Comparison of fission-track (FT) ages of detrital zircons recovered from Atlantic Coastal Plain sediments to FT ages of zircons from bedrock in source terranes in the Appalachians provides a key to understanding the provenance of the sediments and, in turn, the erosional and depositional history of the Atlantic passive margin.
In Appalachian source terranes, the oldest zircon fission-track (ZFT) ages from bedrock in the western Appalachians (defined for this paper as the Appalachian Plateau, Valley and Ridge, and far western Blue Ridge) are notably older than the oldest ages from bedrock in the eastern Appalachians (Piedmont and main part of the Blue Ridge). The age difference is seen both in ZFT sample ages and in individual zircon grain ages and reflects differences in the thermotectonic history of the rocks. In the east, ZFT data indicate that the rocks cooled from temperatures high enough to partially or totally reset ZFT ages during the Paleozoic and (or) Mesozoic. The majority of the rocks are interpreted to have cooled through the ZFT closure temperature (∼235 °C) at various times during the late Paleozoic Alleghanian orogeny. In contrast, most of the rocks sampled in the western Appalachians have never been heated to temperatures high enough to totally reset their ZFT ages. Reflecting their contrasting thermotectonic histories, nearly 80 percent of the sampled western rocks yield one or more zircon grains with very old FT ages, in excess of 800 Ma; zircon grains yielding FT ages this old have not been found in rocks in the Piedmont and main part of the Blue Ridge. The ZFT data suggest that the asymmetry of zircon ages of exposed bedrock in the eastern and western Appalachians was in evidence by no later than the Early Cretaceous and probably by the Late Triassic.
Detrital zircon suites from sands collected in the Atlantic Coastal Plain provide a record of detritus eroded from source terranes in the Appalachians during the Mesozoic and Cenozoic. In Virginia and Maryland, sands of Early Cretaceous through late early Oligocene age do not yield any old zircons comparable in age to the old zircons found in bedrock in the western Appalachians. Very old zircons yielding FT ages >800 Ma are only encountered in Coastal Plain sands of middle early Miocene and younger age.
Miocene and younger fluvial-deltaic deposits associated with the major mid-Atlantic Coastal Plain rivers that now head in the western Appalachians (the Hudson, Delaware, Susquehanna, Potomac, James, and Roanoke) contain abundant clasts of fossiliferous chert and quartzite and other distinctive rock types derived from Paleozoic rocks of the western Appalachians. These distinctive clasts have not been reported in older Coastal Plain sediments.
The ZFT and lithic detritus data indicate that the drainage divide for one or more east-flowing mid-Atlantic rivers migrated west into the western Appalachians, and the river(s) began transporting western Appalachian detritus to the Atlantic Coastal Plain, sometime between the late early Oligocene and middle early Miocene. By no later than late middle Miocene most if not all of the major rivers that now head west of the Blue Ridge were transporting western Appalachian detritus to the Coastal Plain. Prior to the drainage divide migrating into the western Appalachians, the ZFT data are consistent with the dominant source of Atlantic Coastal Plain sediments being detritus from the Piedmont and main part of the Blue Ridge, with possible input from distant volcanic sources.
The ZFT data suggest that the rapid increase in the rate of siliciclastic sediment accumulation in middle Atlantic margin offshore basins that peaked in the middle Miocene and produced almost 30 percent of the total volume of post-rift siliciclastic sediments in the offshore basins began in the early Miocene when Atlantic river(s) gained access to the relatively easily eroded Paleozoic sedimentary rocks of the western Appalachians.
","language":"English","publisher":"American Journal of Science","doi":"10.2475/02.2016.02","usgsCitation":"Naeser, C.W., Naeser, N., Edwards, L.E., Weems, R.E., Southworth, C.S., and Newell, W.L., 2016, Erosional and depositional history of the Atlantic passive margin as recorded in detrital zircon fission-track ages and lithic detritus in Atlantic Coastal plain sediments: American Journal of Science, v. 316, no. 2, p. 110-168, https://doi.org/10.2475/02.2016.02.","productDescription":"59 p.","startPage":"110","endPage":"168","ipdsId":"IP-019078","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":336324,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"316","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-07","publicationStatus":"PW","scienceBaseUri":"58b69a41e4b01ccd54ff3f9c","contributors":{"authors":[{"text":"Naeser, C. W.","contributorId":17582,"corporation":false,"usgs":true,"family":"Naeser","given":"C.","middleInitial":"W.","affiliations":[],"preferred":false,"id":673647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Naeser, N.D.","contributorId":184146,"corporation":false,"usgs":false,"family":"Naeser","given":"N.D.","email":"","affiliations":[],"preferred":false,"id":673648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edwards, Lucy E. 0000-0003-4075-3317 leedward@usgs.gov","orcid":"https://orcid.org/0000-0003-4075-3317","contributorId":2647,"corporation":false,"usgs":true,"family":"Edwards","given":"Lucy","email":"leedward@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":673551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weems, Robert E. 0000-0002-1907-7804 rweems@usgs.gov","orcid":"https://orcid.org/0000-0002-1907-7804","contributorId":2663,"corporation":false,"usgs":true,"family":"Weems","given":"Robert","email":"rweems@usgs.gov","middleInitial":"E.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":673553,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Southworth, C. Scott 0000-0002-7976-7807 ssouthwo@usgs.gov","orcid":"https://orcid.org/0000-0002-7976-7807","contributorId":1608,"corporation":false,"usgs":true,"family":"Southworth","given":"C.","email":"ssouthwo@usgs.gov","middleInitial":"Scott","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":673554,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newell, Wayne L. wnewell@usgs.gov","contributorId":2512,"corporation":false,"usgs":true,"family":"Newell","given":"Wayne","email":"wnewell@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":false,"id":673555,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70170615,"text":"70170615 - 2016 - Mercury in fish and macroinvertebrates from New York's streams and rivers: A compendium of data sources","interactions":[],"lastModifiedDate":"2017-04-21T10:39:00","indexId":"70170615","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":133,"text":"Report","active":false,"publicationSubtype":{"id":2}},"seriesNumber":"16-07","title":"Mercury in fish and macroinvertebrates from New York's streams and rivers: A compendium of data sources","docAbstract":"The U.S. Geological Survey has compiled a list of existing data sets, from selected sources, containing mercury (Hg) concentration data in fish and macroinvertebrate samples that were collected from flowing waters of New York State from 1970 through 2014. Data sets selected for inclusion in this report were limited to those that contain fish and (or) macroinvertebrate data that were collected across broad areas, cover relatively long time periods, and (or) were collected as part of a broader-scale (e.g. national) study or program. In addition, all data sets listed were collected, processed, and analyzed with documented methods, and contain critical sample information (e.g. fish species, fish size, Hg species) that is needed to analyze and interpret the reported Hg concentration data. Fourteen data sets, all from state or federal agencies, are listed in this report, along with selected descriptive information regarding each data source and data set contents. Together, these 14 data sets contain Hg and related data for more than\r\n7,000 biological samples collected from more than 700 unique stream and river locations between 1970 and 2014.","language":"English","publisher":"New York State Energy Research and Development Authority","usgsCitation":"Riva-Murray, K., and Burns, D.A., 2016, Mercury in fish and macroinvertebrates from New York's streams and rivers: A compendium of data sources: Report 16-07, v, 16 p.","productDescription":"v, 16 p.","numberOfPages":"26","ipdsId":"IP-059881","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":340076,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":340075,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://nyserda.ny.gov/publications"}],"country":"United States","state":"New 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York\",\"nation\":\"USA \"}}]}","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4ee4b0c3010a8087c7","contributors":{"authors":[{"text":"Riva-Murray, Karen krmurray@usgs.gov","contributorId":168654,"corporation":false,"usgs":true,"family":"Riva-Murray","given":"Karen","email":"krmurray@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":627885,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Douglas A. 0000-0001-6516-2869 daburns@usgs.gov","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":1237,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas","email":"daburns@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":627886,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70162657,"text":"sir20155158 - 2016 - Water quality and hydrology of Silver Lake, Oceana County, Michigan, with emphasis on lake response to nutrient loading","interactions":[],"lastModifiedDate":"2018-01-08T12:35:15","indexId":"sir20155158","displayToPublicDate":"2016-01-29T16:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5158","title":"Water quality and hydrology of Silver Lake, Oceana County, Michigan, with emphasis on lake response to nutrient loading","docAbstract":"Silver Lake is a 672-acre inland lake located in Oceana County, Michigan, and is a major tourist destination due to its proximity to Lake Michigan and the surrounding outdoor recreational opportunities. In recent years, Silver Lake exhibited patterns of high phosphorus concentrations, elevated chlorophyll a concentrations, and nuisance algal blooms. The U.S. Geological Survey (USGS), in cooperation with the Silver Lake Improvement Board and in collaboration with the Annis Water Resources Institute (AWRI) of Grand Valley State University, designed a study to assess the hydrologic and nutrient inputs to Silver Lake in order to identify the events and conditions that affect the nutrient chemistry and production of algal blooms in the lake. This information can inform water-resource managers in developing various management strategies to prevent or reduce the occurrence of future algal blooms.
\nUSGS and AWRI scientists collected data from November 2012 to December 2014 to provide information for future management decisions for Silver Lake. Silver Lake can be classified as a polymictic lake and has a residence time of approximately 223 days. Based on the mean lake Secchi depth, total phosphorus, and total nitrogen concentrations, Silver Lake is classified as a eutrophic lake. In-situ bioassay results indicate that algal growth in Silver Lake is colimited by both nitrogen and phosphorus. The nutrient budget for Silver Lake was calculated using the BATHTUB model based on 2 years of water-quality data collection. The BATHTUB model, developed by the U.S. Army Corps of Engineers, treats the lake as a well-mixed system with multiple inputs and outlets for both water and dissolved constituents, such as nutrients.
\nBased on results of the BATHTUB model, which were conditioned on observed concentrations and flows, the mean annual input of phosphorus to Silver Lake was approximately 1,342 pounds (lb); the mean annual input of nitrogen to Silver Lake was approximately 51,998 lb. The major measured sources of phosphorus loading to Silver Lake were groundwater and Hunter Creek, whereas the major measured sources of nitrogen to Silver Lake were Hunter Creek, groundwater, and atmospheric deposition. The largest loading of phosphorus and nitrogen to Silver Lake occurred during the spring. Minimal phosphorus deposition (if any) occurred in the lakebed sediment; however, of the nitrogen that entered Silver Lake, approximately 42.2 percent was deposited in the lakebed sediment as simulated by the BATHTUB model.
\nIn addition to measured sources, a septic load model was used to estimate the likely range of septic contribution to groundwater and adjacent surface waters. The likely septic loading scenario estimates that septic systems contribute 47.8 percent of the phosphorus to groundwater and 22.3 percent of phosphorus to Hunter Creek. These results indicate that septic systems are a major source of phosphorus loading to Silver Lake. The likely septic loading scenario indicated that septic systems account for 0.95 percent of the nitrogen load to Hunter Creek and 1.1 percent of the contribution of nitrogen to groundwater.
\nThe BATHTUB model was used to estimate future nutrient loading and eutrophication scenarios based on water-quality data collected from Silver Lake, groundwater, major tributaries, and atmospheric deposition. A separate septic load model was used to estimate the septic contribution to groundwater or directly to surface water, and the nutrient load estimates were modeled using the BATHTUB model to determine subsequent water-quality changes to Silver Lake.
\nDirector, Michigan Water Science Center
U.S. Geological Survey
6520 Mercantile Way Suite 5
Lansing, MI 48911–5991
http://mi.water.usgs.gov/
Arnold Air Force Base occupies about 40,000 acres in Coffee and Franklin Counties, Tennessee. The primary mission of Arnold Air Force Base is to provide risk-reduction information in the development of aerospace products through test and evaluation. This mission is achieved in part through test facilities at Arnold Engineering Development Complex (AEDC), which occupies about 4,000 acres in the center of Arnold Air Force Base. Arnold Air Force Base is underlain by gravel and limestone aquifers, the most productive of which is the Manchester aquifer. Several volatile organic compounds, primarily chlorinated solvents, have been identified in the groundwater at Arnold Air Force Base. In 2011, the U.S. Geological Survey, in cooperation with the U.S. Air Force, Arnold Air Force Base, completed a study of groundwater flow focused on the Arnold Engineering Development Complex area. The Arnold Engineering Development Complex area is of particular concern because within this area (1) chlorinated solvents have been identified in the groundwater, (2) the aquifers are dewatered around below-grade test facilities, and (3) there is a regional groundwater divide.
\nDuring May 2011, when water levels were near seasonal highs, water-level data were collected from 374 monitoring wells; and during September 2011, when water levels were near seasonal lows, water-level data were collected from 376 monitoring wells. Potentiometric surfaces were mapped by contouring altitudes of water levels measured in wells completed in the shallow aquifer, the upper and lower parts of the Manchester aquifer, and the Fort Payne aquifer. Water levels are generally 2 to 14 feet lower in September compared to May. The potentiometric-surface maps for all aquifers indicate a groundwater depression at the J4 test cell. Similar groundwater depressions in the shallow and upper parts of the Manchester aquifer are within the main testing area at the Arnold Engineering Development Complex at dewatering facilities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155165","collaboration":"Prepared in cooperation with the United States Air Force, Arnold Air Force Base","usgsCitation":"Haugh, C.J., and Robinson, J.A., 2016, Potentiometric surfaces of the Arnold Engineering Development Complex area, Arnold Air Force Base, Tennessee, May and September 2011: U.S. Geological Survey Scientific Investigations Report 2015–5165, 23 p., https://dx.doi.org/10.3133/sir20155165.","productDescription":"v, 28 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-059351","costCenters":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"links":[{"id":314981,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5165/sir20155165.pdf","text":"Report","size":"1.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5165"},{"id":314980,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5165/coverthb.jpg"}],"country":"United States","state":"Tennessee","county":"Coffee County, Franklin County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.5,\n 35\n ],\n [\n -86.5,\n 35.75\n ],\n [\n -85.5,\n 35.75\n ],\n [\n -85.5,\n 35\n ],\n [\n -86.5,\n 35\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
http://tn.water.usgs.gov
The U.S. Geological Survey (USGS) installed the first gage to measure the flow of water into California’s Sacramento–San Joaquin River Delta from the Sacramento River in the late 1800s. Today, a network of 35 hydro-acoustic meters measure flow throughout the delta. This region is a critical part of California’s freshwater supply and conveyance system. With the data provided by this flow-station network—sampled every 15 minutes and updated to the web every hour—state and federal water managers make daily decisions about how much freshwater can be pumped for human use, at which locations, and when. Fish and wildlife scientists, working with water managers, also use this information to protect fish species affected by pumping and loss of habitat. The data are also used to help determine the success or failure of efforts to restore ecosystem processes in what has been called the “most managed and highly altered” watershed in the country.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153061","usgsCitation":"Burau, J.R., Ruhl, C.A., and Work, P.A., 2016, Innovation in Monitoring: The U.S. Geological Survey Sacramento-San Joaquin River Delta, California, Flow-Station Network: U.S. Geological Survey Fact Sheet 2015-3061, 6 p., https://dx.doi.org/10.3133/fs20153061.","productDescription":"6 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-044692","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":315073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3061/coverthb.jpg"},{"id":315074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3061/fs20153061.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3061 PDF"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River, San Joaquin River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 37.75\n ],\n [\n -122,\n 38.5\n ],\n [\n -121.25,\n 38.5\n ],\n [\n -121.25,\n 37.75\n ],\n [\n -122,\n 37.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
http://ca.water.usgs.gov
Missouri was the leading producer of lead in the United States—as well as the world—for more than a century. One of the lead sources is known as the Old Lead Belt, located in southeast Missouri. The primary ore mineral in the region is galena, which can be found both in surface deposits and underground as deep as 200 feet. More than 8.5 million tons of lead were produced from the Old Lead Belt before operations ceased in 1972. Although active lead mining has ended, the effects of mining activities still remain in the form of large mine waste piles on the landscape typically near tributaries and the main stem of the Big River, which drains the Old Lead Belt. Six large mine waste piles encompassing more than 2,800 acres, exist within the Big River Basin. These six mine waste piles have been an available source of trace element-rich suspended sediments transported by natural erosional processes downstream into the Big River.
\nA study was performed by the U.S. Geological Survey in cooperation with U.S. Environmental Protection Agency, Region 7, to calculate and characterize suspended-sediment quantity and quality within the Big River basin after reclamation of the mine waste piles ended in 2012. Streamflow and suspended sediments were quantified and sampled at two locations along a 68-mile reach of the Big River between Bonne Terre and Byrnes Mill, Missouri. The results will help regulatory agencies, such as the U.S. Environmental Protection Agency and U.S. Fish and Wildlife Service, determine impaired reaches and ecosystems for remedial and restoration efforts.
\nContinuous stream stage, water temperature, and turbidity, and discrete suspended-sediment concentration data were collected at the two sites between October 2011 and September 2013. Suspended-sediment samples were collected during various hydrologic conditions to develop a regression model between discrete suspended-sediment concentration and continuous turbidity. Suspended sediments collected during stormflow events were analyzed for concentrations of trace elements such as barium, cadmium, lead, and zinc within two sediment size fractions. Event loads and annual loads of suspended sediment and select trace elements in suspended sediments also were calculated.
\nSuspended-sediment loads computed by the regression model increased downstream from about 201,000 tons at the upstream site to about 355,000 tons at the downstream site during the study period. Stormflow-event-based (hereinafter referred to as “event-based”) suspended-sediment loads ranged from 180 to 32,000 tons at the upstream sampling site and 390 to 53,000 tons at the downstream site along the Big River. Although only seven stormflow events at the upstream site and six at the downstream site were sampled, the event-based suspended-sediment loads accounted for nearly 30 percent of the total suspended-sediment loads computed at both sites, indicating most of the suspended sediment transported through the Big River occurs during higher streamflows.
\nSediment quality guidelines, known as the threshold effect concentration and the probable effect concentration, used to assess toxicity of trace-element concentrations in sediments were compared to the cadmium, lead, and zinc concentrations in suspended sediment samples collected during stormflow events. All concentrations of cadmium, lead, and zinc in event-based suspended sediment samples exceeded the threshold and probable effect concentrations. Lead and zinc concentrations in the sediment size fraction less than 0.063 millimeters also exceeded the toxic effect threshold, above which sediment is considered to be heavily polluted causing adverse effects on sediment-dwelling organisms. Concentrations of cadmium and zinc in event-based suspended sediment samples were notably higher in samples from the upstream site than samples from the downstream site, indicating the sources of sediments enriched in these trace elements decrease in the downstream area of the watershed. The reduction in concentration of cadmium and zinc could be from dissolution of the constituents during transport or possibly a decrease in downstream source material. The lead concentration exceedance of the probable effects concentration as well as the threshold effects concentration indicates that lead-rich suspended sediments in the fraction less than 0.063 millimeters are readily available within the Big River Basin for transport. These sediments remain in the system from historical mining, and as the reclamation of mine waste piles in the upstream area of the watershed reduce additional sediment loadings, these fine sediments may be continually released as the river scours the streambed and erodes stream banks causing the lead-rich suspended sediment to remain in a state of equilibrium.
\nBarium concentrations in suspended-sediments were nearly twice as high in stormflow event samples collected at the downstream site as compared to samples from the upstream site. The source of barium in the Big River could be from Mineral Fork and Mill Creek, which flow through the historical barite (barium sulfate, also known as tiff) mining district in Washington County, and discharge into the Big River between the two study sites.
\nTotal trace-element loads and yields in suspended sediments were computed from the sampled events for each year in the study. The total barium loads in suspended sediments were higher for sampled events collected at the downstream site than the upstream site during both study years. Cadmium and zinc loads in suspended sediments were lower at the downstream site than the upstream site, although the decrease in total load was not substantial during the study period. Lead loads in suspended sediments were lower at the downstream site during the first study year, with a slightly higher load downstream in the second year though the increase from upstream to downstream was small. Event-based yields were higher at the upstream site, indicating that readily available sediment sources are closer to the upstream site where more mining affected areas are located. The estimates determined during large precipitation events indicate that large sources of suspended sediments with large concentrations of trace elements are still available for transport within the Big River.
\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155171","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency, Region 7","usgsCitation":"Barr, M.N., 2016, Surface-water quality and suspended-sediment quantity and quality within the Big River Basin, southeastern Missouri, 2011–13: U.S. Geological Survey Scientific Investigations Report 2015–5171, 39 p., https://dx.doi.org/10.3133/sir20155171.","productDescription":"vi, 39 p.","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2011-01-01","ipdsId":"IP-065903","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":314930,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5171/coverthb.jpg"},{"id":314931,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5171/sir20155171.pdf","text":"Report","size":"2.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5171"}],"country":"United States","state":"Missouri","otherGeospatial":"Big River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.80749511718749,\n 38.586820096127674\n ],\n [\n -90.6427001953125,\n 38.45789034424927\n ],\n [\n -90.582275390625,\n 38.371808917147554\n ],\n [\n -90.5767822265625,\n 38.285624966683756\n ],\n [\n -90.5877685546875,\n 38.16479533621134\n ],\n [\n -90.4779052734375,\n 38.06539235133249\n ],\n [\n -90.362548828125,\n 38.013476231041935\n ],\n [\n -90.23071289062499,\n 37.931200459333716\n ],\n [\n -90.1318359375,\n 37.84883250647402\n ],\n [\n -90.0384521484375,\n 37.814123701604466\n ],\n [\n -89.901123046875,\n 37.714244967649265\n ],\n [\n -90.0164794921875,\n 37.57505900514994\n ],\n [\n -90.208740234375,\n 37.58376576718623\n ],\n [\n -90.3900146484375,\n 37.58376576718623\n ],\n [\n -90.538330078125,\n 37.53150992479082\n ],\n [\n -90.703125,\n 37.413800350662875\n ],\n [\n -90.7635498046875,\n 37.28279464911045\n ],\n [\n -90.8404541015625,\n 37.17344871200958\n ],\n [\n -91.07666015625,\n 37.18657859524883\n ],\n [\n -91.23596191406249,\n 37.23470197166817\n ],\n [\n -91.23046875,\n 37.43997405227057\n ],\n [\n -91.109619140625,\n 37.58376576718623\n ],\n [\n -91.1920166015625,\n 37.688167468408025\n ],\n [\n -91.3238525390625,\n 37.76637243960176\n ],\n [\n -91.263427734375,\n 37.84015683604134\n ],\n [\n -91.131591796875,\n 37.883524980871336\n ],\n [\n -91.04919433593749,\n 38.03078569382294\n ],\n [\n -91.0711669921875,\n 38.12591462924157\n ],\n [\n -90.9173583984375,\n 38.16479533621134\n ],\n [\n -90.9228515625,\n 38.25974980039479\n ],\n [\n -90.9613037109375,\n 38.298559092254344\n ],\n [\n -90.999755859375,\n 38.38472766885085\n ],\n [\n -90.94482421875,\n 38.46219172306828\n ],\n [\n -90.8843994140625,\n 38.5008925889646\n ],\n [\n -90.80749511718749,\n 38.60828592850559\n ],\n [\n -90.80749511718749,\n 38.586820096127674\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"
Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road, MS-100
Rolla, MO 65401
Modeling streamflow is an important approach for understanding landscape-scale drivers of flow and estimating flows where there are no streamgage records. In this study conducted by the U.S. Geological Survey in cooperation with Colorado State University, the objectives were to model streamflow metrics on small, ungaged streams in the Upper Colorado River Basin and identify streams that are potentially threatened with becoming intermittent under drier climate conditions. The Upper Colorado River Basin is a region that is critical for water resources and also projected to experience large future climate shifts toward a drying climate. A random forest modeling approach was used to model the relationship between streamflow metrics and environmental variables. Flow metrics were then projected to ungaged reaches in the Upper Colorado River Basin using environmental variables for each stream, represented as raster cells, in the basin. Last, the projected random forest models of minimum flow coefficient of variation and specific mean daily flow were used to highlight streams that had greater than 61.84 percent minimum flow coefficient of variation and less than 0.096 specific mean daily flow and suggested that these streams will be most threatened to shift to intermittent flow regimes under drier climate conditions. Map projection products can help scientists, land managers, and policymakers understand current hydrology in the Upper Colorado River Basin and make informed decisions regarding water resources. With knowledge of which streams are likely to undergo significant drying in the future, managers and scientists can plan for stream-dependent ecosystems and human water users.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds974","collaboration":"Prepared in cooperation with Colorado State University","usgsCitation":"Reynolds, L.V., and Shafroth, P.B., 2016, Modeled streamflow metrics on small, ungaged stream reaches in the Upper Colorado River Basin: U.S. Geological Survey Data Series 974, 11 p., https://dx.doi.org/10.3133/ds974.","productDescription":"Report: vi, 11 p.; Dataset","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070136","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":438644,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H9938M","text":"USGS data release","linkHelpText":"Modeled Streamflow Metrics on Small, Ungaged Stream Reaches in the Upper Colorado River Basin"},{"id":314550,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://dx.doi.org/10.5066/F7H9938M","text":"Geospatial Data"},{"id":314505,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0974/ds974.pdf","text":"Report","size":"4.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 974"},{"id":314503,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0974/coverthb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.79638671875,\n 35.47856499535729\n ],\n [\n -113.18115234375,\n 35.55904339525896\n ],\n [\n -112.664794921875,\n 35.782170703266075\n ],\n [\n -112.203369140625,\n 35.79999392988527\n ],\n [\n -110.335693359375,\n 35.37113502280101\n ],\n [\n -106.171875,\n 34.7506398050501\n ],\n [\n -105.413818359375,\n 34.858890491257824\n ],\n [\n -104.578857421875,\n 35.22767235493586\n ],\n [\n -103.974609375,\n 36.12012758978146\n ],\n [\n -104.029541015625,\n 36.80048816579081\n ],\n [\n -104.2822265625,\n 37.74465712069939\n ],\n [\n -104.403076171875,\n 38.26406296833964\n ],\n [\n -104.38110351562499,\n 38.856820134743636\n ],\n [\n -104.27124023437499,\n 39.64799732373418\n ],\n [\n -104.04052734375,\n 40.212440718286466\n ],\n [\n -103.9306640625,\n 40.588928169693745\n ],\n [\n -104.051513671875,\n 40.79717741518769\n ],\n [\n -104.67773437499999,\n 41.623655390686395\n ],\n [\n -105.084228515625,\n 42.5611728553181\n ],\n [\n -106.12792968749999,\n 42.91620643817353\n ],\n [\n -107.303466796875,\n 43.620170616189924\n ],\n [\n -108.116455078125,\n 43.94537239244209\n ],\n [\n -108.61083984375,\n 44.276671273775186\n ],\n [\n -108.819580078125,\n 44.61393394730626\n ],\n [\n -109.16015624999999,\n 44.84029065139799\n ],\n [\n -109.53369140625,\n 44.92591837128866\n ],\n [\n -110.3466796875,\n 44.87144275016589\n ],\n [\n -110.819091796875,\n 43.96119063892024\n ],\n [\n -110.830078125,\n 41.828642001860544\n ],\n [\n -111.016845703125,\n 41.60722821271717\n ],\n [\n -111.3134765625,\n 41.46742831254425\n ],\n [\n -111.64306640625,\n 41.29431726315258\n ],\n [\n -112.03857421875,\n 40.65563874006118\n ],\n [\n -112.181396484375,\n 40.41349604970198\n ],\n [\n -113.04931640625,\n 37.95286091815649\n ],\n [\n -113.543701171875,\n 37.640334898059486\n ],\n [\n -113.851318359375,\n 37.23907530202184\n ],\n [\n -113.983154296875,\n 36.48314061639213\n ],\n [\n -113.961181640625,\n 35.79999392988527\n ],\n [\n -113.80737304687499,\n 35.43381992014202\n ],\n [\n -113.79638671875,\n 35.47856499535729\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS Fort Collins Science Center
2150 Centre Avenue, Building C
Fort Collins, CO 80526-8118
https://www.fort.usgs.gov/
In the Chesapeake Bay watershed, estimated fluxes of nutrients and sediment from the bay’s nontidal tributaries into the estuary are the foundation of decision making to meet reductions prescribed by the Chesapeake Bay Total Maximum Daily Load (TMDL) and are often the basis for refining scientific understanding of the watershed-scale processes that influence the delivery of these constituents to the bay. Two regression-based flux and trend estimation models, ESTIMATOR and Weighted Regressions on Time, Discharge, and Season (WRTDS), were compared using data from 80 watersheds in the Chesapeake Bay Nontidal Water-Quality Monitoring Network (CBNTN). The watersheds range in size from 62 to 70,189 square kilometers and record lengths range from 6 to 28 years. ESTIMATOR is a constant-parameter model that estimates trends only in concentration; WRTDS uses variable parameters estimated with weighted regression, and estimates trends in both concentration and flux. WRTDS had greater explanatory power than ESTIMATOR, with the greatest degree of improvement evident for records longer than 25 years (30 stations; improvement in median model R2= 0.06 for total nitrogen, 0.08 for total phosphorus, and 0.05 for sediment) and the least degree of improvement for records of less than 10 years, for which the two models performed nearly equally. Flux bias statistics were comparable or lower (more favorable) for WRTDS for any record length; for 30 stations with records longer than 25 years, the greatest degree of improvement was evident for sediment (decrease of 0.17 in median statistic) and total phosphorus (decrease of 0.05). The overall between-station pattern in concentration trend direction and magnitude for all constituents was roughly similar for both models. A detailed case study revealed that trends in concentration estimated by WRTDS can operationally be viewed as a less-constrained equivalent to trends in concentration estimated by ESTIMATOR. Estimates of annual mean flow-adjusted (ESTIMATOR) and flow-normalized (WRTDS) concentration for years initially constituting the end of a water-quality record showed a similar degree of variability as data for additional years were incrementally added and the initial estimates “aged.” On the basis of the results of this broad comparison of the two models, the U.S. Geological Survey is adopting WRTDS as the primary model for estimating constituent fluxes and trends throughout the CBNTN. Nutrient and sediment flux and trend estimates, based on WRTDS, are summarized narratively and tabulated in appendixes for all stations for which fluxes or trends were reported through water year 2012.
\nWRTDS also was used to explore the sensitivity of flux and trend estimates to three data-quality issues common in many large-scale monitoring networks and evident in some of the CBNTN records. The potential effects of inconsistency in annual sampling effort and inconsistency in storm sampling effort were explored by way of a subsampling experiment using eight of the most densely sampled long-term (1985–2012) stations in the CBNTN as baseline datasets. From each dataset, a set of 10 “design guideline” subsamples was selected, consisting of 12 monthly samples and 8 targeted storm samples per year. The selection was conducted in a manner that preserved the overall intensity of storm sampling in the baseline data. These 10 subsamples were further manipulated to create “heterogeneous” subsamples by removing storm samples prior to 2003. The maximum relative difference between flow-normalized flux estimated in a single year from any of the 10 design guideline subsamples and values estimated in the corresponding year from baseline data was smallest for dissolved inorganic nitrogen (median of 8 stations = 6 percent of baseline estimate), but more appreciable for total phosphorus and sediment (medians of 22 and 32 percent, respectively). The maximum relative difference between flow-normalized flux estimated from from the 10 heterogeneous subsamples and values estimated in the corresponding year from baseline data was more pronounced, with medians for 8 stations of 15, 30, and 53 percent of the corresponding baseline estimates for dissolved inorganic nitrogen, total phosphorus, and sediment, respectively. The worst-case maximum relative differences between flow-normalize flux estimated in a single year from the 10 heterogeneous subsamples and values estimated in the corresponding year from baseline data were 25 percent for dissolved inorganic nitrogen, 37 percent for total phosphorus, and 250 percent for sediment. The results for the heterogeneous subsamples indicate that changes in storm sampling frequency can result in appreciable distortion of estimated trends in flow-normalized flux, especially for total phosphorus and sediment. Trend lines estimated from heterogeneous subsamples tended to converge with the trend lines estimated from baseline data after 2003. In contrast, 2003–12 trends based on subsamples truncated by discarding all data prior to the induced heterogeneity in 2003 showed appreciable biases and differences in slope, relative to the corresponding 2003–12 segment of the trend computed from the design guideline subsamples. Overall, the results indicate that for particulate constituents, load and trend estimates computed using long-term records recently converted to CBNTN design guideline sampling protocols will be most reliable if the trend is computed using the entire record, but reported only for the period that design guideline sampling protocols were followed.
\nInconsistencies related to changing laboratory methods were also examined via two manipulative experiments. In the first experiment, increasing and decreasing “stair-step” patterns of changes in censoring level, overall representing a factor-of-five change in the laboratory reporting limit, were artificially imposed on a 27-year record with no censoring and a period-of-record concentration trend of –68.4 percent. Trends estimated on the basis of the manipulated records were broadly similar to the original trend (–63.6 percent for decreasing censoring levels and –70.3 percent for increasing censoring levels), lending a degree of confidence that the survival regression routines upon which WRTDS is based are generally robust to data censoring. The second experiment considered an abrupt disappearance of low-concentration observations of total phosphorus, associated with a laboratory method change and not reflected through censoring, near the middle of a 28-year record. By process of elimination, an upward shift in the estimated flow-normalize concentration trend line around the same time was identified as a likely artifact resulting from the laboratory method change, although a contemporaneous change in watershed processes cannot be ruled out. Decisions as to how to treat records with potential sampling protocol or laboratory methods-related artifacts should be made on a case-by-case basis, and trend results should be appropriately qualified.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155133","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program","usgsCitation":"Chanat, J.G., Moyer, D.L., Blomquist, J.D., Hyer, K.E., and Langland, M.J., 2016, Application of a weighted regression model for reporting nutrient and sediment concentrations, fluxes, and trends in concentration and flux for the Chesapeake Bay Nontidal Water-Quality Monitoring Network, results through water year 2012: U.S. Geological Survey Scientific Investigations Report 2015–5133, 76 p., https://dx.doi.org/10.3133/sir20155133.","productDescription":"Report: viii, 74 p.; 5 Appendixes","numberOfPages":"88","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-063310","costCenters":[{"id":614,"text":"Virginia Water Science 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U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
http://va.water.usgs.gov/
Fatty acid analysis of periphyton is an emerging tool for assessing the condition of a stream ecosystem on the basis of its water quality. The study presented in this report was designed to test the hypothesis that periphyton communities have a fatty acid profile that can detect excessive turbidity and suspended sediment. The fatty acid composition of periphyton was assessed during two seasons upstream and downstream from an underground aqueduct that provides supplemental flows, which are a potential source of turbidity and suspended sediment on the upper Esopus Creek, New York. These data were compared with measurements of periphyton standing crop, diatom community structure and integrity, and basic water-quality parameters. Periphyton standing crop and diatom community integrity indicated little evidence of impairment from the supplemental flows. The relative abundances of two physiologically important fatty acids, γ-linolenic acid (18:3ω6) and eicosapentaenoic acid (20:5ω3), were significantly lower downstream from the supplemental flows and multivariate analyses of fatty acid profiles identified significant differences between sites upstream and downstream from the supplemental flows. Individual fatty acids and summary metrics, however, were not significantly correlated with turbidity or suspended sediment. Together, these results indicate that the supplemental flows may cause some measurable effects but they do not constitute a major disturbance to the periphyton community on the upper Esopus Creek. Fatty acid analysis may have potential as a tool for monitoring changes in periphyton nutritional composition that may reflect water quality and ecosystem health but needs to be further evaluated around a more definitive source of water-quality impairment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155161","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"George, S.D., Ernst, A.G., Baldigo, B.P., and Honeyfield, D.C., 2016, Response of periphyton fatty acid composition to supplemental flows in the upper Esopus Creek, Catskill Mountains, New York: U.S. Geological Survey Scientific Investigations Report 2015–5161, 22 p., with appendixes, https://dx.doi.org/10.3133/sir20155161.","productDescription":"viii, 22 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-050857","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":313924,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5161/coverthb.jpg"},{"id":313925,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5161/sir20155161.pdf","text":"Report","size":"3.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5161"}],"country":"United States","state":"New York","otherGeospatial":"Esopus Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.6466064453125,\n 41.99522219923445\n ],\n [\n -74.6466064453125,\n 42.15118709351198\n ],\n [\n -74.1632080078125,\n 42.15118709351198\n ],\n [\n -74.1632080078125,\n 41.99522219923445\n ],\n [\n -74.6466064453125,\n 41.99522219923445\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
Information requests:
(518) 285-5602
or visit our Web site at:
http://ny.water.usgs.gov
Strategic planning is increasingly recognized as necessary for providing the greatest possible conservation benefits for restoration efforts. Rigorous, science-based resource assessment, combined with acknowledgement of broader basin trends, provides a solid foundation for determining effective projects. It is equally important that methods used to prioritize conservation investments are simple and practical enough that they can be implemented in a timely manner and by a variety of resource managers. With the help of local and regional natural resource professionals, we have developed a broad-scale, spatially-explicit assessment of 146 miles (~20,000 acres) of the Colorado River mainstem in Grand and San Juan Counties, Utah that will function as the basis for a systematic, practical approach to conservation planning and riparian restoration prioritization. For the assessment we have: 1) acquired, modified or created spatial datasets of Colorado River bottomland conditions; 2) synthesized those datasets into habitat suitability models and estimates of natural recovery potential, fire risk and relative cost; 3) investigated and described dominant ecosystem trends and human uses, and; 4) suggested site selection and prioritization approaches. Partner organizations (The Nature Conservancy, National Park Service, Bureau of Land Management and Utah Forestry Fire and State Lands) are using the assessment and datasets to identify and prioritize a suite of restoration actions to increase ecosystem resilience and improve habitat for bottomland species. Primary datasets include maps of bottomland cover types, bottomland extent, maps of areas inundated during high and low flow events, as well as locations of campgrounds, roads, fires, invasive vegetation treatment areas and other features. Assessment of conditions and trends in the project area entailed: 1) assemblage of existing data on geology, changes in stream flow, and predictions of future conditions; 2) identification of fish and wildlife species present and grouping species into Conservation Elements (CEs) based on habitat needs, and: 3) acquisition, review and creation of spatial datasets characterizing vegetation, fluvial geomorphic and human features within the bottomland. Interpretation of aerial imagery and assimilation of pre-existing spatial data were central to our efforts in characterizing resources conditions. Detailed maps of vegetation and channel habitat features in the project area were generated from true color, high resolution (0.3m) imagery flown September 16, 2010. We also mapped channel habitat features at high flow on 1.0-m resolution, publicly available, true color imagery. We obtained additional layers such as land ownership, roads, fire history, non-native vegetation treatment areas, and recreational use features from public sources and project partners. Habitat suitability models were created for groups of terrestrial species by combining spatial datasets with the habitat needs of conservation elements, guided by literature, where available, and extensive use of expert knowledge. Conservation elements for endangered fish species life stages were identified but not modeled. Terrestrial CE’s included: • Riparian Overstory -yellow-billed cuckoo, Bullock’s oriole, black-headed grosbeak, blue grosbeak, warbling vireo, Cooper's hawk, screech owl, saw-whet owl, and bald eagle, (best: tall trees, dense canopy, diverse shrub understory, no tamarisk); • Riparian Understory - southwestern willow flycatcher, common yellowthroat, yellow warbler, yellow-breasted chat, beaver, northern river otter, black-necked garter snake, (best: dense mesic shrubs near still water, no tamarisk); • Bat Feeding - Allen's big-eared bat, Townsend's big-eared bat, fringed myotis, Yuma myotis, big free-tailed bat, spotted bat (best: diverse vegetation, close to still water); • Bat Watering - big free-tailed and spotted bats (best: still water with no tall vegetation); •
","language":"English","publisher":"Colorado Mesa University","usgsCitation":"Christine Rasmussen, and Shafroth, P.B., 2016, Conservation planning for the Colorado River in Utah, 94 p. .","productDescription":"94 p. ","ipdsId":"IP-079063","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":335787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":334302,"type":{"id":15,"text":"Index Page"},"url":"https://www.coloradomesa.edu/water-center/documents/rasmussen_shaftroth_2016_watercenter_cmu.pdf"}],"country":"United States","state":"Utah","otherGeospatial":"Colorado River ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.06677246093749,\n 39.15136267949029\n ],\n [\n -109.3304443359375,\n 38.94659331893374\n ],\n [\n -109.79187011718749,\n 38.49229419236133\n ],\n [\n -110.489501953125,\n 37.913867495923746\n ],\n [\n -110.93994140625,\n 37.37015718405753\n ],\n [\n -110.89599609375,\n 37.17782559332976\n ],\n [\n -110.269775390625,\n 37.735969208590504\n ],\n [\n -109.44580078125,\n 38.453588708941375\n ],\n [\n -109.05029296875,\n 39.11301365149975\n ],\n [\n -109.06677246093749,\n 39.15136267949029\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a6c833e4b025c464286292","contributors":{"authors":[{"text":"Christine Rasmussen","contributorId":178920,"corporation":false,"usgs":false,"family":"Christine Rasmussen","affiliations":[],"preferred":false,"id":661589,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":661588,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193404,"text":"70193404 - 2016 - Geologic and geophysical maps and volcanic history of the Kelton Pass SE and Monument Peak SW Quadrangles, Box Elder County, Utah","interactions":[],"lastModifiedDate":"2018-02-13T15:27:39","indexId":"70193404","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5627,"text":"Miscellaneous Publication","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"16-1DM","title":"Geologic and geophysical maps and volcanic history of the Kelton Pass SE and Monument Peak SW Quadrangles, Box Elder County, Utah","docAbstract":"The Kelton Pass SE and Monument Peak SW 7.5' quadrangles are located in Box Elder County, northwestern Utah (figure 1; plate 1). The northern boundary of the map area is 8.5 miles (13.7 km) south of the Utah-Idaho border, and the southern boundary reaches the edge of mud flats at the north end of Great Salt Lake. Elevations range from 4218 feet (1286 m) along the mud flats to 5078 feet (1548 m) in the Wildcat Hills. Deep Creek forms a prominent drainage between the Wildcat Hills and Cedar Hill. The closest towns are the ranching communities of Snowville, Utah (10 miles [16 km] to the northeast) (figure 1), and Park Valley, Utah (10 miles [16 km] to the west).
The Kelton Pass SE and Monument Peak SW 7.5' quadrangles are located entirely within southern Curlew Valley, which drains south into Great Salt Lake, and extends north of the area shown on figure 1 into Idaho. Curlew Valley is bounded on the west by the Raft River Mountains and on the east by the Hansel Mountains (figure 1). Sedimentary and volcanic bedrock exposures within the quadrangles form the Wildcat Hills, Cedar Hill, and informally named Middle Shield (figure 1). Exposed rocks and deposits are Permian to Holocene in age, and include the Permian quartz sandstone and orthoquartzite of the Oquirrh Formation (Pos), tuffaceous sedimentary rocks of the Miocene Salt Lake Formation (Ts), Pliocene basaltic lava flows (Tb) and dacite (Tdw), Pleistocene rhyolite (Qrw) and basalt (Qb), and Pleistocene and Holocene surficial deposits of alluvial, lacustrine, and eolian origin. Structurally, the map area is situated in the northeastern Basin and Range Province, and is inferred to lie within the hanging wall of the late Miocene detachment faults exposed in the Raft River Mountains to the northwest (e.g., Wells, 1992, 2009; figure 1).
This mapping project was undertaken to produce a comprehensive, large-scale geologic map of the Wildcat Hills, as well as to improve understanding of the volcanic and tectonic evolution of southern Curlew Valley. The resultant publication includes a geologic map of the Kelton Pass SE and Monument Peak SW quadrangles (plate 1), two interpretive geologic cross sections (plate 2), new geophysical data and interpretations, and new geochronology data for volcanic units within and near the quadrangles.
","language":"English","publisher":"Utah Department of Natural Resources","usgsCitation":"Felger, T.J., Miller, D., Langenheim, V., and Fleck, R.J., 2016, Geologic and geophysical maps and volcanic history of the Kelton Pass SE and Monument Peak SW Quadrangles, Box Elder County, Utah: Miscellaneous Publication 16-1DM, Report: 34 p.; 2 Plates.","productDescription":"Report: 34 p.; 2 Plates","ipdsId":"IP-032395","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":351555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347973,"type":{"id":11,"text":"Document"},"url":"https://ugspub.nr.utah.gov/publications/misc_pubs/mp-16-1.pdf"}],"country":"United States","state":"Utah","county":"Box Elder County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.125,\n 41.875\n ],\n [\n -112.875,\n 41.875\n ],\n [\n -112.875,\n 41.75\n ],\n [\n -113.125,\n 41.75\n ],\n [\n -113.125,\n 41.875\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afeea4be4b0da30c1bfc5e1","contributors":{"authors":[{"text":"Felger, Tracey J. 0000-0003-0841-4235 tfelger@usgs.gov","orcid":"https://orcid.org/0000-0003-0841-4235","contributorId":1117,"corporation":false,"usgs":true,"family":"Felger","given":"Tracey","email":"tfelger@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":718917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, David M. 0000-0003-3711-0441 dmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":140769,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","email":"dmiller@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":718915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langenheim, Victoria E. 0000-0003-2170-5213 zulanger@usgs.gov","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":151042,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","email":"zulanger@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":718918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fleck, Robert J. 0000-0002-3149-8249 fleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3149-8249","contributorId":1048,"corporation":false,"usgs":true,"family":"Fleck","given":"Robert","email":"fleck@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":718916,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191236,"text":"70191236 - 2016 - An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States","interactions":[],"lastModifiedDate":"2017-10-02T16:19:59","indexId":"70191236","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1191,"text":"Cartography and Geographic Information Science","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States","docAbstract":"Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15230406.2015.1067829","usgsCitation":"Wendel, J., Buttenfield, B., and Stanislawski, L.V., 2016, An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States: Cartography and Geographic Information Science, v. 43, no. 3, p. 233-249, https://doi.org/10.1080/15230406.2015.1067829.","productDescription":"17 p.","startPage":"233","endPage":"249","ipdsId":"IP-062949","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":346332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"43","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-15","publicationStatus":"PW","scienceBaseUri":"59d35029e4b05fe04cc34d65","contributors":{"authors":[{"text":"Wendel, Jochen","contributorId":196803,"corporation":false,"usgs":false,"family":"Wendel","given":"Jochen","affiliations":[],"preferred":false,"id":711651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buttenfield, Barbara P.","contributorId":145538,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":711652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanislawski, Larry V. 0000-0002-9437-0576 lstan@usgs.gov","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":3386,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","email":"lstan@usgs.gov","middleInitial":"V.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":711650,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70038143,"text":"ds669 - 2016 - Tabulated Transmissivity and Storage Properties of the Floridan Aquifer System in Florida and Parts of Georgia, South Carolina, and Alabama","interactions":[],"lastModifiedDate":"2016-12-02T11:57:07","indexId":"ds669","displayToPublicDate":"2012-04-19T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"669","title":"Tabulated Transmissivity and Storage Properties of the Floridan Aquifer System in Florida and Parts of Georgia, South Carolina, and Alabama","docAbstract":"A goal of the U.S. Geological Survey Groundwater Resources Program is to assess the availability of fresh water within each of the principal aquifers in the United States with the greatest groundwater withdrawals. The Floridan aquifer system (FAS), which covers an area of approximately 100,000 square miles in Florida and parts of Georgia, Alabama, Mississippi, and South Carolina, is one such principal aquifer, having the fifth largest groundwater withdrawals in the Nation, totaling 3.64 billion gallons per day in 2000. Compilation of FAS hydraulic properties is critical to the development and calibration of groundwater flow models that can be used to develop water budgets spatially and temporally, as well as to evaluate resource changes over time. Wells with aquifer test data were identified as Upper Floridan aquifer (UFA), Lower Floridan aquifer (LFA), Floridan aquifer system (FAS, Upper Floridan with some middle and/or Lower Floridan), or middle Floridan confining unit (MCU), based on the identification from the original database or report description, or comparison of the open interval of the well with previously published maps.
This report consolidates aquifer hydraulic property data obtained from multiple databases and reports of the U.S. Geological Survey, various State agencies, and the Water Management Districts of Florida, that are compiled into tables to provide a single information source for transmissivity and storage properties of the FAS as of October 2011. Transmissivity calculated from aquifer pumping tests and specific-capacity data are included. Values for transmissivity and storage coefficients are intended for use in regional or sub regional groundwater flow models; thus, any tests (aquifer pumping tests and specific capacity data) that were conducted with packers or for open intervals less than 30 feet in length are excluded from the summary statistics and tables of this report, but are included in the database.
The transmissivity distribution from the aquifer pumping tests is highly variable. The transmissivity based on aquifer pumping tests (from 1,045 values for the UFA and FAS) ranges from 8 to about 9,300,000 square feet per day (ft2/d) and values of storage coefficient (646 reported) range from 3x10-9 to 0.41. The 64 transmissivity values for the LFA range from about 130 to 4,500,000 ft2/d, and the 17 storage coefficient values range from 7x10-8 to 0.03. The 14 transmissivity values for the MCU range from 1 to about 600,000 ft2/d and the 10 storage coefficient values range from 8x10-8 to 0.03. Transmissivity estimates for the UFA and FAS for 442 specific capacity tests range from approximately 200 to 1,000,000 ft2/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds669","collaboration":"Product of the U.S. Geological Survey Groundwater Resources Program","usgsCitation":"Kuniansky, E.L., and Bellino, J.C., 2016, Tabulated transmissivity and storage properties of the Floridan aquifer system in Florida and parts of Georgia, South Carolina, and Alabama (ver. 1.1, May 2016): U.S. Geological Survey Data Series 669, 37 p., https://pubs.usgs.gov/ds/669.","productDescription":"v, 16 p.; Appendix; 2 Tables; Table 1: 17 inches x 11 inches, Table 2: 17 inches x 11 inches; DS699 FAS AQT DS zip file","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":321206,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table1-v1.1.pdf","text":"Table 1 - Transmissivity and storage coefficients from aquifer pumping tests ","size":"321 KB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 669"},{"id":321207,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table1-v1.1.xls","text":"Table 1 - Excel ","size":"355 KB xls","description":"DS 669"},{"id":321208,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table2-v1.1.pdf","text":"Table 2 -Transmissivity estimates from specific capacity data","size":"157 KB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 669"},{"id":321209,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/669/tables/ds669_table2-v1.1.xls","text":"Table 2 - Excel ","size":"132 KB xls","description":"DS 669"},{"id":321211,"rank":8,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/ds/669/DS669_v1.1FAS_AQT_DATA_04192016.zip","text":"Aquifer Data","size":"1 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 669"},{"id":321205,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/669/versionHist.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"DS 669"},{"id":254562,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/669/","linkFileType":{"id":5,"text":"html"},"description":"DS 669"},{"id":254564,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/669/images/coverthb.jpg"}],"country":"United States","state":"Alabama, Florida, Georgia, South Carolina","otherGeospatial":"Floridan Aquifer System","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -90,25 ], [ -90,34 ], [ -79.5,34 ], [ -79.5,25 ], [ -90,25 ] ] ] } } ] }","edition":"Version 1.1 May 13, 2016","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Surveyr
4446 Pet Lane, Suite 108r
Lutz, FL 33559r
Telephone: (813) 498–5000r
http://fl.water.usgs.gov/
Dilution of aluminum discharged to reservoirs in filter-backwash effluents at water-treatment facilities in Massachusetts was investigated by a field study and computer simulation. Determination of dilution is needed so that permits for discharge ensure compliance with water-quality standards for aquatic life. The U.S. Environmental Protection Agency chronic standard for aluminum, 87 micrograms per liter (μg/L), rather than the acute standard, 750 μg/L, was used in this investigation because the time scales of chronic exposure (days) more nearly match rates of change in reservoir concentrations than do the time scales of acute exposure (hours).
Whereas dilution factors are routinely computed for effluents discharged to streams solely on the basis of flow of the effluent and flow of the receiving stream, dilution determination for effluents discharged to reservoirs is more complex because (1), compared to streams, additional water is available for dilution in reservoirs during low flows as a result of reservoir flushing and storage during higher flows, and (2) aluminum removal in reservoirs occurs by aluminum sedimentation during the residence time of water in the reservoir. Possible resuspension of settled aluminum was not considered in this investigation. An additional concern for setting discharge standards is the substantial concentration of aluminum that can be naturally present in ambient surface waters, usually in association with dissolved organic carbon (DOC), which can bind aluminum and keep it in solution.
A method for dilution determination was developed using a mass-balance equation for aluminum and considering sources of aluminum from groundwater, surface water, and filter-backwash effluents and losses caused by sedimentation, water withdrawal, and spill discharge from the reservoir. The method was applied to 13 reservoirs. Data on aluminum and DOC concentrations in reservoirs and influent water were collected during the fall of 2009. Complete reservoir volume was determined to be available for mixing on the basis of vertical and horizontal aluminum-concentration profiling. Losses caused by settling of aluminum were assumed to be proportional to aluminum concentration and reservoir area. The constant of proportionality, as a function of DOC concentration, was established by simulations in each of five reservoirs that differed in DOC concentration.
In addition to computing dilution factors, the project determined dilution factors that would be protective with the same statistical basis (frequency of exceedance of the chronic standard) as dilutions computed for streams at the 7-day-average 10-year-recurrence annual low flow (the 7Q10). Low-flow dilutions are used for permitting so that receiving waters are protected even at the worst-case flow levels. The low-flow dilution factors that give the same statistical protection are the lowest annual 7-day-average dilution factors with a recurrence of 10 years, termed 7DF10s. Determination of 7DF10 values for reservoirs required that long periods of record be simulated so that dilution statistics could be determined. Dilution statistics were simulated for 13 reservoirs from 1960 to 2004 using U.S. Geological Survey Firm-Yield Estimator software to model reservoir inputs and outputs and present-day values of filter-effluent discharge and aluminum concentration.
Computed settling velocities ranged from 0 centimeters per day (cm/d) at DOC concentrations of 15.5 milligrams per liter (mg/L) to 21.5 cm/d at DOC concentrations of 2.7 mg/L. The 7DF10 values were a function of aluminum effluent discharged. At current (2009) effluent discharge rates, the 7DF10 values varied from 1.8 to 115 among the 13 reservoirs. In most cases, the present-day (2009) discharge resulted in receiving water concentrations that did not exceed the standard at the 7DF10. Exceptions were one reservoir with a very small area and three reservoirs with high concentrations of DOC. Maximum permissible discharges were determined for water-treatment plants by adjusting discharges upward in simulations until the 7DF10 resulted in reservoir concentrations that just met the standard. In terms of aluminum flux, these discharges ranged from 0 to 28 kilograms of aluminum per day.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20115136","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Colman, J.A., Massey, A.J., and Levin, S.B., 2016, Determination of dilution factors for discharge of aluminum-containing wastes by public water-supply treatment facilities into lakes and reservoirs in Massachusetts (ver. 1.1, December 2016): U.S. Geological Survey Scientific Investigations Report 2011–5136, 36 p., https://pubs.usgs.gov/sir/2011/5136.","productDescription":"vi, 36 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"links":[{"id":116564,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2011/5136/images/coverthb.jpg"},{"id":94131,"rank":100,"type":{"id":15,"text":"Index 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\"}}]}","edition":"Version 1.0: Originally posted September 16, 2011; Version 1.1: December 30, 2016","contact":"Director, Massachusetts-Rhode Island Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
(508) 490-5000
http://ma.water.usgs.gov
The U.S. Geological Survey in cooperation with the South Carolina Department of Transportation collected clear-water pier- and contraction-scour data at 116 bridges in the Coastal Plain and Piedmont Physiographic Provinces of South Carolina. Pier-scour depths collected in both provinces ranged from 0 to 8.0 feet. Contraction-scour depths collected in the Coastal Plain ranged from 0 to 3.9 feet. Using hydraulic data estimated with a one-dimensional flow model, predicted clear-water scour depths were computed with scour equations from the Federal Highway Administration Hydraulic Engineering Circular 18 and compared with measured scour. This comparison indicated that predicted clear-water scour depths, in general, exceeded measured scour depths and at times were excessive. Predicted clear-water contraction scour, however, was underpredicted approximately 30 percent of the time by as much as 7.1 feet.
The investigation focused on clear-water pier scour, comparing trends in the laboratory and field data. This comparison indicated that the range of dimensionless variables (relative depth, flow intensity, relative grain size) used in laboratory investigations of pier scour, were similar to the range for field data in South Carolina, further indicating that laboratory relations may have some applicability to field conditions in South Carolina. Variables determined to be important in developing pier scour in laboratory studies were investigated to understand their influence on the South Carolina field data, and many of these variables appeared to be insignificant under field conditions in South Carolina. The strongest explanatory variables were pier width and approach velocity. Envelope curves developed from the field data are useful tools for evaluating reasonable ranges of clear-water pier and contraction scour in South Carolina. A modified version of the Hydraulic Engineering Circular 18 pier-scour equation also was developed as a tool for evaluating clearwater pier scour. The envelope curves and modified equation offer an improvement over the current methods for predicting clear-water scour in South Carolina.
Data from this study were compiled into a database that includes photographs, measured scour depths, predicted scour depths, limited basin characteristics, limited soil data, and modeled hydraulic data. The South Carolina database can be used to compare studied sites with unstudied sites to evaluate the potential for scour at the unstudied sites. In addition, the database can be used to evaluate the performance of various methods for predicting clear-water pier and contraction scour.
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U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
http://sc.water.usgs.gov/
The Missouri River Pallid Sturgeon Effects Analysis (EA) was commissioned by the U.S. Army Corps of Engineers to develop a foundation of understanding of how pallid sturgeon (Scaphirhynchus albus) population dynamics are linked to management actions in the Missouri River. The EA consists of several steps: (1) development of comprehensive, conceptual ecological models illustrating pallid sturgeon population dynamics and links to management actions and other drivers; (2) compilation and assessment of available scientific literature, databases, and models; (3) development of predictive, quantitative models to explore the system dynamics and population responses to management actions; and (4) analysis and assessment of effects of system operations and actions on species’ habitats and populations. This report addresses the second objective, compilation and assessment of relevant information.
\nScientific information on pallid sturgeon and its environment has grown substantially during the last decade. Presently available (2015) information indicates that stocked sturgeon are surviving and growing, and that wild and hatchery sturgeon are spawning in the wild. However, natural recruitment to age-1 and older has not been detected since systematic sampling began in 2005. Population models indicate the sensitivity of population growth to certain demographic variables, in particular early-life stage survival and perhaps adult fecundity. This report documents the existing population models for the pallid sturgeon, and the substantial quantities of information developed through the Pallid Sturgeon Population Assessment Program (PSPAP), the Habitat Assessment and Monitoring Program (HAMP), the Comprehensive Sturgeon Research Project (CSRP), range-wide genetics databases, and related research studies. The reference database compiled for the EA consists of over 190 peer-reviewed documents specifically related to pallid sturgeon and over 12,000 references on the Missouri River system and related species.
\nNotwithstanding the large quantity of information available, the EA faces challenges in synthesizing the information into useful, quantitative models. In particular, critical demographic parameters for population models remain uncertain and the functional relationships between the two main categories of physical management action—changes in flow regime and reengineering channel form—and pallid sturgeon survival responses are obscure. In addition, there is an overarching uncertainty about how physical management actions interact with propagation management actions in view of evolving understanding of genetic structuring of the pallid sturgeon population. Synthesis efforts are also challenged by the fragmentation of information sources among projects and agencies; one objective of this report is to facilitate future assessments by providing documentation of what information is available and where.
\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151226","collaboration":"Prepared in cooperation with the Missouri River Recovery Program","usgsCitation":"Jacobson, R.B., Parsley, M.J., Annis, M.L., Colvin, M.E., Welker, T.L., and James, D.A.,\n2015, Science information to support Missouri River Scaphirhynchus albus (pallid sturgeon)\neffects analysis: U.S. Geological Survey Open-File Report 2015–1226, 78 p.,\nhttps://dx.doi.org/10.3133/ofr20151226.","productDescription":"vii, 78 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-059606","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":314737,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1226/coverthb.jpg"},{"id":314738,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1226/ofr20151226.pdf","text":"Report","size":"5.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1226"}],"country":"United States","state":"Iowa, Kansas, Missouri, Montana, Nebraska, North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.6142578125,\n 37.28279464911045\n ],\n [\n -91.82373046875,\n 37.52715361723378\n ],\n [\n -90.06591796875,\n 38.8225909761771\n ],\n [\n -96.50390625,\n 43.96119063892024\n ],\n [\n -100.26123046875,\n 48.37084770238363\n ],\n [\n -104.04052734375,\n 48.99463598353408\n ],\n [\n -112.8076171875,\n 49.023461463214126\n ],\n [\n -112.60986328125,\n 45.089035564831036\n ],\n [\n -106.74316406249999,\n 40.979898069620155\n ],\n [\n -102.06298828125,\n 38.993572058209466\n ],\n [\n -94.6142578125,\n 37.28279464911045\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"
Director, USGS Columbia Environmental Research Center
4200 New Haven Road
Columbia, MO 65201
Historical data for nine selected streamgages in southwest and south-central Kansas were used in an assessment of streamflow characteristics and trends. This information is required by the U.S. Fish and Wildlife Service and the Kansas Department of Wildlife, Parks and Tourism to assist with the effective management of Etheostoma cragini (Arkansas darter) habitats and populations in the State. Changing streamflow conditions, such as a reduction or elimination of streamflow, may adversely affect the Arkansas darter. Priority basins for the Arkansas darter represented by the selected streamgages include the Cimarron River, Rattlesnake Creek, the North Fork Ninnescah River, the South Fork Ninnescah River, the Medicine Lodge River, and the Chikaskia River.
\nStreamflow conditions were assessed using annual streamflow characteristics computed for the period of record for each of the selected streamgages. Specific streamflow characteristics computed were mean discharge, mean base flow, 90th-percentile flow, 10th-percentile flow, minimum 7-day mean flow, minimum 28-day mean flow, number of days of flow less than 1 cubic foot per second, and number of zero-flow days.
\nTwo of the priority basins had statistically significant decreases in annual mean discharge during the period of record. In the Cimarron River Basin, there was a pronounced multidecadal decrease in the magnitude and variability of annual mean discharge. Concurrently, the percentage of the annual mean discharge that was contributed by base flow increased. In the Rattlesnake Creek Basin, there was a pre-1985 decrease in annual mean discharge. Typically, in these two basins, significant decreases were indicated for mean base flow, 90th-percentile flow, 10th-percentile flow, minimum 7-day mean flow, and minimum 28-day mean flow. No significant trend in annual mean discharge was indicated for the North Fork Ninnescah, South Fork Ninnescah, Medicine Lodge, and Chikaskia River Basins. For the Medicine Lodge and Chikaskia River Basins as well as the downstream part of the South Fork Ninnescah River Basin, a significant increase in mean base flow and 10th-percentile flow was indicated. Also, for the latter two basins, a significant increase was indicated for minimum 7-day mean flow.
\nFactors investigated to explain long-term trends in annual mean discharge, or lack thereof, included precipitation and groundwater withdrawals. Annual precipitation in the study area varied substantially from 1951 to 2013 with no pronounced long-term trend. Thus, a precipitation-related explanation for the significant decrease in annual mean discharge in the Cimarron River and Rattlesnake Creek Basins was not supported. Because the most pronounced decreases in annual mean discharge were in the basin with the largest groundwater-level declines (that is, the Cimarron River Basin), both in terms of magnitude and areal extent, it is likely that groundwater withdrawals were a primary, if not dominant, causative factor.
\nThe occurrence of extremely low-flow (less than 1 cubic foot per second) and zero-flow days varied by basin and year. Typically, such days occurred in the summer and autumn for all basins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155167","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Kansas Department of Wildlife, Parks and Tourism","usgsCitation":"Juracek, K.E., 2015, Streamflow characteristics and trends at selected streamgages in southwest and south-central Kansas: U.S. Geological Survey Scientific Investigations Report 2015–5167, 20 p., https://dx.doi.org/10.3133/sir20155167.","productDescription":"vi, 20 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065381","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":312557,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5167/sir20155167.pdf","text":"Report","size":"3.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5167"},{"id":312556,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5167/coverthb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.0465087890625,\n 37.00255267215955\n ],\n [\n -102.0465087890625,\n 38.69408504756833\n ],\n [\n -96.9488525390625,\n 38.69408504756833\n ],\n [\n -96.9488525390625,\n 37.00255267215955\n ],\n [\n -102.0465087890625,\n 37.00255267215955\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
http://ks.water.usgs.gov
Statistical summaries of streamflow data collected at 184 streamgages in Iowa are presented in this report. All streamgages included for analysis have at least 10 years of continuous record collected before or through September 2013. This report is an update to two previously published reports that presented statistical summaries of selected Iowa streamflow data through September 1988 and September 1996. The statistical summaries include (1) monthly and annual flow durations, (2) annual exceedance probabilities of instantaneous peak discharges (flood frequencies), (3) annual exceedance probabilities of high discharges, and (4) annual nonexceedance probabilities of low discharges and seasonal low discharges. Also presented for each streamgage are graphs of the annual mean discharges, mean annual mean discharges, 50-percent annual flow-duration discharges (median flows), harmonic mean flows, mean daily mean discharges, and flow-duration curves. Two sets of statistical summaries are presented for each streamgage, which include (1) long-term statistics for the entire period of streamflow record and (2) recent-term statistics for or during the 30-year period of record from 1984 to 2013. The recent-term statistics are only calculated for streamgages with streamflow records pre-dating the 1984 water year and with at least 10 years of record during 1984–2013. The streamflow statistics in this report are not adjusted for the effects of water use; although some of this water is used consumptively, most of it is returned to the streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151214","collaboration":"Prepared in cooperation with the Iowa Department of Transportation, the Iowa Highway Research Board (Iowa DOT Research Project TR-669), and the U.S. Army Corps of Engineers","usgsCitation":"Eash, D.A., O’Shea, P.S., Weber, J.R., Nguyen, K.T., Montgomery, N.L., and Simonson, A.J., 2015, Statistical summaries of selected Iowa streamflow data through September 2013: U.S. Geological Survey Open-File Report 2015–1214, 18 p., https://dx.doi.org/10.3133/ofr20151214.","productDescription":"Report: vii, 18 p.; Table; Companion File","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"1984-01-01","ipdsId":"IP-064768","costCenters":[{"id":351,"text":"Iowa Water Science 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\"}}]}","contact":"Director, Iowa Water Science Center
U.S. Geological Survey
P.O. Box 1230
Iowa City, IA 52244
http://ia.water.usgs.gov
Large bottom roughness is a characteristic of most coral reef environments and this has been shown to have a substantial impact on hydrodynamic processes in these environments. In this paper, we evaluate suspended sediment concentration (SSC) data as well detailed hydrodynamic data over a coral reef flat in Ningaloo Reef, Western Australia, to understand how this bottom roughness affects these processes. A well-developed logarithmic velocity layer consistently developed above a canopy layer during the experiment. Estimates of bottom stresses from these logarithmic profiles were comparable with estimates obtained directly from turbulent Reynolds stresses, and an order of magnitude greater than those typically reported for sandy beach environments having similar flow. Nevertheless, the sediment grain size distribution of the suspended load was very fine relative to what should be mobilized by these stresses, indicated the large roughness substantially suppressed sediment transport.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2015","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2015","conferenceDate":"May 11-15, 2015","conferenceLocation":"San Diego, CA","language":"English","publisher":"World Scientific","doi":"10.1142/9789814689977_0086","usgsCitation":"Pomeroy, A.W., Lowe, R.J., Ghisalberti, M., Storlazzi, C.D., Cutter, M., and Symonds, G., 2015, Mechanics of sediment suspension and transport within a fringing reef, in The proceedings of the coastal sediments 2015, San Diego, CA, May 11-15, 2015, 14 p., https://doi.org/10.1142/9789814689977_0086.","productDescription":"14 p.","ipdsId":"IP-063073","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":382162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","city":"Tantabiddi","otherGeospatial":"Ningaloo Reef","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 113.62060546875,\n -22.024545601240337\n ],\n [\n 114.00375366210938,\n -22.024545601240337\n ],\n [\n 114.00375366210938,\n -21.644664169522265\n ],\n [\n 113.62060546875,\n -21.644664169522265\n ],\n [\n 113.62060546875,\n -22.024545601240337\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Pomeroy, A. W. M.","contributorId":146291,"corporation":false,"usgs":false,"family":"Pomeroy","given":"A.","email":"","middleInitial":"W. M.","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":567319,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, R. J.","contributorId":146292,"corporation":false,"usgs":false,"family":"Lowe","given":"R.","email":"","middleInitial":"J.","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":567320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ghisalberti, M.","contributorId":146293,"corporation":false,"usgs":false,"family":"Ghisalberti","given":"M.","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":567321,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":567318,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cutter, M.","contributorId":146294,"corporation":false,"usgs":false,"family":"Cutter","given":"M.","email":"","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":567322,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Symonds, G.","contributorId":146295,"corporation":false,"usgs":false,"family":"Symonds","given":"G.","email":"","affiliations":[{"id":12494,"text":"CSIRO Land and Water, Australia","active":true,"usgs":false}],"preferred":false,"id":567323,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70159763,"text":"70159763 - 2015 - Potential application of radiogenic isotopes and geophysical methods to understand the hydrothermal dystem of the Upper Geyser Basin, Yellowstone National Park","interactions":[],"lastModifiedDate":"2017-04-26T15:10:40","indexId":"70159763","displayToPublicDate":"2015-12-31T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":273,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":4}},"seriesNumber":"NPS/YELL/NRR—2015/1077","title":"Potential application of radiogenic isotopes and geophysical methods to understand the hydrothermal dystem of the Upper Geyser Basin, Yellowstone National Park","docAbstract":"Numerous geochemical and geophysical studies have been conducted at Yellowstone National Park to better understand the hydrogeologic processes supporting the thermal features of the Park. This report provides the first 87Sr/86Sr and 234U/238U data for thermal water from the Upper Geyser Basin (UGB) intended to evaluate whether heavy radiogenic isotopes might provide insight to sources of groundwater supply and how they interact over time and space. In addition, this report summarizes previous geophysical studies made at Yellowstone National Park and provides suggestions for applying non-invasive ground and airborne studies to better understand groundwater flow in the subsurface of the UGB. \r\nMultiple samples from Old Faithful, Aurum, Grand, Oblong, and Daisy geysers characterized previously for major-ion concentrations and isotopes of water (δ2H, δ18O, and 3H) were analyzed for Sr and U isotopes. Concentrations of dissolved Sr and U are low (4.3–128 ng g-1 Sr and 0.026–0.0008 ng g-1 U); consequently only 87Sr/86Sr data are reported for most samples. Values of 87Sr/86Sr for most geysers remained uniform between April and September 2007, but show large increases in all five geysers between late October 2007 and early April, 2008. By late summer of 2008, 87Sr/86Sr values returned to values similar to those observed a year earlier. Similar patterns are not present in major-ion data measured on the same samples. Furthermore, large geochemical differences documented between geysers are not observed in 87Sr/86Sr data, although smaller differences between sites may be present. Sr-isotope data are consistent with a stratified hydrologic system where water erupted in spring and summer of 2007 and summer of 2008 equilibrated with local intracaldera rhyolite flows at shallower depths. Water erupted between October 2007 and April 2008 includes greater amounts of groundwater that circulated deep enough to acquire a radiogenic 87Sr/86Sr, most likely from Archean basement rocks. Details of how the shallow and deep components interact and mechanisms causing these interactions remain unknown, but the data demonstrate the usefulness of obtaining Sr-isotope data from future sample campaigns. \r\nGeophysical methods that would be useful for characterization of the UGB subsurface properties and geothermal system include electromagnetic (EM), gravity, and ambient seismic. A suite of ground-based EM methods could be used in a synergistic combination together with airborne EM surveys to provide data for a range of spatial scales and resolutions. Existing thermal data for the shallow subsurface could be used to relate ground and airborne EM survey data to locations of geothermal fluids near the surface. Gravity surveys would be useful for mapping subsurface density anomalies and possibly monitoring changes in degree of saturation with groundwater. Ambient seismic surveys would be useful for estimating the thickness of unconsolidated deposits that contain the shallow groundwater system. \r\nA study that combines radiogenic isotope tracers with geophysical methods has the potential to better characterize the geothermal workings in the UGB. Insights gained could lead to a better understanding of the geothermal system and how Park infrastructure may cause perturbations. Measurements of radiogenic isotopes from multiple geysers and pools in localized areas within the UGB that are coupled with data from geophysical surveys would help refine conceptual models of mixing between deep- and shallow-derived subsurface fluids.","language":"English","publisher":"National Park Service","usgsCitation":"Paces, J.B., Long, A.J., and Koth, K.R., 2015, Potential application of radiogenic isotopes and geophysical methods to understand the hydrothermal dystem of the Upper Geyser Basin, Yellowstone National Park: Natural Resource Report NPS/YELL/NRR—2015/1077, Report: viii, 33 p.; Appendixes A & B.","productDescription":"Report: viii, 33 p.; Appendixes A & B","ipdsId":"IP-068244","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":340475,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":311583,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/App/Reference/Profile/2224966/"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.060791015625,\n 43.878097874251736\n ],\n [\n -109.70947265625,\n 43.878097874251736\n ],\n [\n -109.70947265625,\n 45.00365115687186\n ],\n [\n -111.060791015625,\n 45.00365115687186\n ],\n [\n -111.060791015625,\n 43.878097874251736\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5901b1bde4b0c2e071a99ba6","contributors":{"authors":[{"text":"Paces, James B. 0000-0002-9809-8493 jbpaces@usgs.gov","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":2514,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"jbpaces@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":580366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Andrew J. 0000-0001-7385-8081 ajlong@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-8081","contributorId":989,"corporation":false,"usgs":true,"family":"Long","given":"Andrew","email":"ajlong@usgs.gov","middleInitial":"J.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":580367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koth, Karl R. kkoth@usgs.gov","contributorId":4817,"corporation":false,"usgs":true,"family":"Koth","given":"Karl","email":"kkoth@usgs.gov","middleInitial":"R.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":580368,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193683,"text":"70193683 - 2015 - Regulating services as measures of ecological resilience on DoD lands","interactions":[],"lastModifiedDate":"2017-12-21T10:20:18","indexId":"70193683","displayToPublicDate":"2015-12-31T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesNumber":"Project RC-201114","title":"Regulating services as measures of ecological resilience on DoD lands","docAbstract":"Knowledge of the capacity and flow of ecosystem services can help DoD land managers make decisions that enhance cost-effectiveness, minimize environmental damage, and maximize resources available for military missions. We demonstrated a methodology to quantify and map selected regulating services (RS), which helps land managers envision tradeoffs. Our objectives were to 1) estimate current capacity of and demand for selected RS within DoD lands, 2) examine the effects of future DoD land management and climate changes on the capacity and flow of these RS, and 3) project how land-use and climate changes in nearby lands affect future demand for RS. Our approach incorporates widely accepted models and equations, remote sensing, GIS analysis, and stakeholder involvement. Required data include land cover/use, soil type, precipitation, and air temperature. We integrated data into the a) Surface Curve Number Method and b) Revised Universal Soil Loss Equation to estimate capacity of sediment, nitrogen (N) and surface-water regulation. Capacities and flows of RS vary greatly across landscapes and are likely to vary as climate changes or development occurs. Analyses of RS capacity and flow can help managers and planners prioritize actions in the context of best management practices and compatible use buffers. Staff surveys indicated that our approach was informative and easy to use. Implementation may be most limited by on-installation personnel time.
","language":"English","publisher":"Environmental Security Technology Certification Program (ESTCP)","publisherLocation":"Alexandria, VA","usgsCitation":"Angermeier, P.L., and Villamagna, A.M., 2015, Regulating services as measures of ecological resilience on DoD lands, v, 93 p.","productDescription":"v, 93 p.","ipdsId":"IP-064228","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":350118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":350117,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://serdp-estcp.org/Program-Areas/Resource-Conservation-and-Resiliency/Natural-Resources/Watershed-Processes-and-Management/RC-201114"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fe3be4b06e28e9c252cd","contributors":{"authors":[{"text":"Angermeier, Paul L. 0000-0003-2864-170X biota@usgs.gov","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":166679,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul","email":"biota@usgs.gov","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719882,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villamagna, Amy M.","contributorId":201421,"corporation":false,"usgs":false,"family":"Villamagna","given":"Amy","email":"","middleInitial":"M.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":725238,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159875,"text":"sir20155170 - 2015 - Methods for estimating flow-duration curve and low-flow frequency statistics for ungaged locations on small streams in Minnesota","interactions":[],"lastModifiedDate":"2015-12-28T12:10:56","indexId":"sir20155170","displayToPublicDate":"2015-12-24T09:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5170","title":"Methods for estimating flow-duration curve and low-flow frequency statistics for ungaged locations on small streams in Minnesota","docAbstract":"Knowledge of the magnitude and frequency of low flows in streams, which are flows in a stream during prolonged dry weather, is fundamental for water-supply planning and design; waste-load allocation; reservoir storage design; and maintenance of water quality and quantity for irrigation, recreation, and wildlife conservation. This report presents the results of a statewide study for which regional regression equations were developed for estimating 13 flow-duration curve statistics and 10 low-flow frequency statistics at ungaged stream locations in Minnesota. The 13 flow-duration curve statistics estimated by regression equations include the 0.0001, 0.001, 0.02, 0.05, 0.1, 0.25, 0.50, 0.75, 0.9, 0.95, 0.99, 0.999, and 0.9999 exceedance-probability quantiles. The low-flow frequency statistics include annual and seasonal (spring, summer, fall, winter) 7-day mean low flows, seasonal 30-day mean low flows, and summer 122-day mean low flows for a recurrence interval of 10 years. Estimates of the 13 flow-duration curve statistics and the 10 low-flow frequency statistics are provided for 196 U.S. Geological Survey continuous-record streamgages using streamflow data collected through September 30, 2012.
\nThe study area includes 196 streamgages located within Minnesota and 50 miles beyond the State’s borders in North Dakota, South Dakota, Iowa, and Wisconsin. The study area was divided into five regions that were developed in a previous study using the concept of hydrologic landscape units. Geographic information system software was used to calculate 18 characteristics investigated as potential explanatory variables in regression analyses for each streamgage drainage basin. Trend analyses indicated statistically significant trends in summer 7-day low flows that were not related to precipitation patterns for 19 streamgages. For 16 of these streamgages, the streamflow record was subset using structural change analysis to identify the most recent period of record without a significant trend. The three remaining streamgages with significant trends were excluded from the final analysis because the effective period of record without a significant trend was less than 10 years.
\nBecause several streams in this study have zero flow as their minimum reported flow, weighted left-censored regression was used to analyze the flow data in an unbiased manner, with weights based on the number of years of record. A total of 115 regression equations were developed in this study to calculate flow-duration curve and low-flow frequency statistics for ungaged locations in the study area. In addition, data from 25 pairs of streamgages were used to develop drainage-area ratio equations that can be used to estimate streamflow statistics at ungaged locations on streams that have a streamgage in another location. Streamflow statistics estimated using regional regression and drainage-area ratio equations were compared among regions. For regions A, D, and E, drainagearea ratio equations were more accurate than regional regression equations for flows, but regional regression equations were more accurate for high flows. For region F, regional regression equations were consistently more accurate than drainage-area ratio equations. For region BC, the pattern in accuracies of regional regression and drainage-area ratio equations between low flows and high flows was not consistent.
\nEquations developed in this study apply only to stream locations where flows are not substantially affected by regulation, diversion, or urbanization. All equations presented in this study will be incorporated into StreamStats, a web-based geographic information system tool developed by the U.S. Geological Survey. StreamStats allows users to obtain streamflow statistics, basin characteristics, and other information for user-selected locations on streams through an interactive map.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155170","collaboration":"Prepared in cooperation with the Minnesota Pollution Control Agency","usgsCitation":"Ziegeweid, J.R., Lorenz, D.L., Sanocki, C.A., and Czuba, C.R., 2015, Methods for estimating flow-duration curve and\nlow-flow frequency statistics for ungaged locations on small streams in Minnesota: U.S. Geological Survey Scientific\nInvestigations Report 2015–5170, 23 p., https://dx.doi.org/10.3133/sir20155170.","productDescription":"Report: vi, 23 p.; Appendix","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-046283","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":312848,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5170/coverthb.jpg"},{"id":312849,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5170/sir20155170.pdf","text":"Report","size":"2.13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5170"},{"id":312850,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5170/downloads/sir20155170_table1-1.xlsx","text":"Table 1-1","size":"89.1 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5170 Appendix 1"}],"country":"United States","state":"Iowa, Minnesota, North Dakota, South Dakota, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.27294921875,\n 49.023461463214126\n ],\n [\n -95.16357421875,\n 49.023461463214126\n ],\n [\n -95.16357421875,\n 49.439556958940855\n ],\n [\n -94.68017578125,\n 49.38237278700955\n ],\n [\n -94.54833984375,\n 49.081062364320736\n ],\n [\n -94.59228515625,\n 48.80686346108517\n ],\n [\n -94.28466796874999,\n 48.76343113791796\n ],\n [\n -93.8232421875,\n 48.69096039092549\n ],\n [\n -93.62548828125,\n 48.60385760823255\n ],\n [\n -93.3837890625,\n 48.67645370777654\n ],\n [\n -92.900390625,\n 48.73445537176822\n ],\n [\n -92.548828125,\n 48.61838518688487\n ],\n [\n -92.46093749999999,\n 48.472921272487824\n ],\n [\n -92.21923828124999,\n 48.4146186174932\n ],\n [\n -91.86767578124999,\n 48.31242790407178\n ],\n [\n -91.51611328125,\n 48.151428143221224\n ],\n [\n -91.14257812499999,\n 48.22467264956519\n ],\n [\n -90.76904296874999,\n 48.31242790407178\n ],\n [\n -90.63720703125,\n 48.22467264956519\n ],\n [\n -90.439453125,\n 48.180738507303836\n ],\n [\n -90.087890625,\n 48.19538740833338\n ],\n [\n -89.82421875,\n 48.09275716032736\n ],\n [\n -89.56054687499999,\n 48.019324184801185\n ],\n [\n -89.93408203124999,\n 47.84265762816538\n ],\n [\n -90.63720703125,\n 47.60616304386874\n ],\n [\n -91.12060546875,\n 47.32393057095941\n ],\n [\n -91.47216796875,\n 47.12995075666307\n ],\n [\n -91.845703125,\n 46.86019101567027\n ],\n [\n -91.82373046875,\n 46.800059446787316\n ],\n [\n -91.3623046875,\n 46.875213396722685\n ],\n [\n -90.72509765625,\n 47.14489748555398\n ],\n [\n -90.1318359375,\n 47.12995075666307\n ],\n [\n -89.8681640625,\n 47.040182144806664\n ],\n [\n -89.67041015625,\n 46.694667307773095\n ],\n [\n -89.8681640625,\n 46.27103747280261\n ],\n [\n -90.32958984375,\n 46.07323062540838\n ],\n [\n -90.90087890624999,\n 45.69083283645816\n ],\n [\n -91.14257812499999,\n 45.13555516012536\n ],\n [\n -91.1865234375,\n 44.54350521320822\n ],\n [\n -90.966796875,\n 44.008620115415354\n ],\n [\n -90.59326171875,\n 43.37311218382002\n ],\n [\n -90.46142578125,\n 42.98857645832184\n ],\n [\n -90.04394531249999,\n 42.68243539838623\n ],\n [\n -89.84619140625,\n 42.24478535602799\n ],\n [\n -90.68115234375,\n 42.04929263868686\n ],\n [\n -91.69189453125,\n 42.58544425738491\n ],\n [\n -92.04345703125,\n 43.197167282501276\n ],\n [\n -93.40576171875,\n 43.11702412135048\n ],\n [\n -95.361328125,\n 42.98857645832184\n ],\n [\n -96.39404296875,\n 42.87596410238254\n ],\n [\n -96.5478515625,\n 42.68243539838623\n ],\n [\n -97.03125,\n 42.956422511073335\n ],\n [\n -97.18505859374999,\n 43.992814500489914\n ],\n [\n -97.8662109375,\n 45.460130637921004\n ],\n [\n -97.88818359375,\n 46.36209301204985\n ],\n [\n -98.10791015625,\n 47.517200697839414\n ],\n [\n -98.15185546874999,\n 48.73445537176822\n ],\n [\n -97.80029296875,\n 48.936934954094035\n ],\n [\n -97.27294921875,\n 49.023461463214126\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
http://mn.water.usgs.gov/
In November 2013, U.S. Geological Survey streamgaging equipment was installed at a historical water-quality station on the Duwamish River, Washington, within the tidal influence at river kilometer 16.7 (U.S. Geological Survey site 12113390; Duwamish River at Golf Course at Tukwila, WA). Publicly available, real-time continuous data includes river streamflow, stream velocity, and turbidity. Between November 2013 and March 2015, the U.S. Geological Survey collected representative samples of water, suspended sediment, or bed sediment from the streamgaging station during 28 periods of differing flow conditions. Samples were analyzed by Washington-State-accredited laboratories for a large suite of compounds, including metals, dioxins/furans, semivolatile compounds including polycyclic aromatic hydrocarbons, pesticides, butytins, polychlorinated biphenyl (PCB) Aroclors and the 209 PCB congeners, volatile organic compounds, hexavalent chromium, and total and dissolved organic carbon. Metals, PCB congeners, and dioxins/furans were frequently detected in unfiltered-water samples, and concentrations typically increased with increasing suspended-sediment concentrations. Chemical concentrations in suspendedsediment samples were variable between sampling periods. The highest concentrations of many chemicals in suspended sediment were measured during summer and early autumn storm periods.
\nMedian chemical concentrations in suspended-sediment samples were greater than median chemical concentrations in fine bed sediment (less than 62.5 µm) samples, which were greater than median chemical concentrations in paired bulk bed sediment (less than 2 mm) samples. Suspended-sediment concentration, sediment particle-size distribution, and general water-quality parameters were measured concurrent with the chemistry sampling. From this discrete data, combined with the continuous streamflow record, estimates of instantaneous sediment and chemical loads from the Green River to the Lower Duwamish Waterway were calculated. For most compounds, loads were higher during storms than during baseline conditions because of high streamflow and high chemical concentrations. The highest loads occurred during dam releases (periods when stored runoff from a prior storm is released from the Howard Hanson Dam into the upper Green River) because of the high river streamflow and high suspended-sediment concentration, even when chemical concentrations were lower than concentrations measured during storm events.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds973","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Conn, K.E., Black, R.W., Vanderpool-Kimura, A.M., Foreman, J.R., Peterson, N.T., Senter, C.A., and Sissel, S.K., 2015, Chemical concentrations and instantaneous loads, Green River to the Lower Duwamish Waterway near Seattle, Washington, 2013–15: U.S. Geological Survey Data Series 973, 46 p., https://dx.doi.org/10.3133/ds973.","productDescription":"Report: vii, 46 p.; Appendix","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-065963","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":312810,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0973/ds973.pdf","text":"Report","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 973 PDF"},{"id":312811,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0973/coverthb.jpg"},{"id":312812,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0973/ds973_appendixa.xlsx","text":"Appendix A","size":"846 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 973 Appendix A XLSX"}],"country":"United States","state":"Washington","otherGeospatial":"Green River, Lower Duwamish Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.4,\n 47.4\n ],\n [\n -122.4,\n 47.6\n ],\n [\n -122.2,\n 47.6\n ],\n [\n -122.2,\n 47.4\n ],\n [\n -122.4,\n 47.4\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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934 Broadway, Suite 300
Tacoma, Washington 98402
http://wa.water.usgs.gov
Federal agencies that oversee land management for much of the Snake Range in eastern Nevada, including the management of Great Basin National Park by the National Park Service, need to understand the potential extent of adverse effects to federally managed lands from nearby groundwater development. As a result, this study was developed (1) to attain a better understanding of aquifers controlling groundwater flow on the eastern side of the southern part of the Snake Range and their connection with aquifers in the valleys, (2) to evaluate the relation between surface water and groundwater along the piedmont slopes, (3) to evaluate sources for Big Springs and Rowland Spring, and (4) to assess groundwater flow from southern Spring Valley into northern Hamlin Valley. The study focused on two areas—the first, a northern area along the east side of Great Basin National Park that included Baker, Lehman, and Snake Creeks, and a second southern area that is the potential source area for Big Springs. Data collected specifically for this study included the following: (1) geologic field mapping; (2) drilling, testing, and water quality sampling from 7 test wells; (3) measuring discharge and water chemistry of selected creeks and springs; (4) measuring streambed hydraulic gradients and seepage rates from 18 shallow piezometers installed into the creeks; and (5) monitoring stream temperature along selected reaches to identify places of groundwater inflow.
\nThe Snake Range was formed by a generally normal-faulted uplift, where late Proterozoic and Cambrian siliciclastic rocks and metamorphic rocks are present at the highest altitudes and younger Paleozoic carbonate rocks are exposed along the flanks. The consolidated rocks are intruded by Jurassic to Tertiary age plutons, which are most common between the Lehman and Snake Creek drainage basins. Older Cenozoic rocks, including Oligocene volcanic rocks and Miocene sedimentary rocks, crop out locally and fill the basins that underlie Snake, Spring, and Hamlin Valleys. Younger Tertiary and Quaternary sedimentary (basin-fill) deposits overlie the older Cenozoic rocks.
\nThe rocks and deposits can be divided into three distinct aquifers. These aquifers include (1) basin-fill aquifers that consist of the permeable parts of the Cenozoic basin fill and some fractured or jointed Cenozoic volcanic rocks, (2) an upper carbonate-rock aquifer that consists of upper Paleozoic carbonate rocks overlying a regionally extensive middle Paleozoic siliciclastic confining unit, and (3) a lower carbonate-rock aquifer that consists of lower Paleozoic carbonate rocks. Secondary openings created by faults, shear zones, fractures, and, in the carbonate rocks, karst solution features, largely determine the water-transmitting properties of the volcanic- and carbonate-rock aquifers. The basin-fill aquifers are composed of a wide variety of rock types and have highly variable hydraulic properties. The three aquifers are stratigraphically and structurally heterogeneous, causing large variations in the ability to store and transmit water. The aquifers are separated by confining units in some areas and are in contact with each other in other areas, yet function as a single, composite aquifer system. Basin-fill aquifers most often overlie or adjoin the lower and upper carbonate-rock aquifers.
\nBaker, Lehman and Snake Creek drainage basins were divided into five hydrologic zones on the basis of climate, geology, and topography. The five zones, from highest to lowest altitudes, are the mountain-upland, karst-limestone, upper-piedmont, lower-piedmont, and valley-lowland zones. The primary hydrologic connection between the mountain-upland and the valley-lowland zones is streamflow. Much of the streamflow from the mountain-upland zone is generated above tree line.
\nGroundwater flow increases in the karst-limestone zone because of increased permeability caused by dissolution, which results in increased streamflow losses. Most of the increased groundwater flow is to springs near faults that form the boundary with the upper-piedmont zone. Thus, groundwater flow from the karst-limestone zone to the upper-piedmont zone was only 10 percent of the combined flow of streams and springs that exit the karst-limestone zone. About 60 percent of the water flowing from Rowland Spring in the Lehman Creek drainage basin was from streamflow losses along Baker Creek. The remaining flow from Rowland Spring comes from local recharge in the karst-limestone zone.
\nIn the upper-piedmont zone, the water table by Baker, Lehman and Snake Creeks was near the water level in the creeks for several hundred feet downstream from the karst-limestone zone. Water levels in piezometers along Snake Creek downstream from its confluence with Spring Creek were far below the streambed, indicating gravity drainage beneath this section of the creek. Estimated vertical hydraulic conductivity along a 3-mile reach of Snake Creek downstream of this confluence was 0.5 foot per day, which was an order of magnitude less than that estimated for Baker and Lehman Creeks. The low vertical hydraulic conductivity in the streambed along the lower reaches of Snake Creek results from chemical precipitation of calcite caused by off-gassing of carbon dioxide derived from springs at the end of the karst-limestone zone.
\nThe younger alluvial deposits thicken rapidly across faults that form the upper boundary of the lower-piedmont zone. The absence of springs or groundwater flow to the creeks upstream of these faults indicates they are not a complete barrier to groundwater flow. The water table was shallow in the valley-lowland zone in the Baker and Lehman Creek drainage basins, whereas the water table was more than 50 feet below land surface in the Snake Creek drainage basin. In contrast to thick basin fill in the valley-lowland zone in the Baker and Lehman Creek drainage basins, fractured and karst limestone underlie basin fill at relatively shallow depths in Snake Creek drainage basin. The underlying limestone acts as a drain for groundwater in the basin fill beneath Snake Creek.
\nA groundwater divide in southern Spring Valley south of Baking Powder Flat separates groundwater flow to the flat from southeastward flow into northern Hamlin Valley. Groundwater flow from southern Spring Valley south of the groundwater divide into northern Hamlin Valley was estimated to range from 6,000 to 11,000 acre-feet per year. This groundwater does not flow to Big Springs in southern Snake Valley; rather, the source of water to Big Springs is groundwater recharge in the Big Spring Wash drainage basin and in nearby smaller drainage basins at the south end of the Snake Range.
\nGroundwater flow from southern Spring Valley continues through the western side of Hamlin Valley before being directed northeast toward the south end of Snake Valley. This flow is constrained by southward-flowing groundwater from Big Spring Wash and northward-flowing groundwater beneath central Hamlin Valley. The redirection to the northeast corresponds to a narrowing of the width of flow in southern Snake Valley caused by a constriction formed by a steeply dipping middle Paleozoic siliciclastic confining unit exposed in the flanks of the mountains and hills on the east side of southern Snake Valley and shallowly buried beneath basin fill in the valley. The narrowing of groundwater flow could be responsible for the large area where groundwater flows to springs or is lost to evapotranspiration between Big Springs in Nevada and Pruess Lake in Utah.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1819","collaboration":"Prepared in cooperation with the National Park Service, Bureau of Land Management, U.S. Fish and Wildlife Service, and U.S. Forest Service","usgsCitation":"Prudic, D.E, Sweetkind, D.S., Jackson, T.R., Dotson, K.E., Plume, R.W., Hatch, C.E., and Halford, K.J. 2015, Evaluating connection of aquifers to springs and streams, Great Basin National Park and vicinity, Nevada: U.S. Geological Survey Professional Paper 1819, 188 p., https://dx.doi.org/10.3133/pp1819.","productDescription":"Report: xxii, 187 p.; Appendixes 1-16","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-034324","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":312322,"rank":16,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1819/pp1819_appendix14.zip","text":"Appendix 14","size":"4.3 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8"},{"id":312320,"rank":14,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1819/pp1819_appendix12.zip","text":"Appendix 12","size":"31 KB","linkFileType":{"id":6,"text":"zip"},"description":"PP 1819 Appendix 12"},{"id":312317,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1819/pp1819_appendix9.zip","text":"Appendix 9","size":"14 KB","linkFileType":{"id":6,"text":"zip"},"description":"PP 1819 Appendix 9"},{"id":312318,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1819/pp1819_appendix10.zip","text":"Appendix 10","size":"24 KB","linkFileType":{"id":6,"text":"zip"},"description":"PP 1819 Appendix 10"},{"id":312319,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1819/pp1819_appendix11.zip","text":"Appendix 11","size":"33 KB","linkFileType":{"id":6,"text":"zip"},"description":"PP 1819 Appendix 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A daily precipitation-runoff model was developed to estimate spatially and temporally distributed recharge for groundwater basins in the San Gorgonio Pass area, southern California. The recharge estimates are needed to define transient boundary conditions for a groundwater-flow model being developed to evaluate the effects of pumping and climate on the long-term availability of groundwater. The area defined for estimating recharge is referred to as the San Gorgonio Pass watershed model (SGPWM) and includes three watersheds: San Timoteo Creek, Potrero Creek, and San Gorgonio River. The SGPWM was developed by using the U.S. Geological Survey INFILtration version 3.0 (INFILv3) model code used in previous studies of recharge in the southern California region, including the San Gorgonio Pass area. The SGPWM uses a 150-meter gridded discretization of the area of interest in order to account for spatial variability in climate and watershed characteristics. The high degree of spatial variability in climate and watershed characteristics in the San Gorgonio Pass area is caused, in part, by the high relief and rugged topography of the area.
\nDaily climate data developed from a network of monitoring sites and published average monthly precipitation maps were used to develop the climate inputs for the SGPWM. Geographic Information System (GIS) data defining land surface altitude, vegetation, soils, surficial geology, and land cover were used to define input parameters representing the physical characteristics of the land surface, root zone, and shallow subsurface underlying the root zone. Model parameterization was based on a previous INFILv3 model developed for an area including the upper parts of the San Timoteo Creek and Potrero Creek drainages and the western part of the San Gorgonio River watershed. The previous INFILv3 model was calibrated by using available streamflow records from the model area. The SGPWM uses an updated INFILv3 version to represent shallow groundwater flow better beneath the root zone that contributes to lateral, downslope seepage rather than deep recharge. The SGPWM calibration was tested by using available streamflow records in the San Gorgonio Pass region.
\nThe SGPWM was used to simulate a 100-year water budget, including recharge and runoff, for water years 1913 through 2012. Results indicated that most recharge came from episodic infiltration of surface-water runoff in the larger stream channels. Results also indicated periods of great variability in recharge and runoff in response to variability in precipitation. More recharge was simulated for the area of the groundwater basin underlying the more permeable alluvial fill of the valley floor compared to recharge in the neighboring upland areas of the less permeable mountain blocks. The greater recharge was in response to the episodic streamflow that discharged from the mountain block areas and quickly infiltrated the permeable alluvial fill of the groundwater basin. Although precipitation at the higher altitudes of the mountain block was more than double precipitation at the lower altitudes of the valley floor, recharge for inter-channel areas of the mountain block was limited by the lower permeability bedrock underlying the thin soil cover, and most of the recharge in the mountain block was limited to the main stream channels underlain by alluvial fill.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155122","collaboration":"Prepared in cooperation with the San Gorgonio Pass Water Agency","usgsCitation":"Hevesi, J.A., and Christensen, A.H., 2015, Estimating natural recharge in San Gorgonio Pass watersheds, California, 1913–2012: U.S. Geological Survey Scientific Investigations Report 2015–5122, 74 p. https://dx.doi.org/10.3133/ SIR20155122.","productDescription":"xii, 74 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-054946","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":312622,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5122/sir20155122.pdf","text":"Report","size":"29.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5122"},{"id":312621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5122/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Gorgonio Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.301025390625,\n 33.43831750748322\n ],\n [\n -116.05682373046875,\n 33.43831750748322\n ],\n [\n -116.05682373046875,\n 34.19135773925218\n ],\n [\n -117.301025390625,\n 34.19135773925218\n ],\n [\n -117.301025390625,\n 33.43831750748322\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
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