The U.S. Geological Survey, in cooperation with the Montana Department of Environmental Quality and the Montana Department of Natural Resources and Conservation, developed regional regression equations based on basin and streamflow characteristics for streamflow-gaging stations through water year 2009 that can be used to estimate streamflow characteristics for ungaged sites in western Montana. The regression equations allow estimation of low-flow frequencies; mean annual and mean monthly streamflows; and the 20-, 50-, and 80-percent durations for annual and monthly duration streamflows for ungaged sites in western Montana that are unaffected by regulation.
\nRegression equations were developed for 54 streamflow characteristics in each of the 4 hydrologic regions generally located in western Montana. Exploratory data analyses determined that of the 40 basin characteristics used as explanatory variables, only 3 were significant in the final equations. These three basin characteristics are contributing drainage area, mean annual precipitation of the drainage basin, and percentage of basin with slopes greater than 50 percent. Low-flow frequency data and daily mean streamflow data from 152 streamflow-gaging stations were used to develop the regression equations. These 152 streamflow-gaging stations were considered to be unregulated (or only the unregulated period of record for regulated streamflow-gaging stations were used). For this study, a streamflow-gaging station is considered to be unregulated if cumulative drainage area upstream from all upstream dams is less than 20 percent of the drainage basin for the streamflow-gaging station, and if no large diversion canals are upstream from the streamflow-gaging station.
\nIn addition to the regional regression equations, two methods for estimating streamflow characteristics based on drainage areas of a nearby streamflow-gaging station are provided. Also, spreadsheet tools are provided for calculating streamflow characteristics based on regression equations or the drainage-area ratio method.
\nAll of the data used to calculate basin characteristics were derived from publicly available data sources and are available through the U.S. Geological Survey Streamstats program (http://water.usgs.gov/osw/streamstats/) for Montana. The primary purpose of the Montana StreamStats application is to provide estimates of basin characteristics and streamflow characteristics for user-selected ungaged sites on Montana streams. The regional regression equations presented in this report have been loaded to the Montana StreamStats application and can be used to derive streamflow characteristics for ungaged sites.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019G","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality and Montana Department of Natural Resources and Conservation","usgsCitation":"McCarthy, P.M., Sando, Roy, Sando, S.K., and Dutton, D.M., 2016, Methods for estimating streamflow characteristics at ungaged sites in western Montana based on data through water year 2009: U.S. Geological Survey Scientific Investigations Report 2015–5019–G, 19 p., https://dx.doi.org/10.3133/sir20155019G.","productDescription":"Report: vii, 19 p.; 10 Tables","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067085","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":319659,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5019G/coverthb.jpg"},{"id":319661,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2015/5019G/sir20155019G_tables.xlsx","text":"Tables 1 through 4, 1–1 through 1–4, and 2–1 through 2–2","size":"483 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015–5019–G Tables"},{"id":319660,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5019G/sir20155019G.pdf","text":"Report","size":"9.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015–5019–G"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.03759765625,\n 48.99463598353408\n ],\n [\n -116.08154296875001,\n 47.97521412341618\n ],\n [\n -115.6201171875,\n 47.39834920035926\n ],\n [\n -114.64233398437499,\n 46.66451741754235\n ],\n [\n -114.43359375,\n 46.52863469527167\n ],\n [\n -114.53247070312499,\n 46.14178273759234\n ],\n [\n -114.43359375,\n 45.87471224890479\n ],\n [\n -114.53247070312499,\n 45.79050946752472\n ],\n [\n -114.576416015625,\n 45.590978249451936\n ],\n [\n -114.334716796875,\n 45.4524242413431\n ],\n [\n -113.9501953125,\n 45.69850658738848\n ],\n [\n -113.7744140625,\n 45.57560020947799\n ],\n [\n -113.7744140625,\n 45.390735154248894\n ],\n [\n -113.433837890625,\n 45.0502402697946\n ],\n [\n -113.51074218749999,\n 44.933696389694674\n ],\n [\n -113.26904296874999,\n 44.78573392716592\n ],\n [\n -113.08227539062499,\n 44.66865287227321\n ],\n [\n -112.9833984375,\n 44.43377984606825\n ],\n [\n -112.8515625,\n 44.33956524809713\n ],\n [\n -112.796630859375,\n 44.457309801319305\n ],\n [\n -112.47802734375,\n 44.4808302785626\n ],\n [\n -112.379150390625,\n 44.457309801319305\n ],\n [\n -112.291259765625,\n 44.574817404670306\n ],\n [\n -111.8408203125,\n 44.55133484083592\n ],\n [\n -111.52221679687499,\n 44.535674532413196\n ],\n [\n -111.42333984375,\n 44.69989765840318\n ],\n [\n -111.346435546875,\n 44.72332018895825\n ],\n [\n -111.060791015625,\n 44.465151013519616\n ],\n [\n -111.060791015625,\n 44.99588261816546\n ],\n [\n -107.22656249999999,\n 45.02695045318546\n ],\n [\n -107.57812499999999,\n 45.99696161820381\n ],\n [\n -108.12744140625,\n 47.234489635299184\n ],\n [\n -111.42333984375,\n 47.2195681123155\n ],\n [\n -112.7197265625,\n 48.06339653776211\n ],\n [\n -113.35693359375,\n 49.009050809382046\n ],\n [\n -116.03759765625,\n 48.99463598353408\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
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The U.S. Geological Survey (USGS), in cooperation with the Montana Department of Natural Resources and Conservation, completed a study to update methods for estimating peak-flow frequencies at ungaged sites in Montana based on peak-flow data at streamflow-gaging stations through water year 2011. The methods allow estimation of peak-flow frequencies (that is, peak-flow magnitudes, in cubic feet per second, associated with annual exceedance probabilities of 66.7, 50, 42.9, 20, 10, 4, 2, 1, 0.5, and 0.2 percent) at ungaged sites. The annual exceedance probabilities correspond to 1.5-, 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.
Regional regression analysis is a primary focus of Chapter F of this Scientific Investigations Report, and regression equations for estimating peak-flow frequencies at ungaged sites in eight hydrologic regions in Montana are presented. The regression equations are based on analysis of peak-flow frequencies and basin characteristics at 537 streamflow-gaging stations in or near Montana and were developed using generalized least squares regression or weighted least squares regression.
All of the data used in calculating basin characteristics that were included as explanatory variables in the regression equations were developed for and are available through the USGS StreamStats application (http://water.usgs.gov/osw/streamstats/) for Montana. StreamStats is a Web-based geographic information system application that was created by the USGS to provide users with access to an assortment of analytical tools that are useful for water-resource planning and management. The primary purpose of the Montana StreamStats application is to provide estimates of basin characteristics and streamflow characteristics for user-selected ungaged sites on Montana streams. The regional regression equations presented in this report chapter can be conveniently solved using the Montana StreamStats application.
Selected results from this study were compared with results of previous studies. For most hydrologic regions, the regression equations reported for this study had lower mean standard errors of prediction (in percent) than the previously reported regression equations for Montana. The equations presented for this study are considered to be an improvement on the previously reported equations primarily because this study (1) included 13 more years of peak-flow data; (2) included 35 more streamflow-gaging stations than previous studies; (3) used a detailed geographic information system (GIS)-based definition of the regulation status of streamflow-gaging stations, which allowed better determination of the unregulated peak-flow records that are appropriate for use in the regional regression analysis; (4) included advancements in GIS and remote-sensing technologies, which allowed more convenient calculation of basin characteristics and investigation of many more candidate basin characteristics; and (5) included advancements in computational and analytical methods, which allowed more thorough and consistent data analysis.
This report chapter also presents other methods for estimating peak-flow frequencies at ungaged sites. Two methods for estimating peak-flow frequencies at ungaged sites located on the same streams as streamflow-gaging stations are described. Additionally, envelope curves relating maximum recorded annual peak flows to contributing drainage area for each of the eight hydrologic regions in Montana are presented and compared to a national envelope curve. In addition to providing general information on characteristics of large peak flows, the regional envelope curves can be used to assess the reasonableness of peak-flow frequency estimates determined using the regression equations.
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\"}}]}","edition":"Version 1.1: February 2018","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
The climatic conditions of the specific time period during which peak-flow data were collected at a given streamflow-gaging station (hereinafter referred to as gaging station) can substantially affect how well the peak-flow frequency (hereinafter referred to as frequency) results represent long-term hydrologic conditions. Differences in the timing of the periods of record can result in substantial inconsistencies in frequency estimates for hydrologically similar gaging stations. Potential for inconsistency increases with decreasing peak-flow record length. The representativeness of the frequency estimates for a short-term gaging station can be adjusted by various methods including weighting the at-site results in association with frequency estimates from regional regression equations (RREs) by using the Weighted Independent Estimates (WIE) program. Also, for gaging stations that cannot be adjusted by using the WIE program because of regulation or drainage areas too large for application of RREs, frequency estimates might be improved by using record extension procedures, including a mixed-station analysis using the maintenance of variance type I (MOVE.1) procedure. The U.S. Geological Survey, in cooperation with the Montana Department of Transportation and the Montana Department of Natural Resources and Conservation, completed a study to provide adjusted frequency estimates for selected gaging stations through water year 2011.
The purpose of Chapter D of this Scientific Investigations Report is to present adjusted frequency estimates for 504 selected streamflow-gaging stations in or near Montana based on data through water year 2011. Estimates of peak-flow magnitudes for the 66.7-, 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities are reported. These annual exceedance probabilities correspond to the 1.5-, 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.
The at-site frequency estimates were adjusted by weighting with frequency estimates from RREs using the WIE program for 438 selected gaging stations in Montana. These 438 selected gaging stations (1) had periods of record less than or equal to 40 years, (2) represented unregulated or minor regulation conditions, and (3) had drainage areas less than about 2,750 square miles.
The weighted-average frequency estimates obtained by weighting with RREs generally are considered to provide improved frequency estimates. In some cases, there are substantial differences among the at-site frequency estimates, the regression-equation frequency estimates, and the weighted-average frequency estimates. In these cases, thoughtful consideration should be applied when selecting the appropriate frequency estimate. Some factors that might be considered when selecting the appropriate frequency estimate include (1) whether the specific gaging station has peak-flow characteristics that distinguish it from most other gaging stations used in developing the RREs for the hydrologic region; and (2) the length of the peak-flow record and the general climatic characteristics during the period when the peak-flow data were collected. For critical structure-design applications, a conservative approach would be to select the higher of the at-site frequency estimate and the weighted-average frequency estimate.
The mixed-station MOVE.1 procedure generally was applied in cases where three or more gaging stations were located on the same large river and some of the gaging stations could not be adjusted using the weighted-average method because of regulation or drainage areas too large for application of RREs. The mixed-station MOVE.1 procedure was applied to 66 selected gaging stations on 19 large rivers.
The general approach for using mixed-station record extension procedures to adjust at-site frequencies involved (1) determining appropriate base periods for the gaging stations on the large rivers, (2) synthesizing peak-flow data for the gaging stations with incomplete peak-flow records during the base periods by using the mixed-station MOVE.1 procedure, and (3) conducting frequency analysis on the combined recorded and synthesized peak-flow data for each gaging station. Frequency estimates for the combined recorded and synthesized datasets for 66 gaging stations with incomplete peak-flow records during the base periods are presented. The uncertainties in the mixed-station record extension results are difficult to directly quantify; thus, it is important to understand the intended use of the estimated frequencies based on analysis of the combined recorded and synthesized datasets. The estimated frequencies are considered general estimates of frequency relations among gaging stations on the same stream channel that might be expected if the gaging stations had been gaged during the same long-term base period. However, because the mixed-station record extension procedures involve secondary statistical analysis with accompanying errors, the uncertainty of the frequency estimates is larger than would be obtained by collecting systematic records for the same number of years in the base period.
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\"}}]}","edition":"Version 1.0: April 6, 2016; Version 1.1: February 26, 2018","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
A large-scale study by the U.S. Geological Survey, in cooperation with the Montana Department of Transportation and the Montana Department of Natural Resources and Conservation, was done to investigate general patterns in peak-flow temporal trends and stationarity through water year 2011 for 24 long-term streamflow-gaging stations (hereinafter referred to as gaging stations) in Montana. Hereinafter, all years refer to water years; a water year is the 12-month period from October 1 through September 30 and is designated by the year in which it ends. The primary focus of the study was to identify general patterns in peak-flow temporal trends and stationarity that are relevant to application of peak-flow frequency analyses within a statewide gaging-station network.
\nTemporal trends were analyzed for two hydrologic variables: annual peak flow and peak-flow timing. Annual peak flow is the maximum instantaneous discharge, in cubic feet per second, recorded each year a gaging station was operated. Peak-flow timing is the day of the annual peak flow (hereinafter referred to as day of peak), recorded each year a gaging station was operated.
\nStudy results provide evidence that annual peak flow for most of the long-term gaging stations can be reasonably considered as stationary for application of peak-flow frequency analyses within a statewide gaging station network. Upward trends in annual peak flow during 1930–76 generally were stronger than downward trends during 1967–2011 for most long-term gaging stations. Statistical distributions of annual peak flow generally were similar among three summary time periods (1930–78, 1979–2011, and the entire period of record). However, for two low-elevation gaging stations in eastern Montana (Poplar River at international boundary [gaging station 06178000] and Powder River at Moorhead, Montana [gaging station 06324500]), substantial downward trends in annual peak flow during 1967–2011 were of similar or stronger magnitude than the upward trends during 1930–76, and the annual-peak-flow medians for 1979–2011 were substantially lower than the medians for the entire period of record.
\nFor peak-flow timing for most long-term gaging stations, differences in trends between 1930–76 and 1967–2011 are variable and not particularly strong. Statistical distributions generally are similar among the summary time periods. However, for two high-elevation gaging stations on a Missouri River headwater tributary (Gallatin River near Gallatin Gateway, Montana [gaging station 06043500] and Gallatin River at Logan, Montana [gaging station 06052500]) and for five high-elevation gaging stations in the Yellowstone River Basin (Yellowstone River at Corwin Springs, Montana [gaging station 06191500], Yellowstone River near Livingston, Montana [gaging station 06192500], Clarks Fork Yellowstone River near Belfry, Montana [gaging station 06207500], Clarks Fork Yellowstone River at Edgar, Montana [gaging station 06208500], and Yellowstone River at Billings, Montana [gaging station 06214500]) downward trends in peak-flow timing during 1967–2011 generally were stronger than upward trends during 1930–1976, and day-of-peak medians for 1979–2011 were considerably less than medians for 1930–78. The downward trends in peak-flow timing for 1967–2011 indicate that the timing of annual peak flows changed from later in the year to earlier in the year. For the seven high-elevation gaging stations, the mean change during 1967–2011 was about 13 days (range of 8 to 22 days).
\nFor most of the high-elevation gaging stations in the Missouri River headwaters, Yellowstone River Basin, and Columbia River Basin, there was general correspondence between trend patterns for annual peak flow and trend patterns for peak-flow timing; that is, during periods when there were upward trends in annual peak flow, there generally also were upward trends in peak-flow timing. Conversely, during periods when there were downward trends in annual peak flow, there generally also were downward trends in peak-flow timing.
\nThe two low-elevation gaging stations in eastern Montana (Poplar River at international boundary [gaging station 06178000] and Powder River at Moorhead, Montana [gaging station 06324500]) had considerable changes in annual-peakflow characteristics after the mid-1970s, which might provide evidence of potential nonstationarity in the peak-flow records. The two low-elevation gaging stations that have potential nonstationarity are located in drainage basins that are strongly affected by agricultural activities that potentially affect the hydrologic regimes. Primary agricultural activities that might alter natural hydrologic conditions include construction of small impoundments (primarily for stock-watering purposes) and irrigation diversions. Temporal variability in these activities might contribute to the potential nonstationarity issues. Changes in climatic characteristics after the mid-1970s also possibly contribute to the potential nonstationarity issues. Lack of considerable indication of potential nonstationarity in annual peak flow for the other long-term gaging stations in this study might indicate that climatic changes have been more pronounced with respect to effects on peak flows in low elevation areas in eastern Montana than in areas represented by the other long-term gaging stations. Another possibility is that climatic changes after the mid-1970s are exacerbated in low-elevation areas where small-impoundment development and potential effects of irrigation diversions might be more extensive.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019B","collaboration":"Prepared in cooperation with the Montana Department of Transportation and Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, S.K., McCarthy, P.M., Sando, Roy, and Dutton, D.M., 2016, Temporal trends and stationarity in annual peak flow and peak-flow timing for selected long-term streamflow-gaging stations in or near Montana through water year 2011: U.S. Geological Survey Scientific Investigations Report 2015–5019–B, 48 p., https://dx.doi.org/10.3133/sir20155019B.","productDescription":"vi, 48 p.","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-060698","costCenters":[{"id":5050,"text":"WY-MT Water Science 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
Process-based hydrological models have a long history dating back to the 1960s. Criticized by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is that these tools are necessary in many situations and, in a certain class of problems, they are the most appropriate type of hydrological model. This is especially the case in situations where knowledge of flow paths or distributed state variables and/or preservation of physical constraints is important. Examples of this include: spatiotemporal variability of soil moisture, groundwater flow and runoff generation, sediment and contaminant transport, or when feedbacks among various Earth’s system processes or understanding the impacts of climate non-stationarity are of primary concern. These are situations where process-based models excel and other models are unverifiable. This article presents this pragmatic view in the context of existing literature to justify the approach where applicable and necessary. We review how improvements in data availability, computational resources and algorithms have made detailed hydrological simulations a reality. Avenues for the future of process-based hydrological models are presented suggesting their use as virtual laboratories, for design purposes, and with a powerful treatment of uncertainty.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.jhydrol.2016.03.026","usgsCitation":"Fatichi, S., Vivoni, E.R., Odgen, F.L., Ivanov, V.Y., Mirus, B.B., Gochis, D., Downer, C.W., Camporese, M., Davison, J.H., Ebel, B.A., Jones, N., Kim, J., Mascaro, G., Niswonger, R.G., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and Tarboton, D., 2016, An overview of current applications, challenges, and future trends in distributed process-based models in hydrology: Journal of Hydrology, v. 537, p. 45-60, https://doi.org/10.1016/j.jhydrol.2016.03.026.","productDescription":"16 p.","startPage":"45","endPage":"60","numberOfPages":"16","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072365","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471090,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2016.03.026","text":"Publisher Index Page"},{"id":319747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"537","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5703821ce4b0328dcb81b35b","contributors":{"authors":[{"text":"Fatichi, Simone","contributorId":152452,"corporation":false,"usgs":false,"family":"Fatichi","given":"Simone","email":"","affiliations":[{"id":18926,"text":"Institute of Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":589430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vivoni, Enrique R.","contributorId":139052,"corporation":false,"usgs":false,"family":"Vivoni","given":"Enrique","email":"","middleInitial":"R.","affiliations":[{"id":12634,"text":"School of Earth and Space Exploration and School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ","active":true,"usgs":false}],"preferred":false,"id":589431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Odgen, Fred L","contributorId":152453,"corporation":false,"usgs":false,"family":"Odgen","given":"Fred","email":"","middleInitial":"L","affiliations":[{"id":6656,"text":"University of Wyoming, Renewable Resources","active":true,"usgs":false}],"preferred":false,"id":589432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ivanov, Valeriy Y","contributorId":152454,"corporation":false,"usgs":false,"family":"Ivanov","given":"Valeriy","email":"","middleInitial":"Y","affiliations":[{"id":6649,"text":"University of Michigan, School of Natural Resources and Environment","active":true,"usgs":false}],"preferred":false,"id":589433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":589434,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gochis, David","contributorId":152455,"corporation":false,"usgs":false,"family":"Gochis","given":"David","email":"","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":589435,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Downer, Charles W","contributorId":152456,"corporation":false,"usgs":false,"family":"Downer","given":"Charles","email":"","middleInitial":"W","affiliations":[{"id":18927,"text":"Engineer Research and Development","active":true,"usgs":false}],"preferred":false,"id":589436,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Camporese, Matteo","contributorId":152457,"corporation":false,"usgs":false,"family":"Camporese","given":"Matteo","email":"","affiliations":[{"id":18928,"text":"University of Padua","active":true,"usgs":false}],"preferred":false,"id":589437,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Davison, Jason H","contributorId":152458,"corporation":false,"usgs":false,"family":"Davison","given":"Jason","email":"","middleInitial":"H","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":589438,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ebel, Brian A. 0000-0002-5413-3963 bebel@usgs.gov","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":2557,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian","email":"bebel@usgs.gov","middleInitial":"A.","affiliations":[{"id":438,"text":"National Research Program - 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2016 - Municipal solid waste landfills harbor distinct microbiomes","interactions":[],"lastModifiedDate":"2018-08-07T11:52:05","indexId":"70177963","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"title":"Municipal solid waste landfills harbor distinct microbiomes","docAbstract":"Landfills are the final repository for most of the discarded material from human society and its “built environments.” Microorganisms subsequently degrade this discarded material in the landfill, releasing gases (largely CH4 and CO2) and a complex mixture of soluble chemical compounds in leachate. Characterization of “landfill microbiomes” and their comparison across several landfills should allow the identification of environmental or operational properties that influence the composition of these microbiomes and potentially their biodegradation capabilities. To this end, the composition of landfill microbiomes was characterized as part of an ongoing USGS national survey studying the chemical composition of leachates from 19 non-hazardous landfills across 16 states in the continental U.S. The landfills varied in parameters such as size, waste composition, management strategy, geography, and climate zone. The diversity and composition of bacterial and archaeal populations in leachate samples were characterized by 16S rRNA gene sequence analysis, and compared against a variety of physical and chemical parameters in an attempt to identify their impact on selection. Members of the Epsilonproteobacteria, Gammaproteobacteria, Clostridia, and candidate division OP3 were the most abundant. The distribution of the observed phylogenetic diversity could best be explained by a combination of variables and was correlated most strongly with the concentrations of chloride and barium, rate of evapotranspiration, age of waste, and the number of detected household chemicals. This study illustrates how leachate microbiomes are distinct from those of other natural or built environments, and sheds light on the major selective forces responsible for this microbial diversity.
","language":"English","publisher":"Frontiers","doi":"10.3389/fmicb.2016.00534","usgsCitation":"Stamps, B.W., Lyles, C.N., Suflita, J.M., Masoner, J.R., Cozzarelli, I.M., Kolpin, D.W., and Stevenson, B.S., 2016, Municipal solid waste landfills harbor distinct microbiomes: Frontiers in Microbiology, v. 7, p. 1-11, https://doi.org/10.3389/fmicb.2016.00534.","productDescription":"Article 534; 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-062988","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":471095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2016.00534","text":"Publisher Index Page"},{"id":330574,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-20","publicationStatus":"PW","scienceBaseUri":"5818582fe4b0bb36a4c6fa13","contributors":{"authors":[{"text":"Stamps, Blake W.","contributorId":176485,"corporation":false,"usgs":false,"family":"Stamps","given":"Blake","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":652503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyles, Christopher N.","contributorId":176484,"corporation":false,"usgs":false,"family":"Lyles","given":"Christopher","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":652504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Suflita, Joseph M.","contributorId":82997,"corporation":false,"usgs":true,"family":"Suflita","given":"Joseph","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":652505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Masoner, Jason R. 0000-0002-4829-6379 jmasoner@usgs.gov","orcid":"https://orcid.org/0000-0002-4829-6379","contributorId":3193,"corporation":false,"usgs":true,"family":"Masoner","given":"Jason","email":"jmasoner@usgs.gov","middleInitial":"R.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":652506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":652507,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652508,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stevenson, Bradley S.","contributorId":176491,"corporation":false,"usgs":false,"family":"Stevenson","given":"Bradley","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":652509,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70192938,"text":"70192938 - 2016 - Role of large- and fine-scale variables in predicting catch rates of larval Pacific lamprey in the Willamette Basin, Oregon","interactions":[],"lastModifiedDate":"2017-11-07T12:50:32","indexId":"70192938","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Role of large- and fine-scale variables in predicting catch rates of larval Pacific lamprey in the Willamette Basin, Oregon","docAbstract":"Pacific lamprey Entosphenus tridentatus is an anadromous fish native to the Pacific Northwest of the USA. That has declined substantially over the last 40 years. Effective conservation of this species will require an understanding of the habitat requirements for each life history stage. Because its life cycle contains extended freshwater rearing (3–8 years), the larval stage may be a critical factor limiting abundance of Pacific lamprey. The objective of our study was to estimate the influence of barriers and habitat characteristics on the catch-per-unit-effort (CPUE) of larval Pacific lamprey in the Willamette River Basin, Oregon, USA. We sampled lampreys at multiple locations in wadeable streams throughout the basin in 2011–13 and used an information theoretic approach to examine the relative influence of fine- and large-scale predictors of CPUE. Pacific lamprey was observed across the basin, but its relative abundance appeared to be limited by the presence of natural and artificial barriers in some sub-basins. Lower velocity habitats such as off-channel areas and pools contained higher densities of larval lamprey; mean Pacific lamprey CPUE in off-channel habitats was 4 and 32 times greater than in pools and riffles respectively. Restoration and conservation strategies that improve fish passage, enhance natural hydrologic and depositional processes and increase habitat heterogeneity will likely benefit larval Pacific lamprey.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12207","usgsCitation":"Schultz, L., Mayfield, M.P., Sheoships, G.T., Wyss, L.A., Clemens, B.J., Whitlock, S.L., and Schreck, C.B., 2016, Role of large- and fine-scale variables in predicting catch rates of larval Pacific lamprey in the Willamette Basin, Oregon: Ecology of Freshwater Fish, v. 25, no. 2, p. 261-271, https://doi.org/10.1111/eff.12207.","productDescription":"11 p.","startPage":"261","endPage":"271","ipdsId":"IP-056803","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":348380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.50280761718751,\n 43.97700467496408\n ],\n [\n -122.01416015625,\n 43.97700467496408\n ],\n [\n -122.01416015625,\n 45.7176863579072\n ],\n [\n -123.50280761718751,\n 45.7176863579072\n ],\n [\n -123.50280761718751,\n 43.97700467496408\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2014-12-19","publicationStatus":"PW","scienceBaseUri":"5a07ea50e4b09af898c8cc75","contributors":{"authors":[{"text":"Schultz, Luke 0000-0002-6751-4626 lschultz@usgs.gov","orcid":"https://orcid.org/0000-0002-6751-4626","contributorId":193171,"corporation":false,"usgs":true,"family":"Schultz","given":"Luke","email":"lschultz@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":720937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mayfield, Mariah P.","contributorId":200089,"corporation":false,"usgs":false,"family":"Mayfield","given":"Mariah","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":720938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sheoships, Gabe T.","contributorId":200090,"corporation":false,"usgs":false,"family":"Sheoships","given":"Gabe","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":720939,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wyss, Lance A.","contributorId":195114,"corporation":false,"usgs":false,"family":"Wyss","given":"Lance","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":720940,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clemens, Benjamin J.","contributorId":195098,"corporation":false,"usgs":false,"family":"Clemens","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":720941,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whitlock, Steven L.","contributorId":171705,"corporation":false,"usgs":false,"family":"Whitlock","given":"Steven","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":720942,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schreck, Carl B. 0000-0001-8347-1139 carl.schreck@usgs.gov","orcid":"https://orcid.org/0000-0001-8347-1139","contributorId":878,"corporation":false,"usgs":true,"family":"Schreck","given":"Carl","email":"carl.schreck@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":717382,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70193182,"text":"70193182 - 2016 - NHDPlus as a geospatial framework for SPARROW modeling","interactions":[],"lastModifiedDate":"2018-03-15T10:26:24","indexId":"70193182","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1975,"text":"Impact Assessment Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"NHDPlus as a geospatial framework for SPARROW modeling","docAbstract":"Successful water-resource management requires thorough knowledge and understanding of the relations among water-quality contaminate sources and the factors that affect the transport throughout a hydrologic system. Surface-water modeling is a valuable tool that can be applied to help advance and achieve the understanding of these dynamic relations. Spatially Referenced Regressions on Watershed Attributes (SPARROW) is one such model that uses nonlinear statistical methods to define conceptual and spatial relations among quantities of contaminant sources, monitored contaminant load, aquatic transport processes. NHDPlus Version 2 (V2) is a digital representation of a hydrologic network of streams and associated catchments. The dataset provides the fundamental spatial framework for SPARROW modeling.","language":"English","publisher":"American Water Resources Association","usgsCitation":"Brakebill, J.W., and Schwarz, G., 2016, NHDPlus as a geospatial framework for SPARROW modeling: Impact Assessment Bulletin, v. 18, no. 3.","ipdsId":"IP-073650","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":348610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347782,"type":{"id":15,"text":"Index Page"},"url":"https://www.awra.org/impact/"}],"volume":"18","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07ea50e4b09af898c8cc73","contributors":{"authors":[{"text":"Brakebill, John W. 0000-0001-9235-6810 jwbrakeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9235-6810","contributorId":1061,"corporation":false,"usgs":true,"family":"Brakebill","given":"John","email":"jwbrakeb@usgs.gov","middleInitial":"W.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":718136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":543,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":718137,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70179445,"text":"70179445 - 2016 - Slow climate velocities of mountain streams portend their role as refugia for cold-water biodiversity","interactions":[],"lastModifiedDate":"2017-01-03T11:21:55","indexId":"70179445","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"Slow climate velocities of mountain streams portend their role as refugia for cold-water biodiversity","docAbstract":"The imminent demise of montane species is a recurrent theme in the climate change literature, particularly for aquatic species that are constrained to networks and elevational rather than latitudinal retreat as temperatures increase. Predictions of widespread species losses, however, have yet to be fulfilled despite decades of climate change, suggesting that trends are much weaker than anticipated and may be too subtle for detection given the widespread use of sparse water temperature datasets or imprecise surrogates like elevation and air temperature. Through application of large water-temperature databases evaluated for sensitivity to historical air-temperature variability and computationally interpolated to provide high-resolution thermal habitat information for a 222,000-km network, we estimate a less dire thermal plight for cold-water species within mountains of the northwestern United States. Stream warming rates and climate velocities were both relatively low for 1968–2011 (average warming rate = 0.101 °C/decade; median velocity = 1.07 km/decade) when air temperatures warmed at 0.21 °C/decade. Many cold-water vertebrate species occurred in a subset of the network characterized by low climate velocities, and three native species of conservation concern occurred in extremely cold, slow velocity environments (0.33–0.48 km/decade). Examination of aggressive warming scenarios indicated that although network climate velocities could increase, they remain low in headwaters because of strong local temperature gradients associated with topographic controls. Better information about changing hydrology and disturbance regimes is needed to complement these results, but rather than being climatic cul-de-sacs, many mountain streams appear poised to be redoubts for cold-water biodiversity this century.
","language":"English","publisher":"PNAS","doi":"10.1073/pnas.1522429113","usgsCitation":"Isaak, D.J., Young, M.K., Luce, C.H., Hostetler, S.W., Wenger, S., Peterson, E.E., Ver Hoef, J., Groce, M.C., Horan, D.L., and Nagel, D.E., 2016, Slow climate velocities of mountain streams portend their role as refugia for cold-water biodiversity: Proceedings of the National Academy of Sciences of the United States of America, v. 113, no. 16, p. 4374-4379, https://doi.org/10.1073/pnas.1522429113.","productDescription":"6 p.","startPage":"4374","endPage":"4379","ipdsId":"IP-072844","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":471098,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4843441","text":"External Repository"},{"id":332732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"16","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-04","publicationStatus":"PW","scienceBaseUri":"586cc697e4b0f5ce109fa957","contributors":{"authors":[{"text":"Isaak, Daniel J.","contributorId":177835,"corporation":false,"usgs":false,"family":"Isaak","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":657245,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Michael K.","contributorId":177836,"corporation":false,"usgs":false,"family":"Young","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":657246,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luce, Charles H.","contributorId":177837,"corporation":false,"usgs":false,"family":"Luce","given":"Charles","email":"","middleInitial":"H.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":657247,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":657244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":657248,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peterson, Erin E.","contributorId":177839,"corporation":false,"usgs":false,"family":"Peterson","given":"Erin","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":657249,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ver Hoef, Jay","contributorId":177840,"corporation":false,"usgs":false,"family":"Ver Hoef","given":"Jay","affiliations":[],"preferred":false,"id":657250,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Groce, Matthew C.","contributorId":177841,"corporation":false,"usgs":false,"family":"Groce","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":657251,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Horan, Dona L.","contributorId":36672,"corporation":false,"usgs":true,"family":"Horan","given":"Dona","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":657252,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nagel, David E.","contributorId":86887,"corporation":false,"usgs":true,"family":"Nagel","given":"David","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":657253,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70169948,"text":"ofr20161056 - 2016 - Construction, calibration, and validation of the RBM10 water temperature model for the Trinity River, northern California","interactions":[],"lastModifiedDate":"2016-04-01T08:35:51","indexId":"ofr20161056","displayToPublicDate":"2016-03-31T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1056","title":"Construction, calibration, and validation of the RBM10 water temperature model for the Trinity River, northern California","docAbstract":"We constructed a one-dimensional daily averaged water-temperature model to simulate Trinity River temperatures for 1980–2013. The purpose of this model is to assess effects of water-management actions on water temperature and to provide water temperature inputs for a salmon population dynamics model. Simulated meteorological data, observed streamflow data, and observed water temperatures were used as model inputs to simulate a continuous 34-year time series of historical daily mean water temperature at eight locations along 112.2 river miles from Lewiston Dam near Weaverville, California, downstream to the Klamath River confluence. To demonstrate the utility of the model to inform management actions, we simulated three management alternatives to assess the effects of bypass flow augmentation in a drought year, 1994, and compared those results to the simulated historical baseline, referred to as the “No Action” alternative scenario. Augmentation flows from the Lewiston Dam bypass consist of temperature-controlled releases capable of cooling downstream water temperatures in hot times of the year, which can reduce the probability of disease outbreaks in fish populations. Outputs from the Trinity River water-temperature model were then used as inputs to an existing water-temperature model of the Klamath River to evaluate the effect of augmentation flow releases on water temperatures in the lower Klamath River.
\nWe structured the Trinity River water-temperature model in River Basin Model-10 (RBM10), which uses a simple equilibrium flow model, assuming discharge in each river segment on each day is transmitted downstream instantaneously. The model uses a heat-budget formulation to quantify heat flux at the air-water interface. Inputs for the heat budget are calculated from daily mean meteorological data, including net shortwave solar radiation, net longwave atmospheric radiation, air temperature, wind speed, vapor pressure, and a psychrometric constant needed to calculate the Bowen ratio. The modeling domain was divided into eight reaches ranging in length from 8.8 to 20.6 miles, which were calibrated and validated separately with observed water temperature data collected irregularly from 1980 to 2013. Root mean square errors of observed and simulated water temperatures for the eight reaches ranged from 0.25 to 1.12 degrees Celsius (°C). Mean absolute errors ranged from 0.18 to 0.89 °C. For model validation, a k-fold cross-validation technique was used. Validation root mean square error and mean absolute error for the eight reaches ranged from 0.24 to 1.11 °C and from 0.18 to 0.89 °C, respectively.
\nAugmentation scenarios were based on historical hydrological and meteorological data, combined with prescribed flow and temperature releases from Lewiston Dam provided by the Bureau of Reclamation. Water releases were scheduled to achieve targeted flows of 2,500, 2,800, and 3,200 cubic feet per second in the lower Klamath River from mid-August through late September, coinciding with the upstream migration of adult fall-run Chinook salmon (Oncorhynchus tshawytscha). Water temperatures simulated at river mile 5.7 on the Klamath River showed a 5 °C decrease from the No Action historical baseline, which was near or greater than 23 °C when augmentation began in mid-August. Thereafter, an approximate 1 °C difference among augmentation scenarios emerged, with the decrease in water temperature commensurate to the level of augmentation. All augmentation scenarios simulated water temperatures equal to or less than 21 °C from mid-August through late September. Water temperatures equal to or greater than 23 °C are of particular interest because of a thermal threshold known to inhibit upstream migration of salmon. When temperatures exceed this approximate 23 °C threshold, Chinook salmon are known to congregate in high densities in thermal refugias and show extended residence times, which can potentially trigger epizootic outbreaks such as of Ichthyophthirius multifiliis (“Ich”) and Flavobacterium columnare (“Columnaris”) that were the causative factors of the Klamath River fish kill in 2002. A model with the ability to simulate water temperatures in response to management actions at the basin scale is a valuable asset for water managers who must make decisions about how best to use limited water resources, which directly affect the state of fisheries in the Klamath Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161056","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Bureau of Reclamation","usgsCitation":"Jones, E.C., Perry, R.W., Risley, J.C., Som, N.A., and Hetrick, N.J., 2016, Construction, calibration, and validation of the RBM10 water temperature model for the Trinity River, northern California: U.S. Geological Survey Open-File Report 2016–1056, 46 p., https://dx.doi.org/10.3133/ofr20161056.","productDescription":"vi, 46 p.","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070848","costCenters":[{"id":654,"text":"Western Fisheries Research 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U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
The U.S. Geological Survey (USGS) South Atlantic Water Science Center (SAWSC) Georgia office, in cooperation with local, State, and other Federal agencies, maintains a long-term hydrologic monitoring network of more than 350 real-time, continuous-record, streamflow-gaging stations (streamgages). The network includes 14 real-time lake-level monitoring stations, 72 real-time surface-water-quality monitors, and several water-quality sampling programs. Additionally, the SAWSC Georgia office operates more than 204 groundwater monitoring wells, 39 of which are real-time. The wide-ranging coverage of streamflow, reservoir, and groundwater monitoring sites allows for a comprehensive view of hydrologic conditions across the State. One of the many benefits this monitoring network provides is a spatially distributed overview of the hydrologic conditions of creeks, rivers, reservoirs, and aquifers in Georgia.
Streamflow and groundwater data are verified throughout the year by USGS hydrographers and made available to water-resource managers, recreationists, and Federal, State, and local agencies. Hydrologic conditions are determined by comparing the statistical analyses of data collected during the current water year to historical data. Changing hydrologic conditions underscore the need for accurate, timely data to allow informed decisions about the management and conservation of Georgia’s water resources for agricultural, recreational, ecological, and water-supply needs and in protecting life and property.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163016","usgsCitation":"Knaak, A.E., Ankcorn, P.D., and Peck, M.F., 2016, Normal streamflows and water levels continue—Summary of hydrologic conditions in Georgia, 2014: U.S. Geological Survey Fact Sheet 2016–3016, 4 p., https://dx.doi.org/10.3133/fs20163016.","productDescription":"4 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070931","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":319376,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3016/fs20163016.pdf","text":"Report","size":"4.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3016"},{"id":319375,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3016/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
Atlantic coastal bays of the US are essential habitat for young of year bluefish (Pomatomus saltatrix). Their residence in these estuaries during critical life stages, high lipid content, and piscivory make bluefish an ideal bioindicator species for evaluating estuarine health. Individual whole fish from four estuaries impacted by Hurricane Sandy were collected in August 2013, analyzed for a suite of persistent organic pollutants (POPs) including polychlorinated biphenyls, polybrominated diphenyl ethers and organochlorine pesticides and evaluated using health metrics. Concentrations in whole bluefish differed by estuary; however, concentrations for many POPs decreased or were similar to those observed prior to the hurricane. Prevalence of the ectoparasitic gill isopod (Lironeca ovalis) varied by estuary and no relationships between contaminants and lesions were observed. Bluefish should be considered for monitoring programs and, if sampled frequently, could be an effective bioindicator of incremental and episodic changes in contaminants within aquatic food webs.
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Sharack, Baker, R.J., Jennifer Samson, and Reilly, T.J., 2016, Young of the year bluefish (Pomatomus saltatrix) as a bioindicator of estuarine health: Establishing a new baseline for persistent organic pollutants after Hurricane Sandy for selected estuaries in New Jersey and New York: Marine Pollution Bulletin, v. 107, no. 2, p. 422-431, https://doi.org/10.1016/j.marpolbul.2016.03.019.","productDescription":"10 p.","startPage":"422","endPage":"431","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066942","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":471114,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpolbul.2016.03.019","text":"Publisher Index Page"},{"id":322036,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey, New York","otherGeospatial":"Barnegat Bay, Great South Bay, Jamaica Bay, Sandy Hook Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.5,\n 39\n ],\n [\n -74.5,\n 41.2\n ],\n [\n -71.5,\n 41.2\n ],\n [\n -71.5,\n 39\n ],\n [\n -74.5,\n 39\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57500788e4b0ee97d51bb87c","contributors":{"authors":[{"text":"Smalling, Kelly L. 0000-0002-1214-4920 ksmall@usgs.gov","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":149769,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L. 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In 2015, the network included about 200 real-time streamgages (hereafter referred to as “gages”), 12 real-time reservoir-level monitoring stations, and 30 groundwater-level monitoring wells. These data and associated analyses provide a unique overview of hydrologic conditions and help improve the understanding of Kansas’s water resources.
Real-time data are verified by the USGS throughout the year with regular measurements of streamflow, lake levels, and groundwater levels. These data are used in protecting life and property; and managing water resources for agricultural, industrial, public supply, ecological, and recreational purposes. Yearly hydrologic conditions are characterized by comparing statistical analyses of current and historical water year (WY) data for the period of record. A WY is the 12-month period from October 1 through September 30 and is designated by the year in which it ends.
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\"}}]}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, Kansas 66049
The permafrost component of the cryosphere is changing dramatically, but the permafrost region is not well monitored and the consequences of change are not well understood. Changing permafrost interacts with ecosystems and climate on various spatial and temporal scales. The feedbacks resulting from these interactions range from local impacts on topography, hydrology, and biology to complex influences on global scale biogeochemical cycling. This review contributes to this focus issue by synthesizing its 28 multidisciplinary studies which provide field evidence, remote sensing observations, and modeling results on various scales. We synthesize study results from a diverse range of permafrost landscapes and ecosystems by reporting key observations and modeling outcomes for permafrost thaw dynamics, identifying feedbacks between permafrost and ecosystem processes, and highlighting biogeochemical feedbacks from permafrost thaw. We complete our synthesis by discussing the progress made, stressing remaining challenges and knowledge gaps, and providing an outlook on future needs and research opportunities in the study of permafrost–ecosystem–climate interactions.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/11/4/040201","usgsCitation":"Grosse, G., Goetz, S., McGuire, A.D., Romanovsky, V.E., and Schuur, E.A., 2016, Review and synthesis: Changing permafrost in a warming world and feedbacks to the Earth System: Environmental Research Letters, v. 11, no. 4, p. 1-10, https://doi.org/10.1088/1748-9326/11/4/040201.","productDescription":"10 p.","startPage":"1","endPage":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073123","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471120,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/11/4/040201","text":"Publisher Index Page"},{"id":323450,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-29","publicationStatus":"PW","scienceBaseUri":"575be4ade4b04f417c27f540","chorus":{"doi":"10.1088/1748-9326/11/4/040201","url":"http://dx.doi.org/10.1088/1748-9326/11/4/040201","publisher":"IOP Publishing","authors":"Grosse Guido, Goetz Scott, McGuire A Dave, Romanovsky Vladimir E, Schuur Edward A G","journalName":"Environmental Research Letters","publicationDate":"3/29/2016","auditedOn":"4/2/2016","publiclyAccessibleDate":"3/30/2016"},"contributors":{"authors":[{"text":"Grosse, Guido","contributorId":146182,"corporation":false,"usgs":false,"family":"Grosse","given":"Guido","email":"","affiliations":[{"id":12916,"text":"Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":638408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goetz, Scott","contributorId":75259,"corporation":false,"usgs":true,"family":"Goetz","given":"Scott","affiliations":[],"preferred":false,"id":638409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGuire, A. 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Relatively few of these assessments, however, have involved the use of process-based simulations of contaminant transport and fate in the subsurface, or compared the predictions from such models with measured data—especially over regional to national scales. To address this need, a process-based groundwater vulnerability assessment (P-GWAVA) system was constructed to use transport-and-fate simulations to predict the concentration of any surface-derived compound at a specified depth in the vadose zone anywhere in the conterminous United States. The system was then used to simulate the concentrations of selected agrichemicals in the vadose zone beneath agricultural areas in multiple locations across the conterminous United States. The simulated concentrations were compared with measured concentrations of the compounds detected in shallow groundwater (that is, groundwater drawn from within a depth of 6.3 ± 0.5 meters [mean ± 95 percent confidence interval] below the water table) in more than 1,400 locations across the United States. The results from these comparisons were used to select the simulation approaches that led to the closest agreement between the simulated and the measured concentrations.
The P-GWAVA system uses computer simulations that account for a broader range of the hydrologic, physical, biological and chemical phenomena known to control the transport and fate of solutes in the subsurface than has been accounted for by any other vulnerability assessment over regional to national scales. Such phenomena include preferential transport and the influences of temperature, soil properties, and depth on the partitioning, transport, and transformation of pesticides in the subsurface. Published methods and detailed soil property data are used to estimate a wide range of model input parameters for each site, including surface albedo, surface crust permeability, soil water content, Brooks-Corey parameters, saturated hydraulic conductivity, macroporosity and sizes of microbial populations, as well as solute partition coefficients, reaction rates, and meso-micropore diffusion rates. To ensure geographic consistency among the predictions, the only site-specific input data that are used are those that are available for all of the 48 conterminous states.
","language":"English","publisher":"U.S Geological Survey","publisherLocation":"Reston, V.A","doi":"10.3133/sir20145189","collaboration":"Prepared in cooperation with National Water-Quality Assessment Program","usgsCitation":"Barbash, J.E., and Voss, F.D., 2016, Design and testing of a process-based groundwater vulnerability assessment (P-GWAVA) system for predicting concentrations of agrichemicals in groundwater across the United States: U.S. Geological Survey Scientific Investigations Report 2014–5189, 210 p., https://dx.doi.org/10.3133/sir20145189.","productDescription":"xvi, 210 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-044178","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":316519,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5189/sir20145189.pdf","text":"Report","size":"7.9 MB","description":"SIR 2014-5189 Report 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U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
http://wa.water.usgs.gov
With climate change becoming more of concern, many ecologists are including climate variables in their system and statistical models. The Standardized Precipitation Evapotranspiration Index (SPEI) is a drought index that has potential advantages in modeling ecological response variables, including a flexible computation of the index over different timescales. However, little development has been made in terms of the choice of timescale for SPEI. We developed a Bayesian modeling approach for estimating the timescale for SPEI and demonstrated its use in modeling wetland hydrologic dynamics in two different eras (i.e., historical [pre-1970] and contemporary [post-2003]). Our goal was to determine whether differences in climate between the two eras could explain changes in the amount of water in wetlands. Our results showed that wetland water surface areas tended to be larger in wetter conditions, but also changed less in response to climate fluctuations in the contemporary era. We also found that the average timescale parameter was greater in the historical period, compared with the contemporary period. We were not able to determine whether this shift in timescale was due to a change in the timing of wet–dry periods or whether it was due to changes in the way wetlands responded to climate. Our results suggest that perhaps some interaction between climate and hydrologic response may be at work, and further analysis is needed to determine which has a stronger influence. Despite this, we suggest that our modeling approach enabled us to estimate the relevant timescale for SPEI and make inferences from those estimates. Likewise, our approach provides a mechanism for using prior information with future data to assess whether these patterns may continue over time. We suggest that ecologists consider using temporally scalable climate indices in conjunction with Bayesian analysis for assessing the role of climate in ecological systems.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.2092","usgsCitation":"Post van der Burg, M., Anteau, M.J., McCauley, L.A., and Wiltermuth, M.T., 2016, A Bayesian approach for temporally scaling climate for modeling ecological systems: Ecology and Evolution, v. 6, no. 9, p. 2978-2987, https://doi.org/10.1002/ece3.2092.","productDescription":"10 p.","startPage":"2978","endPage":"2987","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063141","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471122,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.2092","text":"Publisher Index Page"},{"id":319621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"9","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-28","publicationStatus":"PW","scienceBaseUri":"56fceaabe4b0a6037df29c96","contributors":{"authors":[{"text":"Post van der Burg, Max 0000-0002-3943-4194 maxpostvanderburg@usgs.gov","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":4947,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","email":"maxpostvanderburg@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":625616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anteau, Michael J. 0000-0002-5173-5870 manteau@usgs.gov","orcid":"https://orcid.org/0000-0002-5173-5870","contributorId":3427,"corporation":false,"usgs":true,"family":"Anteau","given":"Michael","email":"manteau@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":625618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCauley, Lisa A. lmccauley@usgs.gov","contributorId":5048,"corporation":false,"usgs":true,"family":"McCauley","given":"Lisa","email":"lmccauley@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":625617,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiltermuth, Mark T. 0000-0002-8871-2816 mwiltermuth@usgs.gov","orcid":"https://orcid.org/0000-0002-8871-2816","contributorId":708,"corporation":false,"usgs":true,"family":"Wiltermuth","given":"Mark","email":"mwiltermuth@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":625619,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170378,"text":"70170378 - 2016 - Trophic magnification of organic chemicals: A global synthesis","interactions":[],"lastModifiedDate":"2018-08-07T12:11:16","indexId":"70170378","displayToPublicDate":"2016-03-25T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Trophic magnification of organic chemicals: A global synthesis","docAbstract":"Production of organic chemicals (OCs) is increasing exponentially, and some OCs biomagnify through food webs to potentially toxic levels. Biomagnification under field conditions is best described by trophic magnification factors (TMFs; per trophic level change in log-concentration of a chemical) which have been measured for more than two decades. Syntheses of TMF behavior relative to chemical traits and ecosystem properties are lacking. We analyzed >1500 TMFs to identify OCs predisposed to biomagnify and to assess ecosystem vulnerability. The highest TMFs were for OCs that are slowly metabolized by animals (metabolic rate kM < 0.01 day–1) and are moderately hydrophobic (log KOW 6–8). TMFs were more variable in marine than freshwaters, unrelated to latitude, and highest in food webs containing endotherms. We modeled the probability that any OC would biomagnify as a combined function of KOW and kM. Probability is greatest (∼100%) for slowly metabolized compounds, regardless of KOW, and lowest for chemicals with rapid transformation rates (kM > 0.2 day–1). This probabilistic model provides a new global tool for screening existing and new OCs for their biomagnification potential.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.6b00201","usgsCitation":"Walters, D., Jardine, T., Cade, B.S., Kidd, K., Muir, D., and Leipzig-Scott, P.C., 2016, Trophic magnification of organic chemicals: A global synthesis: Environmental Science & Technology, v. 50, no. 9, p. 4650-4658, https://doi.org/10.1021/acs.est.6b00201.","productDescription":"9 p.","startPage":"4650","endPage":"4658","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071917","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":320332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-20","publicationStatus":"PW","scienceBaseUri":"5718a852e4b0ef3b7caba67d","contributors":{"authors":[{"text":"Walters, David 0000-0002-4237-2158 waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":147135,"corporation":false,"usgs":true,"family":"Walters","given":"David","email":"waltersd@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":627031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jardine, T.D.","contributorId":168698,"corporation":false,"usgs":false,"family":"Jardine","given":"T.D.","email":"","affiliations":[{"id":13248,"text":"University of Saskatchewan","active":true,"usgs":false}],"preferred":false,"id":627032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cade, Brian S. 0000-0001-9623-9849 cadeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9623-9849","contributorId":1278,"corporation":false,"usgs":true,"family":"Cade","given":"Brian","email":"cadeb@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":627033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kidd, K.A.","contributorId":168699,"corporation":false,"usgs":false,"family":"Kidd","given":"K.A.","email":"","affiliations":[{"id":18889,"text":"University of New Brunswick","active":true,"usgs":false}],"preferred":false,"id":627034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Muir, D.C.G.","contributorId":168700,"corporation":false,"usgs":false,"family":"Muir","given":"D.C.G.","email":"","affiliations":[{"id":6779,"text":"Environment Canada, Burlington, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":627035,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leipzig-Scott, Peter C. pleipzig-scott@usgs.gov","contributorId":5661,"corporation":false,"usgs":true,"family":"Leipzig-Scott","given":"Peter","email":"pleipzig-scott@usgs.gov","middleInitial":"C.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":627036,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70174999,"text":"70174999 - 2016 - Crude oil metabolites in groundwater at two spill sites","interactions":[],"lastModifiedDate":"2018-08-06T13:12:27","indexId":"70174999","displayToPublicDate":"2016-03-24T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Crude oil metabolites in groundwater at two spill sites","docAbstract":"Two groundwater plumes in north central Minnesota with residual crude oil sources have 20 to 50 mg/L of nonvolatile dissolved organic carbon (NVDOC). These values are over 10 times higher than benzene and two to three times higher than Diesel Range Organics in the same wells. On the basis of previous work, most of the NVDOC consists of partial transformation products from the crude oil. Monitoring data from 1988 to 2015 at one of the sites located near Bemidji, MN show that the plume of metabolites is expanding toward a lakeshore located 335 m from the source zone. Other mass balance studies of the site have demonstrated that the plume expansion is driven by the combined effect of continued presence of the residual crude oil source and depletion of the electron accepting capacity of solid phase iron oxide and hydroxides on the aquifer sediments. These plumes of metabolites are not covered by regulatory monitoring and reporting requirements in Minnesota and other states. Yet, a review of toxicology studies indicates that polar metabolites of crude oil may pose a risk to aquatic and mammalian species. Together the results suggest that at sites where residual sources are present, monitoring of NVDOC may be warranted to evaluate the fates of plumes of hydrocarbon transformation products.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12419","usgsCitation":"Bekins, B.A., Cozzarelli, I.M., Erickson, M., Steenson, R., and Thorn, K.A., 2016, Crude oil metabolites in groundwater at two spill sites: Groundwater, v. 54, no. 5, p. 681-691, https://doi.org/10.1111/gwat.12419.","productDescription":"11 p.","startPage":"681","endPage":"691","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072648","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":325698,"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":643546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steenson, Ross","contributorId":173174,"corporation":false,"usgs":false,"family":"Steenson","given":"Ross","email":"","affiliations":[{"id":27173,"text":"San Francisco Bay Regional Water Quality Control Board, Oakland, CA","active":true,"usgs":false}],"preferred":false,"id":643547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thorn, Kevin A. 0000-0003-2236-5193 kathorn@usgs.gov","orcid":"https://orcid.org/0000-0003-2236-5193","contributorId":3288,"corporation":false,"usgs":true,"family":"Thorn","given":"Kevin","email":"kathorn@usgs.gov","middleInitial":"A.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":643548,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70168826,"text":"fs20163011 - 2016 - Climate Change Science Activities of the U.S. Geological Survey in New England","interactions":[],"lastModifiedDate":"2016-03-23T13:51:00","indexId":"fs20163011","displayToPublicDate":"2016-03-23T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3011","title":"Climate Change Science Activities of the U.S. Geological Survey in New England","docAbstract":"The U.S. Geological Survey (USGS) has actively pursued research in the effects of climate change on the hydrology of New England. Ongoing focus areas of climate change science activities of the USGS in New England include the following:
\n• Hydrologic climate-response data (initiating or expanding long-term hydrologic climate-response data collection networks to detect and monitor climate-related changes in hydrology)
• Relations between historical climatic and hydrologic variation (investigating historic relations between long-term climatic variation and hydrologic variation and connecting these relations to future conditions as forecasted by the Interagency Panel on Climate Change (Nakićenović and others, 2000)
• Hydrologic change on natural and human (incorporating climate change scenarios into assessments of how flows in streams or water levels in aquifers may change)
• Relations between climatic and hydrologic variation (characterizing impacts of climate change on floods and droughts, the long-term availability of water supplies for societal and natural uses in response to climate changes)
• Developing tools to forecast ecosystem change and water resources management (developing regionally consistent tools for forecasting ecosystem change and resource management)
This fact sheet presents recent climate change investigations of the USGS in New England using selected recent publications. These publications highlight the broad spectrum of expertise and commitment to understanding the relations of climate change and water resources in New England.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163011","usgsCitation":"U.S. Geological Survey, 2016, Climate change science activities of the U.S. Geological Survey in New England: U.S. Geological Survey Fact Sheet 2016–3011, 4 p., https://dx.doi.org/10.3133/fs20163011.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-070470","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":319052,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20163012","text":"Fact Sheet 2016-3012","size":"1 MB","description":"FS2016-3011"},{"id":319051,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20163010","text":"Fact Sheet 2016-3010","size":"871 KB","description":"FS2016-3011"},{"id":319050,"rank":4,"type":{"id":7,"text":"Companion 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Or visit our Web site at
http://newengland.water.usgs.gov/
The U.S. Geological Survey (USGS) has a long history of working with the Federal Highway Administration (FHWA) and State transportation agencies to provide data and information to address various issues related to water resources and the Nation’s transportation infrastructure. These issues include the following:
\n• Streamgaging data networks
• Flow frequencies and flow statistics
• Water-quality investigations
• Bridge scour and sediment transport
• Hydrologic and hydraulic flood modeling
In New England, the USGS is conducting investigations to improve flood flow estimation techniques, to define channel characteristics at bankfull discharge, and to document storm tide as a result of major coastal storms. Current locally focused investigations include examination of flow frequency in rural, urban, and small watersheds; documentation of extreme inland floods along with flood-frequency updates; examination of the effects of roadway blasting on groundwater quality; and determinations of the effects of road salting on the quality of runoff and receiving waters.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Or visit our Web site at
http://newengland.water.usgs.gov/
The U.S. Geological Survey (USGS) has many ongoing and recent water-related natural hazard activities in New England that can be used to help mitigate the effects of natural hazards in cooperation with other agencies. The themes related to potential hazards and the tools and science to better understand and address them include the following:
\nErosion and landslides
• Fluvial erosion (sediment transport, bridge scour, and bankfull channel geometry characterization)
• Current and historic landslide mapping
Flood documentation and assessment
• Flood high-water marks
• Flood modeling and frequency analysis
• Flood inundation mapping
• Peak-flow regression equations
Drought documentation and assessment
• Drought frequency analysis
• Low-flow frequency and flow duration statistics
• Water use and availability during drought
Hydrologic monitoring
• Streamflow monitoring network
• Groundwater monitoring network
• Tidal monitoring network
• Snow surveys and ice jam monitoring
Tools for natural hazard assessment and mitigation
• Light detection and ranging (lidar) remote sensing technology
• StreamStats Web-based tool for streamflow statistics
• Flood inundation mapper
Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Or visit our Web site at
http://newengland.water.usgs.gov/
Habitat has changed unfavorably during the past 150 y for the desert tortoise Gopherus agassizii, a federally threatened species with declining populations in the Mojave Desert and western Sonoran Desert. To support recovery efforts, we synthesized published information on relationships of desert tortoises with three habitat features (cover sites, forage, and soil) and candidate management practices for improving these features for tortoises. In addition to their role in soil health and facilitating recruitment of annual forage plants, shrubs are used by desert tortoises for cover and as sites for burrows. Outplanting greenhouse-grown seedlings, protected from herbivory, has successfully restored (>50% survival) a variety of shrubs on disturbed desert soils. Additionally, salvaging and reapplying topsoil using effective techniques is among the more ecologically beneficial ways to initiate plant recovery after severe disturbance. Through differences in biochemical composition and digestibility, some plant species provide better-quality forage than others. Desert tortoises selectively forage on particular annual and herbaceous perennial species (e.g., legumes), and forage selection shifts during the year as different plants grow or mature. Nonnative grasses provide low-quality forage and contribute fuel to spreading wildfires, which damage or kill shrubs that tortoises use for cover. Maintaining a diverse “menu” of native annual forbs and decreasing nonnative grasses are priorities for restoring most desert tortoise habitats. Reducing herbivory by nonnative animals, carefully timing herbicide applications, and strategically augmenting annual forage plants via seeding show promise for improving tortoise forage quality. Roads, another disturbance, negatively affect habitat in numerous ways (e.g., compacting soil, altering hydrology). Techniques such as recontouring road berms to reestablish drainage patterns, vertical mulching (“planting” dead plant material), and creating barriers to prevent trespasses can assist natural recovery on decommissioned backcountry roads. Most habitat enhancement efforts to date have focused on only one factor at a time (e.g., providing fencing) and have not included proactive restoration activities (e.g., planting native species on disturbed soils). A research and management priority in recovering desert tortoise habitats is implementing an integrated set of restorative habitat enhancements (e.g., reducing nonnative plants, improving forage quality, augmenting native perennial plants, and ameliorating altered hydrology) and monitoring short- and long-term indicators of habitat condition and the responses of desert tortoises to habitat restoration.
","language":"English","publisher":"U.S. Fish and Wildlife Service","publisherLocation":"Washington, D.C.","doi":"10.3996/052015-JFWM-046","usgsCitation":"Abella, S.R., and Berry, K.H., 2016, Enhancing and restoring habitat for the desert tortoise: Journal of Fish and Wildlife Management, v. 7, no. 1, p. 255-279, https://doi.org/10.3996/052015-JFWM-046.","productDescription":"25 p.","startPage":"255","endPage":"279","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066485","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":488430,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/052015-jfwm-046","text":"Publisher Index Page"},{"id":319189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.027099609375,\n 37.32648861334206\n ],\n [\n -113.75244140624999,\n 37.42252593456307\n ],\n [\n -113.521728515625,\n 37.33522435930641\n ],\n [\n -113.48876953125,\n 37.055177106660814\n ],\n [\n -113.5986328125,\n 36.4566360115962\n ],\n [\n -113.73046875,\n 36.1822249804225\n ],\n [\n -113.79638671875,\n 35.951329861522666\n ],\n [\n -113.895263671875,\n 35.60371874069731\n ],\n [\n -113.675537109375,\n 35.23664622093195\n ],\n [\n -113.51074218749999,\n 35.074964853989556\n ],\n [\n -113.44482421875,\n 34.97600151317591\n ],\n [\n -113.18115234375,\n 34.66032236481892\n ],\n [\n -113.203125,\n 34.379712580462204\n ],\n [\n -113.466796875,\n 34.19817309627726\n ],\n [\n -113.88427734374999,\n 34.125447565116126\n ],\n [\n -114.36767578124999,\n 34.03445260967645\n ],\n [\n -115.13671875,\n 34.17090836352573\n ],\n [\n -115.15869140624999,\n 33.8430453147447\n ],\n [\n -114.664306640625,\n 33.486435450999885\n ],\n [\n -114.81811523437501,\n 33.14675022877648\n ],\n [\n -115.04882812499999,\n 32.94414888814148\n ],\n [\n -115.3564453125,\n 33.119150226768866\n ],\n [\n -115.521240234375,\n 33.330528249028085\n ],\n [\n -116.114501953125,\n 33.779147331286474\n ],\n [\n -116.38916015624999,\n 33.90689555128866\n ],\n [\n -117.22412109375,\n 34.19817309627726\n ],\n [\n -117.630615234375,\n 34.15272698011818\n ],\n [\n -118.02612304687499,\n 34.14363482031264\n ],\n [\n -118.38867187500001,\n 34.288991865037524\n ],\n [\n -118.65234374999999,\n 34.52466147177172\n ],\n [\n -118.80615234374999,\n 34.75966612466248\n ],\n [\n -118.223876953125,\n 35.16482750605027\n ],\n [\n -118.487548828125,\n 35.39800594715108\n ],\n [\n -118.443603515625,\n 35.6126508187567\n ],\n [\n -118.14697265625,\n 35.7286770448517\n ],\n [\n -117.98217773437499,\n 35.639441068973916\n ],\n [\n -117.850341796875,\n 35.64836915737426\n ],\n [\n -117.98217773437499,\n 36.05798104702501\n ],\n [\n -118.048095703125,\n 36.38591277287651\n ],\n [\n -118.21289062499999,\n 36.5978891330702\n ],\n [\n -118.333740234375,\n 37.02886944696474\n ],\n [\n -118.35571289062499,\n 37.25656608611523\n ],\n [\n -118.0810546875,\n 37.23907530202184\n ],\n [\n -117.938232421875,\n 37.448696585910376\n ],\n [\n -117.740478515625,\n 37.56199695314352\n ],\n [\n -117.57568359374999,\n 37.49229399862877\n ],\n [\n -117.2900390625,\n 37.26530995561875\n ],\n [\n -116.79565429687499,\n 37.274052809979054\n ],\n [\n -116.5869140625,\n 37.25656608611523\n ],\n [\n -116.49902343749999,\n 37.204081555898526\n ],\n [\n -116.15844726562501,\n 37.16907157713011\n ],\n [\n -115.927734375,\n 37.142803443716836\n ],\n [\n -115.51025390625,\n 37.38761749978395\n ],\n [\n -114.993896484375,\n 37.24782120155428\n ],\n [\n -114.54345703125,\n 37.25656608611523\n ],\n [\n -114.3017578125,\n 37.212831514455964\n ],\n [\n -114.027099609375,\n 37.32648861334206\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-01","publicationStatus":"PW","scienceBaseUri":"56f25e98e4b0f59b85de6ff1","contributors":{"authors":[{"text":"Abella, Scott R.","contributorId":103940,"corporation":false,"usgs":true,"family":"Abella","given":"Scott","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":623207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":623206,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159388,"text":"sir20155152 - 2016 - Flood-inundation maps for a 12.5-mile reach of Big Papillion Creek at Omaha, Nebraska","interactions":[],"lastModifiedDate":"2016-03-22T10:17:15","indexId":"sir20155152","displayToPublicDate":"2016-03-22T00:00:00","publicationYear":"2016","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":"2015-5152","title":"Flood-inundation maps for a 12.5-mile reach of Big Papillion Creek at Omaha, Nebraska","docAbstract":"Digital flood-inundation maps for a 12.5-mile reach of the Big Papillion Creek from 0.6 mile upstream from the State Street Bridge to the 72nd Street Bridge in Omaha, Nebraska, were created by the U.S. Geological Survey (USGS) in cooperation with the Papio-Missouri River Natural Resources District. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Big Papillion Creek at Fort Street at Omaha, Nebraska (station 06610732). Near-real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at this site.
\nFlood profiles were computed for the 12.5-mile reach by means of a one-dimensional step-backwater model. The model was calibrated by using the current (2015) stage-discharge relation at streamgages for the Big Papillion Creek at Fort Street at Omaha, Nebraska, and the Big Papillion Creek at Q Street at Omaha, Nebraska. The hydraulic model was then used to compute 15 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum for the Big Papillion Creek at Fort Street and ranging from 18 ft (or near bankfull) to 32 ft, which exceeds the “major flood stage” as defined by the National Weather Service. The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging data having a 1.18-ft vertical accuracy and 3.28-ft horizontal resolution) to delineate the area flooded at each flood stage (water level).
\nThe availability of these flood-inundation maps, along with Internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155152","collaboration":"Prepared in cooperation with the Papio-Missouri River Natural Resources District","usgsCitation":"Strauch, K.R., Dietsch, B.J., and Anderson, K.J., 2016, Flood-inundation maps for a 12.5-mile reach of Big Papillion Creek at Omaha, Nebraska: U.S. Geological Survey Scientific Investigations Report 2015–5152, 11 p., https://dx.doi.org/10.3133/sir20155152.","productDescription":"Report: v, 11 p.; Datasets; Metadata","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066029","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":314625,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_river_areas_GRIDS.zip","text":"River area GRIDS","size":"52.9 MB","description":"SIR 2015–5152 River area GRIDS"},{"id":314626,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_river_areas_shapefiles.zip","text":"River area shapefiles","size":"2.2 MB","description":"SIR 2015–5152 River area shapefiles"},{"id":314628,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_metadata_BigPapio_at_Fort_FIM_GRID.txt","text":"GRID metadata","size":"20.0 kb","description":"SIR 2015–5152 GRID metadata"},{"id":314629,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_metadata_BigPapio_at_Fort_FIM_shapefile.txt","text":"Shapefile metadata","size":"20.0 kb","description":"SIR 2015–5152 Shapefile metadata"},{"id":314466,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152.pdf","text":"Report","size":"2.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015–5152"},{"id":314622,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_levee_areas_GRIDS.zip","text":"Levee area GRIDS","size":"1.6 MB","description":"SIR 2015–5152 Levee area GRIDS"},{"id":314623,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_levee_areas_shapefiles.zip","text":"Levee area shapefiles","size":"144 kb","description":"SIR 2015–5152 Levee area shapefiles"},{"id":314465,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5152/coverthb.jpg"}],"country":"United States","state":"Nebraska","city":"Omaha","otherGeospatial":"Big Papillion Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.16676330566406,\n 41.23702755320388\n ],\n [\n -96.16676330566406,\n 41.35052580597025\n ],\n [\n -96.01020812988281,\n 41.35052580597025\n ],\n [\n -96.01020812988281,\n 41.23702755320388\n ],\n [\n -96.16676330566406,\n 41.23702755320388\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Urbanization substantially changes the physicochemical and biological characteristics of streams. The trajectory of negative effect is broadly similar around the world, but the nature and magnitude of ecological responses to urban growth differ among locations. Some heterogeneity in response arises from differences in the level of urban development and attributes of urban water management. However, the heterogeneity also may arise from variation in hydrologic, biological, and physicochemical templates that shaped stream ecosystems before urban development. We present a framework to develop hypotheses that predict how natural watershed and channel attributes in the pre-urban-development state may confer ecological resistance to urbanization. We present 6 testable hypotheses that explore the expression of such attributes under our framework: 1) greater water storage capacity mitigates hydrologic regime shifts, 2) coarse substrates and a balance between erosive forces and sediment supply buffer morphological changes, 3) naturally high ionic concentrations and pH pre-adapt biota to water-quality stress, 4) metapopulation connectivity results in retention of species richness, 5) high functional redundancy buffers trophic function from species loss, and 6) landuse history mutes or reverses the expected trajectory of eutrophication. Data from past comparative analyses support these hypotheses, but rigorous testing will require targeted investigations that account for confounding or interacting factors, such as diversity in urban infrastructure attributes. Improved understanding of the susceptibility or resistance of stream ecosystems could substantially strengthen conservation, management, and monitoring efforts in urban streams. We hope that these preliminary, conceptual hypotheses will encourage others to explore these ideas further and generate additional explanations for the heterogeneity observed in urban streams.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/684839","usgsCitation":"Utz, R.M., Hopkins, K., Beesley, L., Booth, D.B., Hawley, R.J., Baker, M.E., Freeman, M., and Jones, K.L., 2016, Ecological resistance in urban streams: the role of natural and legacy attributes: Freshwater Science, v. 35, no. 1, p. 380-397, https://doi.org/10.1086/684839.","productDescription":"18 p.","startPage":"380","endPage":"397","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066391","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":318934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ebc71be4b0f59b85d99410","contributors":{"authors":[{"text":"Utz, Ryan M.","contributorId":167572,"corporation":false,"usgs":false,"family":"Utz","given":"Ryan","email":"","middleInitial":"M.","affiliations":[{"id":24756,"text":"Chatham University, Pittsburg PA","active":true,"usgs":false}],"preferred":false,"id":622742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hopkins, Kristina G.","contributorId":167573,"corporation":false,"usgs":false,"family":"Hopkins","given":"Kristina G.","affiliations":[{"id":24757,"text":"University of Maryland, Annapolis MD","active":true,"usgs":false}],"preferred":false,"id":622743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beesley, Leah","contributorId":146250,"corporation":false,"usgs":false,"family":"Beesley","given":"Leah","email":"","affiliations":[{"id":16644,"text":"Centre of Excellence in Natural Resource Management, University of Western Australia,","active":true,"usgs":false}],"preferred":false,"id":622744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Booth, Derek B.","contributorId":100873,"corporation":false,"usgs":false,"family":"Booth","given":"Derek","email":"","middleInitial":"B.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":622745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hawley, Robert J.","contributorId":167574,"corporation":false,"usgs":false,"family":"Hawley","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":24758,"text":"Sustainable Streams, LLC, Louisville, KY","active":true,"usgs":false}],"preferred":false,"id":622746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baker, Matthew E.","contributorId":149189,"corporation":false,"usgs":false,"family":"Baker","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":17665,"text":"Department of Geography and Environmental Systems, University of Maryland, Baltimore County, Baltimore, Maryland, US","active":true,"usgs":false}],"preferred":false,"id":622747,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":622741,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jones, Krista L. 0000-0002-0301-4497 kljones@usgs.gov","orcid":"https://orcid.org/0000-0002-0301-4497","contributorId":4550,"corporation":false,"usgs":true,"family":"Jones","given":"Krista","email":"kljones@usgs.gov","middleInitial":"L.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":622748,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70168418,"text":"sir20165021 - 2016 - Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10","interactions":[],"lastModifiedDate":"2016-03-18T08:13:50","indexId":"sir20165021","displayToPublicDate":"2016-03-17T00:00:00","publicationYear":"2016","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":"2016-5021","title":"Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10","docAbstract":"The Albuquerque area of New Mexico has two principal sources of water: (1) groundwater from the Santa Fe Group aquifer system, and (2) surface water from the Rio Grande. From 1960 to 2002, pumping from the Santa Fe Group aquifer system caused groundwater levels to decline more than 120 feet while water-level declines along the Rio Grande in Albuquerque were generally less than 40 feet. These differences in water-level declines in the Albuquerque area have resulted in a great deal of interest in quantifying the river-aquifer interaction associated with the Rio Grande.
In 2003, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, acting as fiscal agent for the Middle Rio Grande Endangered Species Collaborative Program, and the U.S. Army Corps of Engineers, began a study to characterize the hydrogeology of the Rio Grande inner valley alluvial aquifer in the Albuquerque area of New Mexico. The study provides hydrologic data in order to enhance the understanding of rates of water leakage from the Rio Grande to the alluvial aquifer, groundwater flow through the aquifer, and discharge of water from the aquifer to riverside drains. The study area extends about 20 miles along the Rio Grande in the Albuquerque area. Piezometers and surface-water gages were installed in paired transects at eight locations. Nested piezometers, completed at various depths in the alluvial aquifer, and surface-water gages, installed in the Rio Grande and riverside drains, were instrumented with pressure transducers. Water-level and water-temperature data were collected from 2009 to 2010.
Water levels from the piezometers indicated that groundwater movement was usually away from the river towards the riverside drains. Annual mean horizontal groundwater gradients in the inner valley alluvial aquifer ranged from 0.0024 (I-25 East) to 0.0144 (Pajarito East). The median hydraulic conductivity values of the inner valley alluvial aquifer, determined from slug tests, ranged from 30 feet per day (ft/d) (Montaño) to 120 ft/d (Central) for paired transects, with a median hydraulic conductivity for all transects of 50 ft/d. Daily mean groundwater fluxes from the river through the inner valley alluvial aquifer computed using Darcy’s Law and the slug test results ranged from about 0.01 ft/d (Montaño West) to between 1.0 and 2.0 ft/d (Central East). Median annual groundwater fluxes from the river through the inner valley alluvial aquifer determined using the Suzuki-Stallman method was greatest at Alameda East (0.50 ft/d) and lowest at Alameda West (0.25 ft/d). The results from both methods agreed reasonably well.
Seepage investigations conducted by measuring discharge in the east and west riverside drains provided information for computing changes in flow within the drains and for evaluating results from Darcy’s Law and Suzuki-Stallman method flux calculations. Discharge measured in the east riverside drain between the Barelas Bridge and the I-25 bridge indicated that the flow in the east riverside drain increased by an average of 56.5 cubic feet per day per linear foot (ft3/d/ft) of drain. Discharge measured in the west riverside drain between the Central bridge and the I-25 bridge indicated that flow increased between west drain miles 0 and 4, an average of 53.8 ft3/d/ft of drain, and that flow increased between west drain miles 7 and 10, an average of 44.9 ft3/d/ft of drain. In comparison to the seepage measurement results, the groundwater fluxes from the river through the inner valley alluvial aquifer calculated from Darcy’s Law (qslug) and by the Suzuki-Stallman method (qheat) would account for 20–36 percent or 53–95 percent, respectively, of the total flow in the east riverside drain and 22–31 percent or 19–26 percent, respectively, of the total flow in the west drain. These results indicate that the drains likely also receive water from outside the inner valley.
The spatial variability of horizontal hydraulic gradients and groundwater fluxes can be primarily attributed to variability in the distances between the river and riverside drains throughout the study area and geologic heterogeneities in the alluvial aquifer. Temporal variability in the water levels, which control the horizontal hydraulic gradients and fluxes between the Rio Grande and the riverside drains, can be primarily attributed to seasonal fluctuations in river stage and irrigation practices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165021","collaboration":"Prepared in cooperation with Bureau of Reclamation acting as fiscal agent for the Middle Rio Grande Endangered Species Collaborative Program","usgsCitation":"Rankin, D.R., Oelsner, G.P., McCoy, K.J., Moret, G.J.M., Worthington, J.A., and Bandy-Baldwin, K.M., 2016, Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10: U.S. Geological Survey Scientific Investigations Report 2016–5021, 89 p., https://dx.doi.org/10.3133/sir20165021.","productDescription":"viii, 89 p.","numberOfPages":"101","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-033038","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":318926,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5021/sir20165021.pdf","text":"Report","size":"5.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5021"},{"id":318925,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5021/coverthb.jpg"}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Rio Grande","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.7266845703125,\n 34.95011635301367\n ],\n [\n -106.7266845703125,\n 35.290468565908775\n ],\n [\n -106.56051635742188,\n 35.290468565908775\n ],\n [\n -106.56051635742188,\n 34.95011635301367\n ],\n [\n -106.7266845703125,\n 34.95011635301367\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
5338 Montogmery Blvd., NE Suite 400
Albuquerque, NM 87109–1311