":"Version 1.0: Originally posted June 27, 2016; Version 1.1: April 26, 2017","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
https://id.water.usgs.gov
Groundwater is the source of drinking water for ∼1.4 million people in the Coastal Plain Province of Maryland (USA). In addition, groundwater is essential for commercial, industrial, and agricultural uses. Approximately 0.757 × 109 L d–1 (200 million gallons/d) were withdrawn in 2010. As a result of decades of withdrawals from the coastal plain confined aquifers, groundwater levels have declined by as much as 70 m (230 ft) from estimated prepumping levels. Other issues posing challenges to long-term groundwater sustainability include degraded water quality from both man-made and natural sources, reduced stream base flow, land subsidence, and changing recharge patterns (drought) caused by climate change. In Maryland, groundwater supply is managed primarily by the Maryland Department of the Environment, which seeks to balance reasonable use of the resource with long-term sustainability. The chief goal of groundwater management in Maryland is to ensure safe and adequate supplies for all current and future users through the implementation of appropriate usage, planning, and conservation policies. To assist in that effort, the geographic information system (GIS)–based Maryland Coastal Plain Aquifer Information System was developed as a tool to help water managers access and visualize groundwater data for use in the evaluation of groundwater allocation and use permits. The system, contained within an ESRI ArcMap desktop environment, includes both interpreted and basic data for 16 aquifers and 14 confining units. Data map layers include aquifer and confining unit layer surfaces, aquifer extents, borehole information, hydraulic properties, time-series groundwater-level data, well records, and geophysical and lithologic logs. The aquifer and confining unit layer surfaces were generated specifically for the GIS system. The system also contains select groundwater-quality data and map layers that quantify groundwater and surface-water withdrawals. The aquifer information system can serve as a pre- and postprocessing environment for groundwater-flow models for use in water-supply planning, development, and management. The system also can be expanded to include features that evaluate constraints to groundwater development, such as insufficient available drawdown, degraded groundwater quality, insufficient aquifer yields, and well-field interference. Ultimately, the aquifer information system is intended to function as an interactive Web-based utility that provides a broad array of information related to groundwater resources in Maryland’s coastal plain to a wide-ranging audience, including well drillers, consultants, academia, and the general public.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2016.2520(15)","usgsCitation":"Andreasen, D., Nardi, M.R., Staley, A., Achmad, G., and Grace, J.W., 2016, The Maryland Coastal Plain Aquifer Information System: A GIS-based tool for assessing groundwater resources: Special Paper of the Geological Society of America, v. 520, p. 159-170, https://doi.org/10.1130/2016.2520(15).","productDescription":"12 p.","startPage":"159","endPage":"170","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068540","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":324417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"520","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57724023e4b07657d1a793b0","contributors":{"authors":[{"text":"Andreasen, David C.","contributorId":59003,"corporation":false,"usgs":true,"family":"Andreasen","given":"David C.","affiliations":[],"preferred":false,"id":640806,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nardi, Mark R. 0000-0002-7310-8050 mrnardi@usgs.gov","orcid":"https://orcid.org/0000-0002-7310-8050","contributorId":1859,"corporation":false,"usgs":true,"family":"Nardi","given":"Mark","email":"mrnardi@usgs.gov","middleInitial":"R.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":640805,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Staley, Andrew W.","contributorId":43319,"corporation":false,"usgs":true,"family":"Staley","given":"Andrew W.","affiliations":[],"preferred":false,"id":640807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Achmad, Grufron","contributorId":172464,"corporation":false,"usgs":false,"family":"Achmad","given":"Grufron","email":"","affiliations":[{"id":25435,"text":"Maryland Geological Survey","active":true,"usgs":false}],"preferred":false,"id":640808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grace, John W.","contributorId":172465,"corporation":false,"usgs":false,"family":"Grace","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":27050,"text":"Maryland Department of the Environment","active":true,"usgs":false}],"preferred":false,"id":640809,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70174056,"text":"70174056 - 2016 - Inferring river bathymetry via Image-to-Depth Quantile Transformation (IDQT)","interactions":[],"lastModifiedDate":"2016-06-27T11:23:51","indexId":"70174056","displayToPublicDate":"2016-06-27T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Inferring river bathymetry via Image-to-Depth Quantile Transformation (IDQT)","docAbstract":"Conventional, regression-based methods of inferring depth from passive optical image data undermine the advantages of remote sensing for characterizing river systems. This study introduces and evaluates a more flexible framework, Image-to-Depth Quantile Transformation (IDQT), that involves linking the frequency distribution of pixel values to that of depth. In addition, a new image processing workflow involving deep water correction and Minimum Noise Fraction (MNF) transformation can reduce a hyperspectral data set to a single variable related to depth and thus suitable for input to IDQT. Applied to a gravel bed river, IDQT avoided negative depth estimates along channel margins and underpredictions of pool depth. Depth retrieval accuracy (R25 0.79) and precision (0.27 m) were comparable to an established band ratio-based method, although a small shallow bias (0.04 m) was observed. Several ways of specifying distributions of pixel values and depths were evaluated but had negligible impact on the resulting depth estimates, implying that IDQT was robust to these implementation details. In essence, IDQT uses frequency distributions of pixel values and depths to achieve an aspatial calibration; the image itself provides information on the spatial distribution of depths. The approach thus reduces sensitivity to misalignment between field and image data sets and allows greater flexibility in the timing of field data collection relative to image acquisition, a significant advantage in dynamic channels. IDQT also creates new possibilities for depth retrieval in the absence of field data if a model could be used to predict the distribution of depths within a reach.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016WR018730","usgsCitation":"Legleiter, C.J., 2016, Inferring river bathymetry via Image-to-Depth Quantile Transformation (IDQT): Water Resources Research, v. 52, no. 5, p. 3722-3741, https://doi.org/10.1002/2016WR018730.","productDescription":"20 p.","startPage":"3722","endPage":"3741","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072989","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":470836,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr018730","text":"Publisher Index Page"},{"id":324402,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-14","publicationStatus":"PW","scienceBaseUri":"5772401fe4b07657d1a7937e","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":640728,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70174066,"text":"70174066 - 2016 - Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA","interactions":[],"lastModifiedDate":"2016-06-27T11:09:22","indexId":"70174066","displayToPublicDate":"2016-06-27T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA","docAbstract":"Regional analysis of large wildfire potential given climate change scenarios is crucial to understanding areas most at risk in the future, yet wildfire models are not often developed and tested at this spatial scale. We fit three historical climate suitability models for large wildfires (i.e. ≥ 400 ha) in Colorado andWyoming using topography and decadal climate averages corresponding to wildfire occurrence at the same temporal scale. The historical models classified points of known large wildfire occurrence with high accuracies. Using a novel approach in wildfire modeling, we applied the historical models to independent climate and wildfire datasets, and the resulting sensitivities were 0.75, 0.81, and 0.83 for Maxent, Generalized Linear, and Multivariate Adaptive Regression Splines, respectively. We projected the historic models into future climate space using data from 15 global circulation models and two representative concentration pathway scenarios. Maps from these geospatial analyses can be used to evaluate the changing spatial distribution of climate suitability of large wildfires in these states. April relative humidity was the most important covariate in all models, providing insight to the climate space of large wildfires in this region. These methods incorporate monthly and seasonal climate averages at a spatial resolution relevant to land management (i.e. 1 km2) and provide a tool that can be modified for other regions of North America, or adapted for other parts of the world.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-015-1553-5","usgsCitation":"West, A., Kumar, S., and Jarnevich, C.S., 2016, Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA: Climatic Change, v. 134, no. 4, p. 565-577, https://doi.org/10.1007/s10584-015-1553-5.","productDescription":"13 p.","startPage":"565","endPage":"577","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067316","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470838,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://link.springer.com/10.1007/s10584-015-1553-5","text":"External Repository"},{"id":324396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, 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Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"995","title":"Post-Hurricane Joaquin coastal oblique aerial photographs collected from the South Carolina/North Carolina border to Montauk Point, New York, October 7–9, 2015","docAbstract":"The U.S. Geological Survey (USGS), as part of the National Assessment of Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms (Morgan, 2009). On October 7–9, 2015, the USGS conducted an oblique aerial photographic survey of the coast from the South Carolina/North Carolina border to Montauk Point, New York (fig. 1), aboard a Cessna 182 (aircraft) at an altitude of 500 feet (ft) and approximately 1,200 ft offshore fig. 2. This mission was conducted to collect post-Hurricane Joaquin data for assessing incremental changes in the beach and nearshore area since the last surveys, mission flown in September 2014 (Virginia to New York: Morgan, 2015), November 2012 (northern North Carolina: Morgan and others, 2014) and May 2008 (southern North Carolina: unpublished report), and the data can be used to assess of future coastal change.
The photographs in this report are Joint Photographic Experts Group (JPEG) images. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. The photograph locations are an estimate of the position of the aircraft at the time the photograph was taken and do not indicate the location of any feature in the images (see the Navigation Data page). These photographs document the state of the barrier islands and other coastal features at the time of the survey. Pages containing thumbnail images of the photographs, referred to as contact sheets, were created in 5-minute segments of flight time. These segments can be found on the Photos and Maps page. Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet.
In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML file was created using the photographic navigation files. This KML file can be found in the kml folder.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds995","usgsCitation":"Morgan, K.L.M., 2016, Post-Hurricane Joaquin coastal oblique aerial photographs collected from the South Carolina/North Carolina border to Montauk Point, New York, October 7–9, 2015: U.S. Geological Survey Data Series 995, https://dx.doi.org/10.3133/ds995.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074292","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":321248,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0995"},{"id":324389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, New Jersey, New York, North Carolina, South Carolina, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.55224609374999,\n 33.7243396617476\n ],\n [\n -77.816162109375,\n 33.76088200086917\n ],\n [\n -76.431884765625,\n 34.58799745550482\n ],\n [\n -75.41015624999999,\n 35.21869749632885\n ],\n [\n -75.333251953125,\n 35.7286770448517\n ],\n [\n -75.860595703125,\n 36.923547681089296\n ],\n [\n -75.552978515625,\n 37.448696585910376\n ],\n [\n -74.87182617187499,\n 38.53097889440026\n ],\n [\n -74.70703125,\n 39.00211029922512\n ],\n [\n -74.014892578125,\n 39.63953756436671\n ],\n [\n -73.89404296875,\n 40.51379915504413\n ],\n [\n -72.960205078125,\n 40.60561205826018\n ],\n [\n -71.630859375,\n 41.062786068733026\n ],\n [\n -71.817626953125,\n 41.1455697310095\n ],\n [\n -73.10302734375,\n 40.85537053192496\n ],\n [\n -73.948974609375,\n 40.73893324113603\n ],\n [\n -74.1357421875,\n 40.55554790286311\n ],\n [\n -74.38842773437499,\n 39.7240885773337\n ],\n [\n -75.047607421875,\n 39.00211029922512\n ],\n [\n -75.234375,\n 38.47939467327645\n ],\n [\n -76.256103515625,\n 36.94111143010772\n ],\n [\n -75.684814453125,\n 35.68407153314097\n ],\n [\n -75.69580078125,\n 35.38904996691167\n ],\n [\n -76.57470703125,\n 34.92197103616377\n ],\n [\n -77.376708984375,\n 34.7506398050501\n ],\n [\n -78.046875,\n 34.23451236236984\n ],\n [\n -78.837890625,\n 33.916013113401696\n ],\n [\n -78.55224609374999,\n 33.7243396617476\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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600 4th Street South
St. Petersburg, FL 33701
(727) 502–8000
http://coastal.er.usgs.gov
Heterotrophic respiration (HR), the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes our ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic-specific global models is not imminent, we suggest that a useful step to improve this situation would be the development of “Decomposition Functional Types” (DFTs). Analogous to plant functional types (PFTs), DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing modelers and experimentalists to efficiently group and vary these characteristics across space and time. We argue that DFTs should be initially informed by top-down expert opinion, but ultimately developed using bottom-up, data-driven analyses, and provide specific examples of potential dependent and independent variables that could be used. We present an example clustering analysis to show how annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and demonstrate that these groups are distinct from (but complementary to) already-existing PFTs. A similar analysis incorporating observational data could form the basis for future DFTs. Finally, we suggest next steps and critical priorities: collection and synthesis of existing data; more in-depth analyses combining open data with rigorous testing of analytical results; using point measurements and realistic forcing variables to constrain process-based models; and planning by the global modeling community for decoupling decomposition from fixed site data. These are all critical steps to build a foundation for DFTs in global models, thus providing the ecological and climate change communities with robust, scalable estimates of HR.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1380","usgsCitation":"Bond-Lamberty, B., Epron, D., Harden, J.W., Harmon, M.E., Hoffman, F., Kumar, J., McGuire, A.D., and Vargas, R., 2016, Estimating heterotrophic respiration at large scales: Challenges, approaches, and next steps: Ecosphere, v. 7, no. 6, Article e01380; 13 p., https://doi.org/10.1002/ecs2.1380.","productDescription":"Article e01380; 13 p.","ipdsId":"IP-070883","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470841,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1380","text":"Publisher Index Page"},{"id":331662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-27","publicationStatus":"PW","scienceBaseUri":"58492df3e4b06d80b7b093a6","chorus":{"doi":"10.1002/ecs2.1380","url":"http://dx.doi.org/10.1002/ecs2.1380","publisher":"Wiley-Blackwell","authors":"Bond-Lamberty Ben, Epron Daniel, Harden Jennifer, Harmon Mark E., Hoffman Forrest, Kumar Jitendra, David McGuire Anthony, Vargas Rodrigo","journalName":"Ecosphere","publicationDate":"6/2016"},"contributors":{"authors":[{"text":"Bond-Lamberty, Ben","contributorId":172028,"corporation":false,"usgs":false,"family":"Bond-Lamberty","given":"Ben","email":"","affiliations":[{"id":33852,"text":"Univ of Maryland, College Park, MD","active":true,"usgs":false},{"id":13566,"text":"Joint Global Change Research Institute, Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":655178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Epron, Daniel","contributorId":177277,"corporation":false,"usgs":false,"family":"Epron","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":655179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harden, Jennifer W. 0000-0002-6570-8259 jharden@usgs.gov","orcid":"https://orcid.org/0000-0002-6570-8259","contributorId":1971,"corporation":false,"usgs":true,"family":"Harden","given":"Jennifer","email":"jharden@usgs.gov","middleInitial":"W.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":655180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harmon, Mark E.","contributorId":96961,"corporation":false,"usgs":true,"family":"Harmon","given":"Mark","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":655181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoffman, Forrest","contributorId":177278,"corporation":false,"usgs":false,"family":"Hoffman","given":"Forrest","email":"","affiliations":[],"preferred":false,"id":655132,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Jitendra","contributorId":177279,"corporation":false,"usgs":false,"family":"Kumar","given":"Jitendra","email":"","affiliations":[],"preferred":false,"id":655182,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGuire, Anthony D. 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":2493,"corporation":false,"usgs":true,"family":"McGuire","given":"Anthony","email":"ffadm@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":false,"id":655183,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vargas, Rodrigo","contributorId":172036,"corporation":false,"usgs":false,"family":"Vargas","given":"Rodrigo","affiliations":[],"preferred":false,"id":655184,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70174167,"text":"70174167 - 2016 - A comparison of three macroinvertebrate sampling devices for use in conducting rapid-assessment procedures of Delmarva Peninsula wetlands","interactions":[],"lastModifiedDate":"2018-08-10T10:03:33","indexId":"70174167","displayToPublicDate":"2016-06-27T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2898,"text":"Northeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of three macroinvertebrate sampling devices for use in conducting rapid-assessment procedures of Delmarva Peninsula wetlands","docAbstract":"Three types of macroinvertebrate collecting devices, Gerking box traps, D-shaped sweep nets, and activity traps, have commonly been used to sample macroinvertebrates when conducting rapid biological assessments of North American wetlands. We compared collections of macroinvertebrates identified to the family level made with these devices in 6 constructed and 2 natural wetlands on the Delmarva Peninsula of Maryland. We also assessed their potential efficacy in comparisons among wetlands using several proportional and richness attributes. Differences in median diversity among samples from the 3 devices were significant; the sweep-net samples had the greatest diversity and the activity-trap samples had the least diversity. Differences in median abundance were not significant between the Gerking box-trap samples and sweep-net samples, but median abundance among activity-trap samples was significantly lower than among samples of the other 2 devices. Within samples, the proportions of median diversity composed of major class and order groupings were similar among the 3 devices. However the proportions of median abundance composed of the major class and order groupings within activity-trap samples were not similar to those of the other 2 devices. There was a slight but significant increase in the total number of families captured when we combined activity-trap samples with Gerking box-trap samples or with sweep-net samples, and the per-sample median numbers of families of the combined activity-trap and sweep-net samples was significantly higher than that of the combined activity-trap and Gerking box-trap samples. We detected significant differences among wetlands for 4 macroinvertebrate attributes with the Gerking box-trap data, 6 attributes with sweep-net data, and 5 attributes with the activity-trap data. A small, but significant increase in the number of attributes showing differences among wetlands occurred when we combined activity-trap samples with those of the Gerking boxtrap or sweep net.
","language":"English","publisher":"Eagle Hill Institute","publisherLocation":"Steuben, ME","doi":"10.1656/045.023.0209","usgsCitation":"Lowe, T.P., Tebbs, K., and Sparling, D.W., 2016, A comparison of three macroinvertebrate sampling devices for use in conducting rapid-assessment procedures of Delmarva Peninsula wetlands: Northeastern Naturalist, v. 23, no. 2, p. 321-338, https://doi.org/10.1656/045.023.0209.","productDescription":"18 p.","startPage":"321","endPage":"338","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066463","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":324542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.629638671875,\n 37.04202441635081\n ],\n [\n -76.629638671875,\n 39.639537564366684\n ],\n [\n -74.9102783203125,\n 39.639537564366684\n ],\n [\n -74.9102783203125,\n 37.04202441635081\n ],\n [\n -76.629638671875,\n 37.04202441635081\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-21","publicationStatus":"PW","scienceBaseUri":"57739face4b07657d1a90c97","contributors":{"authors":[{"text":"Lowe, T. Peter plowe@usgs.gov","contributorId":172500,"corporation":false,"usgs":true,"family":"Lowe","given":"T.","email":"plowe@usgs.gov","middleInitial":"Peter","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":641016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tebbs, Kerry","contributorId":172511,"corporation":false,"usgs":false,"family":"Tebbs","given":"Kerry","email":"","affiliations":[],"preferred":false,"id":641082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sparling, Donald W.","contributorId":7220,"corporation":false,"usgs":true,"family":"Sparling","given":"Donald","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":641083,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70174046,"text":"fs20163037 - 2016 - Mapping water use—Landsat and water resources in the United States","interactions":[],"lastModifiedDate":"2019-09-20T10:50:09","indexId":"fs20163037","displayToPublicDate":"2016-06-27T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3037","displayTitle":"Mapping Water Use—Landsat and Water Resources in the United States","title":"Mapping water use—Landsat and water resources in the United States","docAbstract":"Using Landsat satellite data, scientists with the U.S. Geological Survey have helped to refine a technique called evapotranspiration mapping to measure how much water crops are using across landscapes and through time. These water-use maps are created using a computer model that integrates Landsat and weather data.
Crucial to the process is the thermal (infrared) band from Landsat. Using the Landsat thermal band with its 100-meter resolution, water-use maps can be created at a scale detailed enough to show how much water crops are using at the level of individual fields anywhere in the world.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163037","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration","usgsCitation":"U.S. Geological Survey, 2016, Mapping water use—Landsat and water resources in the United States (ver. 1.1, September 2019): U.S. Geological Survey Fact Sheet 2016–3037, 2 p., https://doi.org/10.3133/fs20163037.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075235","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":367504,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3037/fs20163037_2.pdf","text":"Report","size":"5.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3037"},{"id":324411,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3037/coverthb2.jpg"},{"id":367505,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2016/3037/versionHist.txt","size":"1.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"}],"edition":"Version 1.0: June 27, 2016; Version 1.1 September 18, 2019","contact":"Director, Earth Resources Observation and Science (EROS) Center
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Spectacled eider Somateria fischeri numbers have declined and they are considered threatened in accordance with the US Endangered Species Act throughout their range. We synthesized the available information for spectacled eiders to construct deterministic, stochastic, and metapopulation models for this species that incorporated current estimates of vital rates such as nest success, adult survival, and the impact of lead poisoning on survival. Elasticities of our deterministic models suggested that the populations would respond most dramatically to changes in adult female survival and that the reductions in adult female survival related to lead poisoning were locally important. We also examined the sensitivity of the population to changes in lead exposure rates. With the knowledge that some vital rates vary with environmental conditions, we cast stochastic models that mimicked observed variation in productivity. We also used the stochastic model to examine the probability that a specific population will persist for periods of up to 50 y. Elasticity analysis of these models was consistent with that for the deterministic models, with perturbations to adult female survival having the greatest effect on population projections. When used in single population models, demographic data for some localities predicted rapid declines that were inconsistent with our observations in the field. Thus, we constructed a metapopulation model and examined the predictions for local subpopulations and the metapopulation over a wide range of dispersal rates. Using the metapopulation model, we were able to simulate the observed stability of local subpopulations as well as that of the metapopulation. Finally, we developed a global metapopulation model that simulates periodic winter habitat limitation, similar to that which might be experienced in years of heavy sea ice in the core wintering area of spectacled eiders in the central Bering Sea. Our metapopulation analyses suggested that no subpopulation is independent and that future management actions may be improved through a metapopulation framework. For example, management actions could include displacement of breeding females from\"sink\" areas that reduce the growth potential of the population as a whole. However, this action is contingent upon dispersal among local populations, for which there is limited information. Thus, we recommend that researchers examine dispersal behavior among areas on the Yukon-Kuskokwim Delta in western Alaska. The metapopulation framework could also be applied at the rangewide scale to address the density-dependent limitation of available polynya habitat during winter that may limit the recovery of small subpopulations, such as that on the Yukon-Kuskokwim Delta. Reductions in other subpopulations may be necessary to ensure an increase in the Yukon-Kuskokwim Delta population. Thus, we recommend that managers consider the interpopulation dynamics of spectacled eiders at different spatial scales in future management actions.
","language":"English","doi":"10.3996/nafa.81.0001","usgsCitation":"Flint, P.L., Grand, J.B., Petersen, M.R., and Rockwell, R.F., 2016, Effects of Lead Exposure, Environmental Conditions, and Metapopulation Processes on Population Dynamics of Spectacled Eiders.: North American Fauna, v. 81, p. 1-41, https://doi.org/10.3996/nafa.81.0001.","productDescription":"iii, 44 p.","startPage":"1","endPage":"41","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057251","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":470842,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/nafa.81.0001","text":"Publisher Index Page"},{"id":438607,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74B2ZCK","text":"USGS data release","linkHelpText":"Spectacled Eider (Somateria fischeri) Nest, Capture, and Resight Records Yukon-Kuskokwim Delta, Alaska"},{"id":324910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","state":"Alaska","otherGeospatial":"Bering Sea, Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.85546875,\n 59.80063426102869\n ],\n [\n -145.283203125,\n 60.02095215374802\n ],\n [\n -146.25,\n 60.02095215374802\n ],\n [\n -147.744140625,\n 59.57885104663186\n ],\n [\n -150.205078125,\n 59.0405546167585\n ],\n [\n -151.435546875,\n 57.32652122521709\n ],\n [\n -153.45703125,\n 56.26776108757582\n ],\n [\n -163.65234374999997,\n 52.482780222078205\n ],\n [\n -177.36328125,\n 50.064191736659104\n ],\n [\n -186.15234374999997,\n 50.12057809796008\n ],\n [\n -195.64453125,\n 54.67383096593114\n ],\n [\n -196.5234375,\n 57.938183012205315\n ],\n [\n -202.060546875,\n 62.471723714758724\n ],\n [\n -222.626953125,\n 72.18180355624855\n ],\n [\n -228.69140625,\n 75.67219739055291\n ],\n [\n -228.603515625,\n 76.51681887717322\n ],\n [\n -219.375,\n 77.13761179723426\n ],\n [\n -210.76171875,\n 77.19617635994676\n ],\n [\n -195.64453125,\n 77.2156395545647\n ],\n [\n -179.912109375,\n 76.78065491639973\n ],\n [\n -162.0703125,\n 75.47513069090051\n ],\n [\n -149.23828125,\n 74.18805166460048\n ],\n [\n -140.888671875,\n 69.7485511291223\n ],\n [\n -141.85546875,\n 59.80063426102869\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-22","publicationStatus":"PW","scienceBaseUri":"5780ceb6e4b0811616822315","contributors":{"authors":[{"text":"Flint, Paul L. 0000-0002-8758-6993 pflint@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-6993","contributorId":3284,"corporation":false,"usgs":true,"family":"Flint","given":"Paul","email":"pflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":641925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grand, J. Barry 0000-0002-3576-4567 barry_grand@usgs.gov","orcid":"https://orcid.org/0000-0002-3576-4567","contributorId":579,"corporation":false,"usgs":true,"family":"Grand","given":"J.","email":"barry_grand@usgs.gov","middleInitial":"Barry","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":641926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Petersen, Margaret R. 0000-0001-6082-3189 mrpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-6082-3189","contributorId":167729,"corporation":false,"usgs":true,"family":"Petersen","given":"Margaret","email":"mrpetersen@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":641927,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rockwell, Robert F.","contributorId":172752,"corporation":false,"usgs":false,"family":"Rockwell","given":"Robert","email":"","middleInitial":"F.","affiliations":[{"id":6989,"text":"American Museum of Natural History","active":true,"usgs":false}],"preferred":false,"id":641928,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70175641,"text":"70175641 - 2016 - Elucidation of taste- and odor-producing bacteria and toxigenic cyanobacteria in a Midwestern drinking water supply reservoir by shotgun metagenomics analysis","interactions":[],"lastModifiedDate":"2016-08-18T09:29:57","indexId":"70175641","displayToPublicDate":"2016-06-24T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":850,"text":"Applied and Environmental Microbiology","active":true,"publicationSubtype":{"id":10}},"title":"Elucidation of taste- and odor-producing bacteria and toxigenic cyanobacteria in a Midwestern drinking water supply reservoir by shotgun metagenomics analysis","docAbstract":"While commonplace in clinical settings, DNA-based assays for identification or enumeration of drinking water pathogens and other biological contaminants remain widely unadopted by the monitoring community. In this study, shotgun metagenomics was used to identify taste-and-odor producers and toxin-producing cyanobacteria over a 2-year period in a drinking water reservoir. The sequencing data implicated several cyanobacteria, including Anabaena spp.,Microcystis spp., and an unresolved member of the order Oscillatoriales as the likely principal producers of geosmin, microcystin, and 2-methylisoborneol (MIB), respectively. To further demonstrate this, quantitative PCR (qPCR) assays targeting geosmin-producing Anabaena and microcystin-producing Microcystis were utilized, and these data were fitted using generalized linear models and compared with routine monitoring data, including microscopic cell counts, sonde-based physicochemical analyses, and assays of all inorganic and organic nitrogen and phosphorus forms and fractions. The qPCR assays explained the greatest variation in observed geosmin (adjusted R2 = 0.71) and microcystin (adjusted R2 = 0.84) concentrations over the study period, highlighting their potential for routine monitoring applications. The origin of the monoterpene cyclase required for MIB biosynthesis was putatively linked to a periphytic cyanobacterial mat attached to the concrete drinking water inflow structure. We conclude that shotgun metagenomics can be used to identify microbial agents involved in water quality deterioration and to guide PCR assay selection or design for routine monitoring purposes. Finally, we offer estimates of microbial diversity and metagenomic coverage of our data sets for reference to others wishing to apply shotgun metagenomics to other lacustrine systems.
","language":"English","publisher":"American Society for Microbiology","doi":"10.1128/AEM.01334-16","usgsCitation":"Otten, T., Graham, J., Harris, T.D., and Dreher, T., 2016, Elucidation of taste- and odor-producing bacteria and toxigenic cyanobacteria in a Midwestern drinking water supply reservoir by shotgun metagenomics analysis: Applied and Environmental Microbiology, v. 82, no. 17, p. 5410-5420, https://doi.org/10.1128/AEM.01334-16.","productDescription":"10 p.","startPage":"5410","endPage":"5420","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070537","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":470846,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1128/aem.01334-16","text":"External Repository"},{"id":326787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Cheney Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.89505004882811,\n 37.82280243352756\n ],\n [\n -97.8823471069336,\n 37.80680022788906\n ],\n [\n -97.87788391113281,\n 37.804087645887726\n ],\n [\n -97.86449432373047,\n 37.80381638220771\n ],\n [\n -97.8610610961914,\n 37.79893346559687\n ],\n [\n -97.85385131835938,\n 37.78916666399649\n ],\n [\n -97.84320831298828,\n 37.7831974274346\n ],\n [\n -97.84492492675781,\n 37.790794553924414\n ],\n [\n -97.8226089477539,\n 37.790523241426946\n ],\n [\n -97.80303955078125,\n 37.7897092979573\n ],\n [\n -97.79239654541016,\n 37.78726741375342\n ],\n [\n -97.78999328613281,\n 37.765286825037926\n ],\n [\n -97.789306640625,\n 37.75904423204625\n ],\n [\n -97.76596069335938,\n 37.756872771856465\n ],\n [\n -97.76184082031249,\n 37.755244134885736\n ],\n [\n -97.7676773071289,\n 37.74031329210266\n ],\n [\n -97.7783203125,\n 37.73461163021697\n ],\n [\n -97.78724670410156,\n 37.72863798965106\n ],\n [\n -97.81917572021484,\n 37.71478815132924\n ],\n [\n -97.82947540283203,\n 37.71478815132924\n ],\n [\n -97.84217834472656,\n 37.71804716978354\n ],\n [\n -97.84767150878905,\n 37.7280949075216\n ],\n [\n -97.84767150878905,\n 37.73678374366597\n ],\n [\n -97.86312103271483,\n 37.75415835698726\n ],\n [\n -97.8775405883789,\n 37.75850137297435\n ],\n [\n -97.89093017578125,\n 37.77532815168286\n ],\n [\n -97.8885269165039,\n 37.78021262835114\n ],\n [\n -97.89230346679688,\n 37.7916084854395\n ],\n [\n -97.90328979492188,\n 37.81168262440736\n ],\n [\n -97.89505004882811,\n 37.82280243352756\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","issue":"17","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57b6dc56e4b03fd6b7d94c35","contributors":{"authors":[{"text":"Otten, Timothy","contributorId":173804,"corporation":false,"usgs":false,"family":"Otten","given":"Timothy","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":645909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":150737,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer L.","email":"jlgraham@usgs.gov","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":645908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harris, Theodore D. 0000-0003-0944-8007 tdharris@usgs.gov","orcid":"https://orcid.org/0000-0003-0944-8007","contributorId":4040,"corporation":false,"usgs":true,"family":"Harris","given":"Theodore","email":"tdharris@usgs.gov","middleInitial":"D.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":645910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dreher, Theo","contributorId":173805,"corporation":false,"usgs":false,"family":"Dreher","given":"Theo","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":645911,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168671,"text":"ofr20161015 - 2016 - Relation between Enterococcus concentrations and turbidity in fresh and saline recreational waters, coastal Horry County, South Carolina, 2003–04","interactions":[],"lastModifiedDate":"2016-12-08T17:14:53","indexId":"ofr20161015","displayToPublicDate":"2016-06-24T10:45:00","publicationYear":"2016","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":"2016-1015","title":"Relation between Enterococcus concentrations and turbidity in fresh and saline recreational waters, coastal Horry County, South Carolina, 2003–04","docAbstract":"Bacteria related to the intestinal tract of humans and other warm-blooded animals have been detected in fresh and saline surface waters used for recreational purposes in coastal areas of Horry County, South Carolina, since the early 2000s. Specifically, concentrations of the facultative anaerobic organism, Enterococcus, have been observed to exceed the single-sample regulatory limit of 104 colony forming units per 100 milliliters of water. Water bodies characterized by these concentrations are identified on the 303(d) list for impaired water in South Carolina; moreover, because current analytical methods used to monitor Enterococcus concentrations take up to 1 day for results to become available, water-quality advisories are not reflective of the actual health risk.
\nTo determine if Enterococcus concentrations in surface water could be assessed in a more rapid manner, an investigation was completed between 2003 and 2004 in the study area of coastal Horry County, South Carolina. The study was designed to assess the relation between Enterococcus concentrations and turbidity, which, unlike Enterococcus concentrations, can be measured continuously by using a multiparameter water-quality sensor and results reported in real time. In 2003, three water-quality data collection stations that included a multiparameter water-quality sensor that measured turbidity were located in three representative surface-water basins in coastal Horry County, South Carolina. All these locations had previous reports of high Enterococcus concentrations. At each station, the water-quality sensor was placed in the water column and continuously measured turbidity, pH, specific conductivity, dissolved oxygen, and temperature. Each water-quality data collection station also monitored instantaneous precipitation and wind speed and direction. Surface-water samples were collected at each station during events characterized by no precipitation and by some recorded precipitation using manual and automatic methods, and analyzed for Enterococcus concentrations. A comparison of Enterococcus concentrations in surface-water samples collected simultaneously using both methods indicated a positive relation, although the average percent relative difference between the methods was 46 percent.
\nDuring a period of no precipitation in February 2004, no relation between turbidity and Enterococcus concentrations was observed for surface-water samples collected at the water-quality data collection station located in the channel that drains a freshwater swamp. In contrast, during periods of precipitation in March and August 2004 at this location, a positive relation was observed between turbidity and Enterococcus concentrations in surface-water samples; that is, water samples characterized by higher turbidity also contained higher Enterococcus concentrations. At the water-quality data collection station located in a channel that drains to the surf zone of the Atlantic Ocean, no relation was observed between turbidity and Enterococcus concentrations during periods of either no precipitation (July 2004) or precipitation (August 2004). At this location, the turbidity was inversely related to relative tide height, high turbidity was observed during low tide when freshwater flowed seaward, and low turbidity was observed during high tide when saline seawater flowed landward.
\nThe positive relation observed between turbidity and Enterococcus concentrations in surface water at the water-quality data collection station located in the channel that drains a freshwater swamp may be attributed to bacterial survival in the abundant channel bed sediments that characterized this more naturalized area. Surface-water bed sediments collected near each water-quality data collection station and the surf zone were incubated in static microcosms in the laboratory and analyzed for Enterococcus concentrations over time. Enterococcus concentrations continued to persist in bed sediments collected in the channel that drains the swamp even after almost 4 months of incubation. Conversely, enterococci were not observed to persist in bed sediments characterized by high specific conductance. Although it is currently (2016) unknown whether this persistence of enterococci demonstrates growth or viability, the data indicate that enterococci can exist in channel bed-sediment environments outside of a host for a long time. This observation confirms previous reports that challenge the use of Enterococcus concentrations as an indicator of the recent introduction of fecal-related material and the associated acute risk to other pathogens.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20161015","collaboration":"Prepared in cooperation with Horry County Stormwater Management ","usgsCitation":"Landmeyer, J.E., and Garigen, T.J., 2016, Relation between Enterococcus concentrations and turbidity in fresh and saline recreational waters, coastal Horry County, South Carolina, 2003–04: U.S. Geological Survey Open-File Report 2016–1015, 21 p., https://dx.doi.org/10.3133/ofr20161015.","productDescription":"Report: viii, 21 p., Appendixes: Tables 1-1, 1-2, 1-3","startPage":"1","endPage":"21","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064657","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":324066,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1015/ofr20161015.pdf","text":"Report","size":"12.3 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South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
(678) 924–6700
https://www.usgs.gov/water/southatlantic/
This report describes the analytical methods and results of a pilot study to enhance the Ohio StreamStats application by adding the ability to obtain water-use information for selected areas in the northeast quadrant of Ohio. Water-use estimates are determined in StreamStats through a simple multistep process.
\nWater-use data used to develop the Ohio StreamStats water-use application were obtained from the Ohio Department of Natural Resources (ODNR) and 2010 countywide estimates of self-supplied domestic water use (hereafter referred to as “domestic water use”) compiled by the U.S. Geological Survey (USGS). With the exception of domestic water uses, monthly time series of reported water uses for 2005–2012 are used to calculate average monthly and average annual withdrawals. Domestic water use is estimated from the USGS 2010 countywide estimates, assuming that water use is distributed uniformly in space and time. Consumptive-use coefficients are used to estimate net withdrawals and facilitate computation of return flows.
\nTemporary water-use registrations for hydraulic fracturing are tabulated separately from the other water uses. Water-use indices are computed by dividing average annual net withdrawals (with and without temporary registrations) by the mean October streamflow estimated with StreamStats. The water-use indices are intended to provide metrics of potential consumptive water use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165077","collaboration":"Prepared in cooperation with the Ohio Water Development Authority and the Muskingum Watershed Conservancy District","usgsCitation":"Koltun, G.F., Nardi, M.R., and Shaffer, K.H., 2016, Estimation of upstream water use with Ohio’s StreamStats application: U.S. Geological Survey Scientific Investigations Report 2016–5077, 13 p., https://dx.doi.org/10.3133/sir20165077.","productDescription":"iv, 12 p.","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-072219","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":324337,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5077/sir20165077.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5077"},{"id":324336,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5077/coverthb.jpg"}],"contact":"Director, Ohio Water Science Center
U.S. Geological Survey
6480 Doubletree Ave
Columbus, OH 43229–1111
http://oh.water.usgs.gov/
European mouflon sheep (Ovis gmelini musimon) were introduced to Kahuku Ranch on Hawai‘i Island in 1968 and 1974 for trophy hunting and have been detrimental to the native ecosystem by trampling, bark stripping, and browsing vegetation. In 2003, Hawai‘i Volcanoes National Park acquired Kahuku Ranch and managers began removing mouflon. The objective of this project was to determine whether hunting has changed the distribution of mouflon in Kahuku, to better understand mouflon behaviour and to expedite eradication efforts. Locations from hunting and GPS telemetry data during 2007–14 were used to determine the effect of hunting on mouflon distribution by examining distance to roads and habitat use. Mouflon seemed to avoid roads after hunting pressure increased and their distribution within vegetation types changed over time. Mouflon without hunting pressure were detected in native shrub habitat in 68% of all observations. Hunted mouflon were encountered less in native shrub habitat and more in other habitats including open forest, closed forest, and areas with no vegetation. These changes suggest that hunting has influenced the distribution of mouflon over time away from native shrub and into other vegetation types where they may be more difficult to control.
","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/PC15039","usgsCitation":"Palupe, B., Leopold, C.R., Hess, S.C., Faford, J., Pacheco, D., and Judge, S.W., 2016, Changes in habitat use and distribution of mouflon in the Kahuku Unit of Hawai‘i Volcanoes National Park: Pacific Conservation Biology, v. 22, no. 4, p. 308-311, https://doi.org/10.1071/PC15039.","productDescription":"4 p.","startPage":"308","endPage":"311","ipdsId":"IP-075091","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":331660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai‘i","otherGeospatial":"Hawai‘i Volcanoes National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.52108764648438,\n 19.129599439736836\n ],\n [\n -155.52108764648438,\n 19.476950206488414\n ],\n [\n -154.97726440429688,\n 19.476950206488414\n ],\n [\n -154.97726440429688,\n 19.129599439736836\n ],\n [\n -155.52108764648438,\n 19.129599439736836\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58492df3e4b06d80b7b093a8","contributors":{"authors":[{"text":"Palupe, Bronson","contributorId":177210,"corporation":false,"usgs":false,"family":"Palupe","given":"Bronson","email":"","affiliations":[],"preferred":false,"id":655074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leopold, Christina R.","contributorId":46817,"corporation":false,"usgs":true,"family":"Leopold","given":"Christina","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":655075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hess, Steven C. 0000-0001-6403-9922 shess@usgs.gov","orcid":"https://orcid.org/0000-0001-6403-9922","contributorId":3156,"corporation":false,"usgs":true,"family":"Hess","given":"Steven","email":"shess@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":false,"id":655076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faford, Jonathan K.","contributorId":177221,"corporation":false,"usgs":false,"family":"Faford","given":"Jonathan K.","affiliations":[],"preferred":false,"id":655077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pacheco, Dexter","contributorId":156310,"corporation":false,"usgs":false,"family":"Pacheco","given":"Dexter","email":"","affiliations":[{"id":20307,"text":"US National Park Service","active":true,"usgs":false}],"preferred":false,"id":655078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Judge, Seth W.","contributorId":8718,"corporation":false,"usgs":true,"family":"Judge","given":"Seth","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":655079,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70174035,"text":"70174035 - 2016 - Nutrient delivery to Lake Winnipeg from the Red-Assiniboine River Basin – A binational application of the SPARROW model","interactions":[],"lastModifiedDate":"2016-08-19T10:08:23","indexId":"70174035","displayToPublicDate":"2016-06-23T17:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1180,"text":"Canadian Water Resources Journal","active":true,"publicationSubtype":{"id":10}},"title":"Nutrient delivery to Lake Winnipeg from the Red-Assiniboine River Basin – A binational application of the SPARROW model","docAbstract":"Excessive phosphorus (TP) and nitrogen (TN) inputs from the Red–Assiniboine River Basin (RARB) have been linked to eutrophication of Lake Winnipeg; therefore, it is important for the management of water resources to understand where and from what sources these nutrients originate. The RARB straddles the Canada–United States border and includes portions of two provinces and three states. This study represents the first binationally focused application of SPAtially Referenced Regressions on Watershed attributes (SPARROW) models to estimate loads and sources of TP and TN by jurisdiction and basin at multiple spatial scales. Major hurdles overcome to develop these models included: (1) harmonization of geospatial data sets, particularly construction of a contiguous stream network; and (2) use of novel calibration steps to accommodate limitations in spatial variability across the model extent and in the number of calibration sites. Using nutrient inputs for a 2002 base year, a RARB TP SPARROW model was calibrated that included inputs from agriculture, forests and wetlands, wastewater treatment plants (WWTPs) and stream channels, and a TN model was calibrated that included inputs from agriculture, WWTPs and atmospheric deposition. At the RARB outlet, downstream from Winnipeg, Manitoba, the majority of the delivered TP and TN came from the Red River Basin (90%), followed by the Upper Assiniboine River and Souris River basins. Agriculture was the single most important TP and TN source for each major basin, province and state. In general, stream channels (historically deposited nutrients and from bank erosion) were the second most important source of TP. Performance metrics for the RARB SPARROW model are similarly robust compared to other, larger US SPARROW models making it a potentially useful tool to address questions of where nutrients originate and their relative contributions to loads delivered to Lake Winnipeg.
","language":"English","publisher":"Taylor and Francis Group","doi":"10.1080/07011784.2016.1178601","collaboration":"International Joint Commission","usgsCitation":"Benoy, G.A., Jenkinson, R., Robertson, D.M., and Saad, D.A., 2016, Nutrient delivery to Lake Winnipeg from the Red-Assiniboine River Basin – A binational application of the SPARROW model: Canadian Water Resources Journal, v. 41, no. 3, p. 429-447, https://doi.org/10.1080/07011784.2016.1178601.","productDescription":"19 p.","startPage":"429","endPage":"447","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068514","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":324322,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":324318,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1080/07011784.2016.1178601"}],"country":"Canada, United States","state":"Manitoba, Minnesota, Saskatchewan","otherGeospatial":"Assiniboine River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.152099609375,\n 50.064191736659104\n ],\n [\n -98.009033203125,\n 50.2612538275847\n ],\n [\n -99.47021484375,\n 50.162824333817284\n ],\n [\n -102.469482421875,\n 51.248163159055906\n ],\n [\n -104.8974609375,\n 50.597186230587035\n ],\n [\n -103.853759765625,\n 49.1888842152458\n ],\n [\n -101.898193359375,\n 48.04870994288686\n ],\n [\n -99.41528320312499,\n 48.268569112964336\n ],\n [\n -97.152099609375,\n 50.064191736659104\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-20","publicationStatus":"PW","scienceBaseUri":"576cfa1de4b07657d1a33c62","contributors":{"authors":[{"text":"Benoy, Glenn A. 0000-0001-6530-7220","orcid":"https://orcid.org/0000-0001-6530-7220","contributorId":172405,"corporation":false,"usgs":false,"family":"Benoy","given":"Glenn","email":"","middleInitial":"A.","affiliations":[{"id":13361,"text":"International Joint Commission, Washington DC","active":true,"usgs":false}],"preferred":false,"id":640604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenkinson, R. Wayne","contributorId":172406,"corporation":false,"usgs":false,"family":"Jenkinson","given":"R. Wayne","affiliations":[{"id":13361,"text":"International Joint Commission, Washington DC","active":true,"usgs":false}],"preferred":false,"id":640605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robertson, Dale M. 0000-0001-6799-0596 dzrobert@usgs.gov","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":150760,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"dzrobert@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":640606,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saad, David A. dasaad@usgs.gov","contributorId":121,"corporation":false,"usgs":true,"family":"Saad","given":"David","email":"dasaad@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":640607,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170435,"text":"ds993 - 2016 - Evaluating the potential effects of hurricanes on long-term sediment accumulation in two micro-tidal sub-estuaries: Barnegat Bay and Little Egg Harbor, New Jersey, U.S.A.","interactions":[],"lastModifiedDate":"2025-05-13T16:49:14.127904","indexId":"ds993","displayToPublicDate":"2016-06-23T11:00:00","publicationYear":"2016","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":"993","title":"Evaluating the potential effects of hurricanes on long-term sediment accumulation in two micro-tidal sub-estuaries: Barnegat Bay and Little Egg Harbor, New Jersey, U.S.A.","docAbstract":"Barnegat Bay, located along the eastern shore of New Jersey, was significantly impacted by Hurricane Sandy in October 2012. Scientists from the U.S. Geological Survey (USGS) developed a multidisciplinary study of sediment transport and hydrodynamics to understand the mechanisms that govern estuarine and wetland responses to storm forcing. This report details the physical and chemical characteristics of surficial and downcore sediments from two areas within the bay. Eleven sites were sampled in both the central portion of the bay near Barnegat Inlet and in the southern portion of the bay in Little Egg Harbor. Laboratory analyses include Be-7, Pb-210, bulk density, porosity, x-radiographs, and grain-size distribution. These data will serve as a critical baseline dataset for understanding the current sedimentological regime and can be applied to future storms for understanding estuarine and wetland evolution.
\nThis report serves as an archive for sedimentological and radiochemical data derived from the surface sediments and box cores. Downloadable data are available as Excel spreadsheets, PDF files, and JPEG files, and include sediment core data plots and x-radiographs, as well as physical-properties, grain-size, alpha-spectroscopy, and gamma-spectroscopy data. Federal Geographic Data Committee metadata are available for analytical datasets in the data downloads page of this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds993","usgsCitation":"Marot, M.E., Smith, C.G., Ellis, A.M., and Wheaton, C.J., 2016, Evaluating the potential effects of hurricanes on long-term sediment accumulation in two micro-tidal sub-estuaries—Barnegat Bay and Little Egg Harbor, New Jersey, U.S.A.: U.S. Geological Survey Data Series 993, https://dx.doi.org/10.3133/ds993.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069912","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":322039,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0993","text":"Report HTML"},{"id":324282,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Barnegat Bay, Little Egg Harbor","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.08767700195312,\n 39.832795514765444\n ],\n [\n -74.16183471679688,\n 39.8480851287529\n ],\n [\n -74.35890197753906,\n 39.562823814514026\n ],\n [\n -74.27375793457031,\n 39.51516565173217\n ],\n [\n -74.19960021972656,\n 39.600397716215824\n ],\n [\n -74.13642883300781,\n 39.68922382844214\n ],\n [\n -74.08973693847656,\n 39.76210275375137\n ],\n [\n -74.08767700195312,\n 39.832795514765444\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
(727) 502-8000
http://coastal.er.usgs.gov/
The Texas Department of State Health Services issued fish consumption advisories in 2003 and 2010 for Leon Creek in San Antonio, Texas, based on elevated concentrations of polychlorinated biphenyls (PCBs) in fish tissues. The U.S. Geological Survey (USGS) measured elevated PCB concentrations in stream-sediment samples collected during 2007–9 from Leon Creek at Lackland Air Force Base (now known as Joint Base San Antonio-Lackland; the sampling site at this base is hereinafter referred to as the “Joint Base site”) and sites on Leon Creek downstream from the base. This report describes the occurrence and concentrations of selected trace elements and halogenated organic compounds (pesticides, flame retardants, and PCBs) and potential sources of PCBs in stream-sediment samples collected from four sites on Leon Creek during 2012–14. In downstream order, sediment samples were collected from Leon Creek at northwest Interstate Highway 410 (Loop 410), Rodriguez Park, Morey Road, and Joint Base. The USGS periodically collected streambed-sediment samples during low flow and suspended-sediment samples during high flow.
\nTrace element concentrations were low compared to the consensus-based sediment-quality guidelines (SQGs) for the threshold effect concentration (TEC) and probable effect concentration (PEC). Adverse effects to benthic biota are not expected at concentrations less than the TEC and are expected at concentrations greater than the PEC. No trace element concentrations were greater than the PEC in any of the samples. Trace element concentrations were greatest at the Morey Road and Joint Base sites and exceeded the TECs by 41 and 27 percent, respectively. Trace element concentrations were lowest at the Rodriguez Park and Loop 410 sites and exceeded the TECs by 18 and 14 percent, respectively.
\nPesticides that have been banned for several decades are commonly detected in Leon Creek stream sediments, particularly the chlordane compounds. Chlordane compounds were detected in 84 percent of the samples and at every sample collection site. The samples collected from the Rodriguez Park site had the most pesticide compounds detected. Only samples collected from the Joint Base site had dichlorodiphenyldichloroethane (DDD), dichlorodiphenyldichloroethylene (DDE), or dichlorodiphenyltrichloroethane (DDT) concentrations greater than the TEC, and a few were also greater than the PEC.
\nFlame retardants were found at every site on Leon Creek where stream sediments were collected; however, a few compounds were frequently detected in the laboratory reagent blanks so their detections in the environmental samples may not be from local sources. Consensus-based SQGs were not available for flame retardants so samples were compared to Environment Canada Federal Environmental Quality Guidelines (FEQGs). The concentrations of flame retardants generally were greater in the suspended-sediment samples than the streambed-sediment samples and greater than the FEQGs in many cases.
\nEighteen PCB congeners were quantified in the sediment samples collected from Leon Creek. The samples collected from the Joint Base site had the most frequent PCB congener detections. Total PCB concentrations, computed as the sum of the 18 congeners by using the Kaplan-Meier method for left-censored environmental data, were much smaller than the TEC of 59.8 micrograms per kilogram (μg/kg). When detected, the concentrations of total PCBs in the stream-sediment samples collected from Leon Creek during 2012–14 ranged from an estimated 0.2 to 8.7 μg/kg.
\nSediment samples collected from Leon Creek by the USGS during 2007–9 and 2012–14 at a total of eight sites following identical field and laboratory methods were evaluated to determine if potential PCB sources could be identified. Total PCB concentrations in the sediment samples collected upstream from the Joint Base site were low or nondetections; while concentrations in the samples collected on and downstream from the Joint Base site were greater. Congeners 180 and 138 constituted the greatest proportion of the PCB mixture in samples collected upstream from, on, and downstream from the Joint Base site. Upstream from the Joint Base site, congeners 180 and 138 constituted 50 percent and 35 percent respectively of the PCBs congeners found in the samples. On and downstream from the Joint Base site, congeners 180 and 138 constituted 80 percent and 13 percent respectively of the PCBs congeners found in the samples. Chi-square (C2) tests also indicate that samples collected from the Loop 410 site were statistically different from samples collected from the Joint Base site and sites downstream. The PCB congener pattern in the Leon Creek samples is most like the congener mixture in Aroclor 1260, which is chemically similar to the PCBs detected in the fish samples that resulted in the 2003 fish consumption advisory.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165039","collaboration":"Prepared in cooperation with the San Antonio River Authority","usgsCitation":"Wilson, J.T., 2016, Occurrence and concentrations of selected trace elements and halogenated organic compounds in stream sediments and potential sources of polychlorinated biphenyls, Leon Creek, San Antonio, Texas, 2012–14: U.S. Geological Survey Scientific Investigations Report 2016–5039, 99 p., https://dx.doi.org/10.3133/sir20165039.","productDescription":"viii, 99 p.","startPage":"1","endPage":"99","numberOfPages":"111","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069499","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":324297,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5039/sir20165039.pdf","text":"Report","size":"1.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5039"},{"id":324296,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5039/coverthb.jpg"}],"country":"United States","state":"Texas","city":"San Antonio","otherGeospatial":"Leon Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.60401153564452,\n 29.2324852813013\n ],\n [\n -98.60401153564452,\n 29.354349397730857\n ],\n [\n -98.5089111328125,\n 29.354349397730857\n ],\n [\n -98.5089111328125,\n 29.2324852813013\n ],\n [\n -98.60401153564452,\n 29.2324852813013\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Despite progress in the implementation of conservation practices, related improvements in water quality have been challenging to measure in larger river systems. In this paper we quantify these downstream effects by applying the empirical U.S. Geological Survey water-quality model SPARROW to investigate whether spatial differences in conservation intensity were statistically correlated with variations in nutrient loads. In contrast to other forms of water quality data analysis, the application of SPARROW controls for confounding factors such as hydrologic variability, multiple sources and environmental processes. A measure of conservation intensity was derived from the USDA-CEAP regional assessment of the Upper Mississippi River and used as an explanatory variable in a model of the Upper Midwest. The spatial pattern of conservation intensity was negatively correlated (p = 0.003) with the total nitrogen loads in streams in the basin. Total phosphorus loads were weakly negatively correlated with conservation (p = 0.25). Regional nitrogen reductions were estimated to range from 5 to 34% and phosphorus reductions from 1 to 10% in major river basins of the Upper Mississippi region. The statistical associations between conservation and nutrient loads are consistent with hydrological and biogeochemical processes such as denitrification. The results provide empirical evidence at the regional scale that conservation practices have had a larger statistically detectable effect on nitrogen than on phosphorus loadings in streams and rivers of the Upper Mississippi Basin.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.5b03543","usgsCitation":"Garcia, A.M., Alexander, R.B., Arnold, J.G., Norfleet, L., White, M.J., Robertson, D.M., and Schwarz, G., 2016, Regional effects of agricultural conservation practices on nutrient transport in the Upper Mississippi River Basin: Environmental Science & Technology, v. 50, no. 13, p. 6991-7000, https://doi.org/10.1021/acs.est.5b03543.","productDescription":"10 p.","startPage":"6991","endPage":"7000","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067273","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":470859,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.5b03543","text":"Publisher Index Page"},{"id":324247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"13","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-21","publicationStatus":"PW","scienceBaseUri":"576ba89fe4b07657d1a17688","chorus":{"doi":"10.1021/acs.est.5b03543","url":"http://dx.doi.org/10.1021/acs.est.5b03543","publisher":"American Chemical Society (ACS)","authors":"García Ana María, Alexander Richard B., Arnold Jeffrey G., Norfleet Lee, White Michael J., Robertson Dale M., Schwarz Gregory","journalName":"Environmental Science & Technology","publicationDate":"7/5/2016","auditedOn":"6/23/2016","publiclyAccessibleDate":"6/21/2016"},"contributors":{"authors":[{"text":"Garcia, Ana Maria 0000-0002-5388-1281 agarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-5388-1281","contributorId":2035,"corporation":false,"usgs":true,"family":"Garcia","given":"Ana","email":"agarcia@usgs.gov","middleInitial":"Maria","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":640414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alexander, Richard B. 0000-0001-9166-0626 ralex@usgs.gov","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":541,"corporation":false,"usgs":true,"family":"Alexander","given":"Richard","email":"ralex@usgs.gov","middleInitial":"B.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":640415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arnold, Jeffrey G.","contributorId":172345,"corporation":false,"usgs":false,"family":"Arnold","given":"Jeffrey","email":"","middleInitial":"G.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":640417,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norfleet, Lee","contributorId":172346,"corporation":false,"usgs":false,"family":"Norfleet","given":"Lee","email":"","affiliations":[{"id":24598,"text":"USDA-NRCS retired","active":true,"usgs":false}],"preferred":false,"id":640418,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"White, Michael J.","contributorId":172348,"corporation":false,"usgs":false,"family":"White","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":640425,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Robertson, Dale M. 0000-0001-6799-0596 dzrobert@usgs.gov","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":150760,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"dzrobert@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":640416,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":543,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":640419,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70173863,"text":"70173863 - 2016 - Stock assessment in inland fisheries: a foundation for sustainable use and conservation","interactions":[],"lastModifiedDate":"2016-08-19T10:21:06","indexId":"70173863","displayToPublicDate":"2016-06-22T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Stock assessment in inland fisheries: a foundation for sustainable use and conservation","docAbstract":"Fisheries stock assessments are essential for science-based fisheries management. Inland fisheries pose challenges, but also provide opportunities for biological assessments that differ from those encountered in large marine fisheries for which many of our assessment methods have been developed. These include the number and diversity of fisheries, high levels of ecological and environmental variation, and relative lack of institutional capacity for assessment. In addition, anthropogenic impacts on habitats, widespread presence of non-native species and the frequent use of enhancement and restoration measures such as stocking affect stock dynamics. This paper outlines various stock assessment and data collection approaches that can be adapted to a wide range of different inland fisheries and management challenges. Although this paper identifies challenges in assessment, it focuses on solutions that are practical, scalable and transferrable. A path forward is suggested in which biological assessment generates some of the critical information needed by fisheries managers to make effective decisions that benefit the resource and stakeholders.
","language":"English","publisher":"Springer","doi":"10.1007/s11160-016-9435-0","usgsCitation":"Lorenzen, K., Cowx, I.G., Entsua-Mensah, R., Lester, N.P., Koehn, J., Randall, R., So, N., Bonar, S.A., Bunnell, D., Venturelli, P.A., Bower, S.D., and Cooke, S., 2016, Stock assessment in inland fisheries: a foundation for sustainable use and conservation: Reviews in Fish Biology and Fisheries, v. 26, no. 3, p. 405-440, https://doi.org/10.1007/s11160-016-9435-0.","productDescription":"36 p.","startPage":"405","endPage":"440","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063445","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":324249,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-04","publicationStatus":"PW","scienceBaseUri":"576ba89fe4b07657d1a1769e","contributors":{"authors":[{"text":"Lorenzen, Kai","contributorId":169476,"corporation":false,"usgs":false,"family":"Lorenzen","given":"Kai","email":"","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":638845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cowx, Ian G.","contributorId":37228,"corporation":false,"usgs":false,"family":"Cowx","given":"Ian","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":638843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Entsua-Mensah, R. 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Low-magnitude aftershocks missing from the network catalog are detected by applying a matched-filter approach to continuous seismic data, with the catalog earthquakes serving as the waveform templates. We measure precise differential arrival times between events, which we use for double-difference event relocation in a 3D seismic velocity model. Most aftershocks are deeper than the mainshock slip, and most occur west of the mapped surface rupture. While the mainshock coseismic and postseismic slip appears to have occurred on the near-vertical, strike-slip West Napa fault, many of the aftershocks occur in a complex zone of secondary faulting. Earthquake locations in the main aftershock zone, near the mainshock hypocenter, delineate multiple dipping secondary faults. Composite focal mechanisms indicate strike-slip and oblique-reverse faulting on the secondary features. The secondary faults were moved towards failure by Coulomb stress changes from the mainshock slip. Clusters of aftershocks north and south of the main aftershock zone exhibit vertical strike-slip faulting more consistent with the West Napa Fault. The northern aftershocks correspond to the area of largest mainshock coseismic slip, while the main aftershock zone is adjacent to the fault area that has primarily slipped postseismically. Unlike most creeping faults, the zone of postseismic slip does not appear to contain embedded stick-slip patches that would have produced on-fault aftershocks. The lack of stick-slip patches along this portion of the fault may contribute to the low productivity of the South Napa aftershock sequence.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120150169","usgsCitation":"Hardebeck, J.L., and Shelly, D.R., 2016, Aftershocks of the 2014 South Napa, California, Earthquake: Complex faulting on secondary faults: Bulletin of the Seismological Society of America, v. 106, no. 3, p. 1100-1109, https://doi.org/10.1785/0120150169.","productDescription":"9 p.","startPage":"1100","endPage":"1109","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066544","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":325841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"South Napa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.44125366210936,\n 38.45789034424927\n ],\n [\n -122.37533569335936,\n 38.38795699631398\n ],\n [\n -122.35198974609375,\n 38.329807044201374\n ],\n [\n -122.34924316406251,\n 38.26406296833964\n ],\n [\n -122.32177734375,\n 38.23278669950994\n ],\n [\n -122.25173950195311,\n 38.23925875585244\n ],\n [\n -122.23663330078124,\n 38.245730236135294\n ],\n [\n -122.24899291992188,\n 38.278078995562105\n ],\n [\n -122.23114013671875,\n 38.30071455572194\n ],\n [\n -122.23526000976561,\n 38.33088431959968\n ],\n [\n -122.29843139648436,\n 38.382574703770246\n ],\n [\n -122.33276367187499,\n 38.42131826945341\n ],\n [\n -122.35198974609375,\n 38.453588708941375\n ],\n [\n -122.40554809570311,\n 38.487994609214795\n ],\n [\n -122.44674682617188,\n 38.504116723098505\n ],\n [\n -122.46322631835938,\n 38.49229419236133\n ],\n [\n -122.44125366210936,\n 38.45789034424927\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-24","publicationStatus":"PW","scienceBaseUri":"579c7e2ae4b0589fa1ca11c0","contributors":{"authors":[{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780 jhardebeck@usgs.gov","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":841,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"jhardebeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":643959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shelly, David R. dshelly@usgs.gov","contributorId":2978,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":643960,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70173952,"text":"70173952 - 2016 - Variability and trends in runoff efficiency in the conterminous United States","interactions":[],"lastModifiedDate":"2017-08-29T09:38:33","indexId":"70173952","displayToPublicDate":"2016-06-22T14:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Variability and trends in runoff efficiency in the conterminous United States","docAbstract":"Variability and trends in water-year runoff efficiency (RE) — computed as the ratio of water-year runoff (streamflow per unit area) to water-year precipitation — in the conterminous United States (CONUS) are examined for the 1951 through 2012 period. Changes in RE are analyzed using runoff and precipitation data aggregated to United States Geological Survey 8-digit hydrologic cataloging units (HUs). Results indicate increases in RE for some regions in the north-central CONUS and large decreases in RE for the south-central CONUS. The increases in RE in the north-central CONUS are explained by trends in climate, whereas the large decreases in RE in the south-central CONUS likely are related to groundwater withdrawals from the Ogallala aquifer to support irrigated agriculture.
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