For wetland restoration success to be maximized, restoration managers need better information regarding how the frequency, depth, and duration of flooding affect soil chemistry and the survival, growth, and morphology of targeted plant species. In a greenhouse study we investigated the impact of four different flooding durations (0 %, 40 %, 60 %, and 100 %) on soil physicochemistry and the responses of seedlings and adults of two species of emergent wetland macrophytes commonly used in restoration efforts (Schoenoplectus acutus and Schoenoplectus californicus). The longest flooding duration, which created more reducing soil conditions, resulted in significantly reduced survival of S. acutus adults (34 ± 21 % survival) and complete mortality of seedlings of both species. Schoenoplectus californicus adults exhibited higher flooding tolerance, showing little impact of flooding on morphology and physiology. A companion field study indicated that S. californicus maintained stem strength regardless of flooding duration or depth, supporting the greenhouse study results. This information serves to improve our understanding of the ecological differences between these species as well as provide restoration managers with better guidelines for targeted elevation and hydrologic regimes for these species in order to enhance the success of restoration plantings and better predict restoration site development.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-015-0713-8","usgsCitation":"Sloey, T.M., Howard, R.J., and Hester, M.W., 2016, Response of Schoenoplectus acutus and Schoenoplectus californicus at different life-history stages to hydrologic regime: Wetlands, v. 36, no. 1, p. 37-46, https://doi.org/10.1007/s13157-015-0713-8.","productDescription":"10 p.","startPage":"37","endPage":"46","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065149","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":310765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-28","publicationStatus":"PW","scienceBaseUri":"56333586e4b048076347eea3","contributors":{"authors":[{"text":"Sloey, Taylor M","contributorId":149516,"corporation":false,"usgs":false,"family":"Sloey","given":"Taylor","email":"","middleInitial":"M","affiliations":[{"id":17763,"text":"University of Louisiana, Lafayette","active":true,"usgs":false}],"preferred":false,"id":578667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howard, Rebecca J. 0000-0001-7264-4364 howardr@usgs.gov","orcid":"https://orcid.org/0000-0001-7264-4364","contributorId":2429,"corporation":false,"usgs":true,"family":"Howard","given":"Rebecca","email":"howardr@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":578666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hester, Mark W.","contributorId":9566,"corporation":false,"usgs":true,"family":"Hester","given":"Mark","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":578668,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70157593,"text":"70157593 - 2016 - Human activities cause distinct dissolved organic matter composition across freshwater ecosystems","interactions":[],"lastModifiedDate":"2016-02-01T13:16:17","indexId":"70157593","displayToPublicDate":"2015-09-29T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Human activities cause distinct dissolved organic matter composition across freshwater ecosystems","docAbstract":"Dissolved organic matter (DOM) composition in freshwater ecosystems is influenced by interactions between physical, chemical, and biological processes that are controlled, at one level, by watershed landscape, hydrology, and their connections. Against this environmental template, humans may strongly influence DOM composition. Yet, we lack a comprehensive understanding of DOM composition variation across freshwater ecosystems differentially affected by human activity. Using optical properties, we described DOM variation across five ecosystem groups of the Laurentian Great Lakes Region: large lakes, Kawartha Lakes, Experimental Lakes Area, urban stormwater ponds, and rivers (n = 184 sites). We determined how between ecosystem variation in DOM composition related to watershed size, land use and cover, water quality measures (conductivity, dissolved organic carbon (DOC), nutrient concentration, chlorophyll a), and human population density. The five freshwater ecosystem groups had distinctive DOM composition from each other. These significant differences were not explained completely through differences in watershed size nor spatial autocorrelation. Instead, multivariate partial least squares regression showed that DOM composition was related to differences in human impact across freshwater ecosystems. In particular, urban/developed watersheds with higher human population densities had a unique DOM composition with a clear anthropogenic influence that was distinct from DOM composition in natural land cover and/or agricultural watersheds. This nonagricultural, human developed impact on aquatic DOM was most evident through increased levels of a microbial, humic-like parallel factor analysis component (C6). Lotic and lentic ecosystems with low human population densities had DOM compositions more typical of clear water to humic-rich freshwater ecosystems but C6 was only present at trace to background levels. Consequently, humans are strongly altering the quality of DOM in waters nearby or flowing through highly populated areas, which may alter carbon cycles in anthropogenically disturbed ecosystems at broad scales.
","language":"English","publisher":"John Wiley & Sons Ltd.","doi":"10.1111/gcb.13094","usgsCitation":"Williams, C.J., Frost, P.C., Morales-Williams, A.M., Larson, J.H., Richardson, W.B., Chiandet, A.S., and Xenopoulos, M.A., 2016, Human activities cause distinct dissolved organic matter composition across freshwater ecosystems: Global Change Biology, v. 22, no. 2, p. 613-626, https://doi.org/10.1111/gcb.13094.","productDescription":"14 p.","startPage":"613","endPage":"626","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064700","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":308689,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-11","publicationStatus":"PW","scienceBaseUri":"560ba83de4b058f706e53a7f","contributors":{"authors":[{"text":"Williams, Clayton J.","contributorId":138631,"corporation":false,"usgs":false,"family":"Williams","given":"Clayton","email":"","middleInitial":"J.","affiliations":[{"id":12468,"text":"Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA","active":true,"usgs":false}],"preferred":false,"id":573702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frost, Paul C.","contributorId":138628,"corporation":false,"usgs":false,"family":"Frost","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12467,"text":"Department of Biology, Trent University, Peterborough, ON CA","active":true,"usgs":false}],"preferred":false,"id":573703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morales-Williams, Ana M.","contributorId":148057,"corporation":false,"usgs":false,"family":"Morales-Williams","given":"Ana","email":"","middleInitial":"M.","affiliations":[{"id":16985,"text":"Trent University & Iowa State University","active":true,"usgs":false}],"preferred":false,"id":573704,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":573701,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richardson, William B. 0000-0002-7471-4394 wrichardson@usgs.gov","orcid":"https://orcid.org/0000-0002-7471-4394","contributorId":3277,"corporation":false,"usgs":true,"family":"Richardson","given":"William","email":"wrichardson@usgs.gov","middleInitial":"B.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":573705,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chiandet, Aisha S.","contributorId":148058,"corporation":false,"usgs":false,"family":"Chiandet","given":"Aisha","email":"","middleInitial":"S.","affiliations":[{"id":16986,"text":"Severn Sound Environmental Association","active":true,"usgs":false}],"preferred":false,"id":573706,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xenopoulos, Marguerite A.","contributorId":138629,"corporation":false,"usgs":false,"family":"Xenopoulos","given":"Marguerite","email":"","middleInitial":"A.","affiliations":[{"id":12467,"text":"Department of Biology, Trent University, Peterborough, ON CA","active":true,"usgs":false}],"preferred":false,"id":573707,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70159326,"text":"70159326 - 2016 - Prediction of plant vulnerability to salinity increase in a coastal ecosystem by stable isotopic composition (δ18O) of plant stem water: a model study","interactions":[],"lastModifiedDate":"2016-08-25T08:32:50","indexId":"70159326","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Prediction of plant vulnerability to salinity increase in a coastal ecosystem by stable isotopic composition (δ18O) of plant stem water: a model study","docAbstract":"Sea level rise and the subsequent intrusion of saline seawater can result in an increase in soil salinity, and potentially cause coastal salinity-intolerant vegetation (for example, hardwood hammocks or pines) to be replaced by salinity-tolerant vegetation (for example, mangroves or salt marshes). Although the vegetation shifts can be easily monitored by satellite imagery, it is hard to predict a particular area or even a particular tree that is vulnerable to such a shift. To find an appropriate indicator for the potential vegetation shift, we incorporated stable isotope 18O abundance as a tracer in various hydrologic components (for example, vadose zone, water table) in a previously published model describing ecosystem shifts between hammock and mangrove communities in southern Florida. Our simulations showed that (1) there was a linear relationship between salinity and the δ18O value in the water table, whereas this relationship was curvilinear in the vadose zone; (2) hammock trees with higher probability of being replaced by mangroves had higher δ18O values of plant stem water, and this difference could be detected 2 years before the trees reached a tipping point, beyond which future replacement became certain; and (3) individuals that were eventually replaced by mangroves from the hammock tree population with a 50% replacement probability had higher stem water δ18O values 3 years before their replacement became certain compared to those from the same population which were not replaced. Overall, these simulation results suggest that it is promising to track the yearly δ18O values of plant stem water in hammock forests to predict impending salinity stress and mortality.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-015-9916-3","usgsCitation":"Zhai, L., Jiang, J., DeAngelis, D.L., and Sternberg, L.D., 2016, Prediction of plant vulnerability to salinity increase in a coastal ecosystem by stable isotopic composition (δ18O) of plant stem water: a model study: Ecosystems, v. 19, no. 1, p. 32-49, https://doi.org/10.1007/s10021-015-9916-3.","productDescription":"18 p.","startPage":"32","endPage":"49","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068438","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":310336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-21","publicationStatus":"PW","scienceBaseUri":"562a08e5e4b011227bf1fdbd","contributors":{"authors":[{"text":"Zhai, Lu","contributorId":147395,"corporation":false,"usgs":false,"family":"Zhai","given":"Lu","affiliations":[{"id":16839,"text":"Department of Biology, University of Miami, Coral Gables, Florida","active":true,"usgs":false}],"preferred":false,"id":578048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jiang, Jiang","contributorId":46838,"corporation":false,"usgs":true,"family":"Jiang","given":"Jiang","affiliations":[],"preferred":false,"id":578049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":578016,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sternberg, Leonel d.S.L","contributorId":67051,"corporation":false,"usgs":true,"family":"Sternberg","given":"Leonel","email":"","middleInitial":"d.S.L","affiliations":[],"preferred":false,"id":578050,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70156836,"text":"70156836 - 2016 - Detecting significant change in stream benthic macroinvertebrate communities in wilderness areas","interactions":[],"lastModifiedDate":"2017-12-01T13:16:49","indexId":"70156836","displayToPublicDate":"2015-08-31T12:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Detecting significant change in stream benthic macroinvertebrate communities in wilderness areas","docAbstract":"A major challenge in the biological monitoring of stream ecosystems in protected wilderness areas is discerning whether temporal changes in community structure are significantly outside of a reference condition that represents natural or acceptable annual variation in population cycles. Otherwise sites could erroneously be classified as impaired. Long-term datasets are essential for understanding these trends, to ascertain whether any changes in community structure significantly beyond the reference condition are permanent shifts or with time move back to within previous limits. To this end, we searched for long-term (>8 years) quantitative data sets of macroinvertebrate communities in wadeable rivers collected by similar methods and time of year in protected wilderness areas with minimal anthropogenic disturbance. Four geographic areas with datasets that met these criteria in the USA were identified, namely: McLaughlin Nature Reserve in California (1 stream), Great Smoky Mountains National Park in Tennesse-North Carolina (14 streams), Wind River Wilderness Areas in Wyoming (3 streams) and Denali National Park and Preserve in Alaska (6 streams).
\nTwo statistical approaches were applied: Taxonomic Distinctness (TD) to describe changes in diversity over time and non-metric multidimensional scaling (MDS) to describe changes over time in community persistence (Jaccards Index) and community stability (Bray–Curtis Index). Control charts were used to determine if years in MDS plots were significantly outside a reference condition. For Hunting Creek, TD showed three years outside natural variation which could be attributed to severe hydrological events but years outside the natural-variation funnel at sites in other geographical areas were inconsistent and could not be explained by environmental variables. TD identified simulated severe pollutant events which caused the removal of entire invertebrate assemblages but not simulated water temperature shifts.
\nWithin a region, both MDS analyses typically identified similar years as exceeding reference condition variation, illustrating the utility of the approach for identifying wider spatial scale effects that influence more than one stream. MDS responded to both simulated water temperature stress and a pollutant event, and generally outlying years on MDS plots could be explained by environmental variables, particularly higher precipitation. Multivariate control charts successfully identified whether shifts in community structure identified by MDS were significant and whether the shift represented a press disturbance (long-term change) or a pulse disturbance. We consider a combination of TD and MDS with control charts to be a potentially powerful tool for determining years significantly outside of a reference condition variation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2015.07.025","usgsCitation":"Milner, A.M., Woodward, A., Freilich, J.E., Black, R.W., and Resh, V.H., 2016, Detecting significant change in stream benthic macroinvertebrate communities in wilderness areas: Ecological Indicators, v. 60, p. 524-537, https://doi.org/10.1016/j.ecolind.2015.07.025.","productDescription":"14 p.","startPage":"524","endPage":"537","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052838","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":307725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, California, North Carolina, Tennesse, Wyoming","otherGeospatial":"Denali National Park and Preserve, Great Smoky Mountains National Park, McLaughlin Nature Reserve, Wind River Wilderness Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.40966796874999,\n 39.027718840211605\n ],\n [\n -122.40966796874999,\n 40.027614437486655\n ],\n [\n -121.26708984374999,\n 40.027614437486655\n ],\n [\n -121.26708984374999,\n 39.027718840211605\n ],\n [\n -122.40966796874999,\n 39.027718840211605\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.19287109375,\n 42.76314586689494\n ],\n [\n -110.19287109375,\n 43.8503744993026\n ],\n [\n -108.52294921875,\n 43.8503744993026\n ],\n [\n -108.52294921875,\n 42.76314586689494\n ],\n [\n -110.19287109375,\n 42.76314586689494\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.87451171875,\n 62.21163423606344\n ],\n [\n -153.87451171875,\n 64.52954819942195\n ],\n [\n -148.18359375,\n 64.52954819942195\n ],\n [\n -148.18359375,\n 62.21163423606344\n ],\n [\n -153.87451171875,\n 62.21163423606344\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.9794921875,\n 35.31736632923788\n ],\n [\n -83.9794921875,\n 36.13787471840729\n ],\n [\n -82.8369140625,\n 36.13787471840729\n ],\n [\n -82.8369140625,\n 35.31736632923788\n ],\n [\n -83.9794921875,\n 35.31736632923788\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"60","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55e56ca2e4b05561fa20866c","contributors":{"authors":[{"text":"Milner, Alexander M.","contributorId":90341,"corporation":false,"usgs":true,"family":"Milner","given":"Alexander","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":570771,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodward, Andrea 0000-0003-0604-9115 awoodward@usgs.gov","orcid":"https://orcid.org/0000-0003-0604-9115","contributorId":3028,"corporation":false,"usgs":true,"family":"Woodward","given":"Andrea","email":"awoodward@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":570770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freilich, Jerome E.","contributorId":147210,"corporation":false,"usgs":false,"family":"Freilich","given":"Jerome","email":"","middleInitial":"E.","affiliations":[{"id":12587,"text":"Olympic National Park, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":570772,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Black, Robert W. 0000-0002-4748-8213 rwblack@usgs.gov","orcid":"https://orcid.org/0000-0002-4748-8213","contributorId":1820,"corporation":false,"usgs":true,"family":"Black","given":"Robert","email":"rwblack@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":570773,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Resh, Vincent H.","contributorId":12169,"corporation":false,"usgs":true,"family":"Resh","given":"Vincent","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":570774,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70158933,"text":"70158933 - 2016 - The Upper Mississippi River floodscape: spatial patterns of flood inundation and associated plant community distributions","interactions":[],"lastModifiedDate":"2015-12-21T13:32:03","indexId":"70158933","displayToPublicDate":"2015-08-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":849,"text":"Applied Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"The Upper Mississippi River floodscape: spatial patterns of flood inundation and associated plant community distributions","docAbstract":"Questions How is the distribution of different plant communities associated with patterns of flood inundation across a large floodplain landscape? Location Thirty-eight thousand nine hundred and seventy hectare of floodplain, spanning 320 km of the Upper Mississippi River (UMR). Methods High-resolution elevation data (Lidar) and 30 yr of daily river stage data were integrated to produce a ‘floodscape’ map of growing season flood inundation duration. The distributions of 16 different remotely sensed plant communities were quantified along the gradient of flood duration. Results Models fitted to the cumulative frequency of occurrence of different vegetation types as a function of flood duration showed that most types exist along a continuum of flood-related occurrence. The diversity of community types was greatest at high elevations (0–10 d of flooding), where both upland and lowland community types were found, as well as at very low elevations (70–180 d of flooding), where a variety of lowland herbaceous communities were found. Intermediate elevations (20–60 d of flooding) tended to be dominated by floodplain forest and had the lowest diversity of community types. Conclusions Although variation in flood inundation is often considered to be the main driver of spatial patterns in floodplain plant communities, few studies have quantified flood–vegetation relationships at broad scales. Our results can be used to identify targets for restoration of historical hydrological regimes or better anticipate hydro-ecological effects of climate change at broad scales.
","language":"English","publisher":"International Association for Vegetation Science","doi":"10.1111/avsc.12189","usgsCitation":"De Jager, N.R., Rohweder, J.J., Yin, Y., and Hoy, E.E., 2016, The Upper Mississippi River floodscape: spatial patterns of flood inundation and associated plant community distributions: Applied Vegetation Science, v. 19, no. 1, p. 164-172, https://doi.org/10.1111/avsc.12189.","productDescription":"9 p.","startPage":"164","endPage":"172","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064126","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":309735,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1111/avsc.12189/abstract"},{"id":309799,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi River","volume":"19","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-18","publicationStatus":"PW","scienceBaseUri":"5618e535e4b0cdb063e3fef0","contributors":{"authors":[{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":576943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rohweder, Jason J. jrohweder@usgs.gov","contributorId":460,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":576944,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yin, Yao yyin@usgs.gov","contributorId":2170,"corporation":false,"usgs":true,"family":"Yin","given":"Yao","email":"yyin@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":576945,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hoy, Erin E. 0000-0002-2853-3242 ehoy@usgs.gov","orcid":"https://orcid.org/0000-0002-2853-3242","contributorId":4523,"corporation":false,"usgs":true,"family":"Hoy","given":"Erin","email":"ehoy@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":576946,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70150308,"text":"70150308 - 2016 - Corn stover harvest increases herbicide movement to subsurface drains: RZWQM simulations","interactions":[],"lastModifiedDate":"2016-04-28T12:51:11","indexId":"70150308","displayToPublicDate":"2015-07-31T12:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3035,"text":"Pest Management Science","active":true,"publicationSubtype":{"id":10}},"title":"Corn stover harvest increases herbicide movement to subsurface drains: RZWQM simulations","docAbstract":"Crop residue removal for bioenergy production can alter soil hydrologic properties and the movement of agrochemicals to subsurface drains. The Root Zone Water Quality Model (RZWQM), previously calibrated using measured flow and atrazine concentrations in drainage from a 0.4 ha chisel-tilled plot, was used to investigate effects of 50 and 100% corn (Zea mays L.) stover harvest and the accompanying reductions in soil crust hydraulic conductivity and total macroporosity on transport of atrazine, metolachlor, and metolachlor oxanilic acid (OXA).
\nThe model accurately simulated field-measured metolachlor transport in drainage. A 3-yr simulation indicated that 50% residue removal decreased subsurface drainage by 31% and increased atrazine and metolachlor transport in drainage 4 to 5-fold when surface crust conductivity and macroporosity were reduced by 25%. Based on its measured sorption coefficient, ~ 2-fold reductions in OXA losses were simulated with residue removal.
\nRZWQM indicated that if corn stover harvest reduces crust conductivity and soil macroporosity, losses of atrazine and metolachlor in subsurface drainage will increase due to reduced sorption related to more water moving through fewer macropores. Losses of the metolachlor degradation product OXA will decrease due to the more rapid movement of the parent compound into the soil.
\nMajor challenges exist in delineating bedrock fracture zones because these cause abrupt changes in geological and hydrogeological properties over small distances. Borehole observations cannot sufficiently capture heterogeneity in these systems. Geophysical techniques offer the potential to image properties and processes in between boreholes. We used three-dimensional cross borehole electrical resistivity tomography (ERT) in a 9 m (diameter) × 15 m well field to capture high-resolution flow and transport processes in a fractured mudstone contaminated by chlorinated solvents, primarily trichloroethylene. Conductive (sodium bromide) and resistive (deionized water) injections were monitored in seven boreholes. Electrode arrays with isolation packers and fluid sampling ports were designed to enable acquisition of ERT measurements during pulsed tracer injections. Fracture zone locations and hydraulic pathways inferred from hydraulic head drawdown data were compared with electrical conductivity distributions from ERT measurements. Static ERT imaging has limited resolution to decipher individual fractures; however, these images showed alternating conductive and resistive zones, consistent with alternating laminated and massive mudstone units at the site. Tracer evolution and migration was clearly revealed in time-lapse ERT images and supported by in situ borehole vertical apparent conductivity profiles collected during the pulsed tracer test. While water samples provided important local information at the extraction borehole, ERT delineated tracer migration over spatial scales capturing the primary hydrogeological heterogeneity controlling flow and transport. The fate of these tracer injections at this scale could not have been quantified using borehole logging and/or borehole sampling methods alone.
","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.12356","usgsCitation":"Robinson, J., Slater, L., Johnson, T.B., Shapiro, A.M., Tiedeman, C.R., Ntlargiannis, D., Johnson, C.D., Day-Lewis, F.D., Lacombe, P., Imbrigiotta, T.E., and Lane, J.W., 2016, Imaging pathways in fractured rock using three-dimensional electrical resistivity tomography: Groundwater, v. 54, no. 2, p. 186-201, https://doi.org/10.1111/gwat.12356.","productDescription":"16 p.","startPage":"186","endPage":"201","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065453","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":314125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","city":"West Trenton","otherGeospatial":"Naval Air Warfare Center (NAWC)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.9,\n 40.2\n ],\n [\n -74.9,\n 40.3\n ],\n [\n -74.8,\n 40.3\n ],\n [\n -74.8,\n 40.2\n ],\n [\n -74.9,\n 40.2\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-14","publicationStatus":"PW","scienceBaseUri":"5694e045e4b039675d005e27","contributors":{"authors":[{"text":"Robinson, Judith","contributorId":152111,"corporation":false,"usgs":false,"family":"Robinson","given":"Judith","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":587973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slater, Lee","contributorId":55707,"corporation":false,"usgs":false,"family":"Slater","given":"Lee","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":587974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Timothy B.","contributorId":49753,"corporation":false,"usgs":false,"family":"Johnson","given":"Timothy","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":587975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - 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Jr. jwlane@usgs.gov","contributorId":1738,"corporation":false,"usgs":true,"family":"Lane","given":"John","suffix":"Jr.","email":"jwlane@usgs.gov","middleInitial":"W.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":false,"id":587982,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70145073,"text":"70145073 - 2016 - Hydrologic response of streams restored with check dams in the Chiricahua Mountains, Arizona","interactions":[],"lastModifiedDate":"2016-04-21T10:41:30","indexId":"70145073","displayToPublicDate":"2015-04-03T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic response of streams restored with check dams in the Chiricahua Mountains, Arizona","docAbstract":"In this study, hydrological processes are evaluated to determine impacts of stream restoration in the West Turkey Creek, Chiricahua Mountains, southeast Arizona, during a summer-monsoon season (June–October of 2013). A paired-watershed approach was used to analyze the effectiveness of check dams to mitigate high flows and impact long-term maintenance of hydrologic function. One watershed had been extensively altered by the installation of numerous small check dams over the past 30 years, and the other was untreated (control). We modified and installed a new stream-gauging mechanism developed for remote areas, to compare the water balance and calculate rainfall–runoff ratios. Results show that even 30 years after installation, most of the check dams were still functional. The watershed treated with check dams has a lower runoff response to precipitation compared with the untreated, most notably in measurements of peak flow. Concerns that downstream flows would be reduced in the treated watershed, due to storage of water behind upstream check dams, were not realized; instead, flow volumes were actually higher overall in the treated stream, even though peak flows were dampened. We surmise that check dams are a useful management tool for reducing flow velocities associated with erosion and degradation and posit they can increase baseflow in aridlands.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.2895","usgsCitation":"Norman, L.M., Brinkerhoff, F.C., Gwilliam, E., Guertin, D.P., Callegary, J.B., Goodrich, D.C., Nagler, P.L., and Gray, F., 2016, Hydrologic response of streams restored with check dams in the Chiricahua Mountains, Arizona: River Research and Applications, v. 32, no. 4, p. 519-527, https://doi.org/10.1002/rra.2895.","productDescription":"9 p.","startPage":"519","endPage":"527","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054961","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471472,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.2895","text":"Publisher Index Page"},{"id":299331,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Chiricahua Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.500732421875,\n 31.549282377352668\n ],\n [\n -109.500732421875,\n 32.14771106595571\n ],\n [\n -109.0997314453125,\n 32.14771106595571\n ],\n [\n -109.0997314453125,\n 31.549282377352668\n ],\n [\n -109.500732421875,\n 31.549282377352668\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-21","publicationStatus":"PW","scienceBaseUri":"551fab98e4b027f0aee3bae8","chorus":{"doi":"10.1002/rra.2895","url":"http://dx.doi.org/10.1002/rra.2895","publisher":"Wiley-Blackwell","authors":"Norman L. 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It is formed where the confluence of California’s two largest rivers (the Sacramento and San Joaquin) meet the ocean tides and has a significant physical gradient from fluvial to tidal. It is a semidiurnal system (two high and two low tides per day). Today, the Delta is one of the most manipulated in the United States. Once composed of many shallow, meandering and braided dendritic channels and dead-end sloughs and wetlands, it is now a network of leveed canals moving clear water around subsided islands. It historically has supported a biologically diverse tidal wetland complex, of which only 3% remains today (Whipple et al., 2012). It has also witnessed a collapse in the native fish populations. The Delta provides critical habitat for native species, however the hydrology and water quality are complicated by manipulations and diversions to satisfy multiple statewide objectives. Today water managers face co-equal goals of water supply to Californians and maintenance of ecosystem health and function. The Delta is a hub for both a multi-hundred-million dollar agricultural industry and a massive north-to-south water delivery system, supplying the primary source of freshwater to Central Valley farmers and drinking water for two-thirds of California’s population. Large pump facilities support the water demand and draw water from the Delta, further altering circulation patterns and redirecting the net flow toward the export facilities (Monsen et al., 2007). Fluvial sedimentation, along with organic accumulation, creates and sustains the Delta landscape. Hydraulic mining for gold in the watershed during the late 1800s delivered an especially large sediment pulse to the Delta. More recently, from 1955 to the present, a significant sediment decline has been observed that is thought to have been caused mostly by the construction of water storage reservoirs that trap the upstream sediment supply (Wright and Schoellhamer, 2004). Today, one concern is whether the volume of sediment supplied from the upper watershed is sufficient to support ecological function and sustain the Delta landscape and ecosystem in the face of climate change, sea level rise, and proposed restoration associated with the Bay Delta Conservation Plan (http://baydeltaconservationplan.com). Ecosystem health is a management focus and 150,000 acres of restoration is currently proposed, therefore it is of increasingly important to understand the quantity of sediment available for marsh and wetland restoration throughout the Bay Delta Estuary. It is also important to understand the pathways for sediment transport and the sediment budget into each of three Delta regions (figure 1) to guide restoration planning, modeling, and management.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Proceedings of the Joint Federal Interagency Conference","conferenceTitle":"Federal Interagency Sedimentation Conference, 10th","conferenceDate":"April 2015","conferenceLocation":"Reno, Nevada","language":"English","publisher":"ACWI Subcommittee on Sedimentation","usgsCitation":"Morgan, T., and Wright, S., 2016, Sediment budgets, transport, and depositional trends in a large tidal delta, in Proceedings of the Joint Federal Interagency Conference, Reno, Nevada, April 2015, p. 893-904.","productDescription":"12 p.","startPage":"893","endPage":"904","ipdsId":"IP-062201","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":332342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":332094,"type":{"id":15,"text":"Index Page"},"url":"https://acwi.gov/sos/pubs/3rdJFIC/Contents/5C-Morgan-King.pdf"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.54998779296874,\n 38.59326051987162\n ],\n [\n -121.63238525390626,\n 38.49229419236133\n ],\n [\n -121.71478271484375,\n 38.14967752360809\n ],\n [\n -121.84112548828125,\n 38.112949789189614\n ],\n [\n -122.00317382812499,\n 38.18422791820727\n ],\n [\n -122.1514892578125,\n 38.14751758025121\n ],\n [\n -122.15698242187499,\n 38.06539235133249\n ],\n [\n -121.76696777343749,\n 37.96368875328558\n ],\n [\n -121.60491943359375,\n 37.80761398306056\n ],\n [\n -121.47857666015625,\n 37.72293542866175\n ],\n [\n -121.23962402343749,\n 37.783740105227224\n ],\n [\n -121.36322021484374,\n 38.17559185481662\n ],\n [\n -121.48956298828124,\n 38.54816542304656\n ],\n [\n -121.54998779296874,\n 38.59326051987162\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"585a51bfe4b01224f329b5ef","contributors":{"authors":[{"text":"Morgan, Tara 0000-0001-5632-5232 tamorgan@usgs.gov","orcid":"https://orcid.org/0000-0001-5632-5232","contributorId":177451,"corporation":false,"usgs":true,"family":"Morgan","given":"Tara","email":"tamorgan@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655854,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wright, Scott 0000-0002-0387-5713 sawright@usgs.gov","orcid":"https://orcid.org/0000-0002-0387-5713","contributorId":1536,"corporation":false,"usgs":true,"family":"Wright","given":"Scott","email":"sawright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655855,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70139559,"text":"70139559 - 2016 - Arsenic cycling in hydrocarbon plumes: secondary effects of natural attenuation","interactions":[],"lastModifiedDate":"2018-08-07T12:22:49","indexId":"70139559","displayToPublicDate":"2015-01-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1866,"text":"Groundwater Monitoring & Remediation","active":true,"publicationSubtype":{"id":10}},"title":"Arsenic cycling in hydrocarbon plumes: secondary effects of natural attenuation","docAbstract":"Monitored natural attenuation is widely applied as a remediation strategy at hydrocarbon spill sites. Natural attenuation relies on biodegradation of hydrocarbons coupled with reduction of electron acceptors, including solid phase ferric iron (Fe(III)). Because arsenic (As) adsorbs to Fe-hydroxides, a potential secondary effect of natural attenuation of hydrocarbons coupled with Fe(III) reduction is a release of naturally occurring As to groundwater. At a crude-oil-contaminated aquifer near Bemidji, Minnesota, anaerobic biodegradation of hydrocarbons coupled to Fe(III) reduction has been well documented. We collected groundwater samples at the site annually from 2009 to 2013 to examine if As is released to groundwater and, if so, to document relationships between As and Fe inside and outside of the dissolved hydrocarbon plume. Arsenic concentrations in groundwater in the plume reached 230 µg/L, whereas groundwater outside the plume contained less than 5 µg/L As. Combined with previous data from the Bemidji site, our results suggest that (1) naturally occurring As is associated with Fe-hydroxides present in the glacially derived aquifer sediments; (2) introduction of hydrocarbons results in reduction of Fe-hydroxides, releasing As and Fe to groundwater; (3) at the leading edge of the plume, As and Fe are removed from groundwater and retained on sediments; and (4) downgradient from the plume, patterns of As and Fe in groundwater are similar to background. We develop a conceptual model of secondary As release due to natural attenuation of hydrocarbons that can be applied to other sites where an influx of biodegradable organic carbon promotes Fe(III) reduction.
","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.12316","usgsCitation":"Cozzarelli, I.M., Schreiber, M.E., Erickson, M., and Ziegler, B.A., 2016, Arsenic cycling in hydrocarbon plumes: secondary effects of natural attenuation: Groundwater Monitoring & Remediation, v. 54, no. 1, p. 35-45, https://doi.org/10.1111/gwat.12316.","productDescription":"11 p.","startPage":"35","endPage":"45","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060529","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":297603,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","city":"Bemidji","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.12443542480469,\n 47.559268133942446\n ],\n [\n -95.12443542480469,\n 47.58463133843904\n ],\n [\n -95.06916046142578,\n 47.58463133843904\n ],\n [\n -95.06916046142578,\n 47.559268133942446\n ],\n [\n -95.12443542480469,\n 47.559268133942446\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-01-21","publicationStatus":"PW","scienceBaseUri":"54dd2a56e4b08de9379b2fef","chorus":{"doi":"10.1111/gwat.12316","url":"http://dx.doi.org/10.1111/gwat.12316","publisher":"Wiley-Blackwell","authors":"Cozzarelli Isabelle M., Schreiber Madeline E., Erickson Melinda L., Ziegler Brady A.","journalName":"Groundwater","publicationDate":"1/21/2015","auditedOn":"2/24/2015"},"contributors":{"authors":[{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":539445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schreiber, Madeline E.","contributorId":138959,"corporation":false,"usgs":false,"family":"Schreiber","given":"Madeline","email":"","middleInitial":"E.","affiliations":[{"id":12594,"text":"Department of Geosciences, Virginia Tech, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":539446,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erickson, Melinda L. 0000-0002-1117-2866 merickso@usgs.gov","orcid":"https://orcid.org/0000-0002-1117-2866","contributorId":3671,"corporation":false,"usgs":true,"family":"Erickson","given":"Melinda L.","email":"merickso@usgs.gov","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":539447,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ziegler, Brady A.","contributorId":138960,"corporation":false,"usgs":false,"family":"Ziegler","given":"Brady","email":"","middleInitial":"A.","affiliations":[{"id":12594,"text":"Department of Geosciences, Virginia Tech, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":539448,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70154886,"text":"70154886 - 2016 - Natural flow regimes of the Ozark-Ouachita Interior Highlands region","interactions":[],"lastModifiedDate":"2016-01-11T10:40:54","indexId":"70154886","displayToPublicDate":"2014-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Natural flow regimes of the Ozark-Ouachita Interior Highlands region","docAbstract":"Natural flow regimes represent the hydrologic conditions to which native aquatic organisms are best adapted. We completed a regional river classification and quantitative descriptions of each natural flow regime for the Ozark–Ouachita Interior Highlands region of Arkansas, Missouri and Oklahoma. On the basis of daily flow records from 64 reference streams, seven natural flow regimes were identified with mixture model cluster analysis: Groundwater Stable, Groundwater, Groundwater Flashy, Perennial Runoff, Runoff Flashy, Intermittent Runoff and Intermittent Flashy. Sets of flow metrics were selected that best quantified nine ecologically important components of these natural flow regimes. An uncertainty analysis was performed to avoid selecting metrics strongly affected by measurement uncertainty that can result from short periods of record. Measurement uncertainties (bias, precision and accuracy) were assessed for 170 commonly used flow metrics. The ranges of variability expected for select flow metrics under natural conditions were quantified for each flow regime to provide a reference for future assessments of hydrologic alteration. A random forest model was used to predict the natural flow regimes of all stream segments in the study area based on climate and catchment characteristics, and a map was produced. The geographic distribution of flow regimes suggested distinct ecohydrological regions that may be useful for conservation planning. This project provides a hydrologic foundation for future examination of flow–ecology relationships in the region. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.2838","usgsCitation":"Leasure, D.R., Magoulick, D.D., and Longing, S., 2016, Natural flow regimes of the Ozark-Ouachita Interior Highlands region: River Research and Applications, v. 32, no. 1, p. 18-35, https://doi.org/10.1002/rra.2838.","productDescription":"18 p.","startPage":"18","endPage":"35","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052517","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":305764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.16455078125,\n 33.08233672856376\n ],\n [\n -91.16455078125,\n 33.742612777346885\n ],\n [\n -90.50537109375,\n 34.79576153473033\n ],\n [\n -89.80224609374999,\n 35.97800618085568\n ],\n [\n -90.3076171875,\n 36.01356058518153\n ],\n [\n -90.087890625,\n 36.35052700542763\n ],\n [\n -90.19775390625,\n 36.491973470593685\n ],\n [\n -90.72509765625,\n 36.491973470593685\n ],\n [\n -90.4833984375,\n 37.19533058280065\n ],\n [\n -90.24169921875,\n 37.33522435930641\n ],\n [\n -90.3955078125,\n 37.82280243352756\n ],\n [\n -90.3515625,\n 38.22091976683121\n ],\n [\n -90.63720703125,\n 38.35888785866677\n ],\n [\n -91.42822265625,\n 38.39333888832238\n ],\n [\n -92.57080078125,\n 38.28993659801203\n ],\n [\n -92.8125,\n 38.496593518947556\n ],\n [\n -93.22998046875,\n 38.51378825951165\n ],\n [\n -93.515625,\n 38.34165619279593\n ],\n [\n -93.93310546875,\n 38.03078569382294\n ],\n [\n -94.0869140625,\n 37.61423141542417\n ],\n [\n -94.0869140625,\n 37.33522435930641\n ],\n [\n -94.6142578125,\n 37.317751851636906\n ],\n [\n -94.8779296875,\n 37.00255267215955\n ],\n [\n -95.11962890625,\n 36.61552763134925\n ],\n [\n -95.2734375,\n 36.31512514748051\n ],\n [\n -95.47119140625,\n 36.12012758978146\n ],\n [\n -95.69091796875,\n 35.97800618085568\n ],\n [\n -95.80078125,\n 35.67514743608467\n ],\n [\n -95.49316406249999,\n 35.28150065789119\n ],\n [\n -95.51513671875,\n 34.95799531086792\n ],\n [\n -95.91064453125,\n 34.867904962568716\n ],\n [\n -96.48193359375,\n 34.70549341022544\n ],\n [\n -96.48193359375,\n 34.397844946449865\n ],\n [\n -95.888671875,\n 34.17999758688084\n ],\n [\n -94.482421875,\n 34.14363482031264\n ],\n [\n -94.46044921875,\n 33.5963189611327\n ],\n [\n -94.10888671875,\n 33.54139466898275\n ],\n [\n -94.04296874999999,\n 32.99023555965106\n ],\n [\n -91.16455078125,\n 33.08233672856376\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2014-11-11","publicationStatus":"PW","scienceBaseUri":"55a78438e4b0183d66e45e92","contributors":{"authors":[{"text":"Leasure, Douglas R.","contributorId":145643,"corporation":false,"usgs":false,"family":"Leasure","given":"Douglas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":564872,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":564313,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Longing, S. D.","contributorId":145644,"corporation":false,"usgs":false,"family":"Longing","given":"S. 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We used 25 boreholes, 15 static cone-penetration tests, and 22 shallow-seismic profiles to define the geometry of basal- and lateral-slip surfaces; and 9 multitemporal maps to quantify the spatial and temporal distribution of normal faults, thrust faults, back-tilted surfaces, strike-slip faults, flank ridges, folds, ponds, and springs. We infer that the slip surface is a repeating series of steeply sloping surfaces (risers) and gently sloping surfaces (treads). Stretching of earth-flow material created normal faults at risers, and shortening of earth-flow material created thrust faults, back-tilted surfaces, and ponds at treads. Individual pairs of risers and treads formed quasi-discrete kinematic zones within the earth flow that operated in unison to transmit pulses of sediment along the length of the flow. The locations of strike-slip faults, flank ridges, and folds were not controlled by basal-slip surface topography but were instead dependent on earth-flow volume and lateral changes in the direction of the earth-flow travel path. The earth-flow travel path was strongly influenced by inactive earth-flow deposits and pre-earth-flow drainages whose positions were determined by tectonic structures. The implications of our results that may be applicable to other earth flows are that structures with strikes normal to the direction of earth-flow motion (e.g., normal faults and thrust faults) can be used as a guide to the geometry of basal-slip surfaces, but that depths to the slip surface (i.e., the thickness of an earth flow) will vary as sediment pulses are transmitted through a flow.","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2014.04.039","usgsCitation":"Guerriero, L., Coe, J.A., Revellio, P., Grelle, G., Pinto, F., and Guadagno, F., 2016, Influence of slip-surface geometry on earth-flow deformation, Montaguto earth flow, southern Italy: Geomorphology, v. 219, p. 285-305, https://doi.org/10.1016/j.geomorph.2014.04.039.","productDescription":"21 p. 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2016 - Integrated water flow model and modflow-farm process: A comparison of theory, approaches, and features of two integrated hydrologic models","interactions":[],"lastModifiedDate":"2016-12-19T16:50:28","indexId":"70178484","displayToPublicDate":"2011-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5239,"text":"California Natural Resources Agency","active":true,"publicationSubtype":{"id":2}},"title":"Integrated water flow model and modflow-farm process: A comparison of theory, approaches, and features of two integrated hydrologic models","docAbstract":"Effective modeling of conjunctive use of surface and subsurface water resources requires simulation of land use-based root zone and surface flow processes as well as groundwater flows, streamflows, and their interactions. Recently, two computer models developed for this purpose, the Integrated Water Flow Model (IWFM) from the California Department of Water Resources and the MODFLOW with Farm Process (MF-FMP) from the US Geological Survey, have been applied to complex basins such as the Central Valley of California. As both IWFM and MFFMP are publicly available for download and can be applied to other basins, there is a need to objectively compare the main approaches and features used in both models. This paper compares the concepts, as well as the method and simulation features of each hydrologic model pertaining to groundwater, surface water, and landscape processes. The comparison is focused on the integrated simulation of water demand and supply, water use, and the flow between coupled hydrologic processes. The differences in the capabilities and features of these two models could affect the outcome and types of water resource problems that can be simulated.","language":"English","publisher":"California Department of Water Resources","collaboration":"California Department of Water Resources","usgsCitation":"Dogrul, E.C., Schmid, W., Hanson, R.T., Kadir, T., and Chung, F., 2016, Integrated water flow model and modflow-farm process: A comparison of theory, approaches, and features of two integrated hydrologic models: California Natural Resources Agency, i-ix, 70 p. .","productDescription":"i-ix, 70 p. ","ipdsId":"IP-014776","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":332299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":331154,"type":{"id":15,"text":"Index Page"},"url":"https://baydeltaoffice.water.ca.gov/modeling/hydrology/IWFM/Publications/downloadables/Reports/IWFM%20and%20MF-FMP%20TIR-1%20(DWR-USGS%20Nov2011).pdf"}],"publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58590009e4b03639a6025e2f","contributors":{"authors":[{"text":"Dogrul, Emin C.","contributorId":177560,"corporation":false,"usgs":false,"family":"Dogrul","given":"Emin","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":656220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmid, Wolfgang","contributorId":84020,"corporation":false,"usgs":false,"family":"Schmid","given":"Wolfgang","affiliations":[{"id":13040,"text":"Department of Hydrology and Water Resources, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":656221,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanson, Randall T. 0000-0002-9819-7141 rthanson@usgs.gov","orcid":"https://orcid.org/0000-0002-9819-7141","contributorId":801,"corporation":false,"usgs":true,"family":"Hanson","given":"Randall","email":"rthanson@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":656222,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kadir, Tariq","contributorId":26208,"corporation":false,"usgs":true,"family":"Kadir","given":"Tariq","email":"","affiliations":[],"preferred":false,"id":656223,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chung, Francis","contributorId":54488,"corporation":false,"usgs":true,"family":"Chung","given":"Francis","email":"","affiliations":[],"preferred":false,"id":656224,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70141846,"text":"ofr20151021 - 2015 - GIS-Based Identification of Areas with Mineral Resource Potential for Six Selected Deposit Groups, Bureau of Land Management Central Yukon Planning Area, Alaska","interactions":[],"lastModifiedDate":"2020-01-15T07:25:06","indexId":"ofr20151021","displayToPublicDate":"2020-01-15T08:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1021","displayTitle":"GIS-based identification of areas with mineral resource potential for six selected deposit groups, Bureau of Land Management Central Yukon Planning Area, Alaska","title":"GIS-Based Identification of Areas with Mineral Resource Potential for Six Selected Deposit Groups, Bureau of Land Management Central Yukon Planning Area, Alaska","docAbstract":"This study, covering the Bureau of Land Management (BLM) Central Yukon Planning Area (CYPA), Alaska, was prepared to aid BLM mineral resource management planning. Estimated mineral resource potential and certainty are mapped for six selected mineral deposit groups: (1) rare earth element (REE) deposits associated with peralkaline to carbonatitic intrusive igneous rocks, (2) placer and paleoplacer gold, (3) platinum group element (PGE) deposits associated with mafic and ultramafic intrusive igneous rocks, (4) carbonate-hosted copper deposits, (5) sandstone uranium deposits, and (6) tin-tungsten-molybdenum-fluorspar deposits associated with specialized granites. These six deposit groups include most of the strategic and critical elements of greatest interest in current exploration.
\nThis study has used a data-driven, geographic information system (GIS)-based method for evaluating the mineral resource potential across the large region of the CYPA. This method systematically and simultaneously analyzes geoscience data from multiple geospatially referenced datasets and uses individual subwatersheds (12-digit hydrologic unit codes or HUCs) as the spatial unit of classification. The final map output indicates an estimated potential (high, medium, low) for a given mineral deposit group and indicates the certainty (high, medium, low) of that estimate for any given subwatershed (HUC). Accompanying tables describe the data layers used in each analysis, the values assigned for specific analysis parameters, and the relative weighting of each data layer that contributes to the estimated potential and certainty determinations. Core datasets used include the U.S. Geological Survey (USGS) Alaska Geochemical Database (AGDB2), the Alaska Division of Geologic and Geophysical Surveys Web-based geochemical database, data from an anticipated USGS geologic map of Alaska, and the USGS Alaska Resource Data File. Map plates accompanying this report illustrate the mineral prospectivity for the six deposit groups across the CYPA and estimates of mineral resource potential. There are numerous areas, some of them large, rated with high potential for one or more of the selected deposit groups within the CYPA.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151021","collaboration":"Prepared in cooperation with the Alaska Division of Geological and Geophysical Surveys","usgsCitation":"Jones, J.V., III, Karl, S.M., Labay, K.A., Shew, N.B., Granitto, M., Hayes, T.S., Mauk, J.L., Schmidt, J.M., Todd, E., Wang, B., Werdon, M.B., and Yager, D.B., 2015, GIS-based identification of areas with mineral resource potential for six selected deposit groups, Bureau of Land Management Central Yukon Planning Area, Alaska: U.S. Geological Survey Open-File Report 2015–1021, 78 p., 5 appendixes, 12 pls., https://dx.doi.org/10.3133/ofr20151021.","productDescription":"Report: vii, 78 p.; 12 Plates: 11 inches x 16.3 inches; Metadata; 5 Appendices","numberOfPages":"86","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-056688","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":371212,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_appxD.xlsx","text":"Appendix D","size":"17.2 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":" - Lithology keyword search terms for an anticipated U.S. Geological Survey geologic map of Alaska."},{"id":371213,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_appxE.zip","text":"Appendix E","size":"411 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":" - Scoring results for HUC analysis of selected deposit groups (folder containing Excel spreadsheet and geospatial data files)"},{"id":371214,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021.zip","text":"Complete data package","size":"451 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":" - A single ZIP file that contains the report, appendixes, metadata and plates."},{"id":371215,"rank":10,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_meta.txt","size":"83.3 KB","linkFileType":{"id":2,"text":"txt"}},{"id":371211,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_appxC.xlsx","text":"Appendix C","size":"38.6 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":" - Alaska Resource Data File (ARDF) mineral deposit keyword and scoring templates."},{"id":371191,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1021/coverthb3.jpg"},{"id":371192,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021.pdf","text":"Report","size":"2.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1021"},{"id":371210,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_appxB.pdf","text":"Appendix B","size":"138 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":" - Igneous rock geochemistry peer-reviewed literature sources."},{"id":371208,"rank":11,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_metadatafaq.pdf","text":"Metadata FAQ","size":"243 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":371209,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_appxE.zip","text":"Appendix A","size":"28.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":" - Stream-sediment geochemistry summary statistics and percentile cutoffs."},{"id":371207,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2015/1021/ofr20151021_plates.pdf","text":"Plates 1-12","size":"39.0 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":" - A single PDF file that contains the 12 plates not included in the report."}],"country":"United States","state":"Alaska","otherGeospatial":"Central Yukon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -165.05859375,\n 62.431074232920906\n ],\n [\n -165.05859375,\n 71.1877539181316\n ],\n [\n -141.328125,\n 71.1877539181316\n ],\n [\n -141.328125,\n 62.431074232920906\n ],\n [\n -165.05859375,\n 62.431074232920906\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Science Center
The effects of concentrated animal feeding operations (CAFOs) on water quality were investigated at 54 agricultural stream sites throughout the North Carolina Coastal Plain during 2012 and 2013. Three general watershed land-use types were examined during the study, including 18 background watersheds with no active CAFOs (BK sites), 18 watersheds with one or more active swine CAFOs but no poultry CAFOs (SW sites), and 18 watersheds with at least one active swine CAFO and one active dry-litter poultry CAFO (SP sites). The watershed drainage areas for these 54 stream sites ranged from 1.2 to 17.5 square miles. Conventional fertilizers used for crop production are the primary source of nutrients at the BK sites. Animal-waste manures represent an additional source of nutrients at the SW and SP study sites.
\nLand cover, soil drainage, and CAFO attributes were compiled for each watershed. Water-quality field measurements were made and samples were collected at the 54 primary sites during 6 bimonthly sampling periods from June 2012 to April 2013. An additional 23 secondary sites were sampled once during April 2013 to provide supplemental data at stream locations directly adjacent or in close proximity to swine CAFOs and (or) background agricultural areas within 9 of the primary watersheds. The watershed drainage areas for the 23 secondary sites ranged from 0.2 to 8.9 square miles. Water temperature, specific conductance, dissolved-oxygen concentration, and pH were measured directly in the streams. Water samples were analyzed for major ions, nutrients, and stable isotopes, including delta hydrogen-2 (δ2H) and delta oxygen-18 (δ18O) of water and delta nitrogen-15 (δ15N) and δ18O of dissolved nitrate plus nitrite.
\nMost of the water-quality properties and constituents varied significantly among the six sampling periods, changing both seasonally and in response to hydrologic conditions. The differences noted among the sampling periods indicate that the interactions between seasonal climatic differences, streamflow conditions, and instream biotic and abiotic processes are complex and their integrated effects can have varying degrees of influence on individual nutrients.
\nWater-quality differences were noted for the SW and SP land-use groups relative to the BK group. Median values of specific conductance, several major ions (magnesium, sodium, potassium, and chloride), and nitrogen fractions (ammonia plus organic nitrogen, ammonia, nitrate plus nitrite, total nitrogen, and δ15N of nitrate plus nitrite) were higher for the SW and SP groups compared to the BK group. No significant differences in water temperature, dissolved oxygen, calcium, total organic nitrogen, orthophosphate, total phosphorus, or δ18O of nitrate plus nitrite were noted among the land-use groups. When compared on the basis of land-use type, there was an overall measurable effect of CAFO waste manures on stream water quality for the SW and SP watershed groups.
\nSome individual sites within the SW and SP groups showed no measurable CAFO effects on water quality despite having CAFOs present upstream. An evaluation of sodium plus potassium concentrations coupled with δ15N values of nitrate plus nitrite proved valuable for distinguishing which SW and SP sites had a water-quality signature indicative of CAFO waste manures. Sites with CAFO manure effects were characterized by higher sodium plus potassium concentrations (commonly between 11 and 33 milligrams per liter) and δ15N values of nitrate plus nitrite (commonly between 11 and 26 parts per thousand) relative to sites reflecting background agricultural conditions, which commonly had sodium plus potassium concentrations between 6 and 14 milligrams per liter and δ15N values of nitrate plus nitrite between 6 and 15 parts per thousand. On the basis of the results of this study, land applications of waste manure at swine CAFOs influenced ion and nutrient chemistry in many of the North Carolina Coastal Plain streams that were studied.
\nA classification tree model was developed to examine relations of watershed environmental attributes among the study sites with and without CAFO manure effects. Model results indicated that variations in swine barn density, percentage of wetlands, and total acres available for applying swine-waste manures had an important influence on those watersheds where CAFO effects on water quality were either evident or mitigated. Measurable effects of CAFO waste manures on stream water quality were most evident in those SW and SP watersheds having lower percentages of wetlands combined with higher swine barn densities and (or) higher total acres available for applying waste manure at the swine CAFOs. Stream water quality was similar to background agricultural conditions in SW and SP watersheds with lower swine barn densities coupled with higher percentages of wetlands or lower acres available for swine manure applications. The model provides a useful tool for exploring and identifying similar, unmonitored watersheds in the North Carolina Coastal Plain with potential CAFO manure influences on water quality that might warrant further examination.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155080","collaboration":"Prepared in cooperation with the North Carolina Department of Environment and Natural Resources, Division of Water Resources","usgsCitation":"Harden, S.L., 2015, Surface-water quality in agricultural watersheds of the North Carolina Coastal Plain associated with concentrated animal feeding operations: U.S. Geological Survey Scientific Investigations Report 2015-5080, Report: ix, 55 p.; 7 Appendices, https://doi.org/10.3133/sir20155080.","productDescription":"Report: ix, 55 p.; 7 Appendices","numberOfPages":"70","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2012-01-02","temporalEnd":"2013-12-31","ipdsId":"IP-060201","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Poultry CAFO attribute data by facility"},{"id":366495,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_3.xlsx","text":"Appendix A3-3","size":"37 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Active swine CAFO permits in sites"},{"id":366496,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_4.xlsx","text":"Appendix A3-4","size":"24 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Swine CAFO attribute data by permit"},{"id":366499,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_7.xlsx","text":"Appendix A3-7","size":"12 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Poultry CAFO attribute data by study site"},{"id":366500,"rank":14,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA4/sir20155080_appendixA4_2.xlsx","text":"Appendix A4-2","size":"13 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled water-quality data for precipitation samples"},{"id":366506,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA3/sir20155080_appendixA3_5.xlsx","text":"Appendix A3-5","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Summary of swine CAFO attribute data by watershed study site"},{"id":366501,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA4/sir20155080_appendixA4_3.xlsx","text":"Appendix A4-3","size":"14 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Results of field 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samples"},{"id":301481,"rank":19,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA6/sir20155080_appendixA6.xlsx","text":"Appendix A6","size":"26 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Data used for distinguishing sites with manure influences"},{"id":301480,"rank":17,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA5/sir2015-5080_appendixA5.pdf","text":"Appendix A5","size":"11.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Detailed Evaluations of the April 2013 Water-Quality Dataset"},{"id":301477,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5080/downloads/sir2015-5080_appendixA2/sir20155080_appendixA2_1.xlsx","text":"Appendix A2-1","size":"28 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Compiled land-cover data for each study 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In this study, we recover archival records and apply a rating curve approach to develop the first instrumental estimates of daily delta inflow and sediment loads to San Francisco Bay (1849–1929). The total sediment load is constrained using sedimentation/erosion estimated from bathymetric survey data to produce continuous daily sediment transport estimates from 1849 to 1955, the time period prior to sediment load measurements. We estimate that ∼55% (45–75%) of the ∼1500 ± 400 million tons (Mt) of sediment delivered to the estuary between 1849 and 2011 was the result of anthropogenic alteration in the watershed that increased sediment supply. Also, the seasonal timing of sediment flux events has shifted because significant spring-melt floods have decreased, causing estimated springtime transport (April 1st to June 30th) to decrease from ∼25% to ∼15% of the annual total. By contrast, wintertime sediment loads (December 1st to March 31st) have increased from ∼70% to ∼80%. A ∼35% reduction of annual flow since the 19th century along with decreased sediment supply has resulted in a ∼50% reduction in annual sediment delivery. The methods developed in this study can be applied to other systems for which unanalyzed historic data exist.","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.jhydrol.2015.08.043","usgsCitation":"Moftakhari, H., Jay, D., Talke, S., and Schoellhamer, D., 2015, Estimation of historic flows and sediment loads to San Francisco Bay,1849–2011: Journal of Hydrology, v. 529, no. 3, p. 1247-1261, https://doi.org/10.1016/j.jhydrol.2015.08.043.","productDescription":"5 p.","startPage":"1247","endPage":"1261","numberOfPages":"5","ipdsId":"IP-061977","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":471491,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2015.08.043","text":"Publisher Index Page"},{"id":347672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","otherGeospatial":"San Francisco Bay area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.70629882812499,\n 38.06539235133249\n ],\n [\n -122.56072998046875,\n 38.25543637637947\n ],\n [\n -122.40142822265625,\n 38.28346905497185\n ],\n [\n -121.78344726562499,\n 38.35027253825765\n ],\n [\n -121.37420654296875,\n 38.31795595794451\n ],\n [\n -121.22589111328126,\n 37.93553306183642\n ],\n [\n -121.25335693359374,\n 37.572882155556194\n ],\n [\n -121.44012451171874,\n 37.40289194122376\n ],\n [\n -121.761474609375,\n 37.21720611325497\n ],\n [\n -122.20642089843749,\n 37.17126017626408\n ],\n [\n -122.47283935546874,\n 37.199706196161735\n ],\n [\n -122.70629882812499,\n 38.06539235133249\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"529","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7c657e4b0bc0bec09c90f","contributors":{"authors":[{"text":"Moftakhari, H.R.","contributorId":174830,"corporation":false,"usgs":false,"family":"Moftakhari","given":"H.R.","email":"","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":649393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jay, D.A.","contributorId":174832,"corporation":false,"usgs":false,"family":"Jay","given":"D.A.","email":"","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":649395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talke, S.A.","contributorId":174831,"corporation":false,"usgs":false,"family":"Talke","given":"S.A.","email":"","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":649394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schoellhamer, David H. 0000-0001-9488-7340 dschoell@usgs.gov","orcid":"https://orcid.org/0000-0001-9488-7340","contributorId":631,"corporation":false,"usgs":true,"family":"Schoellhamer","given":"David H.","email":"dschoell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":649392,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70161471,"text":"sir20155190 - 2015 - Flood-inundation maps for the Schoharie Creek at Prattsville, New York, 2014","interactions":[],"lastModifiedDate":"2016-02-22T08:46:30","indexId":"sir20155190","displayToPublicDate":"2016-02-18T15:15:00","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":"2015-5190","title":"Flood-inundation maps for the Schoharie Creek at Prattsville, New York, 2014","docAbstract":"Digital flood-inundation maps for a 2.6-mile reach of the Schoharie Creek at Prattsville, New York, were created by the U.S. Geological Survey (USGS) in cooperation with the New York State Department of Environmental Conservation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Schoharie Creek at Prattsville (station number 01350000). Near-real-time stages at this streamgage may be obtained online from the USGS National Water Information System (http://waterdata.usgs.gov/) or the National Weather Service Advanced Hydrologic Prediction Service (http://water.weather.gov/ahps/), which also forecasts flood hydrographs at this site. National Weather Service-forecasted peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas and depths of flood inundation.
\nFlood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated by using the stage-discharge relation (rating 82.0) at the Schoharie Creek at Prattsville streamgage (station 01350000) and high-water marks from the flood of August 28, 2011. The hydraulic model was then used to compute 17 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to greater than the highest recorded water level at the streamgage. The simulated water-surface profiles were then combined with a geographic information system digital elevation model, derived from light detection and ranging (lidar) data having a 0.61-foot vertical root-mean squared error and 6.6-foot horizontal resolution, in order to delineate the area flooded at each water level.
\nThese flood-inundation maps, along with near-real-time stage data from USGS streamgages and forecasted stage data from the National Weather Service, can provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155190","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Nystrom, E.A., 2016, Flood-inundation maps for the Schoharie Creek at Prattsville, New York, 2014: U.S. Geological Survey Scientific Investigations Report 2015–5190, 12 p., 17 sheets, https://dx.doi.org/10.3133/sir20155190.","productDescription":"Report: vii, 15 p.; 17 Sheets: 17.00 x 22.00 inches or smaller; Application Sites; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067775","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":316401,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2015/5190/downloads/sir20155190_sheet10-prattsville-stage18.pdf","text":"Sheet10—stage of 18.0 feet","size":"6.11 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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
In Arctic ecosystems, freshwater fish migrate seasonally between productive shallow water habitats that freeze in winter and deep overwinter refuge in rivers and lakes. How these movements relate to seasonal hydrology is not well understood. We used passive integrated transponder tags and stream wide antennae to track 1035 Arctic grayling in Crea Creek, a seasonally flowing beaded stream on the Arctic Coastal Plain, Alaska. Migration of juvenile and adult fish into Crea Creek peaked in June immediately after ice break-up in the stream. Fish that entered the stream during periods of high flow and cold stream temperature traveled farther upstream than those entering during periods of lower flow and warmer temperature. We used generalized linear models to relate migration of adult and juvenile fish out of Crea Creek to hydrology. Most adults migrated in late June – early July, and there was best support (Akaike weight = 0.46; w i ) for a model indicating that the rate of migration increased with decreasing discharge. Juvenile migration occurred in two peaks; the early peak consisted of larger juveniles and coincided with adult migration, while the later peak occurred shortly before freeze-up in September and included smaller juveniles. A model that included discharge, minimum stream temperature, year, season, and mean size of potential migrants was most strongly supported (w i = 0.86). Juvenile migration rate increased sharply as daily minimum stream temperature decreased, suggesting fish respond to impending freeze-up. We found fish movements to be intimately tied to the strong seasonality of discharge and temperature, and demonstrate the importance of small stream connectivity for migratory Arctic grayling during the entire open-water period. The ongoing and anticipated effects of climate change and petroleum development on Arctic hydrology (e.g. reduced stream connectivity, earlier peak flows, increased evapotranspiration) have important implications for Arctic freshwater ecosystems.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-015-0453-x","usgsCitation":"Heim, K.C., Wipfli, M.S., Whitman, M.S., Arp, C.D., Adams, J., and Falke, J.A., 2015, Seasonal cues of Arctic grayling movement in a small Arctic stream: the importance of surface water connectivity: Environmental Biology of Fishes, v. 99, no. 1, p. 49-65, https://doi.org/10.1007/s10641-015-0453-x.","productDescription":"17 p.","startPage":"49","endPage":"65","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060031","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":318109,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"99","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-26","publicationStatus":"PW","scienceBaseUri":"56c599ace4b0946c6521edf6","contributors":{"authors":[{"text":"Heim, Kurt C.","contributorId":138832,"corporation":false,"usgs":false,"family":"Heim","given":"Kurt","email":"","middleInitial":"C.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":620695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":619796,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitman, Matthew S.","contributorId":67961,"corporation":false,"usgs":false,"family":"Whitman","given":"Matthew","email":"","middleInitial":"S.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":620696,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":620697,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Jeff","contributorId":167002,"corporation":false,"usgs":false,"family":"Adams","given":"Jeff","email":"","affiliations":[],"preferred":false,"id":620698,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":620699,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70160003,"text":"sir20155177 - 2015 - Transport and deposition of asbestos-rich sediment in the Sumas River, Whatcom County, Washington","interactions":[],"lastModifiedDate":"2016-06-23T15:03:46","indexId":"sir20155177","displayToPublicDate":"2016-02-08T01:15:00","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":"2015-5177","title":"Transport and deposition of asbestos-rich sediment in the Sumas River, Whatcom County, Washington","docAbstract":"Heavy sediment loads in the Sumas River of Whatcom County, Washington, increase seasonal turbidity and cause locally acute sedimentation. Most sediment in the Sumas River is derived from a deep-seated landslide of serpentinite that is located on Sumas Mountain and drained by Swift Creek, a tributary to the Sumas River. This mafic sediment contains high amounts of naturally occurring asbestiform chrysotile. A known human-health hazard, asbestiform chrysotile comprises 0.25–37 percent, by mass, of the total suspended sediment sampled from the Sumas River as part of this study, which included part of water year 2011 and all of water years 2012 and 2013. The suspended-sediment load in the Sumas River at South Pass Road, 0.6 kilometers (km) downstream of the confluence with Swift Creek, was 22,000 tonnes (t) in water year 2012 and 49,000 t in water year 2013. The suspended‑sediment load at Telegraph Road, 18.8 km downstream of the Swift Creek confluence, was 22,000 t in water year 2012 and 27,000 t in water year 2013. Although hydrologic conditions during the study were wetter than normal overall, the 2-year flood peak was only modestly exceeded in water years 2011 and 2013; runoff‑driven geomorphic disturbance to the watershed, which might have involved mass wasting from the landslide, seemed unexceptional. In water year 2012, flood peaks were modest, and the annual streamflow was normal. The fact that suspended-sediment loads in water year 2012 were equivalent at sites 0.6 and 18.8 km downstream of the sediment source indicates that the conservation of suspended‑sediment load can occur under normal hydrologic conditions. The substantial decrease in suspended-sediment load in the downstream direction in water year 2013 was attributed to either sedimentation in the intervening river reach, transfer to bedload as an alternate mode of sediment transport, or both.
The sediment in the Sumas River is distinct from sediment in most other river systems because of the large percentage of asbestiform chrysotile in suspension. The suspended sediment carried by the Sumas River consists of three major components: (1) a relatively dense, largely non-flocculated material that settles rapidly out of suspension; (2) a lighter component containing relatively high proportions of flocculated material, much of it composed of asbestiform chrysotile; and (3) individual chrysotile fibers that are too small to flocculate or settle out, and remain in suspension as wash load (these fibers are on the order of microns in length and tenths of microns in diameter). Whereas the bulk density of the first (heaviest) component of suspended sediment was between 1.5 and 1.6 grams per cubic centimeter (g/cm3), the bulk density of the flocculated material was an order of magnitude lower (0.16 g/cm3), even after 24 hours of settling. Soon after immersion in water, the fresh chrysotile fibers derived from the Swift Creek landslide seem to flocculate readily into large bundles, or floccules, that exhibit settling velocities characteristic of coarse silts and fine sands (30 and 250 micrometers). In quiescent water within this river system, the floccules settle out quickly, but still leave between 2.4 and 19.5 million chrysotile fibers per liter in the clear overlying water. Consistent with the results from previous laboratory research, the amounts of asbestiform chrysotile in the water column in Swift Creek, as well as in the Sumas River close to and downstream of its confluence with Swift Creek, were determined to be directly correlated with pH. This observation offers a possible alternative to either turbidity or suspended‑sediment concentration as a surrogate for the concentration of fresh asbestiform chrysotile in suspension.
Continued movement and associated erosion of the landslide through mass wasting and runoff will maintain large sediment loads in Swift Creek and in the Sumas River for the foreseeable future. Given the present channel morphology of the river system, aggradation (that is, sediment accumulation) in Swift Creek and the Sumas River are also likely to continue.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155177","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Curran, C.A., Anderson, S.W., Barbash, J.E., Magirl, C.S., Cox, S.E., Norton, K.K., Gendaszek, A.S., Spanjer, A.R., and Foreman, J.R., 2016, Transport and deposition of asbestos-rich sediment in the Sumas River, Whatcom County, Washington: U.S. Geological Survey Scientific Investigations Report 2015–5177, 51 p., https://dx.doi.org/10.3133/sir20155177.","productDescription":"Report: viii, 51 p.; Appendixes A-H","startPage":"1","endPage":"51","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066836","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":316682,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5177/sir20155177.pdf","text":"Report","size":"3.1 MB","description":"SIR 2015-5177 PDF"},{"id":316683,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5177/sir20155177_appendixa.xlsx","text":"Appendix A","size":"25 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix A"},{"id":316684,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5177/sir20155177_appendixb.xlsx","text":"Appendix B","size":"51 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix B"},{"id":316685,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5177/sir20155177_appendixc.xlsx","text":"Appendix C","size":"6.5 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix C"},{"id":316686,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5177/sir20155177_appendixd.xlsx","text":"Appendix D","size":"15 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix D"},{"id":316687,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5177/sir20155177_appendixe.xlsx","text":"Appendix E","size":"13 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix E"},{"id":316688,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5177/sir20155177_appendixf.xlsx","text":"Appendix F","size":"234 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix F"},{"id":316689,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5177/sir20155177_appendixg.xlsx","text":"Appendix G","size":"29 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix G"},{"id":316690,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5177/sir20155177_appendixh.xlsx","text":"Appendix H","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix H"},{"id":316511,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5177/coverthb.jpg"}],"country":"United States","state":"Washington","county":"Whatcom County","otherGeospatial":"Sumas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.35267639160156,\n 49.00139345263396\n ],\n [\n -122.36160278320311,\n 48.99733908118444\n ],\n [\n -122.37876892089842,\n 48.98562459864604\n ],\n [\n -122.37670898437499,\n 48.97751295870069\n ],\n [\n -122.3650360107422,\n 48.972555195463336\n ],\n [\n -122.36709594726562,\n 48.94820993324199\n ],\n [\n -122.35748291015625,\n 48.91347483782297\n ],\n [\n -122.33070373535155,\n 48.90354608612109\n ],\n [\n -122.32315063476562,\n 48.87826399706969\n ],\n [\n -122.310791015625,\n 48.86381134898791\n ],\n [\n -122.28675842285158,\n 48.84212454876025\n ],\n [\n -122.25997924804688,\n 48.83941303819501\n ],\n [\n -122.20985412597656,\n 48.871038194878636\n ],\n [\n -122.18788146972655,\n 48.907608086640366\n ],\n [\n -122.17758178710939,\n 48.950013693526294\n ],\n [\n -122.17758178710939,\n 48.98787759766659\n ],\n [\n -122.17689514160158,\n 49.002294379248696\n ],\n [\n -122.35267639160156,\n 49.00139345263396\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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Tacoma, Washington 98402
http://wa.water.usgs.gov
Long-term changes in nitrate concentration and flux between the middle of the 20th century and the first decade of the 21st century were estimated for the Des Moines River and the Middle Illinois River, two Midwestern Corn Belt streams, using a novel weighted regression approach that is able to detect subtle changes in solute transport behavior over time. The results show that the largest changes in flow-normalized concentration and flux occurred between 1960 and 1980 in both streams, with smaller or negligible changes between 1980 and 2004. Contrasting patterns were observed between (1) nitrate export linked to non-point sources, explicitly runoff of synthetic fertilizer or other surface sources and (2) nitrate export presumably associated with point sources such as urban wastewater or confined livestock feeding facilities, with each of these modes of transport important under different domains of streamflow. Surface runoff was estimated to be consistently most important under high-flow conditions during the spring in both rivers. Nitrate export may also have been considerable in the Des Moines River even under some conditions during the winter when flows are generally lower, suggesting the influence of point sources during this time. Similar results were shown for the Middle Illinois River, which is subject to significant influence of wastewater from the Chicago area, where elevated nitrate concentrations were associated with at the lowest flows during the winter and fall. By modeling concentration directly, this study highlights the complex relationship between concentration and streamflow that has evolved in these two basins over the last 50 years. This approach provides insights about changing conditions that only become observable when stationarity in the relationship between concentration and streamflow is not assumed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2015.03.062","usgsCitation":"Kelly, V.J., Stets, E., and Crawford, C.G., 2015, Long-term changes in nitrate conditions over the 20th century in two Midwestern Corn Belt streams: Journal of Hydrology, v. 525, p. 559-571, https://doi.org/10.1016/j.jhydrol.2015.03.062.","productDescription":"13 p.","startPage":"559","endPage":"571","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059752","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":471511,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2015.03.062","text":"Publisher Index Page"},{"id":314258,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa","city":"Keosaque, Peoria","otherGeospatial":"Des Moines River, Middle Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.01221466064453,\n 40.70380607385548\n ],\n [\n -92.01221466064453,\n 40.76676160346336\n ],\n [\n -91.9071578979492,\n 40.76676160346336\n ],\n [\n -91.9071578979492,\n 40.70380607385548\n ],\n [\n -92.01221466064453,\n 40.70380607385548\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.72396850585936,\n 40.602484146302096\n ],\n [\n -89.72396850585936,\n 40.76182096906601\n ],\n [\n -89.44656372070312,\n 40.76182096906601\n ],\n [\n -89.44656372070312,\n 40.602484146302096\n ],\n [\n -89.72396850585936,\n 40.602484146302096\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"525","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56977530e4b039675d00a6c0","contributors":{"authors":[{"text":"Kelly, Valerie J. vjkelly@usgs.gov","contributorId":4161,"corporation":false,"usgs":true,"family":"Kelly","given":"Valerie","email":"vjkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":588484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stets, Edward G. estets@usgs.gov","contributorId":3593,"corporation":false,"usgs":true,"family":"Stets","given":"Edward G.","email":"estets@usgs.gov","affiliations":[],"preferred":false,"id":588485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crawford, Charles G. 0000-0003-1653-7841 cgcrawfo@usgs.gov","orcid":"https://orcid.org/0000-0003-1653-7841","contributorId":1064,"corporation":false,"usgs":true,"family":"Crawford","given":"Charles","email":"cgcrawfo@usgs.gov","middleInitial":"G.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":588486,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70161998,"text":"70161998 - 2015 - Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network","interactions":[],"lastModifiedDate":"2019-12-12T10:54:01","indexId":"70161998","displayToPublicDate":"2016-01-11T16:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network","docAbstract":"The U.S. Geological Survey is developing new Landsat science products. One, named Dynamic Surface Water Extent (DSWE), is focused on the representation of ground surface inundation as detected in cloud-/shadow-/snow-free pixels for scenes collected over the U.S. and its territories. Characterization of DSWE uncertainty to facilitate its appropriate use in science and resource management is a primary objective. A unique evaluation dataset developed from data made publicly available through the Everglades Depth Estimation Network (EDEN) was used to evaluate one candidate DSWE algorithm that is relatively simple, requires no scene-based calibration data, and is intended to detect inundation in the presence of marshland vegetation. A conceptual model of expected algorithm performance in vegetated wetland environments was postulated, tested and revised. Agreement scores were calculated at the level of scenes and vegetation communities, vegetation index classes, water depths, and individual EDEN gage sites for a variety of temporal aggregations. Landsat Archive cloud cover attribution errors were documented. Cloud cover had some effect on model performance. Error rates increased with vegetation cover. Relatively low error rates for locations of little/no vegetation were unexpectedly dominated by omission errors due to variable substrates and mixed pixel effects. Examined discrepancies between satellite and in situ modeled inundation demonstrated the utility of such comparisons for EDEN database improvement. Importantly, there seems no trend or bias in candidate algorithm performance as a function of time or general hydrologic conditions, an important finding for long-term monitoring. The developed database and knowledge gained from this analysis will be used for improved evaluation of candidate DSWE algorithms as well as other measurements made on Everglades surface inundation, surface water heights and vegetation using radar, lidar and hyperspectral instruments. Although no other sites have such an extensive in situ network or long-term records, the broader applicability of this and other candidate DSWE algorithms is being evaluated in other wetlands using this work as a guide. Continued interaction among DSWE producers and potential users will help determine whether the measured accuracies are adequate for practical utility in resource management.
","language":"English","publisher":"MDPI","doi":"10.3390/rs70912503","usgsCitation":"Jones, J., 2015, Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network: Remote Sensing, v. 9, no. 7, p. 12503-12538, https://doi.org/10.3390/rs70912503.","productDescription":"36 p.","startPage":"12503","endPage":"12538","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066317","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471512,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs70912503","text":"Publisher Index Page"},{"id":314188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.331787109375,\n 24.831610355586918\n ],\n [\n -80.31280517578125,\n 24.831610355586918\n ],\n [\n -80.31280517578125,\n 26.561506704037942\n ],\n [\n -81.331787109375,\n 26.561506704037942\n ],\n [\n -81.331787109375,\n 24.831610355586918\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-23","publicationStatus":"PW","scienceBaseUri":"5694d22de4b039675d005dc0","contributors":{"authors":[{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":588290,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70159957,"text":"sir20155176 - 2015 - Decision analysis to support development of the Glen Canyon Dam long-term experimental and management plan","interactions":[],"lastModifiedDate":"2024-03-04T20:23:08.804716","indexId":"sir20155176","displayToPublicDate":"2016-01-07T16:30:00","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":"2015-5176","title":"Decision analysis to support development of the Glen Canyon Dam long-term experimental and management plan","docAbstract":"The U.S. Geological Survey, in cooperation with the Bureau of Reclamation, National Park Service, and Argonne National Laboratory, completed a decision analysis to use in the evaluation of alternatives in the Environmental Impact Statement concerning the long-term management of water releases from Glen Canyon Dam and associated management activities. Two primary decision analysis methods, multicriteria decision analysis and the expected value of information, were used to evaluate the alternative strategies against the resource goals and to evaluate the influence of uncertainty.
\nA total of 18 performance metrics associated with 8 out of 12 resource goals (fundamental objectives) were developed by the Bureau of Reclamation and National Park Service in partnership with subject-matter teams composed of Federal, State, tribal, and private experts. A total of 19 long-term strategies associated with 7 alternatives were developed by the Bureau of Reclamation, National Park Service, Argonne National Laboratory, U.S. Geological Survey, and Cooperating Agencies. The 19 long-term strategies were evaluated against the 18 performance metrics using a series of coupled simulation models, taking into account the effects of several important sources of uncertainty. A total of 27 Federal, State, tribal, and nongovernmental agencies were invited by the Assistant Secretary of Interior to participate in a swing-weighting exercise to understand the range of perspectives about how to place relative value on the resource goals and performance metrics; 14 of the 27 chose to participate. The results of the swing-weighting exercise were combined with the evaluation of the alternatives to complete a multicriteria decision analysis. The effects of uncertainty on the ranking of long-term strategies were evaluated through calculation of the value of information.
\nThe alternatives and their long-term strategies differed across performance metrics, producing unavoidable tradeoffs; thus, there was no long-term strategy that was dominated by another across all performance metrics. When the performance of each alternative was weighted across performance metrics, three alternatives (B, D, and G) were top-ranked depending on the set of weights proposed: Alternative B was favored by those stakeholders that placed a high value on hydropower; Alternative G was favored by those stakeholders that placed a high value on the restoration of natural processes, like beachbuilding and natural vegetation; and Alternative D was favored by the remaining stakeholders. Surprisingly, these rankings were not sensitive to the critical uncertainties that were evaluated; that is, the choice of a preferred long-term strategy was sensitive to the value-based judgment about how to place relative weight on the resource goals but was not sensitive to the uncertainties in the system dynamics that were evaluated in this analysis. The one area of uncertainty that did slightly affect the ranking of alternatives was the long-term pattern of hydrological input; because of this sensitivity, some attention to the possible effects of climate change is warranted.
\nThe results of the decision analysis are meant to serve as only one of many sources of information that can be used to evaluate the alternatives proposed in the Environmental Impact Statement. These results only focus on those resource goals for which quantitative performance metrics could be formulated and evaluated; there are other important aspects of the resource goals that also need to be considered. Not all the stakeholders who were invited to participate in the decision analysis chose to do so; thus, the Bureau of Reclamation, National Park Service, and U.S. Department of Interior may want to consider other input.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155176","collaboration":"Prepared in cooperation with the Bureau of Reclamation, National Park Service, and Argonne National Laboratory","usgsCitation":"Runge, M.C., LaGory, K.E., Russell, Kendra, Balsom, J.R., Butler, R.A., Coggins, L.G., Jr., Grantz, K.A., Hayse, John, Hlohowskyj, Ihor, Korman, Josh, May, J.E., O’Rourke, D.J., Poch, L.A., Prairie, J.R., VanKuiken, J.C., Van Lonkhuyzen, R.A., Varyu, D.R., Verhaaren, B.T., Vesekla, T.D., Williams, N.T., Wuthrich, K.K., Yackulic, C.B., Billerbeck, R.P., and Knowles, G.W., 2015, Decision analysis to support development of the Glen Canyon Dam Long-Term Experimental and Management Plan: U.S. Geological Survey Scientific Investigations Report 2015–5176, 64 p., https://dx.doi.org/10.3133/sir20155176.","productDescription":"xi, 64 p.","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070238","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":312032,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5176/sir20155176.pdf","text":"Report","size":"2.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5176"},{"id":312031,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5176/coverthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.70825195312501,\n 35.074964853989556\n ],\n [\n -114.70825195312501,\n 37.00255267215955\n ],\n [\n -111.258544921875,\n 37.00255267215955\n ],\n [\n -111.258544921875,\n 35.074964853989556\n ],\n [\n -114.70825195312501,\n 35.074964853989556\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
12100 Beech Forest Rd., Ste 4039
Laurel, MD 20708-4039
Historical lead and zinc mining in the Tri-State Mining District (TSMD), located in parts of southeast Kansas, southwest Missouri, and northeast Oklahoma, has resulted in a substantial ongoing input of lead and zinc to the environment (Juracek, 2006; Juracek and Becker, 2009). In response to concern about the mining-related contamination, southeast Cherokee County, Kansas, was listed on the U.S. Environmental Protection Agency’s (USEPA) National Priority List as a Superfund hazardous waste site (fig. 1). To provide some of the information needed to support remediation efforts in the Cherokee County Superfund site, a study was begun in 2009 by the U.S. Geological Survey (USGS) that was requested and funded by USEPA. As part of the study, surficial-soil sampling was used to investigate the extent and magnitude of mining-related lead and zinc contamination in the flood plains of the Spring River and several tributaries within the Superfund site. In mining-affected areas, flood-plain soils had lead and zinc concentrations that far exceeded background levels as well as probable-effects guidelines for toxic aquatic biological effects (Juracek, 2013). Lead- and zinc-contaminated flood plains are a concern, in part, because they represent a long-term source of contamination to the fluvial environment.
\nAn important issue is the within-site representativeness of the surficial-soil samples collected. Specifically, the question is whether or not the samples collected provide an acceptable representation of the lead and zinc concentrations at each site for the purpose of characterizing and comparing sites. The distribution of mining-contaminated sediment on flood plains is determined by several factors including the size and density of the contaminated particles, flood-plain width and topography, flood characteristics (frequency, magnitude, duration), and fluvial geomorphic processes. To evaluate within-site representativeness, additional samples were simultaneously collected to assess within-site variability. In this paper, the specific objectives were to:
\n","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2015 Joint Federal Interagency Conference on Sedimentation and Hydrologic Modeling (SEDHYD 2015)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2015 Joint Federal Interagency Conference on Sedimentation and Hydrologic Modeling (SEDHYD 2015)","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, NV","language":"English","publisher":"SEDHYD","usgsCitation":"Juracek, K.E., 2015, Representativeness of soil samples collected to assess mining-related contamination of flood plains in southeast Kansas, in 2015 Joint Federal Interagency Conference on Sedimentation and Hydrologic Modeling (SEDHYD 2015), Reno, NV, April 19-23, 2015, 8 p.","productDescription":"8 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045073","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":325365,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":325364,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2015/proceedings"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"578dfdb9e4b0f1bea0e0f8e6","contributors":{"authors":[{"text":"Juracek, Kyle E. 0000-0002-2102-8980 kjuracek@usgs.gov","orcid":"https://orcid.org/0000-0002-2102-8980","contributorId":2022,"corporation":false,"usgs":true,"family":"Juracek","given":"Kyle","email":"kjuracek@usgs.gov","middleInitial":"E.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":642666,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70175692,"text":"70175692 - 2015 - A semi-automated tool for reducing the creation of false closed depressions from a filled LIDAR-derived digital elevation model","interactions":[],"lastModifiedDate":"2018-07-24T10:59:55","indexId":"70175692","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"A semi-automated tool for reducing the creation of false closed depressions from a filled LIDAR-derived digital elevation model","docAbstract":"
Closed depressions on the land surface can be identified by ‘filling’ a digital elevation model (DEM) and subtracting the filled model from the original DEM. However, automated methods suffer from artificial ‘dams’ where surface streams cross under bridges and through culverts. Removal of these false depressions from an elevation model is difficult due to the lack of bridge and culvert inventories; thus, another method is needed to breach these artificial dams. Here, we present a semi-automated workflow and toolbox to remove falsely detected closed depressions created by artificial dams in a DEM. The approach finds the intersections between transportation routes (e.g., roads) and streams, and then lowers the elevation surface across the roads to stream level allowing flow to be routed under the road. Once the surface is corrected to match the approximate location of the National Hydrologic Dataset stream lines, the procedure is repeated with sequentially smaller flow accumulation thresholds in order to generate stream lines with less contributing area within the watershed. Through multiple iterations, artificial depressions that may arise due to ephemeral flow paths can also be removed. Preliminary results reveal that this new technique provides significant improvements for flow routing across a DEM and minimizes artifacts within the elevation surface. Slight changes in the stream flow lines generally improve the quality of flow routes; however some artificial dams may persist. Problematic areas include extensive road ditches, particularly along divided highways, and where surface flow crosses beneath road intersections. Limitations do exist, and the results partially depend on the quality of data being input. Of 166 manually identified culverts from a previous study by Doctor and Young in 2013, 125 are within 25 m of culverts identified by this tool. After three iterations, 1,735 culverts were identified and cataloged. The result is a reconditioned elevation dataset, which retains the karst topography for further analysis, and a culvert catalog.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"National Cave and Karst Research Institute Symposium 5, Proceedings of the 14th Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Karst","largerWorkSubtype":{"id":15,"text":"Monograph"},"conferenceTitle":"14th Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Karst","conferenceDate":"October 5-9, 2015","conferenceLocation":"Rochester, MN","language":"English","publisher":"National Cave and Karst Research Institute","doi":"10.5038/9780991000951.1057","usgsCitation":"Wall, J., Doctor, D.H., and Terziotti, S., 2015, A semi-automated tool for reducing the creation of false closed depressions from a filled LIDAR-derived digital elevation model, in National Cave and Karst Research Institute Symposium 5, Proceedings of the 14th Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Karst, Rochester, MN, October 5-9, 2015, p. 255-262, https://doi.org/10.5038/9780991000951.1057.","productDescription":"8 p.","startPage":"255","endPage":"262","ipdsId":"IP-066736","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":471533,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarcommons.usf.edu/sinkhole_2015/ProceedingswithProgram/GIS_Databases_and_Maps/5","text":"External Repository"},{"id":328120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57c7ffade4b0f2f0cebfc214","contributors":{"authors":[{"text":"Wall, John","contributorId":206495,"corporation":false,"usgs":false,"family":"Wall","given":"John","email":"","affiliations":[],"preferred":false,"id":646084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","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":646083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terziotti, Silvia 0000-0003-3559-5844 seterzio@usgs.gov","orcid":"https://orcid.org/0000-0003-3559-5844","contributorId":1613,"corporation":false,"usgs":true,"family":"Terziotti","given":"Silvia","email":"seterzio@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":646085,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176497,"text":"70176497 - 2015 - A 2-D process-based model for suspended sediment dynamics: A first step towards ecological modeling","interactions":[],"lastModifiedDate":"2016-09-19T14:35:11","indexId":"70176497","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A 2-D process-based model for suspended sediment dynamics: A first step towards ecological modeling","docAbstract":"In estuaries suspended sediment concentration (SSC) is one of the most important contributors to turbidity, which influences habitat conditions and ecological functions of the system. Sediment dynamics differs depending on sediment supply and hydrodynamic forcing conditions that vary over space and over time. A robust sediment transport model is a first step in developing a chain of models enabling simulations of contaminants, phytoplankton and habitat conditions.
This works aims to determine turbidity levels in the complex-geometry delta of the San Francisco estuary using a process-based approach (Delft3D Flexible Mesh software). Our approach includes a detailed calibration against measured SSC levels, a sensitivity analysis on model parameters and the determination of a yearly sediment budget as well as an assessment of model results in terms of turbidity levels for a single year, water year (WY) 2011.
Model results show that our process-based approach is a valuable tool in assessing sediment dynamics and their related ecological parameters over a range of spatial and temporal scales. The model may act as the base model for a chain of ecological models assessing the impact of climate change and management scenarios. Here we present a modeling approach that, with limited data, produces reliable predictions and can be useful for estuaries without a large amount of processes data.