A study was conducted by the U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, to reconstruct streamflows for use in the RiverWare model. Methods and data used to estimate daily reconstructed streamflows at 53 sites in selected subbasins in northern New Jersey and southeastern New York are presented in the report. These subbasins contain one or more surface-water diversions that are operated or have been operated in the past by water purveyors or the New Jersey Department of Environmental Protection. Reconstructed streamflows are estimates of those streamflows that would have occurred without the effects of changes in reservoir storage or surface-water diversions by water purveyors.
Reconstructed flows at 47 sites were determined from monthly observed streamflows, changes in reservoir storage, and surface-water diversions. Monthly reconstructed streamflows were calculated directly for those months with sufficient data. Missing monthly reconstructed flows were estimated from relations between selected calculated values of monthly reconstructed flows and monthly observed flows at selected index gages. Daily reconstructed flows were determined from the disaggregation of monthly reconstructed flows on the basis of daily observed flows at selected streamgages. At six sites, reconstructed flows were determined from relations between discrete measurements of observed streamflows and daily observed streamflows at selected index gages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185068","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Hickman, R.E., and McHugh, A.R., 2018, Methods used to reconstruct historical daily streamflows in northern New Jersey and southeastern New York, water years 1922–2010: U.S. Geological Survey Scientific Investigations Report 2018–5068, 75 p., https://doi.org/10.3133/sir20185068.","productDescription":"viii, 75 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070147","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":437761,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J965CZ","text":"USGS data release","linkHelpText":"Data and equations used to reconstruct historical daily streamflows in northern New Jersey and southeastern New York, water years 1922-2010"},{"id":357153,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5068/coverthb2.jpg"},{"id":357154,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5068/sir20185068.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5068"},{"id":357155,"rank":3,"type":{"id":30,"text":"Data Release"},"url":" https://doi.org/10.5066/F7J965CZ","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and equations used to reconstruct historical daily streamflows in northern New Jersey and southeastern New York, water years 1922–2010"}],"country":"United States","state":"New Jersey, New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.049,\n 40.402\n ],\n [\n -73.906,\n 40.402\n ],\n [\n -73.906,\n 41.396\n ],\n [\n -75.049,\n 41.396\n ],\n [\n -75.049,\n 40.402\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
ivers and streams are highly vulnerable to fragmentation from roads due to their prevalence in the landscape. Road-stream crossings are far more numerous than other anthropogenic barriers such as dams; these crossing structures (culverts, bridges, fords, and tide gates) have been demonstrated to impede the passage of aquatic organisms. However, road-stream crossings vary widely in the extent to which they serve as a barrier. It is important to identify barrier severity to facilitate prioritization of restoration activities, since proactively addressing all structures is not feasible. In 2015 the North Atlantic Landscape Conservation Collaborative (LCC) funded a project managed by the North Atlantic Aquatic Connectivity Cooperative (NAACC) to develop a unified protocol for assessing aquatic road-stream crossings focusing on aquatic connectivity. The NAACC relied on rapid field-based assessments, which have been shown to be a useful tool for gathering information necessary for prioritization. However, the rapid assessment protocol developed from the NAACC initiative is not applicable to tidal crossings as it does not address two-directional flow, daily water depth fluctuations, or many of the species likely present in coastal habitats.
The goal of this report is to provide the background necessary to create guidelines and rapid assessment tools for assessing risk posed to aquatic organism passage at tidal crossings. To accomplish these goals, this report identifies species present in tidally influenced coastal wetlands, the unique traits they may display that puts them at risk for detrimental impact from impeded passage, and passage threats unique to tidal crossings that are not addressed by protocols designed for non-tidal systems. Species lists were compiled through literature reviews and discussions with regional researchers and managers familiar with coastal ecosystems or fish passage concerns. Life history traits, environmental sensitivities, and movement patterns for each species were compiled to build a database that can be queried to identify species that are highly vulnerable to impeded passage at tidal crossings (Available at: https://umass.box.com/s/w5mhokxjxshyxmr7si2v0gzcypcitu9d). These risk factors for species, combined with passage threats associated with specific crossing characteristics are discussed in this report. The species list is thorough enough to provide a baseline summary of the types of threats experienced by aquatic organisms at tidal road-stream crossings, but it is not exhaustive. Unique ecosystems, species assemblages, management goals, and prioritization models may require different approaches and solutions. Thus, care must be taken to ensure that assessment tools are appropriate to a project’s target species, habitats, and scale.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Becker, S., Jackson, S., Jordaan, A., and Roy, A.H., 2018, Impacts of tidal road-stream crossings on aquatic organism passage: Cooperator Science Series 131-2018, ii, 57 p.","productDescription":"ii, 57 p.","ipdsId":"IP-090405","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":359809,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":359808,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://digitalmedia.fws.gov/digital/collection/document/id/2238/"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c0108d5e4b0815414cc2dfd","contributors":{"authors":[{"text":"Becker, Sarah","contributorId":210890,"corporation":false,"usgs":false,"family":"Becker","given":"Sarah","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":752685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, Scott","contributorId":210891,"corporation":false,"usgs":false,"family":"Jackson","given":"Scott","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":752686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jordaan, Adrian","contributorId":210892,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":752687,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":752684,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198107,"text":"sir20185087 - 2018 - Tidal flushing of mercury from the Bremerton Naval Complex through the PSNS015 stormwater drain system to Sinclair Inlet, Kitsap County, Washington, 2011 -12","interactions":[],"lastModifiedDate":"2018-09-07T16:38:05","indexId":"sir20185087","displayToPublicDate":"2018-09-07T08:27:21","publicationYear":"2018","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":"2018-5087","title":"Tidal flushing of mercury from the Bremerton Naval Complex through the PSNS015 stormwater drain system to Sinclair Inlet, Kitsap County, Washington, 2011 -12","docAbstract":"The sediments of Sinclair Inlet, in Puget Sound, Washington, have elevated levels of contaminants including mercury. The Bremerton Naval Complex is adjacent to Sinclair Inlet, and has known areas of historical soil mercury contamination. The U.S. Geological Survey, in cooperation with the U.S. Navy, has been investigating the potential for mercury sources on the Bremerton Naval Complex to recontaminate recently remediated marine sediment. In 2011–12, the U.S. Geological Survey conducted three tidal- related sampling campaigns to characterize mercury dynamics in the largest stormwater drain system on the Bremerton Naval Complex, which passes through the soils of an area known as Site 2 that has elevated soil mercury concentrations. The sampling campaigns confirmed that the stormwater drain system, PSNS015, serves as a conduit for seawater transport more than 250 m landward of the contaminated soils that subsequently facilitates mercury transport to Sinclair Inlet.
During the December 2011 reconnaissance sampling campaign, no freshwater source of mercury to PSNS015 was identified. There was heavy precipitation preceding and stormwater runoff generated during the reconnaissance survey, which suggests that the primary source of mercury in PSNS015 is not precipitation-induced. During the May 2012 spring-tide sampling campaign, the water in PSNS015 drained to Sinclair Inlet during a negative low tide, and the highest filtered total mercury concentration in the stormwater drain system (60 ng/L) was measured during the lower-low tide in the freshwater flowing into the seaward-most stormwater drain vault from either up-pipe or local groundwater intrusion. Similar conditions were not observed during the June 2012 companion neap-tide sampling campaign, when the water-level elevation of the positive low tide in Sinclair Inlet dropped only slightly below the stormwater drain vault elevation, the water in the seaward-most stormwater vault was brackish rather than fresh, and the filtered total mercury concentration never exceeded 24 ng/L. Particulate total mercury concentrations and dynamics during the spring- and neap-tide sampling campaigns were variable, with higher concentrations (as much as 133 ng/L) measured throughout the neap-tide study compared to those measured during the spring-tide study (as much as 4.34 ng/L). The highest filtered total mercury concentration of all sampling campaigns (1,140 ng/L) was measured during ebb tide in a nearshore monitoring well that represents groundwater discharging from the contaminated soils directly to Sinclair Inlet along an unwalled part of the shoreline.
The results suggest that mercury extracted from Site 2 soils can be carried to Sinclair Inlet during ebb tides by at least two mechanisms: (1) through groundwater directly to Sinclair Inlet along an unwalled part of the shoreline or (2) through the stormwater drain system when the water level in Sinclair Inlet drops below the water level in the stormwater drain system. The data can be used to guide future modifications to the seawall and stormwater drain system that aim to hydraulically disconnect the stormwater drain system from the surrounding contaminated soils.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185087","collaboration":"Prepared in cooperation with U.S. Department of the Navy","usgsCitation":"Conn, K.E., Paulson, A.J., Dinicola, R.S., and DeWild, J.F., 2018, Tidal flushing of mercury from the Bremerton Naval Complex through the PSNS015 stormwater drain system to Sinclair Inlet, Kitsap County, Washington, 2011 -12: U.S. Geological Survey Scientific Investigations Report 2018-5087, 23 p., https://doi.org/10.3133/sir20185087.","productDescription":"vi, 23 p.","onlineOnly":"Y","ipdsId":"IP-097597","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":357094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5087/coverthb.jpg"},{"id":357095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5087/sir20185087.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5087"}],"country":"United States","state":"Washington","county":"Kitsap County","otherGeospatial":"Sinclair Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.86285400390624,\n 47.43737696208075\n ],\n [\n -122.178955078125,\n 47.43737696208075\n ],\n [\n -122.178955078125,\n 48.21003212234042\n ],\n [\n -122.86285400390624,\n 48.21003212234042\n ],\n [\n -122.86285400390624,\n 47.43737696208075\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Changes in land-use practices and the extirpation (local extinction) of beaver populations in the early 20th century during European settlement are believed to have resulted in many changes in how streams in the Western United States function. Some of the negative changes that have resulted include stream channelization, soil erosion, changing vegetation, water turbidity, and a loss of overland flow. Efforts to restore streams and reduce soil erosion by water have included reintroductions of beaver, incorporating Native American traditional knowledge of dry-land farming techniques, and the installation of rigid check-dams. Many of these efforts have been successful in improving both intermittent and perennial stream function. Therefore, stakeholders in the Southern Rockies Landscape Conservation Cooperative (SRLCC) have identified a need to prioritize streams within their region of interest for the installation of check-dams to continue restoration and conservation efforts and to improve sediment catchment.
Using Natural Resource Conservation Service soil databases, topographic features derived from digital elevation models, stream networks, and regional climatic patterns, I developed a ranking system for watershed potential erosion rates and suitability for check-dam placement across the SRLCC. This ranking system serves as a first step for land managers to prioritize areas for check-dam installation based on relatively static factors (soil properties, topography, and hydrology) that can contribute to rates of soil erosion by water and the stability of check-dams. Many other relatively dynamic factors over time can contribute to rates of soil erosion by water, such as recent wildfire events, changes in weather patterns and extreme climate events, and changing land-use such as grazing, logging, mining, development, and cultivation. These factors that influence vegetative and biological soil crusts cover are also important elements to the potential erosion of soil by water. Because of this, SRLCC stakeholders might consider further evaluation of the watersheds identified here as high ranking. Final watershed prioritization among the high-ranking watersheds identified here should include current knowledge of land-use and land-cover estimates to identify areas at risk for soil erosion or degree of existing erosion problems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181127","usgsCitation":"Ironside, K.E., 2018, Southern Rockies Landscape Conservation Cooperative unit watershed erosion potential prioritization for check-dam installation: U.S. Geological Survey Open-File Report 2018–1127, 15 p., https://doi.org/10.3133/ofr20181127.","productDescription":"Report: v, 15 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-096570","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":356855,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SEUC93","text":"Data release","description":"USGS Data Release","linkHelpText":"Watershed potential erosion rate ranking system and check-dam placement suitability data within the Southern Rockies Landscape Conservation Cooperative (SRLCC)"},{"id":356853,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1127/coverthb.jpg"},{"id":356854,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1127/ofr20181127.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1127"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.173197,\n 32.416412\n ],\n [\n -103.499364,\n 32.416412\n ],\n [\n -103.499364,\n 43.335375\n ],\n [\n -116.173197,\n 43.335375\n ],\n [\n -116.173197,\n 32.416412\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"SBSC Staff,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Conceptual and numerical models were developed to understand and simulate monthly flow-weighted dissolved-solids concentrations in the Colorado River at Imperial Dam. The ability to simulate dissolved-solids concentrations at this location will help the Bureau of Reclamation satisfy the binational agreement on the volume and salinity of Colorado River water delivered to Mexico. A robust spatial- and temporal-resolution dataset that consists of river discharge and dissolved-solids concentration and load information between January 1990 and September 2016 for 10 sites on canals, drains, tributaries, and the main stem of the Colorado River between Hoover and Imperial Dams was generated. Daily mean dissolved-solids concentrations were estimated and monthly mean dissolved-solids loads were computed for each site. Spatial and temporal load patterns, and historical and current controls on loads and concentrations, were analyzed in order to develop a conceptual model of dissolved-solids transport between Hoover and Imperial Dams. Two numerical models describing the relations between dissolved-solids concentrations and components controlling dissolved-solids concentrations and loads were developed, calibrated, and verified.
Between January 1990 and September 2016, there was a 98.8-million-acre-feet loss of water and a 57.0-million-ton loss of dissolved-solids load from the Colorado River between Hoover and Imperial Dams. Between Hoover and Parker Dams, about 69.0 million acre-feet of water was lost and 51.1 million tons of dissolved solids were lost; between Parker and Imperial Dams, about 29.8 million acre-feet of water was lost and 5.9 million tons of dissolved solids were lost. Water was removed from the river at a relatively consistent rate over the 25-year study period through water transfers to California and Arizona, evapotranspiration from crop irrigation, transpiration processes of riparian vegetation, and evaporation from the river main stem. Dissolved solids were removed from the river between Hoover and Parker Dams at a relatively constant rate through water transfers to California and Arizona, and water pumped from the river for irrigation within the Mohave Valley. A small amount of dissolved solids are gained by the river from inflow from the Bill Williams River. Between Parker and Imperial Dams, however, dissolved solids were not removed from the river at a consistent rate over the study period. Dissolved solids were generally removed from the river from 1990 to 2012, then gained by the river from 2012 to 2015, and then removed from the river from 2015 through 2016. Dissolved solids are assumed to be removed from the river and accumulated within the floodplain sediments and aquifers during irrigation processes; some dissolved solids may also be removed from the river through uptake by crops and riparian vegetation. Dissolved solids accumulated on the landscape and in the floodplain aquifer during irrigation are transported to the river during periods when the hydraulic gradient between the floodplain aquifer and the river is increased, causing a gain in dissolved solids in the river. Dissolved-solids gains in the river occur during periods of relatively low river discharge, such as during the winter months and during drier climatic conditions.
Two numerical models were developed and coefficients were estimated by using data from a May 2008-September 2016 calibration period. One model simulates concentrations at Imperial Dam based on the Colorado River system downstream from Parker Dam, and the other model simulates concentrations at Imperial Dam based on the Colorado River system downstream from Hoover Dam. Both models simulated monthly flow-weighted concentrations of dissolved solids for the Colorado River at Imperial Dam, which corresponded well with observed concentrations for the entire study period. The models are more sensitive to input variables of monthly discharge of the Colorado River below Parker Dam and monthly flow-weighted dissolved-solids concentrations of the Colorado River below Hoover Dam and Parker Dam than to the rate of change in concentration with respect to time and the combined discharge of the Colorado River Indian Reservation Main Canal and the Palo Verde Canal. The calibrated models can be used to run scenarios of future monthly flow-weighted dissolved-solids concentrations in the Colorado River at Imperial Dam. Although the models are expected to provide concentration estimates within 18 milligrams per liter (Parker Dam to Imperial Dam model) to 22 milligrams per liter (Hoover Dam to Imperial Dam model), 95 percent of the time, the error of future scenarios increases as uncertainty in the estimated future input variables increases.
Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
This report presents geochemical data for surface water, streambed sediment, and fish tissue samples collected during low-flow conditions in 20 to 24 Sierra Nevada streams during 2011 and 2012. The dataset is part of a larger study designed to assess the factors that control mercury concentrations in fish tissue and to develop a model that predicts mercury concentration in the tissue of selected fish species in Sierra Nevada streams. The ranges of total mercury concentration observed in different matrices of water and sediment from 24 locations were as follows: below detection to 0.86 nanograms per liter in filtered water, below detection to 4.06 nanograms per liter in suspended particulates (greater than 0.3 micrometer in diameter), 1.1 to 381 nanograms per gram in bed sediment less than 2 millimeters, and 28.1 to 1,410 nanograms per gram in bed sediment less than 0.063 millimeters. The ratio of monomethyl mercury to total mercury ranged as follows: below detection to 19.2 percent in filtered water, below detection to 51.7 percent in suspended particles (greater than 0.3 micrometer), and below detection to 7.6 percent in streambed sediment less than 2 millimeters. Fish from 3 species collected at 20 locations had the following range in total mercury concentration (all concentrations wet weight): 10 to 292 nanograms per gram in rainbow trout (293 fish, 19 locations), 13 to 386 nanograms per gram in brown trout (33 fish, 10 locations), and 159 nanograms per gram in hardhead (1 fish). Concentrations of selenium in fish (wet weight) ranged from 60 to 420 nanograms per gram in rainbow trout (66 fish, 19 locations) and from 180 to 240 nanograms per gram in brown trout (6 fish, 2 locations).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1056","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Stumpner, E.B., Alpers, C.N., Marvin-DiPasquale, M., Agee, J.L., Kakouros, E., Arias, M.R., Kieu, L.H., Roth, D.A., Slotton, D.G., and Fleck, J.A., 2018, Geochemical data for water, streambed sediment, and fish tissue from the Sierra Nevada Mercury Impairment Project, 2011–12: U.S. Geological Survey Data Series 1056, 133 p., https://doi.org/10.3133/ds1056.","productDescription":"Report: xiv, 133 p.","onlineOnly":"Y","ipdsId":"IP-053615","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":356551,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1056/ds_1056.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Data Series 1056"},{"id":356550,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1056/coverthb.jpg"}],"country":"United States","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 37.5\n ],\n [\n -119.5,\n 37.5\n ],\n [\n -119.5,\n 40\n ],\n [\n -122,\n 40\n ],\n [\n -122,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey (USGS) uses Maintenance of Variance Extension Type 1 (MOVE.1) regression to transfer streamflows measured at long-term continuous-record streamgaging stations to partial-record (PR) streamgaging stations where intermittent base-flow measurements are available. MOVE.1 regression is used widely throughout the hydrologic community to extend historic low flows and low-flow statistics at continuous-record streamgaging stations to streamgaging stations that have access to only a partial record of low flows. The method correlates base-flow measurements at PR streamgaging stations with daily mean streamflows measured at index stations that exhibit similar streamflow characteristics.
Following changes in the computing platform for storing, processing, retrieving, and publishing National Water Information System (NWIS) hydrologic data, legacy Statistical Analysis System (SAS) code developed by the USGS to implement the MOVE.1 regression was no longer suitable for reading and processing NWIS streamflow data. To migrate the MOVE.1 program so that it could continue to read streamflow data using the new hydrologic data platform, the SAS code was re-written in R, an open source programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The work described in this report was performed in a study conducted by USGS in cooperation with the New Jersey Department of Environmental Protection.
During migration from SAS to R, graphical and tabular output generated by the R script was compared to output produced by the legacy SAS code to ensure that equations used to perform the MOVE.1 regression remained the same. An option to perform censored MOVE.1 regression was added to extend the MOVE.1 methodology to cases where one or more measured continuous-record or PR streamgaging station flows are zero valued. In addition to permitting censored regression, the new R script includes an option to perform piecewise MOVE.1 regression when the relation between PR station and index station low flows varies significantly across the range of index station streamflows.
Together with traditional MOVE.1 regression, censored, and piecewise MOVE.1 regression methods implemented by the R script offer less biased estimates than ordinary least squares regression for the annual 7-day 10-year and other low-flow statistics at PR stations for a range of base-flow conditions. The R script is used to implement the MOVE.1 regression methods across a variety of computing platforms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181089","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Colarullo, S.J., Sullivan, S.L., and McHugh, A.R., 2018, Implementation of MOVE.1, censored MOVE.1, and piecewise MOVE.1 low-flow regressions with applications at partial-record streamgaging stations in New Jersey: U.S. Geological Survey Open-File Report 2018–1089, 20 p., https://doi.org/10.3133/ofr20181089.","productDescription":"v, 20 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-089567","costCenters":[{"id":470,"text":"New Jersey Water Science 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Jersey\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
The maps and graphs in this summary describe national streamflow conditions for water year 2017 (October 1, 2016, to September 30, 2017) in the context of streamflow ranks relative to the 88-year period of 1930–2017, unless otherwise noted. The illustrations are based on observed data from the U.S. Geological Survey (USGS) National Streamflow Network (U.S. Geological Survey, 2018a). The period of 1930–2017 was used because the number of streamgages before 1930 was too small to provide representative data for computing statistics for most regions of the country.
In the summary, reference is made to the term “runoff,” which is the depth to which a river basin, State, or other geographic area would be covered with water if all the streamflow within the area during a specified period was uniformly distributed on it. The value of runoff quantifies the magnitude of water flowing through the Nation’s rivers and streams in measurement units that can be compared from one area to another. In this summary, runoff for a specified period and geographic area is computed from all streamgages with complete record in the geographic area.
In all the graphics, a rank of 1 indicates the highest annual flow of all years analyzed and 88 indicates the lowest annual flow of all years. Rankings of streamflow are grouped into much below normal, below normal, normal, above normal, and much above normal based on percentiles of flow (less than 10 percent, 10–24 percent, 25–75 percent, 76–90 percent, and greater than 90 percent, respectively; U.S. Geological Survey, 2018b). States or water-resources regions are presented in the text in order of ranking; a highest or lowest rank is not shown when there are ties in the rankings. Some of the data used to produce the maps and graphs are provisional and subject to change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183056","usgsCitation":"Jian, X., Wolock, D.M., Brady, S.J., and Lins, H.F., 2018, Streamflow—Water year 2017: U.S. Geological Survey Fact Sheet 2018–3056, 6 p., https://doi.org/10.3133/fs20183056.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-098559","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":356612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3056/fs20183056.pdf","text":"Report","size":"622 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018–3056"},{"id":356611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3056/coverthb.jpg"}],"country":"United States","contact":"U.S. Geological Survey
MS 415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
From December 2 to 4, 2015, NatureServe and the U.S. Geological Survey organized and hosted a biodiversity and ecological informatics workshop at the U.S. Department of the Interior in Washington, D.C. The workshop objective was to identify user-driven future directions and areas of collaboration in advanced applications of environmental data applied to forecasting and decision making for the sustainability of biodiversity and ecosystem services. Substantial effort to recruit attendees from diverse Federal, State, and private sector organizations successfully attracted participants from 20 Federal agencies and 48 different institutions in the academic, nonprofit, State government, and commercial sectors; the total number of attendees ranged from 100 to 144 during the 3-day workshop. The first one-half of the workshop was divided into 7 plenary sessions and 3 sets of lightning talk sessions organized by sector, providing 48 oral and visual plenary presentations that shared diverse perspectives on biodiversity and ecological informatics, including original biospatial analyses from 6 graduate student map contest winners. The second one-half of the workshop focused on 10 breakout sessions with participant-driven themes from the environmental data sphere and concluded with an address by the Director of the U.S. Fish and Wildlife Service. The workshop was structured to encourage interactivity. About 80–90 percent of attendees provided direct feedback using clicker devices for specific questions related to biodiversity and ecological data uses and needs, and 10 breakout session leaders shared the highlights of their group discussions during the final workshop plenary sessions. Participants were encouraged to use the Twitter hashtag #ShareUrData. Over lunch on day 2 there were 20 simultaneous presentations of tools and apps during a special “Tools Café” session.
The 10 participant-defined breakout session topics are listed below:
Numerous common themes that emerged from the workshop include the following:
Director, Core Science Analytics Synthesis and Libraries Program
U.S. Geological Survey
W 6th Ave Kipling Street
Lakewood, CO 80225
Rapid population growth and declining annual recharge to aquifers in the upper Santa Cruz Basin area of southern Arizona, have increased the demand for additional groundwater resources. This demand is predicted to escalate in the future because of higher temperatures, longer droughts, less aquifer recharge, and decreased river and stream base flow. We conducted geologic studies to help evaluate and better understand groundwater resources in the basin. Results of these studies are presented in this report, which summarizes the basin geologic framework and hydrogeology, and presents a threedimensional (3D) hydrogeologic model for the Rio Rico and Nogales 7.5′ quadrangles. Three major hydrogeologic units are displayed in the 3D model; a lower basement confining unit, consisting of Jurassic, Cretaceous, and Tertiary (Paleocene and Oligocene) rocks; a middle unit composed entirely of the Miocene Nogales Formation; and an upper unit consisting of late Miocene to Holocene surficial deposits. The Nogales Formation and the late Miocene to Holocene sediments are the main aquifers in the upper Santa Cruz Basin. The 3D model integrates the hydrogeologic units and faults to define the geometry, structure, and thickness of the aquifer system that provides water to Nogales and surrounding communities of southernmost Arizona. The report includes an EarthVision 3D Viewer, consisting of software enabling the user to view data interactively in 3D space to help explain the internal complexities of the basin geometry, structure, stratigraphy, and hydrology. The 3D model is a synthesis of geologic data from geologic maps, cross sections, and lithologic descriptions and interpretations; and geophysical data including gravity, magnetic data, and airborne electromagnetic data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185062","usgsCitation":"Page, W.R., Bultman, M.W., VanSistine, D.P., Menges, C.M., Gray, Floyd, and Pantea, M.P., 2018, Geologic framework and hydrogeology of the Rio Rico and Nogales 7.5’ quadrangles, upper Santa Cruz Basin, Arizona, with three-dimensional hydrogeologic model: U.S. Geological Survey Scientific Investigations Report 2018–5062, 34 p., https://doi.org/10.3133/sir20185062.","productDescription":"Report: vi, 34 p.; Data release","onlineOnly":"Y","ipdsId":"IP-085666","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":356167,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5062/coverthb.jpg"},{"id":356184,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QJ7GHT","text":"USGS data release","linkHelpText":"Data Release for Geologic Framework and Hydrogeology of the Rico Rico and Nogales 7.5' quadrangles, Upper Santa Cruz basin, Arizona, with 3-Dimensional hydrogeologic model"},{"id":356168,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5062/sir20185062.pdf","text":"Report","size":"23.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5062"}],"country":"United States","state":"Arizona","otherGeospatial":"Rio Rico and Nogales 7.5’ Quadrangles, Upper Santa Cruz Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111,\n 31.33\n ],\n [\n -110.875,\n 31.33\n ],\n [\n -110.875,\n 31.5\n ],\n [\n -111,\n 31.5\n ],\n [\n -111,\n 31.33\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS 980
Denver, CO 80225
Digital flood-inundation maps for an 8-mile (mi) reach of the Huron River near Hamburg, Michigan (station number 04172000), from downstream of Rickett Road to Strawberry Lake, were created by the U.S. Geological Survey (USGS), in cooperation with Green Oak and Hamburg Townships, Michigan, and the U.S. Army Corps of Engineers. The flood-inundation maps also include a 1.16-mi reach of the Ore Lake Tributary until it joins the Huron River, approximately 2.22 mi downstream of Rickett Road. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website 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 Huron River near Hamburg, Michigan (station number 04172000). 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 (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/. The NWS Advanced Hydrologic Prediction Service also provides forecasted flood hydrographs at this website.
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the current stage-discharge relation at the Huron River near Hamburg, Mich., streamgage and was calibrated to water levels determined with stage sensors (pressure transducers) temporarily deployed along the stream reach. The hydraulic model was used to compute a set of water-surface profiles for flood stages ranging from 7.0 to 10.5 feet (ft). This range represents stages just above 6.0 (bankfull) to 2.04 ft above the maximum recorded stage at the USGS streamgage on the Huron River near Hamburg, Mich. (station number 04172000). The computed water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging [lidar] data having a 0.49-ft vertical accuracy and 3.8-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with Internet information regarding current stage and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information critical for flood-response activities such as evacuations, road closures, and postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185048","collaboration":"Prepared in cooperation with Green Oak and Hamburg Townships, Michigan and the U.S. Army Corps of Engineers","usgsCitation":"Prokopec, J.G., 2018, Hydraulic modeling and flood-inundation mapping for the Huron River and Ore Lake Tributary, Livingston County, Michigan: U.S. Geological Survey Scientific Investigations Report 2018–5048, 13 p., https://doi.org/10.3133/sir20185048.","productDescription":"Report: vii, 13 p.; 2 Data releases","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084641","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":437793,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H1TX91","text":"USGS data release","linkHelpText":"Geospatial data for a Flood-Inundation Mapping Study of the Huron River near Hamburg, Michigan"},{"id":362849,"rank":4,"type":{"id":30,"text":"Data Release"},"url":" https://www.sciencebase.gov/catalog/item/5c953d27e4b09388245a6d33 ","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial data for a Flood-Inundation Mapping Study of the Huron River near Hamburg, Michigan"},{"id":356059,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5048/coverthb.jpg"},{"id":356060,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5048/sir20185048.pdf","text":"Report","size":"1.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5048"},{"id":356061,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79G5M11","text":"USGS data release","description":"USGS data release","linkHelpText":"Huron River near Hamburg, Michigan, flood-inundation model and field data"}],"country":"United States","state":"Michigan","county":"Livingston County","otherGeospatial":"Huron River, Ore Lake Tributary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.84233474731445,\n 42.4333\n ],\n [\n -83.7667,\n 42.4333\n ],\n [\n -83.7667,\n 42.490960223200396\n ],\n [\n -83.84233474731445,\n 42.490960223200396\n ],\n [\n -83.84233474731445,\n 42.4333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way
Suite 5
Lansing, MI 48911
An exploratory discrete Bayesian network (BN) was developed to assess the potential of this type of model for estimating the magnitudes and uncertainties of an arbitrary subset of unmeasured water-quality parameters given the measured complement of parameters historically measured at a U.S. Geological Survey streamgage. Water-quality data for 27 water-quality parameters from 596 discrete measurements at U.S. Geological Survey streamgage 03374100 White River at Hazleton, Indiana, were used to develop this BN. Data for each of the water-quality parameters were discretized into five intervals based on the quintiles of the measured values. The 596 discrete measurements were randomly partitioned into a training set with 80 percent of the data and a testing set with 20 percent of the data to identify, estimate, and assess the training and testing accuracy of the Bayesian network.
A BN with 28 nodes was formed from the 27 water-quality parameters and the month of sample collection. Based on data in the training set, a network with 53 directed edges and month as the target node was identified by minimizing the negative log-likelihood function for all nodes treated, in turn, as the target variable. The edge structure determines the number and magnitude of elements in conditional probability tables associated with all nodes.
The effectiveness of the BN was assessed on the basis of correct classification rates to one of the five discrete intervals, which were computed separately for the training and testing datasets and for two conditioning variable sets. The selected sets of conditioning variables represent two of many possible sets of measured parameters on which to base estimates of unmeasured parameters. The first set includes only the month of sample collection (month), and an expanded set includes month and six other continuously measurable parameters, referred to as the ContMeasSet, all of which were obtained from the discrete data.
Results indicated that the training dataset had average correct classification rates of 41.7- and 61.2-percent rates conditioned on the month and ContMeasSet sets, respectively. The testing dataset had somewhat lower average correct classification rates of 40.8 and 56.5 percent for the two conditioning variable sets. When conditioned on month only, the average correct classification rate for the testing dataset was only slightly lower than the average correct classification rate in the training dataset, indicating little model overfitting. When using the ContMeasSet, however, the average decrease in accuracy between training and testing sets was 4.9 percent. The training and testing datasets and both sets of conditioning variables, however, indicate that the BN would substantially outperform a random assignment model, which would be expected to have a 20-percent correct classification rate. In addition, the edge structure of the BN depicts how information can flow through the network, which may help prioritize parameters for measurement to facilitate estimation of unmeasured parameters. Finally, extension of a static BN, like the one developed in this report, to a dynamic BN may provide a basis for using high-frequency or continuous water-quality data to extend information in time between discrete water-quality samples, and this integration could mitigate some of the limitations of high-frequency and discrete water-quality sampling methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185053","collaboration":"National Water-Quality Program","usgsCitation":"Holtschlag, D.J., 2018, An exploratory Bayesian network for estimating the magnitudes and uncertainties of selected water-quality parameters at streamgage 03374100 White River at Hazleton, Indiana, from partially observed data: U.S. Geological Survey Scientific Investigations ReportDirector, Michigan Water Science Center
U.S. Geological Survey
6520 Mercantile Way, Suite 5
Lansing, MI 48911
Nitrate concentrations in the Cedar River in Iowa and Minnesota have increased from an average of less than 1.0 milligram per liter in the early 1900s to more than 5.0 milligrams per liter in the 2000s and have resulted in periodic water-quality impairment of the river. Spatial differences and temporal changes in nitrogen and phosphorus transport in the Cedar River Basin are described for the period from 2000 to 2015. Data used to estimate nitrogen and phosphorus transport were collected by the U.S. Geological Survey as part of six base-flow synoptic studies and by the Minnesota Pollution Control Agency and the Iowa Department of Natural Resources as part of their long-term stream and river monitoring programs. The Cedar River transported an annual average of 53,100 tons of total nitrogen and 2,510 tons of total phosphorus during 2000–15. Three subbasins yielded an annual average of more than 30 pounds per acre (lb/acre) of nitrogen to the Cedar River, whereas two subbasins yielded an annual average of less than 20 lb/acre of nitrogen. The average annual total phosphorus yield from the Little Cedar River subbasin (0.35 lb/acre) was only about 16 percent of the yield from the greatest total phosphorus yielding Lower Cedar River subbasin (more than 1.0 lb/acre). The annual total nitrogen and total phosphorus loads did not change significantly during the study. The relation between annual stream runoff and annual total nitrogen and total phosphorus yields was not spatially uniform across the Cedar River Basin. The Beaver Creek, Black Hawk Creek, and Wolf Creek subbasins yielded the most, and the Main Stem Middle Cedar River, the Lower Cedar River, and the Little Cedar River subbasins yielded the least amount of nitrogen for a given amount of runoff. The Lower Cedar River and Wolf Creek subbasins yielded the most and the West Fork Cedar River and the Little Cedar River subbasins yielded the least phosphorus for a given amount of runoff. The results of this study describe nutrient transport during 2000–15 that can be used to evaluate future progress of nutrient reduction strategies in the Cedar River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185090","collaboration":"Prepared in cooperation with the City of Cedar Rapids, Iowa","usgsCitation":"Kalkhoff, S.J., 2018, Transport of nitrogen and phosphorus in the Cedar River Basin, Iowa and Minnesota, 2000–15: U.S. Geological Survey Scientific Investigations Report 2018–5090, 44 p., https://doi.org/10.3133/sir20185090.","productDescription":"ix, 44 p.","numberOfPages":"58","onlineOnly":"N","ipdsId":"IP-090101","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":355857,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5090/sir20185090.pdf","text":"Report","size":"18.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5090"},{"id":355856,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5090/coverthb.jpg"}],"country":"United States","state":"Iowa, Minnesota","otherGeospatial":"Cedar River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.75,\n 41.25\n ],\n [\n -90.5,\n 41.25\n ],\n [\n -90.5,\n 44\n ],\n [\n -93.75,\n 44\n ],\n [\n -93.75,\n 41.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street
Room 269
Iowa City, Iowa 52240-4105
The Brandon Road Lock and Dam on the Des Plaines River near Joliet, Illinois, has been identified for potential implementation of aquatic nuisance species (ANS) control measures. To provide additional information concerning the flow hydraulics and mixing characteristics of the lock and downstream approach channel, the U.S. Geological Survey performed a detailed study of the site between December 2014 and October 2015, which included the collection and analysis of bathymetric, hydrodynamic, and dye tracer data. Synthesis of these data allowed a characterization of the site for future use in feasibility studies of potential ANS control technologies. The results of this study show a highly dynamic system driven primarily by lock operations but influenced by channel characteristics, industrial withdrawals, and meteorological forcing. Lock operation produces rapidly varying flows in the downstream approach channel, including transient oscillations that produce bidirectional flows. When the lock is not in operation, flows in the approach channel are primarily driven by leakage and wind forcing. Uniform concentrations of dissolved constituents in the lock chamber can be achieved by injection of the constituent into the existing lock filling and emptying system; however, valve and gate leakage can inhibit the mixing at the downstream end of the lock and substantially affects the ability to maintain a treated lock chamber at a uniform target concentration at tailwater level. Proper understanding of these hydraulic factors should be accounted for if the lock is to be used to deliver any dissolved constituent or operated in a way to prevent upstream passage of floating ANS. Moreover, extremely variable flow conditions including bidirectional flows and upstream return flows must be considered when implementing any ANS control technologies in the approach channel.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185094","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Engel, F.L., Jackson, P.R., and Murphy, E.A., 2018, Flow hydraulics and mixing characteristics in and downstream from Brandon Road Lock, Joliet, Illinois: U.S. Geological Survey Scientific Investigations Report 2018–5094, 32 p., https://doi.org/10.3133/sir20185094.","productDescription":"Report: vii, 32 p.; 8 Data Releases","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091674","costCenters":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":437814,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7K935SH","text":"USGS data release","linkHelpText":"Multibeam bathymetry and sediment depth data at select locations on the Des Plaines River near Joliet, Illinois, February 1314, 2017"},{"id":437813,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7S180NN","text":"USGS data release","linkHelpText":"Miscellaneous flow discharge measurements collected downstream of Brandon Road Lock and Dam"},{"id":437812,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73F4MRV","text":"USGS data release","linkHelpText":"Bathymetric survey of the Brandon Road Dam Spillway, Joliet, Illinois"},{"id":437811,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74747Z7","text":"USGS data release","linkHelpText":"Rhodamine WT dye concentrations measured at fixed locations in the Des Plaines River near Brandon Road Lock and Dam near Rockdale, Illinois (October 20-21,2015)"},{"id":437810,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70G3H8C","text":"USGS data release","linkHelpText":"Spatial distribution of Rhodamine WT dye concentration measured in the Des Plaines River near Brandon Road Lock and Dam near Rockdale, Illinois (October 20-21, 2015)"},{"id":437809,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77W69B7","text":"USGS data release","linkHelpText":"Rhodamine WT dye concentration profiles measured at fixed stations in the Brandon Road Lock chamber near Rockdale, Illinois (October 20, 2015)"},{"id":437808,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VQ30S2","text":"USGS data release","linkHelpText":"Water surface elevation in the Brandon Road Lock chamber near Rockdale, Illinois (October 19-21, 2015)"},{"id":355952,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F70G3H8C","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Spatial distribution of Rhodamine WT dye concentration measured in the Des Plaines River near Brandon Road Lock and Dam near Rockdale, Illinois (October 20–21, 2015) "},{"id":355948,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P26X39","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Acoustic Doppler current profiler velocity and discharge measurements collected in and near the lock chamber of Brandon Road Lock and Dam, Joliet, Illinois, USA in December 2014 "},{"id":355949,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F73F4MRV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetric survey of the Brandon Road Dam Spillway, Joliet, Illinois"},{"id":355950,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F77W69B7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Rhodamine WT dye concentration profiles measured at fixed stations in the Brandon Road Lock chamber near Rockdale, Illinois (October 20, 2015)"},{"id":355953,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7VQ30S2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water surface elevation in the Brandon Road Lock chamber near Rockdale, Illinois (October 19–21, 2015)"},{"id":355944,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5094/coverthb.jpg"},{"id":355945,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5094/sir20185094.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5094"},{"id":355946,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7K935SH ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Multibeam bathymetry and sediment depth data at select locations on the Des Plaines River near Joliet, Illinois, February 13–14, 2017"},{"id":355947,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7S180NN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Miscellaneous flow discharge measurements collected downstream of Brandon Road Lock and Dam"},{"id":355951,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F74747Z7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Rhodamine WT dye concentrations measured at fixed locations in the Des Plaines River near Brandon Road Lock and Dam near Rockdale, Illinois (October 20–21, 2015)"}],"country":"United States","state":"Illinois","city":"Joliet","otherGeospatial":"Brandon Road Lock","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.3333,\n 41.37474755643594\n ],\n [\n -88.03276062011719,\n 41.37474755643594\n ],\n [\n -88.03276062011719,\n 41.6333\n ],\n [\n -88.3333,\n 41.6333\n ],\n [\n -88.3333,\n 41.37474755643594\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave.
Urbana, Illinois 61801
The U.S. Geological Survey’s Soil-Water-Balance (SWB) code was developed as a tool to estimate distribution and timing of net infiltration out of the root zone by means of an approach that uses readily available data and minimizes user effort required to begin a SWB application. SWB calculates other components of the water balance, including soil moisture, reference and actual evapotranspiration, snowfall, snowmelt, canopy interception, and crop-water demand. SWB is based on a modified Thornthwaite-Mather soil-water-balance approach, with components of the soil-water balance calculated at a daily time step. Net-infiltration calculations are computed by means of a rectangular grid of computational elements, which allows the calculated infiltration rates to be imported into grid-based regional groundwater-flow models. SWB makes use of gridded datasets, including datasets describing hydrologic soil groups, moisture-retaining capacity, flow direction, and land use. Climate data may be supplied in gridded or tabular form. The SWB 2.0 code described in this report extends capabilities of the original SWB version 1.0 model by adding new options for representing physical processes and additional data input and output capabilities. New methods included in SWB 2.0 allow for direct gridded input of externally calculated water-budget components (fog, septic, and storm-sewer leakage), simulation of canopy interception by several alternative processes, and a crop-water demand method for estimating irrigation amounts. New input and output capabilities allow for grids with differing spatial extents and projections to be combined without requiring the user to resample and resize the grids before use.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Groundwater in Book 6: Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A59","collaboration":"Water Availability and Use Science ProgramDirector, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Sandbars along the Colorado River are used as campsites by river runners and hikers and are an important recreational resource within Grand Canyon National Park, Arizona. Regulation of the flow of river water through Glen Canyon Dam has reduced the amount of sediment available to be deposited as sandbars, has reduced the magnitude and frequency of flooding events, and has increased the magnitude of baseflows. This has caused widespread erosion of sandbars and has allowed native and non-native vegetation to expand on open sand. Previous studies show an overall decline in campsite area despite the use of controlled floods to rebuild sandbars. Monitoring of campsites since 1998 has shown changes in campsite area, but the factors that cause gains and losses in campsite area have not been quantified. These factors include, among others, changes in sandbar volume and slope under different dam flow regimes that include controlled floods, gullying caused by monsoonal rains, vegetation expansion, and reworking of sediment by aeolian processes.
Using 4-band aerial imagery and digital elevation models (DEMs) derived from total-station survey data, we analyzed topographic and vegetation change at 35 of 37 long-term monitoring sites (2 sites were excluded because topographic measurements do not overlap with measurements of campsite area) using data collected between 2002 and 2009 to quantify the factors affecting the size of campsite area. Over the course of the study period, there was a net loss in campsite area of 2,431 square meters (m2). We find that (1) 53 percent of the net loss was caused by topographic change associated with controlled floods and erosion of those flood deposits, (2) 47 percent of the net loss was caused by increases in vegetation cover, the majority of which occurred in high-elevation campsite area, and (3) gullying was significant at certain sites but overall was a minor factor.
Sites in critical reaches—sections of river where campsites are infrequent or where there is high demand by river runners—were subjected to more erosion and changes in sandbar slope than sites in noncritical reaches, suggesting that campsite area is less stable in those reaches. There was also a greater increase in vegetation cover at sites in noncritical reaches than at sites in critical reaches. Our results show a continuation of sandbar erosion and vegetation encroachment that has been occurring at campsites since construction of the dam.
A new campsite survey methodology using a tablet-based geographic information system (GIS) approach was also developed in an effort to map campsite area on digital orthophotographs. Using a series of repeat measurements, we evaluated the inherent uncertainty in mapping campsite area, the accuracy of the new tablet-based method, and if there is any bias between the tablet method and the total-station method that is currently used. We find that uncertainty associated with surveyor judgment while using the total-station method is about 15 percent, which is higher than a previously reported uncertainty of 10 percent. Use of the tablet method adds additional uncertainty; however, the benefits of being able to quantify factors that lead to campsite-area change in the field may outweigh the additional error. Future campsite monitoring may need to consist of a combination of total-station and orthophotograph techniques.
SBSC Staff,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Surface water samples were collected by the U.S. Geological Survey and multiple cooperators during base flow/irrigation runoff and storm runoff conditions from 12 sites throughout California, over 2 consecutive years beginning in April 2015, from both urban and agriculturally dominated watersheds. Water samples were analyzed by gas chromatography/mass spectrometry and liquid chromatrography/tandem mass spectrometry for a suite of 157 pesticides and degradates. Suspended sediments associated with these water samples were analyzed by gas chromatography/mass spectrometry for a suite of 131 pesticides and degradates. Overall, 85 pesticides and degradates were detected in the water: 32 fungicides, 25 herbicides, 27 insecticides, and 1 synergist. In the suspended sediment, 29 pesticides were detected: 9 fungicides, 10 herbicides, and 10 insecticides. Sixteen pesticides (bifenthrin, carbendazim, chlorpyrifos, clothianidin, diazinon, diuron, fenpyroximate, fipronil, fipronil sulfone, fluopicolide, imidacloprid, metolachlor, novaluron, oxyflurofen, permethrin, and simazine) were detected in the water at concentrations that were above at least one aquatic life benchmark value as defined by the U.S Environmental Protection Agency.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1088","collaboration":"Prepared in cooperation with the Central Valley Regional Water Quality Control Board","usgsCitation":"Sanders, C.J., Orlando, J.L., and Hladik, M.L., 2018, Detections of current-use pesticides at 12 surface water sites in California during a 2-year period beginning in 2015: U.S. Geological Survey Data Series 1088, 40 p., https://doi.org/10.3133/ds1088.","productDescription":"Report: viii; 40 p.; 2 Tables","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086505","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":355893,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1088/ds1088.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 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\"}}]}","contact":"The U.S. Geological Survey, in cooperation with the Virginia Department of Environmental Quality, has quantified several measures of rating stability and the frequency and magnitude of changes to ratings through time for 174 real-time continuous streamgages active in Virginia as of September 30, 2013. Generalized additive models (GAMs) were fitted through all available flow measurements for all the streamgages in Virginia’s real-time network as of September 30, 2013, with at least 20 flow measurements with positive flow values. For each measurement with a positive flow value, residuals from the GAM curve were calculated. Time series of these residuals were used to identify major changes to the control (the stream feature or features which control the relation between stage and flow); the measurements in the periods of equilibrium between major changes were assigned to rating families. Of the 127 rating families that were identified as being distinct at sites, documented explanations were found for 67 of them. The most common reasons for the control to change enough to warrant a new rating family are moving the streamgage (28 times), floods (26 times), and construction activities (13 times). Provisional flow data from any streamgage that has recently experienced a major flood, regardless of historical stability, are more uncertain than usual until post-flood evidence emerges that the rating is stable, or if the rating has changed, until it is known to be well defined.
A direct comparison between provisional flow data (those data originally displayed on the web in near-real time) and flow data approved for publication following subsequent flow measurements and review could not be made because provisional flow data have not been archived. As a substitute, alternative flow (AltFlow) tables were constructed for periods with complete records of shifts and ratings. Alternate flows consist of $Q$same, the flow value from the shifted rating table used to compute the daily flow value at the time of the most recent flow measurement that corresponds to the gage height of each day’s daily flow value, and $Q$prev, the flow value from the shifted rating table in effect at the time of the previous flow measurement that corresponds to the gage height of each day’s daily flow value.
Several metrics that summarize AltFlow tables were computed and evaluated; particular importance was given to how well the metrics agreed with the descriptive stability class developed from interviews with hydrographers. Of these stability metrics, at least four were determined to be meaningful and to represent different aspects of control stability that might be relevant to water managers: total root mean square error between log-transformed $Q$prev and $Q$same, percentage of days when the difference between $Q$prev and $Q$same is greater than (>) $Q$same, the sum of absolute AltFlow error divided by total flow, and percentage of days with zero difference between $Q$prev and $Q$same.
Three other meaningful metrics of control stability or provisional flow-data quality were computed: R2 (coefficient of determination) of GAMs from the flow measurements, percentage of total estimated days, and percentage of estimated days in the winter. Correlations among metrics varied, indicating they responded to different aspects of control stability. Relations among the various stability metrics and quantitative basin and site characteristics were weak. Although quantitative relations between stability metrics and basin and site characteristics were all weak, some common patterns still emerged. Controls and ratings on large streams [>500-square mile (mi2) drainage area] and at high elevations (>1,000 feet) were more likely to be stable than controls and ratings on small streams (less than (<) 100-mi2 drainage area) and at low elevations (<500 feet). There were exceptions to both generalizations, and streamgages that were intermediate in both characteristics varied widely in stability.
Typical timing of record computation changed during water years 1991–2013. From 1991 through 2001, the median number of days between the start date of the shift and the date it was created fluctuated between about 240 and about 300 days and decreased by about 4 months from 2001 to 2002. Only in 2012 and 2013 did one-half of the shifts have a delay of about 60 days between start date and final modification.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175137","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality","usgsCitation":"Messinger, T., and Burgholzer, R.W., 2018, Rating stability, and frequency and magnitude of shifts, for streamgages in Virginia through water year 2013: U.S. Geological Survey Scientific Investigations Report 2017–5137, 91 p., https://doi.org/10.3133/sir20175137. 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streamgages in Virginia, B, residual of flow and date with a LOESS smoother, and C, residual of flow and Julian date with a LOESS smoother"},{"id":355787,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate01.pdf","text":"Plate 1","size":"230 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- A, Long-term stage-flow relations at selected streamgages in Virginia with a generalized additive model smoother, B, seasonal variation of relations between residual of flow and date with a LOESS smoother, and C, seasonal variation of relations between residual of flow and Julian date with a LOESS smoother"},{"id":355789,"rank":9,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate03.pdf","text":"Plate 3","size":"360 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- AltFlow hydrographs for four selected streamgages in Virginia, water years 1991–2013"},{"id":355786,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_appendix04.pdf","text":"Appendix 4","size":"24.3 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Plots showing daily time series of $Q$same and $Q$prev"},{"id":355790,"rank":10,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate04.pdf","text":"Plate 4","size":"20.6 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Matrix of proposed stability metrics developed using daily flow, shifted ratings, and flow measurements for streamgages in Virginia, water years 1991–2013"},{"id":355793,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_table20.xlsx","text":"Table 20","size":"50.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Summary of selected rating stability metrics for streamgages in Virginia, water years 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flow, water years 1991–2013 "},{"id":355781,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5137/coverthb.jpg"},{"id":355785,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_appendix03.xlsx","text":"Appendix 3","size":"640 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- UNIX shell scripts used to develop AltFlow record"}],"country":"United 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\"}}]}","contact":"Director, Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
The UFINCH (Unit Flows In Networks of Channels) computer application can be used to simulate daily and unit flows in networks of streams based on geospatial data in the National Hydrography Dataset NHDPlus (with value added attributes), and U.S. Geoogical Survey daily streamflow data from a downstream (or base) streamgage. Among streamflow augmentation methods, UFINCH has the unique capability to estimate time series of flows from a single base (downstream) streamgage to many upstream reaches, while conserving flows within the basin. UFINCH also provides a simple statistical model to adjust simulated flows to better match continuous flows from data at an upstream streamgage. Parameters of the statistical model are estimated using overlapping periods of record at the two streamgages, but the adjustment can be applied to all years of record available at the base streamgage. This report describes the main features of UFINCH and presents results from a sample application. Interactive graphical user interfaces and automated geographical information processing facilitate flow-data retrievals provide an intuitive environment for efficient and effective generation of flow information in a network. UFINCH is coded in the Matlab programming language and can be run in the Matlab programming environment, with supporting statistical, optimization, and mapping toolboxes, or from compiled code on a Microsoft Windows computer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165074","collaboration":"National Water Quality Program","usgsCitation":"Holtschlag, D.J., 2018, UFINCH—A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages: U.S. Geological Survey Scientific Investigations Report 2016-5074, 17 p., https://doi.org/10.3133/sir20165074.","productDescription":"Report: iv, 17 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-073436","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":355884,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5074/sir20165074.pdf","text":"Report","size":"21.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5074"},{"id":355883,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5074/coverthb.jpg"},{"id":355885,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7319TC5","text":"USGS data release","description":"USGS data release","linkHelpText":"Code, data, executables, and other information used to run Unit Flows in Networks of Channels (UFINCH)—A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages"}],"contact":"Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way
Suite 5
Lansing, MI 48911
Streams in the Loup River Basin are sensitive to groundwater withdrawals because of the close hydrologic connection between groundwater and surface water. Groundwater discharge is the primary component of streamflow in the Loup River Basin and constitutes more than 90 percent of streamflow in the central part of the Sand Hills. To improve the understanding of geologic controls and various climatic and land-use changes on groundwater discharge, the U.S. Geological Survey (USGS), in cooperation with the Upper Loup Natural Resources District (NRD), the Lower Loup NRD, and the Nebraska Environmental Trust, studied the spatial and temporal characteristics of groundwater discharge within the Loup River Basin. This report documents the methods of data collection and analysis, which include the collection of approximately 350 river miles of aerial thermal infrared imagery and continuous groundwater-level and temperature data from six streamflow-gaging stations within the Loup River Basin.
The results from the stream reconnaissance and examination of aerial thermal infrared imagery demonstrated the influence of the surficial and subsurface geology on the spatial characteristics of groundwater discharge to streams in the Loup River Basin. At the headwaters of the South Loup River, streamflow is sustained and increased from focused groundwater discharge emanating from Quaternary deposits at many small (less than 0.1 cubic foot per second) focused points. The volume of water produced from this dense network of focused groundwater discharge points along the North Fork South Loup River is sufficient to provide approximately 40 percent of the flow measured at the South Loup River at Arnold, Nebraska streamflow-gaging station (USGS station 06781600) during the irrigation season. Approximately 5 miles downstream from the South Loup River at Arnold, Nebr., streamflow-gaging station, the river incises into Pliocene-age sand and gravel deposits, which provide additional groundwater discharge to the stream. The streamflow of the South Loup River increases by a factor of 5 across a 62-mile reach of the middle South Loup River.
Increases in streamflow along the upper Dismal River result from a dense network of focused groundwater discharge points within semiconsolidated Pliocene-age deposits. Below the Dismal River near Thedford, Nebr., streamflow-gaging station (USGS station 06775900), the Dismal River incises into the Ogallala Formation over a short reach before flowing over coarser, more permeable Quaternary-age alluvial deposits. Diffuse groundwater discharge sustains and increases the streamflow of the lower Dismal River in this reach.
Groundwater sapping was evident on some stream reaches and has increased the size and flow of focused groundwater discharge points. Previous researchers have documented streambed incision and groundwater sapping on the upper Dismal River that have created and enlarged focused groundwater discharge points capturing additional groundwater. Similar processes appear to have played a role in the formation of larger focused groundwater discharge points, which sustain the flow of the middle South Loup River. The constant flow of groundwater into the South Loup River has removed finer-grained Quaternary sediments and further exposed Pliocene-age gravel deposits. Headward erosion is evident where some of the large focused groundwater discharge points have incised their own draws and terminate in bowl-like depressions away from the stream.
Within the Loup River NRDs, the percentage of groundwater-irrigated land in a stream basin is one factor that affects groundwater discharge to streams. A striking example was at the South Loup River at Saint Michael, Nebr., groundwater and streamflow-gaging station (USGS station 06784000) where the shallow groundwater levels declined below the level of the stream during the middle to late part of the growing season (July to September) when consumptive groundwater use was at its peak. The South Loup River Basin above the South Loup River at Saint Michael, Nebr., streamflow-gaging station has the highest percentage of groundwater-irrigated row crops of all the basins examined in this study. Continuous groundwater and surface-water levels measured at the North Loup River at the Taylor, Nebr., streamflow-gaging station (USGS station 06786000) indicate that the stream is receiving groundwater throughout the year; however, when consumptive groundwater use peaks during the middle to late part of the growing season (July to September), the difference in elevation between the groundwater level and the stream elevation decreases, which indicates a reduction in the amount of groundwater discharge received.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185093","collaboration":"Prepared in cooperation with the Upper Loup and Lower Loup Natural Resources Districts and the Nebraska Environmental Trust","usgsCitation":"Hobza, C.M., and Schepers, A.R., 2018, Groundwater discharge characteristics for selected streams within the Loup River Basin, Nebraska, 2014–16: U.S. Geological Survey Scientific Investigations Report 2018–5093, 50 p., https://doi.org/10.3133/sir20185093.","productDescription":"Report: vi, 50 p.; Data Release","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-092216","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":355914,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72Z14TP","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Aerial thermal infrared imagery, focused groundwater discharge points, water temperature, streambed temperature, and vertical hydraulic gradient data collected along the South Loup, Dismal, and North Loup Rivers, Nebraska, 2014–16"},{"id":355912,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5093/coverthb.jpg"},{"id":355913,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5093/sir20185093.pdf","text":"Report","size":"20.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5093"}],"country":"United States","state":"Nebraska","otherGeospatial":"Loup River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102,\n 40\n ],\n [\n -97,\n 40\n ],\n [\n -97,\n 42.75\n ],\n [\n -102,\n 42.75\n ],\n [\n -102,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Regional regression is a common tool used to estimate daily flow-duration curves (FDCs) at ungaged locations. In this report, several refinements to a particular implementation of the regional regression method for estimating FDCs are evaluated by consideration of different methodological options through a leave-one-out cross-validation procedure in the 19 major river basins of the conterminous United States. Regression analyses in this report are based on streamflow data from water years 1981–2013 (October 1, 1980 to September 30, 2013) from 1,378 mostly undisturbed watersheds. Linear regression using selected basin characteristics at 27 quantiles with nonexceedance probabilities ranging from 0.02 to 99.98 percent was applied. The regression computations were primarily by weighted least squares, with left-censored Gaussian regression solved by maximum likelihood in the presence of zero-valued quantiles.
The regional regression method as applied to the FDC estimation problem includes several methodological options that require determination of the better of two or more choices. The options considered in this report include (1) the setting of the maximum number of basin characteristics considered in the regression models for each region, (2) the method of placing the quantiles into groups (“flow regimes”) having the same basin characteristics used as independent variables, (3) the maximum number of candidate models retained from regressions at the single-quantile level that are retained for testing of the best model at the flow-regime scale, and (4) whether drainage area should be forced into the models. In all, 5 binary options were considered for most regions, resulting in 32 methodological combinations. Leave-one-out cross-validation predictions of FDC quantiles at each streamgage used in the study were used to evaluate compared options. Various performance measures were computed based on the predicted quantiles; these were combined by region and the methods were ranked for each measure.
Based on examination of the ranked methods compared across the measures, the following treatments produced the more accurate results: (1) using fewer basin characteristics (of the two options considered), (2) utilizing a variance of the unit FDC-based method of determining the flow regimes rather than fixed regimes, (3) retaining more models from the quantile-level regressions regime-wide consideration, and (4) forcing drainage area into the regression models. Results of analyses also indicate that performance varies more by region than by methodological option, with FDCs in arid regions and those with a large value of a measure of intraregional FDC heterogeneity being harder to predict, particularly with respect to the low-flow quantiles.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185072","collaboration":"Water Availability and Use Science Program","usgsCitation":"Over, T.M., Farmer, W.H., and Russell, A.M., 2018, Refinement of a regression-based method for prediction of flow-duration curves of daily streamflow in the conterminous United States: U.S. Geological Survey Scientific Investigations Report 2018–5072, 34 p., https://doi.org/10.3133/sir20185072.","productDescription":"Report: vi, 34 p.; Appendixes 1–3; Data Release","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-085859","costCenters":[{"id":344,"text":"Illinois Water Science 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\"}}]}\n\n\n","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave.
Urbana, Illinois 61801
The California State Water Resources Control Board initiated a regional monitoring program in July 2015 to determine where and to what degree groundwater quality may be adversely impacted by oil and gas development activities. A key issue in the implementation of the regional groundwater monitoring program is that each year, detailed characterization work can be done in only a few of California’s 487 onshore oil and gas fields. The first step in monitoring groundwater near petroleum development is to prioritize oil and gas fields using consistent statewide analysis of available data that indicate potential risk of groundwater to oil and gas development.
The U.S. Geological Survey compiled data for four factors that characterize the intensity of petroleum resource development and proximity to groundwater resources: petroleum-well density, volume of water injected in oil fields, vertical proximity of groundwater resources to oil and gas resource development, and water-well density. An overall priority ranking for each field was determined by computing summary metrics, analyzing statewide distributions of summary metrics for all oil and gas fields, using those distributions to define relative categories of potential risk for each factor, and combining relative risk rankings for different factors into an overall priority ranking. This preliminary assessment does not represent an evaluation of groundwater risk to oil and gas development, which needs to be based on detailed analysis and data related to development activities including well stimulation, well integrity issues, produced water ponds, and underground injection.
Based on the prioritization analysis, 22 percent (107 fields) of the total number of oil and gas fields in California were ranked as high priority, 23 percent (114 fields) as moderate priority, and 55 percent (266 fields) as low priority. These results indicate that between 100 and 200 oil fields are principal candidates for the next steps in the regional monitoring program. The land area of fields that ranked high priority accounted for 41 percent of the total field area (3,392 square miles). More than half of the high priority fields were in the southern San Joaquin Valley and the Los Angeles Basin. Some of the larger fields tended to have higher rankings because of greater intensity of petroleum development, sometimes coupled with proximity to groundwater resources.
The U.S. Geological Survey, in collaboration with the California State Water Resources Control Board and other agencies, has begun regional groundwater monitoring near oil and gas fields selected for study through the California Oil, Gas, and Groundwater cooperative program. Groundwater monitoring includes compiling, analyzing, and developing three-dimensional visualizations of existing data, including geological frameworks, salinity mapping, identification of surface features that could potentially affect groundwater quality, locations and depths of oil/gas and water wells, cataloging well-construction integrity issues, and evaluating the directions of groundwater flow. These analyses are required to determine where existing wells should be monitored and where new monitoring wells may need to be drilled.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185065","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Davis, T.A., Landon, M.K., and Bennett, G.L., 2018, Prioritization of oil and gas fields for regional groundwater monitoring based on a preliminary assessment of petroleum resource development and proximity to California’s groundwater resources: U.S. Geological Survey Scientific Investigations Report 2018–5065, 115 p.,\nhttps://doi.org/10.3133/sir20185065.","productDescription":"Report: viii, 115 p.; Tables 1-6; Appendix Tables 1-7","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-073272","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437823,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FJ2DV3","text":"USGS data 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\"}}]}","contact":"The purpose of this study was to evaluate the effects of irrigation and cover crops as conservation practices on water quality in groundwater and streams. Bucks Branch, a stream in the Nanticoke River watershed in southwestern Delaware, was identified as having one of the highest concentrations of nitrate in all surface-water sites sampled by the Delaware Department of Natural Resources and Environmental Control (DNREC). The study site is on two adjacent fields bordering Bucks Branch, one that has used irrigation since 2000 and one with dryland farming; both under conservation tillage and long-term rotation of corn, soybean, and small grain crops. A streamgage was installed near the study site fields to measure streamflow and water quality. The study area is typical of farming practices and environmental conditions throughout much of the intensively farmed agricultural land of the Coastal Plain of Delaware and surrounding parts of Maryland. Monitoring was conducted from January 2014 through June 2016. Corn was grown on both fields during the two growing seasons of the study period, and cover crops were planted before or shortly after harvest on both fields. During the second year of data collection, the effects of radish and rye grass cover crops on nutrient transport were studied.
The combined results from data collected for this study show that water and nitrate moved below the root zone year round when soil moisture was high, especially after significant rainfall and frequently after irrigation. Soil water sampled 2 to 3 weeks after nutrients were applied had nitrate concentrations greater than 50 milligrams per liter as nitrogen (mg/L as N) and may be a significant source of nitrate to groundwater. Whereas recharge containing elevated nitrate concentrations also occurred under the dryland field, it was less frequent and of lower concentration than recharge under the irrigated field.
Nitrate was present in all groundwater samples from these sites. Groundwater estimated to have recharged within 10 years or less had higher median concentrations of nitrate than in older water samples. The oldest groundwater encountered was over 30 years old, and had traveled along the longest, deep flowpaths from upland fields to the stream. The median nitrate concentration was 18 mg/L as N in younger water (less than 10 years old) beneath the irrigated field, compared to about 10 mg/L as N in younger water beneath the dryland field. Samples from the shallow upland wells in both study fields showed little, if any, evidence of denitrification. Several samples from deeper wells and from wells near forested riparian zone wetlands that border both fields did show partial denitrification.
A mixing model estimated that between 12 and 22 percent of the nitrate discharging to the stream was lost through uptake and denitrification upstream of the streamgage on Bucks Branch. Continuous data collected at this site and evidence of denitrification in the surface-water samples showed a greater potential for loss of nitrate during the warmer months than the colder months. This pattern was similar to that seen below the streamgage at the most downstream site in the watershed.
A mixed cover crop of radishes and rye was planted prior to removal (radishes) and just after harvest (rye) of the corn crop on the irrigated field. Rye grass was planted shortly after crop harvest on the dryland field. Cover crop biomass samples collected while radishes were growing and after they were killed by freezing temperatures indicates that the early planted radish crop effectively scavenged available nitrogen from the soil. Whereas radish biomass initially held more nitrogen than rye, at 55 to 8 pounds per acre, respectively, leaching of inorganic nitrate following radish die-off was minimal. Soil-water nitrate concentrations during the cover-crop growing period were lower than during the growing season prior to planting of the cover crop. There also was an increase in soil fertility and dissolved organic nitrate in samples of soil water that was likely related to increased soil microbial metabolism. Results indicate that cover crops stored plant nutrients over the winter and did not increase shallow groundwater concentrations of nitrate.
Although conservation practices such as cover crops and nutrient management have been applied to these fields, there was still significant leaching of nitrate to groundwater, especially under the irrigated field. This will likely continue to be a challenge in this area and other parts of the Coastal Plain where soil moisture capacity is relatively low and managing irrigation around rainfall is difficult. Cover crops, when planted in standing corn, are one practice that can effectively pull nitrate from below the root zone to the top layer of soil, thus limiting the amount of potential nitrate leaching to groundwater. Irrigation management that would lower average soil moisture conditions during the growing season also could potentially limit nitrogen transport.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185020","collaboration":"Prepared in cooperation with the Delaware Department of Natural Resources and Environmental Control and the Delaware Geological Survey","usgsCitation":"Denver, J.M., Soroka, A.M., Reyes, B., Lester, T.R., Bringman, D.A., and Brownley, M.S., 2018, Monitoring the water-quality response of agricultural conservation practices in the Bucks Branch watershed, Sussex County, Delaware, 2014–16: U.S. Geological Survey Scientific Investigations Report 2018–5020, 43 p., https://doi.org/10.3133/sir20185020.","productDescription":"ix, 43 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-088518","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":355731,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5020/sir20185020.pdf","text":"Report","size":"8.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5020"},{"id":355732,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77H1HK5","text":"USGS data release","description":"USGS data release","linkHelpText":"Chemistry analysis results for groundwater, soil-pore water, soil and plant material collected from two agricultural sites in the Nanticoke and Chester River watersheds on the Delmarva Peninsula from 2013 to 2016"},{"id":355730,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5020/coverthb.jpg"}],"country":"United States","state":"Delaware","county":"Sussex County","otherGeospatial":"Bucks Branch Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.6833,\n 38.6833\n ],\n [\n -75.6167,\n 38.6833\n ],\n [\n -75.6167,\n 38.7333\n ],\n [\n -75.6833,\n 38.7333\n ],\n [\n -75.6833,\n 38.6833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228