Because of a lack of clarity about the status of golden eagles (Aquila chrysaetos) in coastal southern California, the USGS, in collaboration with local, State, and other Federal agencies, began a multi-year survey and tracking program of golden eagles to address questions regarding habitat use, movement behavior, nest occupancy, genetic population structure, and human impacts on eagles. Golden eagle trapping and tracking efforts began in September 2014. During trapping efforts from September 29, 2014, to February 23, 2016, 27 golden eagles were captured. During trapping efforts from February 24, 2016, to February 23, 2017, an additional 10 golden eagles (7 females and 3 males) were captured in San Diego, Orange, and western Riverside Counties. Biotelemetry data for 26 of the 37 golden eagles that were transmitting data from February 24, 2016, to February 23, 2017 are presented. These eagles ranged as far north as northern Nevada and southern Wyoming, and as far south as La Paz, Baja California, Mexico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1051","collaboration":"Prepared in cooperation with the San Diego Association of Governments (SANDAG) California Department of Fish and Wildlife, Bureau of Land Management and U.S. Fish and Wildlife Service","usgsCitation":"Tracey, J.A., Madden, M.C., Sebes, J.B., Bloom, P.H., Katzner, T.E., and Fisher, R.N., 2017, Biotelemetery data for golden eagles (Aquila chrysaetos) captured in coastal southern California, February 2016–February 2017: U.S. Geological Survey Data Series 1051, 35 p., https://doi.org/10.3133/ds1051.","productDescription":"v, 35 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-085815","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":341226,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1051/coverthb.jpg"},{"id":341227,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1051/ds1051.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1051"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 26\n ],\n [\n -106,\n 26\n ],\n [\n -106,\n 42\n ],\n [\n -122,\n 42\n ],\n [\n -122,\n 26\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
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3020 State University Drive East
Sacramento, California 95819
Results from 1,041 groundwater samples collected during 1986‒2015 from 16 geologic units in Pennsylvania, associated with 25 or more groundwater samples with concentrations of radon-222, were evaluated in an effort to identify variations in radon-222 activities or concentrations and to classify potential radon-222 exposure from groundwater and indoor air. Radon-222 is hereafter referred to as “radon.” Radon concentrations in groundwater greater than or equal to the proposed U.S. Environmental Protection Agency (EPA) maximum contaminant level (MCL) for public-water supply systems of 300 picocuries per liter (pCi/L) were present in about 87 percent of the water samples, whereas concentrations greater than or equal to the proposed alternative MCL (AMCL) for public water-supply systems of 4,000 pCi/L were present in 14 percent. The highest radon concentrations were measured in groundwater from the schists, gneisses, and quartzites of the Piedmont Physiographic Province.
In this study, conducted by the U.S. Geological Survey in cooperation with the Pennsylvania Department of Health and the Pennsylvania Department of Environmental Protection, groundwater samples were aggregated among 16 geologic units in Pennsylvania to identify units with high median radon concentrations in groundwater. Graphical plots and statistical tests were used to determine variations in radon concentrations in groundwater and indoor air. Median radon concentrations in groundwater samples and median radon concentrations in indoor air samples within the 16 geologic units were classified according to proposed and recommended regulatory limits to explore potential radon exposure from groundwater and indoor air. All of the geologic units, except for the Allegheny (Pa) and Glenshaw (Pcg) Formations in the Appalachian Plateaus Physiographic Province, had median radon concentrations greater than the proposed EPA MCL of 300 pCi/L, and the Peters Creek Schist (Xpc), which is in the Piedmont Physiographic Province, had a median radon concentration greater than the EPA proposed AMCL of 4,000 pCi/L. Median concentrations of radon in groundwater and indoor air were determined to differ significantly among the geologic units (Kruskal-Wallis test, significance probability, p<0.001), and Tukey’s test indicated that radon concentrations in groundwater and indoor air in the Peters Creek Schist (Xpc) were significantly higher than those in the other units. Also, the Peters Creek Schist (Xpc) was determined to be the area with highest potential of radon exposure from groundwater and indoor air and one of two units with the highest percentage of population assumed to be using domestic self-supplied water (81 percent), which puts the population at greater potential of exposure to radon from groundwater.
Potential radon exposure determined from classification of geologic units by median radon concentrations in groundwater and indoor air according to proposed and recommended regulatory limits is useful for drawing general conclusions about the presence, variation, and potential radon exposure in specific geologic units, but the associated data and maps have limitations. The aggregated indoor air radon data have spatial accuracy limitations owing to imprecision of geocoded test locations. In addition, the associated data describing geologic units and the public water supplier’s service areas have spatial and interpretation accuracy limitations. As a result, data and maps associated with this report are not recommended for use in predicting individual concentrations at specific sites nor for use as a decision-making tool for property owners to decide whether to test for radon concentrations at specific locations. Instead, the data and maps are meant to promote awareness regarding potential radon exposure in Pennsylvania and to point out data gaps that exist throughout the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175018","collaboration":"Prepared in cooperation with the Pennsylvania Department of Health and the Pennsylvania Department of Environmental Protection","usgsCitation":"Gross, E.L., 2017, Evaluation of radon occurrence in groundwater from 16 geologic units in Pennsylvania, 1986–2015, with application to potential radon exposure from groundwater and indoor air: U.S. Geological Survey Scientific Investigations Report 2017–5018, 24 p., https://doi.org/10.3133/sir20175018.","productDescription":"vi, 24 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081317","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":339920,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5018/coverthb.jpg"},{"id":340851,"rank":3,"type":{"id":30,"text":"Data 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\"}}]}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Studies using global positioning system (GPS) telemetry rarely result in 100% fix success rates (FSR), which may bias datasets because data loss is systematic rather than a random process. Previous spatially explicit models developed to correct for sampling bias have been limited to small study areas, a small range of data loss, or were study-area specific. We modeled environmental effects on FSR from desert to alpine biomes, investigated the full range of potential data loss (0–100% FSR), and evaluated whether animal body position can contribute to lower FSR because of changes in antenna orientation based on GPS detection rates for 4 focal species: cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus nelsoni), and mule deer (Odocoileus hemionus). Terrain exposure and height of over story vegetation were the most influential factors affecting FSR. Model evaluation showed a strong correlation (0.88) between observed and predicted FSR and no significant differences between predicted and observed FSRs using 2 independent validation datasets. We found that cougars and canyon-dwelling bighorn sheep may select for environmental features that influence their detectability by GPS technology, mule deer may select against these features, and elk appear to be nonselective. We observed temporal patterns in missed fixes only for cougars. We provide a model for cougars, predicting fix success by time of day that is likely due to circadian changes in collar orientation and selection of daybed sites. We also provide a model predicting the probability of GPS fix acquisitions given environmental conditions, which had a strong relationship (r 2 = 0.82) with deployed collar FSRs across species.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.758","usgsCitation":"Ironside, K.E., Mattson, D.J., Choate, D., Stoner, D., Arundel, T.R., Hansen, J.R., Theimer, T., Holton, B., Jansen, B., Sexton, J.O., Longshore, K.M., Edwards, T.C., and Peters, M., 2017, Variable terrestrial GPS telemetry detection rates: Addressing the probability of successful acquisitions: Wildlife Society Bulletin, v. 41, no. 2, p. 329-341, https://doi.org/10.1002/wsb.758.","productDescription":"13 p.","startPage":"329","endPage":"341","ipdsId":"IP-055599","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":500018,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/5007fd72bf21419c89d80ab4052700e0","text":"External Repository"},{"id":438347,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PG1PT2","text":"USGS data release","linkHelpText":"Variable Terrestrial GPS Telemetry Detection Rates: Parts 1 - 7Data"},{"id":341101,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-27","publicationStatus":"PW","scienceBaseUri":"59154632e4b01a342e6912da","contributors":{"authors":[{"text":"Ironside, Kirsten E. 0000-0003-1166-3793 kironside@usgs.gov","orcid":"https://orcid.org/0000-0003-1166-3793","contributorId":3379,"corporation":false,"usgs":true,"family":"Ironside","given":"Kirsten","email":"kironside@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":694743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mattson, David J. david_mattson@usgs.gov","contributorId":3662,"corporation":false,"usgs":true,"family":"Mattson","given":"David","email":"david_mattson@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":694744,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Choate, David","contributorId":172339,"corporation":false,"usgs":false,"family":"Choate","given":"David","affiliations":[],"preferred":false,"id":694745,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stoner, David","contributorId":191912,"corporation":false,"usgs":false,"family":"Stoner","given":"David","affiliations":[],"preferred":false,"id":694746,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arundel, Terence R. 0000-0003-0324-4249 tarundel@usgs.gov","orcid":"https://orcid.org/0000-0003-0324-4249","contributorId":139242,"corporation":false,"usgs":true,"family":"Arundel","given":"Terence","email":"tarundel@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":694747,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hansen, Jered R.","contributorId":191913,"corporation":false,"usgs":false,"family":"Hansen","given":"Jered","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":694748,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Theimer, Tad","contributorId":191914,"corporation":false,"usgs":false,"family":"Theimer","given":"Tad","affiliations":[],"preferred":false,"id":694749,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Holton, Brandon","contributorId":191915,"corporation":false,"usgs":false,"family":"Holton","given":"Brandon","affiliations":[],"preferred":false,"id":694750,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jansen, Brian","contributorId":191917,"corporation":false,"usgs":false,"family":"Jansen","given":"Brian","email":"","affiliations":[],"preferred":false,"id":694752,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sexton, Joseph O.","contributorId":191918,"corporation":false,"usgs":false,"family":"Sexton","given":"Joseph","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":694753,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Longshore, Kathleen M. 0000-0001-6621-1271 longshore@usgs.gov","orcid":"https://orcid.org/0000-0001-6621-1271","contributorId":2677,"corporation":false,"usgs":true,"family":"Longshore","given":"Kathleen","email":"longshore@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":694754,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Edwards, Thomas C. Jr. 0000-0002-0773-0909 tce@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-0909","contributorId":2061,"corporation":false,"usgs":true,"family":"Edwards","given":"Thomas","suffix":"Jr.","email":"tce@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":false,"id":694751,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peters, Michael","contributorId":191919,"corporation":false,"usgs":false,"family":"Peters","given":"Michael","email":"","affiliations":[],"preferred":false,"id":694755,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70187639,"text":"70187639 - 2017 - Impact of tile drainage on evapotranspiration in South Dakota, USA, based on high spatiotemporal resolution evapotranspiration time series from a multi-satellite data fusion system","interactions":[],"lastModifiedDate":"2017-07-24T10:04:07","indexId":"70187639","displayToPublicDate":"2017-05-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1942,"text":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Impact of tile drainage on evapotranspiration in South Dakota, USA, based on high spatiotemporal resolution evapotranspiration time series from a multi-satellite data fusion system","docAbstract":"Soil drainage is a widely used agricultural practice in the midwest USA to remove excess soil water to potentially improve the crop yield. Research shows an increasing trend in baseflow and streamflow in the midwest over the last 60 years, which may be related to artificial drainage. Subsurface drainage (i.e., tile) in particular may have strongly contributed to the increase in these flows, because of its extensive use and recent gain in the popularity as a yield-enhancement practice. However, how evapotranspiration (ET) is impacted by tile drainage on a regional level is not well-documented. To explore spatial and temporal ET patterns and their relationship to tile drainage, we applied an energy balance-based multisensor data fusion method to estimate daily 30-m ET over an intensively tile-drained area in South Dakota, USA, from 2005 to 2013. Results suggest that tile drainage slightly decreases the annual cumulative ET, particularly during the early growing season. However, higher mid-season crop water use suppresses the extent of the decrease of the annual cumulative ET that might be anticipated from widespread drainage. The regional water balance analysis during the growing season demonstrates good closure, with the average residual from 2005 to 2012 as low as -3 mm. As an independent check of the simulated ET at the regional scale, the water balance analysis lends additional confidence to the study. The results of this study improve our understanding of the influence of agricultural drainage practices on regional ET, and can affect future decision making regarding tile drainage systems.
","language":"English","publisher":"IEEE","doi":"10.1109/JSTARS.2017.2680411","usgsCitation":"Yang, Y., Anderson, M.C., Gao, F., Hain, C., Kustas, W.P., Meyers, T.P., Crow, W., Finocchiaro, R.G., Otkin, J., Sun, L., and Yang, Y., 2017, Impact of tile drainage on evapotranspiration in South Dakota, USA, based on high spatiotemporal resolution evapotranspiration time series from a multi-satellite data fusion system: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 10, no. 6, p. 2250-2564, https://doi.org/10.1109/JSTARS.2017.2680411.","productDescription":"15 p.","startPage":"2250","endPage":"2564","ipdsId":"IP-079712","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469857,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/470bf08e5c7a445296ba2463f1e54c42","text":"External Repository"},{"id":341160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59154631e4b01a342e6912d4","contributors":{"authors":[{"text":"Yang, Yun","contributorId":191965,"corporation":false,"usgs":false,"family":"Yang","given":"Yun","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":694887,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Martha C.","contributorId":96579,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","email":"","middleInitial":"C.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":694888,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gao, Feng 0000-0002-1865-2846","orcid":"https://orcid.org/0000-0002-1865-2846","contributorId":70671,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":694894,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hain, Christopher","contributorId":191966,"corporation":false,"usgs":false,"family":"Hain","given":"Christopher","email":"","affiliations":[{"id":16239,"text":"NASA Marshall Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":694895,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kustas, William P.","contributorId":29962,"corporation":false,"usgs":false,"family":"Kustas","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":694896,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meyers, Tilden P.","contributorId":146138,"corporation":false,"usgs":false,"family":"Meyers","given":"Tilden","email":"","middleInitial":"P.","affiliations":[{"id":16598,"text":"NOAA/ATDD","active":true,"usgs":false}],"preferred":false,"id":694898,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Finocchiaro, Raymond G. rfinocchiaro@usgs.gov","contributorId":3673,"corporation":false,"usgs":true,"family":"Finocchiaro","given":"Raymond","email":"rfinocchiaro@usgs.gov","middleInitial":"G.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":694900,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Crow, Wade","contributorId":94563,"corporation":false,"usgs":false,"family":"Crow","given":"Wade","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":694899,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Otkin, Jason","contributorId":106176,"corporation":false,"usgs":false,"family":"Otkin","given":"Jason","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":694901,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sun, Liang","contributorId":119410,"corporation":false,"usgs":false,"family":"Sun","given":"Liang","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":694902,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Yang, Yang","contributorId":85292,"corporation":false,"usgs":false,"family":"Yang","given":"Yang","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":694903,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70178728,"text":"sir20165156 - 2017 - Magnitude of flood flows for selected annual exceedance probabilities for streams in Massachusetts","interactions":[],"lastModifiedDate":"2017-05-10T16:40:49","indexId":"sir20165156","displayToPublicDate":"2017-05-10T17:10:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5156","title":"Magnitude of flood flows for selected annual exceedance probabilities for streams in Massachusetts","docAbstract":"The U.S. Geological Survey, in cooperation with the Massachusetts Department of Transportation, determined the magnitude of flood flows at selected annual exceedance probabilities (AEPs) at streamgages in Massachusetts and from these data developed equations for estimating flood flows at ungaged locations in the State. Flood magnitudes were determined for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEPs at 220 streamgages, 125 of which are in Massachusetts and 95 are in the adjacent States of Connecticut, New Hampshire, New York, Rhode Island, and Vermont. AEP flood flows were computed for streamgages using the expected moments algorithm weighted with a recently computed regional skewness coefficient for New England.
Regional regression equations were developed to estimate the magnitude of floods for selected AEP flows at ungaged sites from 199 selected streamgages and for 60 potential explanatory basin characteristics. AEP flows for 21 of the 125 streamgages in Massachusetts were not used in the final regional regression analysis, primarily because of regulation or redundancy. The final regression equations used generalized least squares methods to account for streamgage record length and correlation. Drainage area, mean basin elevation, and basin storage explained 86 to 93 percent of the variance in flood magnitude from the 50- to 0.2-percent AEPs, respectively. The estimates of AEP flows at streamgages can be improved by using a weighted estimate that is based on the magnitude of the flood and associated uncertainty from the at-site analysis and the regional regression equations. Weighting procedures for estimating AEP flows at an ungaged site on a gaged stream also are provided that improve estimates of flood flows at the ungaged site when hydrologic characteristics do not abruptly change.
Urbanization expressed as the percentage of imperviousness provided some explanatory power in the regional regression; however, it was not statistically significant at the 95-percent confidence level for any of the AEPs examined. The effect of urbanization on flood flows indicates a complex interaction with other basin characteristics. Another complicating factor is the assumption of stationarity, that is, the assumption that annual peak flows exhibit no significant trend over time. The results of the analysis show that stationarity does not prevail at all of the streamgages. About 27 percent of streamgages in Massachusetts and about 42 percent of streamgages in adjacent States with 20 or more years of systematic record used in the study show a significant positive trend at the 95-percent confidence level. The remaining streamgages had both positive and negative trends, but the trends were not statistically significant. Trends were shown to vary over time. In particular, during the past decade (2004–2013), peak flows were persistently above normal, which may give the impression of positive trends. Only continued monitoring will provide the information needed to determine whether recent increases in annual peak flows are a normal oscillation or a true trend.
The analysis used 37 years of additional data obtained since the last comprehensive study of flood flows in Massachusetts. In addition, new methods for computing flood flows at streamgages and regionalization improved estimates of flood magnitudes at gaged and ungaged locations and better defined the uncertainty of the estimates of AEP floods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165156","collaboration":"Prepared in cooperation with the Massachusetts Department of Transportation","usgsCitation":"Zarriello, P.J., 2017, Magnitude of flood flows at selected annual exceedance probabilities for streams in Massachusetts: U.S. Geological Survey Scientific Investigations Report 2016–5156, 54 p., https://doi.org/10.3133/sir20165156.","productDescription":"Report: ix, 54 p.; Tables; 1 Appendix","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-065274","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"links":[{"id":341058,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5156/sir20165156_table11.csv","text":"Table 11","size":"47.9 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Magnitude and variance of selected flood 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studies"},{"id":341060,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5156/sir20165156_table13.csv","text":"Table 13","size":"28.6 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Comparison of newly estimated flood flows to prior studies"},{"id":341047,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5156/sir20165156_table03.csv","text":"Table 3 ","size":"85.5 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Flood flows for Massachusetts and adjacent States through water year 2013"},{"id":340865,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5156/coverthb.jpg"},{"id":340867,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5156/sir20165156_appendix03.xlsx","text":"Appendix 3 ","size":"92.5 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5156 Appendix 3","linkHelpText":"- Workbook for estimating flood flows at gaged and ungaged sites in Massachusetts "},{"id":340870,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5156/sir20165156_table04.xlsx","text":"Table 4","size":"71.6 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5156 Table 4","linkHelpText":"- Streamgage and basin characteristics"},{"id":341059,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5156/sir20165156_table04.csv","text":"Table 4 ","size":"30.1 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Streamgage and basin characteristics"},{"id":340866,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5156/sir20165156.pdf","text":"Report","size":"5.41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5156"}],"country":"United 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The current (2015) streamgage network in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York was evaluated in order to design a network that would meet the hydrologic needs of many partners and serve a variety of purposes and interests, including estimation of streamflow statistics at ungaged sites. This study was done by the U.S. Geological Survey, in cooperation with the Pennsylvania Department of Environmental Protection and the Susquehanna River Basin Commission. The study area includes the Commonwealth of Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York. For this study, 229 streamgages were identified as reference streamgages that could be used to represent ungaged watersheds. Criteria for a reference streamgage are a minimum of 10 years of continuous record, minimally altered streamflow, and a drainage area less than 1,500 square miles. Some of the reference streamgages have been discontinued but provide historical hydrologic information valuable in the determination of streamflow characteristics of ungaged watersheds. Watersheds in the study area not adequately represented by a reference streamgage were identified by examining a range of basin characteristics, the extent of geographic coverage, and the strength of estimated streamflow correlations between gaged and ungaged sites.
Basin characteristics were determined for the reference streamgage watersheds and the 1,662 12-digit hydrologic unit code (HUC12) subwatersheds in Pennsylvania and the Susquehanna River Basin using a geographic information system (GIS) spatial analysis and nationally available GIS datasets. Basin characteristics selected for this study include drainage area, mean basin elevation, mean basin slope, percentage of urbanized area, percentage of forested area, percentage of carbonate bedrock, mean annual precipitation, and soil thickness. A GIS spatial analysis was used to identify HUC12 subwatersheds outside the range of basin characteristics of the reference streamgages. There were 320 HUC12 subwatersheds, or 19 percent of the study area, with basin characteristics outside the range represented by the reference streamgage watersheds.
A GIS spatial analysis was used to identify geographic gaps in the streamgage network. For each streamgage, a watershed area, called the gage statistical area (GSA), was delineated. The GSA shows the drainage area within a specific drainage-area ratio of the streamgage for transfer of streamflow statistics from that streamgage to ungaged sites on the valid statistical reach of the GSA for a streamgage. In Pennsylvania, a drainage-area ratio of 0.33–3 times the drainage area of the ungaged site was found to perform as well as, if not better than, more traditional ratios such as 0.5–1.5 (or 2) for transfer of selected streamflow statistics. A total of 1,102 HUC12 subwatersheds, or 66 percent of the study area, are outside the GSA for a reference streamgage.
The USGS Baseline Streamflow Estimator (BaSE) program was used to determine how well HUC12 subwatersheds outside the streamgage GSAs are represented by the reference streamgage network in Pennsylvania, based on estimated streamflow correlation. The centroid of each HUC12 subwatershed was run through the BaSE program to determine the reference streamgage with the highest estimated streamflow correlation. There were 929 HUC12 subwatersheds in Pennsylvania, or 56 percent of the State, with an estimated correlation coefficient less than 0.96.
The results from the basin characteristic, geographic, and streamflow correlation analyses were combined to identify 1,405 HUC12 subwatersheds in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York that lack a representative reference, based on at least one identified gap. Of the 1,405 HUC12 subwatersheds, 139 exhibited all three gaps, indicating a 8-percent gap in the reference streamgage network.
Streamgages in areas with similar hydrologic characteristics and in close proximity to one another can potentially provide similar information (termed streamgages with high substitution potential). Streamgages were considered to have a high substitution potential with a nearby streamgage(s) if (1) the streamflow correlation coefficient was equal to or greater than 0.96, (2) the streamgages had 10 years of concurrent record, and (3) the streamgages are in the same watershed within the GSA of the streamgage. Seventy-four current (2015) streamgages with high substitution potential with at least one other streamgage were identified in the study area. Although these identified streamgages have a high substitution potential, they provide valuable streamflow information to a stakeholder. Selected primary uses of these streamgages were identified to determine the overall need for an individual streamgage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165149","collaboration":"Prepared in cooperation with the Pennsylvania Department of Environmental Protection and the Susquehanna River Basin Commission ","usgsCitation":"Sloto, R.A., Stuckey, M.H., and Hoffman, S.A., 2017, Evaluation of the streamgage network for estimating streamflow statistics at ungaged sites in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York: U.S. Geological Survey Scientific Investigations Report 2016–5149, 102 p., https://doi.org/10.3133/sir20165149.","productDescription":"vi, 102 p.","numberOfPages":"112","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069147","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":340754,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5149//sir20165149.pdf","text":"Report","size":"85.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5149"},{"id":340753,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5149/coverthb.jpg"}],"country":"United States","state":"New York, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.77197265625,\n 39.70718665682654\n ],\n [\n -74.805908203125,\n 39.70718665682654\n ],\n [\n -74.805908203125,\n 42.97250158602597\n ],\n [\n -78.77197265625,\n 42.97250158602597\n ],\n [\n -78.77197265625,\n 39.70718665682654\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
The Equus Beds aquifer in south-central Kansas is a primary water source for the city of Wichita. The Equus Beds aquifer storage and recovery (ASR) project was developed to help the city of Wichita meet increasing current and future demands. The Equus Beds ASR project is a recent part of an 80-year cooperative water science effort with the city of Wichita. The U.S. Geological Survey (USGS) Kansas Water Science Center characterizes river and aquifer water-quality and quantity and evaluates changes that may or may not be related to ASR. The USGS data are used by the city of Wichita to make informed management decisions, satisfy regulatory requirements, and serve as a baseline to detect any subsequent changes that may be related to ASR.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip174","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., 2017, Eighty years of cooperative water science (ver. 1.1, October 2019): U.S. Geological Survey General Information Product 174, 2 p., https://doi.org/10.3133/gip174.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-086697","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":368327,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/gip/0174/versionHist.txt","text":"Version History","linkFileType":{"id":2,"text":"txt"},"description":"GIP 174 Version History"},{"id":368326,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0174/gip174.pdf","text":"Report","size":"1.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 174"},{"id":346650,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0174/coverthb3.jpg"}],"country":"United States","state":"Kansas","county":"Wichita County","otherGeospatial":"Equus Beds Aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-101.485,38.7002],[-101.1293,38.7001],[-101.1254,38.264],[-101.5405,38.2631],[-101.5669,38.2633],[-101.5694,38.7004],[-101.485,38.7002]]]},\"properties\":{\"name\":\"Wichita\",\"state\":\"KS\"}}]}","edition":"Version 1.0: May 9, 2017; Version 1.1: October 15, 2019","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Wildfire is increasingly a concern in the USA, where 10 million acres burned in 2015. Climate is a primary driver of wildfire, and understanding fire-climate relationships is crucial for informing fire management and modeling the effects of climate change on fire. In the southwestern USA, fire-climate relationships have been informed by tree-ring data that extend centuries prior to the onset of fire exclusion in the late 1800s. Variability in cool-season precipitation has been linked to fire occurrence, but the effects of the summer North American monsoon on fire are less understood, as are the effects of climate on fire seasonality. We use a new set of reconstructions for cool-season (October–April) and monsoon-season (July–August) moisture conditions along with a large new fire scar dataset to examine relationships between multi-seasonal climate variability, fire extent, and fire seasonality in the Jemez Mountains, New Mexico (1599–1899 CE). Results suggest that large fires burning in all seasons are strongly influenced by the current year cool-season moisture, but fires burning mid-summer to fall are also influenced by monsoon moisture. Wet conditions several years prior to the fire year during the cool season, and to a lesser extent during the monsoon season, are also important for spring through late-summer fires. Persistent cool-season drought longer than 3 years may inhibit fires due to the lack of moisture to replenish surface fuels. This suggests that fuels may become increasingly limiting for fire occurrence in semi-arid regions that are projected to become drier with climate change.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-017-1958-4","usgsCitation":"Margolis, E.Q., Woodhouse, C.A., and Swetnam, T.W., 2017, Drought, multi-seasonal climate, and wildfire in northern New Mexico: Climatic Change, v. 142, no. 3, p. 433-446, https://doi.org/10.1007/s10584-017-1958-4.","productDescription":"14 p.","startPage":"433","endPage":"446","ipdsId":"IP-086088","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469860,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://link.springer.com/10.1007/s10584-017-1958-4","text":"External Repository"},{"id":340995,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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Mexico\",\"nation\":\"USA \"}}]}","volume":"142","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-25","publicationStatus":"PW","scienceBaseUri":"5912d536e4b0e541a03d451d","contributors":{"authors":[{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":694568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodhouse, Connie A.","contributorId":187601,"corporation":false,"usgs":false,"family":"Woodhouse","given":"Connie","email":"","middleInitial":"A.","affiliations":[{"id":32413,"text":"University of Arizona, Tucson, AZ, USA, 85721","active":true,"usgs":false}],"preferred":false,"id":694569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swetnam, Thomas W.","contributorId":191872,"corporation":false,"usgs":false,"family":"Swetnam","given":"Thomas","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":694570,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187561,"text":"70187561 - 2017 - Development of a coastal drought index using salinity data","interactions":[],"lastModifiedDate":"2017-05-09T11:20:57","indexId":"70187561","displayToPublicDate":"2017-05-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"Development of a coastal drought index using salinity data","docAbstract":"A critical aspect of the uniqueness of coastal drought is the effects on the salinity dynamics of creeks, rivers, and estuaries. The location of the freshwater–saltwater interface along the coast is an important factor in the ecological and socioeconomic dynamics of coastal communities. Salinity is a critical response variable that integrates hydrologic and coastal dynamics including sea level, tides, winds, precipitation, streamflow, and tropical storms. The position of the interface determines the composition of freshwater and saltwater aquatic communities as well as the freshwater availability for water intakes. Many definitions of drought have been proposed, with most describing a decline in precipitation having negative impacts on the water supply. Indices have been developed incorporating data such as rainfall, streamflow, soil moisture, and groundwater levels. These water-availability drought indices were developed for upland areas and may not be ideal for characterizing coastal drought. The availability of real-time and historical salinity datasets provides an opportunity for the development of a salinity-based coastal drought index. An approach similar to the standardized precipitation index (SPI) was modified and applied to salinity data obtained from sites in South Carolina and Georgia. Using the SPI approach, the index becomes a coastal salinity index (CSI) that characterizes coastal salinity conditions with respect to drought periods of higher-saline conditions and wet periods of higher-freshwater conditions. Evaluation of the CSI indicates that it provides additional coastal response information as compared to the SPI and the Palmer hydrologic drought index, and the CSI can be used for different estuary types and for comparison of conditions along coastlines.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-15-00171.1","usgsCitation":"Conrads, P., and Darby, L.S., 2017, Development of a coastal drought index using salinity data: Bulletin of the American Meteorological Society, v. 98, no. 4, p. 753-766, https://doi.org/10.1175/BAMS-D-15-00171.1.","productDescription":"14 p.","startPage":"753","endPage":"766","ipdsId":"IP-067018","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":340993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"98","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-24","publicationStatus":"PW","scienceBaseUri":"5912d536e4b0e541a03d451b","contributors":{"authors":[{"text":"Conrads, Paul 0000-0003-0408-4208 pconrads@usgs.gov","orcid":"https://orcid.org/0000-0003-0408-4208","contributorId":764,"corporation":false,"usgs":true,"family":"Conrads","given":"Paul","email":"pconrads@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":694571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Darby, Lisa S.","contributorId":191873,"corporation":false,"usgs":false,"family":"Darby","given":"Lisa","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":694572,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187573,"text":"ofr20171050 - 2017 - Geophysical data collected during the 2014 minute 319 pulse flow on the Colorado River below Morelos Dam, United States and Mexico","interactions":[],"lastModifiedDate":"2017-05-09T18:05:56","indexId":"ofr20171050","displayToPublicDate":"2017-05-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1050","title":"Geophysical data collected during the 2014 minute 319 pulse flow on the Colorado River below Morelos Dam, United States and Mexico","docAbstract":"Geophysical methods were used to monitor infiltration during a water release, referred to as a “pulse flow,” in the Colorado River delta in March and April 2014. The pulse flow was enabled by Minute 319 of the 1944 United States–Mexico Treaty concerning water of the Colorado River. Fieldwork was carried out by the U.S. Geological Survey and the Centro de Investigación Científica y de Educación Superior de Ensenada as part of a binational effort to monitor the hydrologic effects of the pulse flow along the limitrophe (border) reach of the Colorado River and into Mexico. Repeat microgravity measurements were made at 25 locations in the southern limitrophe reach to quantify aquifer storage change during the pulse flow. Observed increases in storage along the river were greater with distance to the south, and the amount of storage change decreased away from the river channel. Gravity data at four monitoring well sites indicate specific yield equal to 0.32±0.05. Electromagnetic induction methods were used at 12 transects in the limitrophe reach of the river along the United States– Mexico border, and farther south into Mexico. These data, which are sensitive to variation in soil texture and water content, suggest relatively homogeneous conditions. Repeat direct-current resistivity measurements were collected at two locations to monitor groundwater elevation. Results indicate rapid groundwater-level rise during the pulse flow in the limitrophe reach and smaller variation at a more southern transect. Together, these data are useful for hydrogeologic characterization and hydrologic model development. Electronic data files are provided in the accompanying data release (Kennedy and others, 2016a).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171050","collaboration":"Prepared in Cooperation with Universidad Autónoma de Baja California and Centro de Investigación Científica y de Educación Superior de Ensenada","usgsCitation":"Kennedy, J.R., Callegary, J.B., Macy, J.P., Reyes-Lopez, J., Pérez-Flores, M., 2017, Geophysical data collected during the 2014 minute 319 pulse flow on the Colorado River below Morelos Dam, United States and Mexico: U.S. Geological Survey Open-File Report 2017–1050, 48 p., https://doi.org/10.3133/ofr20171050.","productDescription":"Report: vii, 48 Pp.; Data Release","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-067382","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":438349,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7K935M8","text":"USGS data release","linkHelpText":"Geophysical Data Collected during the 2014 Minute 319 Pulse Flow"},{"id":341001,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1050/ofr20171050.pdf","text":"Report","size":"8.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1050"},{"id":341000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1050/coverthb.jpg"},{"id":341002,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7K935M8","text":"Data Release"}],"country":"Mexico, United States","state":"Arizona, Baja California, California","otherGeospatial":"Colorado River, Morelos Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115,\n 32.33333\n ],\n [\n -114.5,\n 32.33333\n ],\n [\n -114.5,\n 32.75\n ],\n [\n -115,\n 32.75\n ],\n [\n -115,\n 32.33333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
In accordance with guidelines set forth by the Office of Water Quality in the Water Mission Area of the U.S. Geological Survey, a quality-assurance plan has been created for use by the Washington Water Science Center (WAWSC) in conducting water-quality activities. This qualityassurance plan documents the standards, policies, and procedures used by the WAWSC for activities related to the collection, processing, storage, analysis, and publication of water-quality data. The policies and procedures documented in this quality-assurance plan for water-quality activities complement the quality-assurance plans for surface-water and groundwater activities at the WAWSC.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171044","usgsCitation":"Conn, K.E., Huffman, R.L., and Barton, Cynthia, 2017, Quality-assurance plan for water-quality activities in the U.S. Geological Survey Washington Water Science Center: U.S. Geological Survey Open-File Report 2017–1044, 66 p., https://doi.org/10.3133/ofr20171044.","productDescription":"vi, 66 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-083785","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":340969,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1044/ofr20171044.pdf","text":"Report","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1044"},{"id":340968,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1044/coverthb.jpg"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Rapid growth in unconventional oil and gas (UOG) has produced jobs, revenue, and energy, but also concerns over spills and environmental risks. We assessed spill data from 2005 to 2014 at 31 481 UOG wells in Colorado, New Mexico, North Dakota, and Pennsylvania. We found 2–16% of wells reported a spill each year. Median spill volumes ranged from 0.5 m3 in Pennsylvania to 4.9 m3 in New Mexico; the largest spills exceeded 100 m3. Seventy-five to 94% of spills occurred within the first three years of well life when wells were drilled, completed, and had their largest production volumes. Across all four states, 50% of spills were related to storage and moving fluids via flowlines. Reporting rates varied by state, affecting spill rates and requiring extensive time and effort getting data into a usable format. Enhanced and standardized regulatory requirements for reporting spills could improve the accuracy and speed of analyses to identify and prevent spill risks and mitigate potential environmental damage. Transparency for data sharing and analysis will be increasingly important as UOG development expands. We designed an interactive spills data visualization tool (http://snappartnership.net/groups/hydraulic-fracturing/webapp/spills.html) to illustrate the value of having standardized, public data.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.6b05749","usgsCitation":"Patterson, L.A., Konschnik, K.E., Wiseman, H., Fargione, J., Maloney, K.O., Kiesecker, J.M., Nicot, J., Baruch-Mordo, S., Entrekin, S., Trainor, A., and Saiers, J., 2017, Unconventional oil and gas spills: Risks, mitigation priorities, and state reporting requirements: Environmental Science & Technology, v. 51, no. 5, p. 2563-2573, https://doi.org/10.1021/acs.est.6b05749.","productDescription":"11 p.","startPage":"2563","endPage":"2573","ipdsId":"IP-076744","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":469868,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.6b05749","text":"Publisher Index Page"},{"id":340919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-21","publicationStatus":"PW","scienceBaseUri":"591183b0e4b0e541a03c1a4a","contributors":{"authors":[{"text":"Patterson, Lauren A.","contributorId":177289,"corporation":false,"usgs":false,"family":"Patterson","given":"Lauren","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":694403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Konschnik, Katherine E.","contributorId":191826,"corporation":false,"usgs":false,"family":"Konschnik","given":"Katherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":694404,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiseman, Hannah","contributorId":191827,"corporation":false,"usgs":false,"family":"Wiseman","given":"Hannah","affiliations":[],"preferred":false,"id":694405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fargione, Joseph","contributorId":191828,"corporation":false,"usgs":false,"family":"Fargione","given":"Joseph","affiliations":[],"preferred":false,"id":694406,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":694402,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kiesecker, Joseph M.","contributorId":146679,"corporation":false,"usgs":false,"family":"Kiesecker","given":"Joseph","email":"","middleInitial":"M.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":694407,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nicot, Jean-Philippe","contributorId":175575,"corporation":false,"usgs":false,"family":"Nicot","given":"Jean-Philippe","email":"","affiliations":[],"preferred":false,"id":694408,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Baruch-Mordo, Sharon","contributorId":191830,"corporation":false,"usgs":false,"family":"Baruch-Mordo","given":"Sharon","email":"","affiliations":[],"preferred":false,"id":694409,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Entrekin, Sally","contributorId":147949,"corporation":false,"usgs":false,"family":"Entrekin","given":"Sally","affiliations":[{"id":16964,"text":"University of Central Arkansas","active":true,"usgs":false}],"preferred":false,"id":694410,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Trainor, Anne","contributorId":191831,"corporation":false,"usgs":false,"family":"Trainor","given":"Anne","affiliations":[],"preferred":false,"id":694411,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Saiers, James","contributorId":191832,"corporation":false,"usgs":false,"family":"Saiers","given":"James","affiliations":[],"preferred":false,"id":694412,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70185642,"text":"ofr20171038 - 2017 - Report of the workshop on evidence-based design of national wildlife health programs","interactions":[],"lastModifiedDate":"2017-05-12T09:53:57","indexId":"ofr20171038","displayToPublicDate":"2017-05-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1038","title":"Report of the workshop on evidence-based design of national wildlife health programs","docAbstract":"This report summarizes a Wildlife Disease Association sponsored workshop held in 2016. The overall objective of the workshop was to use available evidence and selected subject matter expertise to define the essential functions of a National Wildlife Health Program and the resources needed to deliver a robust and reliable program, including the basic infrastructure, workforce, data and information systems, governance, organizational capacity, and essential features, such as wildlife disease surveillance, diagnostic services, and epidemiological investigation. This workshop also provided the means to begin the process of defining the essential attributes of a national wildlife health program that could be scalable and adaptable to each nation’s needs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171038","collaboration":"Prepared in cooperation with the Wildlife Disease Association","usgsCitation":"Nguyen, N.T., Duff, J.P., Gavier-Widén, D., Grillo, T., He, H., Lee, H., Ratanakorn, P., Rijks, J.M., Ryser-Degiorgis, M.-P., Sleeman, J.M., Stephen, C., Tana, T., Uhart, M., and Zimmer, P., 2017, Report of the workshop on evidence-based design of national wildlife health programs: U.S. Geological Survey Open-File Report 2017–1038, 18 p., https://doi.org/10.3133/ofr20171038.","productDescription":"vi, 18 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-083971","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":340849,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1038/coverthb.jpg"},{"id":340850,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1038/ofr20171038.pdf","text":"Report","size":"811 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1038"}],"contact":"Director, National Wildlife Health Center
U.S. Geological Survey,
6006 Schroeder Road
Madison, WI 53711
Acknowledgments
1. Introduction
2. Introductory presentations
3. Facilitated small group discussions
4. Plenary session on additional need for a national wildlife health program
5. Plenary session on key features of a national wildlife health program
6. Next steps
Selected References
Appendixes
Concern over declining pollinators has led to multiple conservation initiatives for improving forage for bees in agroecosystems. Using data available through the Pollinator Library (npwrc.usgs.gov/pollinator/), we summarize plant–pollinator interaction data collected from 2012–2015 on lands managed by the U.S. Fish and Wildlife Service and private lands enrolled in U.S. Department of Agriculture conservation programs in eastern North Dakota (ND). Furthermore, we demonstrate how plant–pollinator interaction data from the Pollinator Library and seed cost information can be used to evaluate hypothetical seeding mixes for pollinator habitat enhancements. We summarize records of 314 wild bee and 849 honey bee (Apis mellifera L.) interactions detected on 63 different plant species. The wild bee observations consisted of 46 species, 15 genera, and 5 families. Over 54% of all wild bee observations were represented by three genera―Bombus, Lassioglossum, and Melissodes. The most commonly visited forbs by wild bees were Monarda fistulosa, Sonchus arvensis, and Zizia aurea. The most commonly visited forbs by A. mellifera were Cirsium arvense, Melilotus officinalis, and Medicago sativa. Among all interactions, 13% of A. mellifera and 77% of wild bee observations were made on plants native to ND. Our seed mix evaluation shows that mixes may often need to be tailored to meet the unique needs of wild bees and managed honey bees in agricultural landscapes. Our evaluation also demonstrates the importance of incorporating both biologic and economic information when attempting to design cost-effective seeding mixes for supporting pollinators in a critically important part of the United States.
","language":"English","publisher":"Entomological Society of America","doi":"10.1093/ee/nvx070","usgsCitation":"Otto, C., O’Dell, S., Bryant, R.B., Euliss, N., Bush, R., and Smart, M., 2017, Using publicly available data to quantify plant–pollinator interactions and evaluate conservation seeding mixes in the Northern Great Plains: Environmental Entomology, v. 46, no. 3, p. 565-578, https://doi.org/10.1093/ee/nvx070.","productDescription":"14 p.","startPage":"565","endPage":"578","ipdsId":"IP-081790","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":340909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-02","publicationStatus":"PW","scienceBaseUri":"591183b1e4b0e541a03c1a50","contributors":{"authors":[{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":694286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Dell, Samuel sodell@usgs.gov","contributorId":152473,"corporation":false,"usgs":true,"family":"O’Dell","given":"Samuel","email":"sodell@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":694397,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bryant, R. B.","contributorId":191824,"corporation":false,"usgs":false,"family":"Bryant","given":"R.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":694287,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Euliss, Ned H. Jr.","contributorId":178233,"corporation":false,"usgs":false,"family":"Euliss","given":"Ned H. Jr.","affiliations":[],"preferred":false,"id":694288,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bush, Rachel","contributorId":191796,"corporation":false,"usgs":false,"family":"Bush","given":"Rachel","email":"","affiliations":[],"preferred":false,"id":694289,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smart, Matthew 0000-0003-0711-3035 msmart@usgs.gov","orcid":"https://orcid.org/0000-0003-0711-3035","contributorId":174424,"corporation":false,"usgs":true,"family":"Smart","given":"Matthew","email":"msmart@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":694290,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187551,"text":"70187551 - 2017 - Historical analysis of riparian vegetation change in response to shifting management objectives on the Middle Rio Grande","interactions":[],"lastModifiedDate":"2017-05-08T15:44:02","indexId":"70187551","displayToPublicDate":"2017-05-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Historical analysis of riparian vegetation change in response to shifting management objectives on the Middle Rio Grande","docAbstract":"Riparian ecosystems are valuable to the ecological and human communities that depend on them. Over the past century, they have been subject to shifting management practices to maximize human use and ecosystem services, creating a complex relationship between water policy, management, and the natural ecosystem. This has necessitated research on the spatial and temporal dynamics of riparian vegetation change. The San Acacia Reach of the Middle Rio Grande has experienced multiple management and river flow fluctuations, resulting in threats to its riparian and aquatic ecosystems. This research uses remote sensing data, GIS, a review of management decisions, and an assessment of climate to both quantify how riparian vegetation has been altered over time and provide interpretations of the relationships between riparian change and shifting climate and management objectives. This research focused on four management phases from 1935 to 2014, each highlighting different management practices and climate-driven river patterns, providing unique opportunities to observe a direct relationship between river management, climate, and riparian response. Overall, we believe that management practices coupled with reduced surface river-flows with limited overbank flooding influenced the compositional and spatial patterns of vegetation, including possibly increasing non-native vegetation coverage. However, recent restoration efforts have begun to reduce non-native vegetation coverage.
","language":"English","publisher":"MDPI","doi":"10.3390/land6020029","usgsCitation":"Petrakis, R., van Leeuwen, W., Villarreal, M.L., Tashjian, P., Dello Russo, R., and Scott, C.A., 2017, Historical analysis of riparian vegetation change in response to shifting management objectives on the Middle Rio Grande: Land, v. 6, no. 2, p. 1-23, https://doi.org/10.3390/land6020029.","productDescription":"Article 29; 23 p.","startPage":"1","endPage":"23","ipdsId":"IP-079082","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469864,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land6020029","text":"Publisher Index Page"},{"id":438351,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7154F84","text":"USGS data release","linkHelpText":"Middle Rio Grande Multitemporal Land Cover Classifications - 1935, 1962, 1987, 1999, and 2014"},{"id":340957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Middle Rio Grande","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.03979492187499,\n 33.61919376817004\n ],\n [\n -106.75140380859374,\n 33.61919376817004\n ],\n [\n -106.75140380859374,\n 34.27197081112463\n ],\n [\n -107.03979492187499,\n 34.27197081112463\n ],\n [\n -107.03979492187499,\n 33.61919376817004\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-22","publicationStatus":"PW","scienceBaseUri":"591183afe4b0e541a03c1a44","contributors":{"authors":[{"text":"Petrakis, Roy E. 0000-0001-8932-077X rpetrakis@usgs.gov","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":174623,"corporation":false,"usgs":true,"family":"Petrakis","given":"Roy","email":"rpetrakis@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":694506,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Leeuwen, Willem","contributorId":148978,"corporation":false,"usgs":false,"family":"van Leeuwen","given":"Willem","email":"","affiliations":[],"preferred":false,"id":694507,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":694505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tashjian, Paul","contributorId":191857,"corporation":false,"usgs":false,"family":"Tashjian","given":"Paul","email":"","affiliations":[],"preferred":false,"id":694508,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dello Russo, Regina","contributorId":191858,"corporation":false,"usgs":false,"family":"Dello Russo","given":"Regina","email":"","affiliations":[],"preferred":false,"id":694509,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Christopher A.","contributorId":31664,"corporation":false,"usgs":true,"family":"Scott","given":"Christopher","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":694510,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187326,"text":"ofr20171047 - 2017 - Characterization of peak streamflows and flood inundation at selected areas in North Carolina following Hurricane Matthew, October 2016","interactions":[],"lastModifiedDate":"2017-08-29T15:36:32","indexId":"ofr20171047","displayToPublicDate":"2017-05-05T12:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1047","title":"Characterization of peak streamflows and flood inundation at selected areas in North Carolina following Hurricane Matthew, October 2016","docAbstract":"The passage of Hurricane Matthew through central and eastern North Carolina during October 7–9, 2016, brought heavy rainfall, which resulted in major flooding. More than 15 inches of rain was recorded in some areas. More than 600 roads were closed, including Interstates 95 and 40, and nearly 99,000 structures were affected by floodwaters. Immediately following the flooding, the U.S. Geological Survey documented 267 high-water marks, of which 254 were surveyed. North Carolina Emergency Management documented and surveyed 353 high-water marks. Using a subset of these highwater marks, six flood-inundation maps were created for hard-hit communities. Digital datasets of the inundation areas, study reach boundary, and water-depth rasters are available for download. In addition, peak gage-height data, peak streamflow data, and annual exceedance probabilities (in percent) were determined for 24 U.S. Geological Survey streamgages located near the heavily flooded communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171047","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Musser, J.W., Watson, K.M., and Gotvald, A.J., 2017, Characterization of peak streamflows and flood inundation at selected areas in North Carolina following Hurricane Matthew, October 2016 (ver. 2.0, August 2017): U.S. Geological Survey Open-File Report 2017–1047, 23 p., https://doi.org/10.3133/ofr20171047.","productDescription":"Report: v, 23 p.; Data Release, Version History","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-085645","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":340658,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75X276T","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood inundation, flood depth, and high-water marks for selected areas in North Carolina from the October 2016 flood"},{"id":340659,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1047/ofr20171047.pdf","text":"Report","size":"4.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1047"},{"id":340657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1047/coverthb3.jpg"},{"id":342197,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2017/1047/versionHist.txt","size":"2.31 MB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.75,\n 34\n ],\n [\n -76.75,\n 34\n ],\n [\n -76.75,\n 36.116667\n ],\n [\n -79.75,\n 36.116667\n ],\n [\n -79.75,\n 34\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: October 2016; Version 1.1: June 2017; Version 2.0: August 2017","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Development from extracting oil and gas resources can have unintended effects on multiple ecosystem functions, with cascading effects on wildlife, ecosystem services, and local economies. Big-game hunting opportunities may be closely related to these effects, but empirical analyses of impacts of energy development on hunting are limited. We examined the influence of oil and gas development density on harvest efficiency, or harvest per unit of hunter effort, within all hunt areas in Wyoming, USA, from 2008 to 2014 for 3 big-game species: elk (Cervus canadensis), mule deer (Odocoileus hemionus), and pronghorn (Antilocapra americana). Using harvest/hunter day as the response variable, we compared linear mixed-effects models for each species that included total well density (i.e., all wells constructed up to the year of record), active well density (i.e., only those wells currently producing oil or gas in that year), or neither as a predictor variable. We used well densities as indicators of development in the absence of data specifying the locations of other oil and gas infrastructure (e.g., roads, well pads). Models also accounted for the fixed effects of road density, hunter density, proportion of the area that is public land with unrestricted hunter access, proportion of the area that is forested, year of observation, and random effects of variation among hunt areas nested within associated game herd units. Presence of oil and gas wells had a positive influence on harvest efficiency for elk and mule deer. Although there was no overall effect to pronghorn, there was a negative influence of wells on juvenile pronghorn harvest efficiency. Changes in harvest efficiency due to expanding oil and gas development could alter the time spent hunting by hunters and their chances of harvesting an animal. This could have subsequent impacts on hunter satisfaction, game populations, and economic revenue generated from recreational hunters.
","language":"English","publisher":"The Wildlife Society","publisherLocation":"Hoboken, NJ","doi":"10.1002/jwmg.21205","usgsCitation":"Dorning, M., Garman, S.L., Diffendorfer, J., Semmens, D.J., Hawbaker, T., and Bagstad, K.J., 2017, Oil and gas development influences big-game hunting in Wyoming: Journal of Wildlife Management, v. 81, no. 3, p. 379-392, https://doi.org/10.1002/jwmg.21205.","productDescription":"14 p.","startPage":"379","endPage":"392","ipdsId":"IP-075405","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":340855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Center","active":true,"usgs":true}],"preferred":true,"id":694192,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70182142,"text":"sir20175010 - 2017 - Simulation of groundwater flow in the glacial aquifer system of northeastern Wisconsin with variable model complexity","interactions":[],"lastModifiedDate":"2017-05-04T15:33:55","indexId":"sir20175010","displayToPublicDate":"2017-05-04T12:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5010","title":"Simulation of groundwater flow in the glacial aquifer system of northeastern Wisconsin with variable model complexity","docAbstract":"The U.S. Geological Survey, National Water-Quality Assessment seeks to map estimated intrinsic susceptibility of the glacial aquifer system of the conterminous United States. Improved understanding of the hydrogeologic characteristics that explain spatial patterns of intrinsic susceptibility, commonly inferred from estimates of groundwater age distributions, is sought so that methods used for the estimation process are properly equipped. An important step beyond identifying relevant hydrogeologic datasets, such as glacial geology maps, is to evaluate how incorporation of these resources into process-based models using differing levels of detail could affect resulting simulations of groundwater age distributions and, thus, estimates of intrinsic susceptibility.
This report describes the construction and calibration of three groundwater-flow models of northeastern Wisconsin that were developed with differing levels of complexity to provide a framework for subsequent evaluations of the effects of process-based model complexity on estimations of groundwater age distributions for withdrawal wells and streams. Preliminary assessments, which focused on the effects of model complexity on simulated water levels and base flows in the glacial aquifer system, illustrate that simulation of vertical gradients using multiple model layers improves simulated heads more in low-permeability units than in high-permeability units. Moreover, simulation of heterogeneous hydraulic conductivity fields in coarse-grained and some fine-grained glacial materials produced a larger improvement in simulated water levels in the glacial aquifer system compared with simulation of uniform hydraulic conductivity within zones. The relation between base flows and model complexity was less clear; however, the relation generally seemed to follow a similar pattern as water levels. Although increased model complexity resulted in improved calibrations, future application of the models using simulated particle tracking is anticipated to evaluate if these model design considerations are similarly important for understanding the primary modeling objective - to simulate reasonable groundwater age distributions.
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\"}}]}","publicComments":"National Water-Quality Assessment","contact":"Director, Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
This assessment was conducted to fulfill the requirements of section 712 of the Energy Independence and Security Act of 2007 and to improve understanding of factors influencing carbon balance in ecosystems of Hawai‘i. Ecosystem carbon storage, carbon fluxes, and carbon balance were examined for major terrestrial ecosystems on the seven main Hawaiian islands in two time periods: baseline (from 2007 through 2012) and future (projections from 2012 through 2061). The assessment incorporated observed data, remote sensing, statistical methods, and simulation models. The national assessment has been completed for the conterminous United States, using methodology described in SIR 2010-5233, with results provided in three regional reports (PP 1804, PP 1797, and PP 1897), and for Alaska, with results provided in PP 1826.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1834","usgsCitation":"Selmants, P.C., Giardina, C.P., Jacobi, J.D., and Zhu, Zhiliang, eds., 2017, Baseline and projected future carbon storage and carbon fluxes in ecosystems of Hawai‘i: U.S. Geological Survey Professional Paper 1834, 134 p., https://doi.org/10.3133/pp1834.","productDescription":"vii, 134 p.","numberOfPages":"146","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-077242","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":340802,"rank":18,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/pp1787","text":"Professional Paper 1787","linkHelpText":"- Baseline and projected future carbon storage and greenhouse-gas fluxes in the Great Plains Region of the United States"},{"id":340799,"rank":15,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/pp1826","text":"Professional Paper 1826","linkHelpText":"- Baseline and projected future carbon storage and greenhouse-gas fluxes in ecosystems of Alaska"},{"id":340797,"rank":13,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_chapter8.pdf","text":"Chapter 8. Projected Future Carbon Storage and Carbon Fluxes in Terrestrial Ecosystems of Hawai‘i From Changes in Climate, Land Use, and Disturbance","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826","linkHelpText":"- By Benjamin M. Sleeter, Jinxun Liu, Colin J. Daniel, Todd J. Hawbaker, Tamara S. Wilson, Lucas B. Fortini, James D. Jacobi, Paul C. Selmants, Christian P. Giardina, Creighton M. Litton, and R. Flint Hughes"},{"id":340796,"rank":12,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_chapter7.pdf","text":"Chapter 7. Baseline and Projected Future Aquatic Carbon Fluxes to Nearshore Waters in Hawai‘i","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826","linkHelpText":"- By Richard A. MacKenzie, Ayron M. Strauch, Tracy N. Wiegner, Steven L. Colbert, Edward G. Stets, and Robert G. Streigl"},{"id":340794,"rank":10,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_chapter5.pdf","text":"Chapter 5. Wildland Fires and Greenhouse Gas Emissions in Hawai‘i","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826","linkHelpText":"- By Todd J. Hawbaker, Clay Trauernicht, Stephen M. Howard, Creighton M. Litton, Christian P. Giardina, James D. Jacobi, Lucas B. Fortini, R. Flint Hughes, Paul C. Selmants, and Zhiliang Zhu"},{"id":340791,"rank":7,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_chapter2.pdf","text":"Chapter 2. Baseline Land Cover","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826","linkHelpText":"- By James D. Jacobi, Jonathan P. Price, Lucas B. Fortini, Samuel M. Gon III, and Paul Berkowitz"},{"id":340821,"rank":5,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_executive_summary.pdf","text":"Executive Summary","size":"250 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826","linkHelpText":"- By Paul C. Selmants, Christian P. Giardina, James D. Jacobi, Lucas B. Fortini, R. Flint Hughes, Todd J. Hawbaker, Richard A. MacKenzie, Benjamin M. Sleeter, and Zhiliang Zhu"},{"id":340803,"rank":19,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20105233","text":"Scientific Investigations Report 2010-5233","linkHelpText":"- A method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of the United States under present conditions and future scenarios"},{"id":340798,"rank":14,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_chapter9.pdf","text":"Chapter 9. Hawai‘i Carbon Balance","size":"600 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826","linkHelpText":"- By Paul C. Selmants, Christian P. Giardina, Benjamin M.Sleeter, Jinxun Liu, and Zhiliang Zhu"},{"id":340786,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834.pdf","text":"Report","size":"23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826"},{"id":340787,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1834/coverthb.jpg"},{"id":340788,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_cover.pdf","text":"Covers","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826"},{"id":340789,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_front.pdf","text":"Front Matter","size":"241 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826"},{"id":340790,"rank":6,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_chapter1.pdf","text":"Chapter 1. Scope and Methodology","size":"665 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826","linkHelpText":"- By Christian P. Giardina, Paul C. Selmants, and James D. Jacobi"},{"id":340792,"rank":8,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_chapter3.pdf","text":"Chapter 3. Projecting End-of-Century Shifts in the Spatial Pattern of Plant-Available Water Across Hawai‘i to Assess Implications to Vegetation Shifts","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826","linkHelpText":"- By Lucas B. Fortini, James D. Jacobi, and Jonathan P. Price"},{"id":340793,"rank":9,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_chapter4.pdf","text":"Chapter 4. Influence of Invasive Species on Carbon Storage in Hawai‘i’s Ecosystems","size":"620 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826","linkHelpText":"- By R. Flint Hughes, Gregory P. Asner, Creighton M. Litton, Paul C. Selmants, Todd J. Hawbaker, James D. Jacobi, Christian P. Giardina, and Benjamin M. Sleeter"},{"id":340795,"rank":11,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/pp/1834/a/pp1834_chapter6.pdf","text":"Chapter 6. Baseline Carbon Storage and Carbon Fluxes in Terrestrial Ecosystems of Hawai‘i","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826","linkHelpText":"- By Paul C. Selmants, Christian P. Giardina, Sinan Sousan, David E. Knapp, Heather L. Kimball, Todd J. Hawbaker, Alvaro Moreno, Jami Seirer, Steve W. Running, Tomoaki Miura, Rafael Bergstrom, R. Flint Hughes, Creighton M. Litton, and Gregory P. Asner"},{"id":342057,"rank":20,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DB80B9","text":"Hawaii Land Cover and Habitat Status","linkHelpText":"(Chapter 2)"},{"id":340800,"rank":16,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/pp1804","text":"Professional Paper 1804","linkHelpText":"- Baseline and projected future carbon storage and greenhouse-gas fluxes in ecosystems of the Eastern United States"},{"id":340801,"rank":17,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/pp1797","text":"Professional Paper 1797","linkHelpText":"- Baseline and projected future carbon storage and greenhouse-gas fluxes in ecosystems of the Western United States"}],"country":"United 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\"}}]}","contact":"Land Change Science Program
U.S. Geological Survey
12201 Sunrise Valley Drive
MS 519A National Center
Reston, VA 20192
We map the 385-kilometer (km) long surface trace of the right-lateral, strike-slip Denali Fault between the Totschunda-Denali Fault intersection in Alaska, United States and the village of Haines Junction, Yukon, Canada. In Alaska, digital elevation models based on light detection and ranging and interferometric synthetic aperture radar data enabled our fault mapping at scales of 1:2,000 and 1:10,000, respectively. Lacking such resources in Yukon, we developed new structure-from-motion digital photogrammetry products from legacy aerial photos to map the fault surface trace at a scale of 1:10,000 east of the international border. The section of the fault that we map, referred to as the Eastern Denali Fault, did not rupture during the 2002 Denali Fault earthquake (moment magnitude 7.9). Seismologic, geodetic, and geomorphic evidence, along with a paleoseismic record of past ground-rupturing earthquakes, demonstrate Holocene and contemporary activity on the fault, however. This map of the Eastern Denali Fault surface trace complements other data sets by providing an openly accessible digital interpretation of the location, length, and continuity of the fault’s surface trace based on the accompanying digital topography dataset. Additionally, the digitized fault trace may provide geometric constraints useful for modeling earthquake scenarios and related seismic hazard.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171049","usgsCitation":"Bender, A.M., and Haeussler, P.J., 2017, Eastern Denali Fault surface trace map, eastern Alaska and Yukon, Canada: U.S. Geological Survey Open-File Report 2017–1049, 10 p., https://doi.org/10.3133/ofr20171049.","productDescription":"iii, 10 p.","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084514","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":438353,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T151WC","text":"USGS data release","linkHelpText":"Eastern Denali Fault Surface Trace Map, Eastern Alaska and Adjacent Canada, 1978-2008"},{"id":422373,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_105646.htm","linkFileType":{"id":5,"text":"html"}},{"id":340824,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1049/coverthb.jpg"},{"id":340825,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1049/ofr20171049.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1049"}],"country":"Canada, United States","state":"Alaska, Yukon","otherGeospatial":"Denali Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -147,\n 60\n ],\n [\n -135,\n 60\n ],\n [\n -135,\n 64\n ],\n [\n -147,\n 64\n ],\n [\n -147,\n 60\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Science Center
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
The U.S. Geological Survey Monthly Water Balance Model Futures Portal (https://my.usgs.gov/mows/) is a user-friendly interface that summarizes monthly historical and simulated future conditions for seven hydrologic and meteorological variables (actual evapotranspiration, potential evapotranspiration, precipitation, runoff, snow water equivalent, atmospheric temperature, and streamflow) at locations across the conterminous United States (CONUS).
The estimates of these hydrologic and meteorological variables were derived using a Monthly Water Balance Model (MWBM), a modular system that simulates monthly estimates of components of the hydrologic cycle using monthly precipitation and atmospheric temperature inputs. Precipitation and atmospheric temperature from 222 climate datasets spanning historical conditions (1952 through 2005) and simulated future conditions (2020 through 2099) were summarized for hydrographic features and used to drive the MWBM for the CONUS. The MWBM input and output variables were organized into an open-access database. An Open Geospatial Consortium, Inc., Web Feature Service allows the querying and identification of hydrographic features across the CONUS. To connect the Web Feature Service to the open-access database, a user interface—the Monthly Water Balance Model Futures Portal—was developed to allow the dynamic generation of summary files and plots based on plot type, geographic location, specific climate datasets, period of record, MWBM variable, and other options. Both the plots and the data files are made available to the user for download
Director, USGS Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Aim
The distributions of exotic species reflect patterns of human-mediated dispersal, species climatic tolerances and a suite of other biotic and abiotic factors. The relative importance of each of these factors will shape how the spread of exotic species is affected by ongoing economic globalization and climate change. However, patterns of trade may be correlated with variation in scientific sampling effort globally, potentially confounding studies that do not account for sampling patterns.
Location
Global.
Time period
Museum records, generally from the 1800s up to 2015.
Major taxa studied
Plant species exotic to the United States.
Methods
We used data from the Global Biodiversity Information Facility (GBIF) to summarize the number of plant species with exotic occurrences in the United States that also occur in each other country world-wide. We assessed the relative importance of trade and climatic similarity for explaining variation in the number of shared species while evaluating several methods to account for variation in sampling effort among countries.
Results
Accounting for variation in sampling effort reversed the relative importance of trade and climate for explaining numbers of shared species. Trade was strongly correlated with numbers of shared U.S. exotic plants between the United States and other countries before, but not after, accounting for sampling variation among countries. Conversely, accounting for sampling effort strengthened the relationship between climatic similarity and species sharing. Using the number of records as a measure of sampling effort provided a straightforward approach for the analysis of occurrence data, whereas species richness estimators and rarefaction were less effective at removing sampling bias.
Main conclusions
Our work provides support for broad-scale climatic limitation on the distributions of exotic species, illustrates the need to account for variation in sampling effort in large biodiversity databases, and highlights the difficulty in inferring causal links between the economic drivers of invasion and global patterns of exotic species occurrence.
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When nature and humanity change Earth’s landscapes - through flood or fire, public policy, natural resources management, or economic development - the results are often dramatic and lasting.
Wildfires can reshape ecosystems. Hurricanes with names like Sandy or Katrina will howl for days while altering the landscape for years. One growing season in the evolution of drought-resistant genetics can transform semiarid landscapes into farm fields.
In the past, valuable land cover maps created for understanding the effects of those events - whether changes in wildlife habitat, water-quality impacts, or the role land use and land cover play in affecting weather and climate - came out at best every 5 to 7 years. Those high quality, high resolution maps were good, but users always craved more: even higher quality data, additional land cover and land change variables, more detailed legends, and most importantly, more frequent land change information.
Now a bold new initiative called Land Change Monitoring, Assessment, and Projection (LCMAP) promises to fulfill that demand.
Developed at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, LCMAP provides definitive, timely information on how, why, and where the planet is changing. LCMAP’s continuous monitoring process can detect changes as they happen every day that Landsat satellites acquire clear observations. The result will be to place near real-time information in the hands of land and resource managers who need to understand the effects these changes have on landscapes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip172","usgsCitation":"Young, S.M., 2017, Land Change Monitoring, Assessment, and Projection (LCMAP) revolutionizes land cover and land change research: U.S. Geological Survey General Information Product 172, 4 p., https://doi.org/10.3133/gip172.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-083103","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":340452,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0172/coverthb.jpg"},{"id":340453,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0172/gip172.pdf","size":"7.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 172"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198