In this paper, we develop and validate a rigorous modeling framework, based on Duhamel's Theorem, for the unsteady one-dimensional vertical transport of a solute across a flat sediment-water interface (SWI) and through the benthic biolayer of a turbulent stream. The modeling framework is novel in capturing the two-way coupling between evolving solute concentrations above and below the SWI and in allowing for a depth-varying diffusivity. Three diffusivity profiles within the sediment (constant, exponentially decaying, and a hybrid model) are evaluated against an extensive set of previously published laboratory measurements of turbulent mass transfer across the SWI. The exponential diffusivity profile best represents experimental observations and its reference diffusivity scales with the permeability Reynolds number, a dimensionless measure of turbulence at the SWI. The depth over which turbulence-enhanced diffusivity decays is of the order of centimeters and comparable to the thickness of the benthic biolayer. Thus, turbulent mixing across the SWI may serve as a universal transport mechanism, supplying the nutrient and energy fluxes needed to sustain microbial growth, and nutrient processing, in the benthic biolayer of stream and coastal sediments.
Irrigated agriculture has grown rapidly over the last 50 years, helping food production keep pace with population growth, but also leading to significant habitat and biodiversity loss globally. Now, in some regions, land degradation and overtaxed water resources mean historical production levels may need to be reduced. We demonstrate how analytically supported planning for habitat restoration in stressed agricultural landscapes can recover biodiversity and create co-benefits during transitions to sustainability. We apply our approach in California's San Joaquin Valley where groundwater regulations are driving significant land use change. We link agricultural-economic and land use change models to generate plausible landscapes with different cropping patterns, including temporary fallowing and permanent retirement. We find that a large fraction of the reduced cultivation is met through temporary fallowing, but still estimate over 86,000 hectares of permanent retirement. We then apply systematic conservation planning to identify optimized restoration solutions that secure at least 10,000 hectares of high quality habitat for each of five representative endangered species, accounting for spatially varying opportunity costs specific to each plausible future landscape. The analyses identified consolidated areas common to all land use scenarios where restoration could be targeted to enhance habitat by utilizing land likely to be retired anyway, and by shifting some retirement from regions with low habitat value to regions with high habitat value. We also show potential co-benefits of retirement (derived from avoided nitrogen loadings and soil carbon sequestration), though these require careful consideration of additionality. Our approach provides a generalizable means to inform multi-benefit adaptation planning in response to agricultural stressors.
","language":"English","publisher":"Frontiers","doi":"10.3389/fsufs.2020.00138","usgsCitation":"Bryant, B.P., Kelsey, T.R., Vogl, A.L., Wolny, S.A., MacEwan, D.J., Selmants, P., Biswas, T., and Butterfield, H.S., 2020, Shaping land use change and ecosystem restoration in a water-stressed agricultural landscape to achieve multiple benefits: Frontiers in Sustainable Food Systems, v. 4, 138, 15 p., https://doi.org/10.3389/fsufs.2020.00138.","productDescription":"138, 15 p.","ipdsId":"IP-119117","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":455470,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fsufs.2020.00138","text":"Publisher Index Page"},{"id":378080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.00390625,\n 35.04798673426734\n ],\n [\n -118.740234375,\n 36.03133177633187\n ],\n [\n -119.39941406249999,\n 37.125286284966805\n ],\n [\n -120.89355468749999,\n 38.61687046392973\n ],\n [\n -121.83837890625,\n 40.44694705960048\n ],\n [\n -122.34374999999999,\n 40.613952441166596\n ],\n [\n -122.84912109375,\n 40.38002840251183\n ],\n [\n -122.73925781250001,\n 39.06184913429154\n ],\n [\n -121.55273437499999,\n 37.84015683604136\n ],\n [\n -120.78369140624999,\n 37.00255267215955\n ],\n [\n -119.94873046875,\n 35.97800618085566\n ],\n [\n -119.00390625,\n 35.04798673426734\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","noUsgsAuthors":false,"publicationDate":"2020-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Bryant, Benjamin P.","contributorId":239716,"corporation":false,"usgs":false,"family":"Bryant","given":"Benjamin","email":"","middleInitial":"P.","affiliations":[{"id":47984,"text":"Woods Institute for the Environment, Stanford University","active":true,"usgs":false}],"preferred":false,"id":797755,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelsey, T. Rodd","contributorId":239717,"corporation":false,"usgs":false,"family":"Kelsey","given":"T.","email":"","middleInitial":"Rodd","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":797756,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vogl, Adrian L.","contributorId":239718,"corporation":false,"usgs":false,"family":"Vogl","given":"Adrian","email":"","middleInitial":"L.","affiliations":[{"id":47984,"text":"Woods Institute for the Environment, Stanford University","active":true,"usgs":false}],"preferred":false,"id":797757,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wolny, Stacie A.","contributorId":239719,"corporation":false,"usgs":false,"family":"Wolny","given":"Stacie","email":"","middleInitial":"A.","affiliations":[{"id":47984,"text":"Woods Institute for the Environment, Stanford University","active":true,"usgs":false}],"preferred":false,"id":797758,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"MacEwan, Duncan J.","contributorId":239720,"corporation":false,"usgs":false,"family":"MacEwan","given":"Duncan","email":"","middleInitial":"J.","affiliations":[{"id":47987,"text":"ERA Economics","active":true,"usgs":false}],"preferred":false,"id":797759,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Selmants, Paul 0000-0001-6211-3957 pselmants@usgs.gov","orcid":"https://orcid.org/0000-0001-6211-3957","contributorId":192591,"corporation":false,"usgs":true,"family":"Selmants","given":"Paul","email":"pselmants@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":797760,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Biswas, Tanushree","contributorId":239721,"corporation":false,"usgs":false,"family":"Biswas","given":"Tanushree","email":"","affiliations":[{"id":47984,"text":"Woods Institute for the Environment, Stanford University","active":true,"usgs":false}],"preferred":false,"id":797761,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Butterfield, H. Scott","contributorId":192141,"corporation":false,"usgs":false,"family":"Butterfield","given":"H.","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":797762,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70212719,"text":"ofr20201072 - 2020 - Cottonwoods, water, and people-Integrating analysis of tree rings with observations of elders from the Eastern Shoshone and Northern Arapaho Tribes of the Wind River Reservation, Wyoming","interactions":[],"lastModifiedDate":"2020-09-01T23:30:54.314533","indexId":"ofr20201072","displayToPublicDate":"2020-08-31T12:55:00","publicationYear":"2020","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":"2020-1072","displayTitle":"Cottonwoods, Water, and People—Integrating Analysis of Tree Rings with Observations of Elders from the Eastern Shoshone and Northern Arapaho Tribes of the Wind River Reservation, Wyoming","title":"Cottonwoods, water, and people-Integrating analysis of tree rings with observations of elders from the Eastern Shoshone and Northern Arapaho Tribes of the Wind River Reservation, Wyoming","docAbstract":"We assessed the history of flow and riparian ecosystem change along the Wind River using cottonwood tree-ring data, streamgage records, historical temperature and precipitation data, drought indices, and local observations and Traditional Ecological Knowledge from elders of the Eastern Shoshone and Northern Arapaho Tribes of the Wind River Reservation, Wyoming. This assessment identified impacts that have occurred to riparian resources of concern to the Tribes, which will assist in prioritizing drought planning efforts. Impacts included reduced abundance, reduced regeneration, and increased mortality in cottonwoods (Populus deltoides and P. angustifolia); an increase in invasive species, especially Russian olive (Elaeagnus angustifolia), that are gradually replacing cottonwoods and other native woody plants; decreased abundance of native and culturally important plants; reduced abundance of culturally important fish; reduced volume and changes to the timing of flows; and changes in river course. This assessment documented the biophysical and social factors that have contributed to riparian ecosystem change and to reduced water availability and flows, including agricultural diversion, drought, and fire. Cottonwoods along the Wind River are as much as 300 years old. By relating tree-ring width to recorded streamflows, we were able to reconstruct streamflows confidently back to the 1850s and speculatively back to the mid-1700s. Extending the historical record of streamflows allows for a more-complete understanding of hydroclimatic variability and provides a foundation for developing preparedness and response strategies for drought management. Ring width of cottonwood trees at the Boysen Site was more strongly correlated to river flow than to local precipitation or temperature, indicating that growth of trees is controlled more by montane snowmelt than by local weather. Therefore, tree rings are a better indicator of water supply than of the local conditions controlling water demand. The extended flow record from tree rings revealed the occurrence of a major period of low flow from 1870 to 1910 that was not evident in the shorter instrumental records of flow and weather. Information from tree rings, streamflow measurements, drought indices, and elder observations all suggest that the early 2000s drought was the most severe, sustained drought in the last century. Our results illustrate how drought is experienced in different ways across locations and sectors, which underscores the importance of using multiple indicators for drought management. These results will contribute to ongoing assessment, monitoring, and planning efforts at the Wind River Reservation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201072","collaboration":"Prepared in cooperation with the Eastern Shoshone Tribe of the Wind River Reservation, Wyoming, the Northern Arapaho Tribe of the Wind River Reservation, Wyoming, and Colorado State University","usgsCitation":"McNeeley, S.M., Friedman, J.M., Beeton, T.A., and Thaxton, R.D., 2020, Cottonwoods, water, and people—Integrating analysis of tree rings with observations of elders from the Eastern Shoshone and Northern Arapaho Tribes of the Wind River Reservation, Wyoming: U.S. Geological Survey Open-File Report 2020–1072, 33 p., https://doi.org/10.3133/ofr20201072.","productDescription":"Report: iv, 33 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-113563","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":378034,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S1UIAL","text":"USGS data release","linkHelpText":"Tree-Ring Data Collected in 2017 and 2018 From Cottonwood Trees Along the Wind River in Wind River Indian Reservation, Wyoming"},{"id":377898,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1072/coverthb.jpg"},{"id":377899,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1072/ofr20201072.pdf","text":"Report","size":"13.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1072"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wind River Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.44442749023438,\n 42.83569550641452\n ],\n [\n -108.160400390625,\n 42.83569550641452\n ],\n [\n -108.160400390625,\n 43.54456658436357\n ],\n [\n -109.44442749023438,\n 43.54456658436357\n ],\n [\n -109.44442749023438,\n 42.83569550641452\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Avenue, Bldg. C
Fort Collins, CO 80526
Global warming-related changes to freshwater ecosystems in Arctic and Subarctic regions have been magnified by nutrient input from increasing waterfowl populations. To gain insight into how these changes might affect ecosystem function, we conducted a mesocosm experiment in the Subarctic by enriching N and P (1 ×, 10 ×, and 20 × treatments) and increasing mean water temperatures ≤ 3°C. We measured responses of two species of larval amphibians, periphyton, and phytoplankton. Wood frog (Rana sylvatica) larvae developed quicker (odds ratio [OR] for 1°C increase = 0.903, 95% CI 0.892–0.912) and were more likely to metamorphose (OR 1.076, 95% CI 0.022–14.73) in warmer waters. Boreal chorus frogs (Pseudacris maculata) also developed quicker with warmer temperatures (OR 0.880, 95% CI 0.860–0.900), despite a non-significant trend toward reduced survival (OR 0.853, 95% CI 0.696–1.039). Periphyton and phytoplankton concentrations increased with nutrient additions, as did size of wood frog metamorphs. Periphyton and phytoplankton did not vary with temperature, but periphyton was limited by tadpole abundance. Our results highlight the potential for non-linear responses to ecosystem change, with species-specific consumer and ecosystem responses that depend on the magnitude of changes.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-020-04392-x","usgsCitation":"Davenport, J., Fishback, L., and Hossack, B., 2020, Effects of experimental warming and nutrient enrichment on wetland communities at the Arctic’s edge: Hydrobiologia, v. 847, p. 3677-3690, https://doi.org/10.1007/s10750-020-04392-x.","productDescription":"14 p.","startPage":"3677","endPage":"3690","ipdsId":"IP-107678","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":378694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Manitoba","city":"Churchill","otherGeospatial":"Hudson Bay Lowlands region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.415771484375,\n 55.83214387781303\n ],\n [\n -92.010498046875,\n 55.83214387781303\n ],\n [\n -92.010498046875,\n 59.50087955346417\n ],\n [\n -97.415771484375,\n 59.50087955346417\n ],\n [\n -97.415771484375,\n 55.83214387781303\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"847","noUsgsAuthors":false,"publicationDate":"2020-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Davenport, Jon M.","contributorId":126727,"corporation":false,"usgs":false,"family":"Davenport","given":"Jon M.","affiliations":[{"id":6583,"text":"University of Montana, Division of Biological Sciences, Missoula, MT, USA 59812","active":true,"usgs":false}],"preferred":false,"id":799488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fishback, LeeAnn","contributorId":168514,"corporation":false,"usgs":false,"family":"Fishback","given":"LeeAnn","email":"","affiliations":[{"id":25316,"text":"Churchill Northern Studies Centre, P.O. Box 610, Churchill, Manitoba, R0B 0E0, Canada","active":true,"usgs":false}],"preferred":false,"id":799489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":799490,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216470,"text":"70216470 - 2020 - Exploring the potential of ground-penetrating radar (GPR) to measure the extent of chronic disturbance in peatlands: Examples from acid mine drainage and peat fire","interactions":[],"lastModifiedDate":"2020-11-23T14:39:05.647854","indexId":"70216470","displayToPublicDate":"2020-08-31T09:38:40","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Exploring the potential of ground-penetrating radar (GPR) to measure the extent of chronic disturbance in peatlands: Examples from acid mine drainage and peat fire","docAbstract":"Peatlands are accumulations of partially decayed organic soil that cover approximately 3% of Earth’s surface and have been shown to serve essential environmental and ecological functions such as sequestering carbon, purifying water, and providing habitat for organisms. However, peatlands are threatened by pressures from agriculture, urban development, mining, and climate change. Geophysical methods have been used in peatlands to determine peat volume and carbon stocks (e.g., Comas et al., 2017), observe differences in humification and water content (e.g., Ulriksen, 1982), guide engineering projects (e.g., Jol and Smith, 1995), learn about subsurface greenhouse gas dynamics (Wright and Comas, 2016), observe seasonal variations in pore water salinity (Walter et al., 2018), and assess hydrological processes (Hare et al., 2017). Among various geophysical methods, ground penetrating radar (GPR) is arguably the most popular for studying peat properties given the method’s sensitivity to variations in water content and ability to resolve major structural properties within the peat at high spatial resolution. Though less widely applied, frequency-domain analysis of GPR may also yield useful information.
","conferenceTitle":"18th International Conference on Ground Penetrating Radar","conferenceDate":"June 14-19, 2020","conferenceLocation":"Golden, Colorado","language":"English","publisher":"Society of Exploration Geologists","doi":"10.1190/gpr2020-015.1","usgsCitation":"Terry, N., Runkel, R.L., Werkema, D.D., Rutila, E., Comas, X., Warren, M., Kristiyono, A., and Murdiyarso, D., 2020, Exploring the potential of ground-penetrating radar (GPR) to measure the extent of chronic disturbance in peatlands: Examples from acid mine drainage and peat fire, 18th International Conference on Ground Penetrating Radar, Golden, Colorado, June 14-19, 2020, p. 53-56, https://doi.org/10.1190/gpr2020-015.1.","productDescription":"4 p.","startPage":"53","endPage":"56","ipdsId":"IP-117032","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":380686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-11-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":805218,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":805220,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Werkema, Dale D.","contributorId":40488,"corporation":false,"usgs":false,"family":"Werkema","given":"Dale","email":"","middleInitial":"D.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":805219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rutila, Elizabeth 0000-0003-0288-9678","orcid":"https://orcid.org/0000-0003-0288-9678","contributorId":224637,"corporation":false,"usgs":false,"family":"Rutila","given":"Elizabeth","email":"","affiliations":[{"id":40900,"text":"Oakridge Institute for Science and Education","active":true,"usgs":false}],"preferred":false,"id":805371,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Comas, Xavier","contributorId":176879,"corporation":false,"usgs":false,"family":"Comas","given":"Xavier","affiliations":[],"preferred":false,"id":805221,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Warren, Matthew","contributorId":245034,"corporation":false,"usgs":false,"family":"Warren","given":"Matthew","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":805222,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kristiyono, Agus 0000-0001-6433-3902","orcid":"https://orcid.org/0000-0001-6433-3902","contributorId":245036,"corporation":false,"usgs":false,"family":"Kristiyono","given":"Agus","email":"","affiliations":[{"id":49058,"text":"Indonesian Agency for Assessment and Application of Technology (BPPT)","active":true,"usgs":false}],"preferred":false,"id":805223,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Murdiyarso, Daniel","contributorId":243962,"corporation":false,"usgs":false,"family":"Murdiyarso","given":"Daniel","email":"","affiliations":[{"id":48776,"text":"cifor","active":true,"usgs":false}],"preferred":false,"id":805224,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228420,"text":"70228420 - 2020 - Science in action or science inaction? Evaluating the implementation of \"best available science\" in hydropower relicensing","interactions":[],"lastModifiedDate":"2022-02-10T15:29:05.82825","indexId":"70228420","displayToPublicDate":"2020-08-31T08:47:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1510,"text":"Energy Policy","active":true,"publicationSubtype":{"id":10}},"title":"Science in action or science inaction? Evaluating the implementation of \"best available science\" in hydropower relicensing","docAbstract":"Over the next two decades, half of all hydropower projects in the USA will require relicensing by the Federal Regulatory Commission (FERC). Relicensing proceedings invoke a range of informational sources and agency regulators are tasked with using the “best available science” (BAS) to make informed decisions about hydropower operations and management. Although embraced as the standard, BAS is not well-defined. The Kennebec and Penobscot River watersheds in Maine provide an ideal opportunity for studying BAS in the relicensing process. Using citation analysis and an online survey, we identified informational sources used in relicensing decisions for dams in this system and assessed agency perceptions of BAS. Analysis of relicensing documents (n=62) demonstrates that FERC and licensee documents are highly similar in citation composition. National Oceanic and Atmospheric Administration (NOAA) documents typically cite more sources and are three times more likely to cite peer-reviewed sources than FERC and licensee documents. Survey data reveals that federal and state agency respondents (n=49) rate peer-reviewed literature highly as BAS, followed by university, agency, and expert sources while industry and community sources rate poorly. Federal respondents report using peer-reviewed/academic sources more frequently and expert sources less frequently than state respondents. Overall, the agreement between individuals with respect to the valuation of sources is low. The reported differences in information use may be linked to disparities in the access to certain sources of information, particularly peer-reviewed literature. Enhanced understanding of information use may aid in identifying pathways for better informed relicensing decisions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.enpol.2020.111457","usgsCitation":"Vogel, S.K., Jansujwicz, J.S., Sponarski, C.C., and Zydlewski, J.D., 2020, Science in action or science inaction? Evaluating the implementation of \"best available science\" in hydropower relicensing: Energy Policy, v. 143, 111457, 10 p., https://doi.org/10.1016/j.enpol.2020.111457.","productDescription":"111457, 10 p.","ipdsId":"IP-111262","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":455481,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.enpol.2020.111457","text":"Publisher Index Page"},{"id":395769,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Kennebec River, Penobscot River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.63110351562499,\n 43.34914966389313\n ],\n [\n -68.170166015625,\n 43.34914966389313\n ],\n [\n -68.170166015625,\n 47.017716353979225\n ],\n [\n -70.63110351562499,\n 47.017716353979225\n ],\n [\n -70.63110351562499,\n 43.34914966389313\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"143","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vogel, Sarah K.","contributorId":275755,"corporation":false,"usgs":false,"family":"Vogel","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":834263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jansujwicz, Jessica S.","contributorId":275757,"corporation":false,"usgs":false,"family":"Jansujwicz","given":"Jessica","email":"","middleInitial":"S.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":834264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sponarski, Carly C.","contributorId":275759,"corporation":false,"usgs":false,"family":"Sponarski","given":"Carly","email":"","middleInitial":"C.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":834265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":834262,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218497,"text":"70218497 - 2020 - Coarse sediment dynamics in a large glaciated river system: Holocene history and storage dynamics dictate contemporary climate sensitivity","interactions":[],"lastModifiedDate":"2021-03-08T12:38:47.66941","indexId":"70218497","displayToPublicDate":"2020-08-31T07:06:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1786,"text":"Geological Society of America Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Coarse sediment dynamics in a large glaciated river system: Holocene history and storage dynamics dictate contemporary climate sensitivity","docAbstract":"The gravel-bedded White River drains a 1279 km2 basin in Washington State, with lowlands sculpted by continental glaciation and headwaters on an actively glaciated stratovolcano. Chronic aggradation along an alluvial fan near the river’s mouth has progressively reduced flood conveyance. In order to better understand how forecasted climate change may influence coarse sediment delivery and aggradation rates in this lowland depositional setting, we assessed the contemporary delivery and routing of coarse sediment through the watershed; this assessment was based on a rich set of topographic, sedimentologic, and hydrologic data from the past century, with a focus on repeat high-resolution topographic surveys from the past decade.
We found that most of the lower river’s contemporary bed-load flux originates from persistent erosion of alluvial deposits in the lower watershed. This erosion is a response to a drop in local base level caused by a major avulsion across the fan in 1906 and then augmented by subsequent dredging. The 1906 avulsion and modern disequilibrium valley profiles reflect landscape conditioning by continental glaciation and a massive mid-Holocene lahar. In the proglacial headwaters, infrequent large sediment pulses have accomplished most of the observed coarse sediment export, with exported material blanketing downstream valley floors; during typical floods, transported bed material is largely sourced from erosion of these valley floor deposits. Throughout the watershed, we observe decadal-scale coarse sediment dynamics strongly related to the filling or emptying of valley-scale sediment storage over 102−104 yr time scales, often in response to major disturbances that either emplace large deposits or influence their redistribution. Paraglacial responses in large watersheds are suggested to be inherently complicated and punctuated as a result of internal landform interactions and stochastic/threshold-dependent events. We argue, in combination, that Holocene disturbance, storage dynamics, and human flow modification make coarse sediment fluxes in the lower White River relatively insensitive to decadal climate variability. Results highlight the degree to which river sensitivity to contemporary disturbance, climatic or otherwise, may be contingent on local and idiosyncratic watershed histories, underscoring the need to unpack those histories while demonstrating the utility of watershed-scale high-resolution topography toward that end.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35530.1","usgsCitation":"Anderson, S.W., and Jaeger, K.L., 2020, Coarse sediment dynamics in a large glaciated river system: Holocene history and storage dynamics dictate contemporary climate sensitivity: Geological Society of America Bulletin, 24 p., https://doi.org/10.1130/B35530.1.","productDescription":"24 p.","ipdsId":"IP-106664","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":436809,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HT46KB","text":"USGS data release","linkHelpText":"Supporting Data for Sediment Studies in the White River Watershed"},{"id":383708,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"White River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.68981933593749,\n 46.72856582519053\n ],\n [\n -121.58843994140625,\n 46.72856582519053\n ],\n [\n -121.58843994140625,\n 47.31648293428332\n ],\n [\n -122.68981933593749,\n 47.31648293428332\n ],\n [\n -122.68981933593749,\n 46.72856582519053\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811209,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811210,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216697,"text":"70216697 - 2020 - Permafrost hydrogeology","interactions":[],"lastModifiedDate":"2020-12-01T13:39:38.019573","indexId":"70216697","displayToPublicDate":"2020-08-29T07:38:34","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Permafrost hydrogeology","docAbstract":"Groundwater processes are often overlooked in permafrost environments, but subsurface storage and routing can strongly influence water and biogeochemical cycling in northern catchments. Groundwater flow in permafrost regions is controlled by the temporal and spatial distribution of frozen ground, causing the hydrogeologic framework to be temperature-dependent. Most flow occurs in geologic units above the permafrost table (supra-permafrost aquifers) or below the permafrost base (sub-permafrost aquifers). In the context of climate change, thawing permafrost is altering groundwater flowpaths and thereby inducing positive trends in river baseflow in many discontinuous permafrost basins. Activated groundwater systems can provide new conduits for flushing Arctic basins and transporting nutrients to basin outlets. The thermal and hydraulic physics that govern groundwater flow in permafrost regions are strongly coupled and more complex than those in non-permafrost settings. Recent research activity in permafrost hydrogeological modeling has resulted in several mainstream groundwater models (e.g., SUTRA, FEFLOW, HYDRUS) offering users advanced capabilities for simulating processes in aquifers that experience dynamic freeze-thaw. This chapter relies on field examples to review key processes and conditions that control groundwater dynamics in permafrost settings and presents an up-to-date synthesis of the mathematical representation of heat transfer and groundwater flow in northern landscapes.
Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The U.S. Geological Survey, in cooperation with Citizens Energy Group, conducted a bathymetric survey of Morse and Geist Reservoirs in central Indiana in April and May of 2016 with a multibeam echosounder. Both reservoirs serve as water supply, flood control, and recreational resources for the city of Indianapolis and the surrounding communities.
Morse and Geist Reservoirs were surveyed to create updated bathymetric maps, determine storage capacities (volume) at specified water-surface elevations, and compare current conditions to historical surveys. Bathymetric data were collected using a high-resolution multibeam echosounder, and supplemental data were collected in coves and other shallow areas using an acoustic Doppler current profiler. The data were processed and combined using HYPACK and ArcMap software to develop a triangulated irregular network, a 5-foot gridded bathymetric dataset, a reservoir capacity table, and a bathymetric contour map for each reservoir.
The computed volume of Morse Reservoir was 23,136 acre-feet (7.54 billion gallons) with a surface area of 1,439 acres (62.7 million square feet). The computed volume of Geist Reservoir was 21,146 acre-feet (6.89 billion gallons) with a surface area of 1,853 acres (80.7 million square feet).
Between 1996 and 2016, lake bottom elevations have increased by a mean of 0.32 feet in Morse Reservoir and 0.27 feet in Geist Reservoir. The data indicate higher sedimentation rates in the upper parts of each reservoir as compared to near the dam and higher sedimentation rates in Morse Reservoir (0.5 inch per year) than in Geist Reservoir (0.2 inch per year). The differences between the current and historical surveys may be due to sedimentation, differences in accuracy between previous surveys, or a combination of both.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205067","collaboration":"Prepared in cooperation with Citizens Energy Group","usgsCitation":"Boldt, J.A., and Martin, Z.W., 2020, Bathymetric surveys of Morse and Geist Reservoirs in central Indiana made with a multibeam echosounder, 2016, and comparison with previous surveys: U.S. Geological Survey Scientific Investigations Report 2020–5067, 39 p., https://doi.org/10.3133/sir20205067.","productDescription":"Report: viii, 39 p.; Data Release; Additional Reports","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-116783","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":377662,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5067/sir20205067.pdf","text":"Report","size":"31.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5067"},{"id":377911,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2020/5067/sir20205067_Morse_Reservoir_2016.pdf","text":"Bathymetric Map of Morse Reservoir near Noblesville, Indiana, 2016","size":"28.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"— High resolution file"},{"id":377912,"rank":5,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2020/5067/sir20205067_Geist_Reservoir_2016.pdf","text":"Bathymetric Map of Geist Reservoir near Fishers, Indiana, 2016","size":"23.4 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"— High resolution file"},{"id":377663,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A2ITC6","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetry of Morse and Geist Reservoirs in central Indiana, 2016"},{"id":377661,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5067/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Hamilton County, Marion County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-85.8617,40.2201],[-85.863,40.139],[-85.8624,39.9436],[-85.8625,39.9286],[-85.9369,39.9272],[-85.9379,39.87],[-85.9541,39.8696],[-85.9518,39.6969],[-85.9523,39.638],[-86.248,39.6335],[-86.3268,39.6318],[-86.3281,39.8526],[-86.328,39.8662],[-86.325,39.8662],[-86.3267,39.9238],[-86.2967,39.9246],[-86.2757,39.925],[-86.2385,39.9259],[-86.239,39.9549],[-86.2417,40.0419],[-86.242,40.1304],[-86.2424,40.1807],[-86.2435,40.2152],[-86.1285,40.2176],[-86.0135,40.2186],[-85.9015,40.2194],[-85.8617,40.2201]]]},\"properties\":{\"name\":\"Hamilton\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Somatic growth exerts strong control on patterns in the abundance of animal populations via effects on maturation, fecundity, and survival rates of juveniles and adults. In this paper, we quantify abiotic and biotic drivers of rainbow trout growth in the Colorado River, AZ, and the resulting impact on spatial and temporal variation in abundance. Inferences are based on approximately 10,000 observations of individual growth grates obtained through an intensive mark‐recapture effort conducted over five years (2012‐2016) in a 130 km‐long study segment downstream of Glen Canyon Dam. Prey availability, turbidity‐driven feeding efficiency, and intra‐specific competition were the dominant drivers of rainbow trout growth. Discharge, water temperature, and solar insulation were also evaluated but had a smaller influence. Mixed‐effect models explained 79‐82% of the variability in observed growth rates, with fixed covariate effects explaining 79‐87% of the total variation in growth parameters across five reaches and 18 quarterly sampling intervals. Reductions in growth owing in part to a phosphorous‐driven decline in prey availability, led to substantive weight loss and poor fish condition. This in turn lowered survival rates and delayed maturation, which led to a rapid decline in abundance and later recruitments. Reductions in feeding efficiency, due to episodic inputs of fine sediment from tributaries, and warmer water temperatures, contributed to reduced growth in downstream reaches, which led to more severe declines in abundance. Somatic growth rates increased following the population collapse due to reduced competition, and in the absence of substantive increases in prey availability. Our study elucidates important linkages between abiotic and biotic factors, somatic growth, and vital rates, and demonstrates how variation in somatic growth influences temporal and spatial patterns in abundance.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1427","usgsCitation":"Korman, J., Yard, M.D., Dzul, M.C., Yackulic, C., Dodrill, M., Deemer, B., and Kennedy, T., 2020, Changes in prey, turbidity, and competition reduce somatic growth and cause the collapse of a fish population: Ecological Monographs, v. 91, no. 1, e01427, https://doi.org/10.1002/ecm.1427.","productDescription":"e01427","ipdsId":"IP-116364","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436811,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90ODKZ3","text":"USGS data release","linkHelpText":"Rainbow trout growth data and growth covariate data downstream of Glen Canyon Dam in the Colorado River, Arizona, 2012 - 2016"},{"id":378203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, 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 -111.6925048828125,\n 36.76309161490538\n ],\n [\n -111.3519287109375,\n 36.76309161490538\n ],\n [\n -111.3519287109375,\n 37.00035919622158\n ],\n [\n -111.6925048828125,\n 37.00035919622158\n ],\n [\n -111.6925048828125,\n 36.76309161490538\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"91","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Korman, Josh","contributorId":139960,"corporation":false,"usgs":false,"family":"Korman","given":"Josh","email":"","affiliations":[{"id":13333,"text":"Ecometric Research Inc.","active":true,"usgs":false}],"preferred":false,"id":798084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798068,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dzul, Maria C. 0000-0002-4798-5930 mdzul@usgs.gov","orcid":"https://orcid.org/0000-0002-4798-5930","contributorId":5469,"corporation":false,"usgs":true,"family":"Dzul","given":"Maria","email":"mdzul@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798069,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798070,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dodrill, Michael J. 0000-0002-7038-7170","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":206439,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798071,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798072,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kennedy, Theodore 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":221741,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798073,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215621,"text":"70215621 - 2020 - Sediment record of mining legacy and water quality from a drinking-water reservoir, Aztec, New Mexico, USA","interactions":[],"lastModifiedDate":"2020-10-26T14:07:39.321219","indexId":"70215621","displayToPublicDate":"2020-08-27T09:02:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1534,"text":"Environmental Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sediment record of mining legacy and water quality from a drinking-water reservoir, Aztec, New Mexico, USA","docAbstract":"The record of mining legacy and water quality was investigated in sediments collected in 2018 from four trenches in the Aztec, New Mexico, drinking-water reservoir #1. Bulk chemical analysis of sediments with depth in the reservoir revealed variable trace-element (uranium, vanadium, arsenic, copper, sulfur, silver, lead, and zinc) concentrations, which appear to coincide with historical mining and milling operations. Cesium-137 age dating, which identified the location of the 1963 radioactive fallout maximum, combined with the known age of the bottom and top of the sediment trenches, was used to estimate a polynomial sedimentation rate (average rate = 1.7 cm/yr). The clay size fraction (< 0.004 mm) was the dominant grain-size fraction of the sediments. Abundant fine-grained phyllosilicate (clay) minerals, predominantly montmorillonite and kaolinite, may explain sorption properties of trace elements. Scanning electron microscopy evaluation of sediments from two trenches showed copper and zinc associated with sulfur, and arsenic associated with iron and aluminum oxides. Results from laboratory batch experiments indicated that uranium, vanadium, and arsenic were released when sediments were reacted with a 150 mg/L sodium bicarbonate solution whereas copper was released when sediments were reacted with 2 mMol/L acetic acid. Observed concentrations from the two leach tests were below regulatory thresholds for delivery of solids to a landfill and were below drinking-water standards. Diatom relative abundance indicates that the water quality in the reservoir was not impaired by high metal concentrations.
The United States faces growing challenges to its water supply, infrastructure, and aquatic ecosystems because of population growth, climate change, floods and droughts, and aging water delivery systems. To help address these challenges, the U.S. Geological Survey (USGS) Water Resources Mission Area has established new strategic priorities that capitalize on the operational and scientific strengths of the USGS to address these complex societal issues. The USGS Integrated Water Availability Assessment Program within the Water Resources Mission Area will provide nationally consistent assessments of water available for human and ecological needs and identify factors that limit water availability. Integrated Water Availability Assessments will assess current conditions, long-term trends, factors responsible for current conditions and observed trends, and provide forecasts of future changes at national and regional scales.
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Drone use in wildlife biology has greatly increased as they become cheaper and easier to deploy in the field. In this paper we describe a less invasive method of using drones and exploring their limitations for studying colonial nesting waterbirds. Western Grebes, like most colonial nesting waterbirds, can be very sensitive to human interaction. Using a 3DR Solo quad copter equipped with a high-resolution digital camera we were able to effectively map and monitor a Western Grebe breeding colony throughout the nesting period with a series of 6 flights. We were able to use drone collected aerial imagery to model nest survival while minimizing disturbance to the birds. However, we were not able to deploy the drone at all of our study sites. Our ability to effectively deploy the drone was hindered by the environmental and vegetation characteristics of a site. Drone technology can be a useful tool, especially when studying a species sensitive to human interaction. However, there researchers should carefully consider their species and study site to evaluate if a drone is the proper tool to meet their objectives.
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Ty","contributorId":280032,"corporation":false,"usgs":false,"family":"Matthews","given":"Ty","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":835916,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212769,"text":"70212769 - 2020 - Concentrations and size distribution of TiO2 and Ag engineered particles in five wastewater treatment plants in the United States","interactions":[],"lastModifiedDate":"2020-09-10T20:47:33.696034","indexId":"70212769","displayToPublicDate":"2020-08-26T11:10:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Concentrations and size distribution of TiO2 and Ag engineered particles in five wastewater treatment plants in the United States","title":"Concentrations and size distribution of TiO2 and Ag engineered particles in five wastewater treatment plants in the United States","docAbstract":"The growing use of engineered particles (e.g., nanosized and pigment sized particles, 1 to 100 nm and 100 to 300 nm, respectively) in a variety of consumer products increases the likelihood of their release into the environment. Wastewater treatment plants (WWTPs) are an important pathways of introduction of engineered particles to the aquatic systems. This study reports the concentrations, removal efficiencies, and particle size distributions of Ag and TiO2 engineered particles in five WWTPs in three states in the United States. The concentration of Ag engineered particles was quantified as the total Ag concentration, whereas the concentration of TiO2 engineered particles was quantified using mass-balance calculations and shifts in the elemental ratio of Ti/Nb above their natural background elemental ratio. Ratios of Ti/Nb in all WWTP influents, activated sludges, and effluents were 2–12 times higher (e.g., 519 to 3243) than the natural background Ti/Nb ratio (e.g., 267 ± 9), indicating that 49–92% of Ti originates from anthropogenic sources. The concentration of TiO2 engineered particles (in μg TiO2 L−1) in the influent, activated sludge, and effluent varied within the ranges of 70–670, 3570–6700, and 7–30, respectively. The concentration of Ag engineered particles (in μg Ag L−1) in the influent, activated sludge, and effluent varied within the ranges of 0.11–0.33, 1.45–1.65, and 0.01–0.04, respectively. The overall removal efficiency (e.g., effluent/influent concentrations) of TiO2 engineered particles (e.g., 90 to 96%) was higher than that for Ag engineered particles (e.g., 82 to 95%). Particles entering WWTPs are in the nanosized range for Ag (e.g., >99%) and a mixture of nanosized (e.g., 15 to 90%) and pigment sized particles (e.g., 10 to 85%) for TiO2. Nearly all Ag (>99%) and 55 to 100% of TiO2 particles discharged to surface water with WWTP effluent are within the nanosize range. This study provides evidence that TiO2 and Ag engineered nanomaterials enter aquatic systems with WWTP effluents, and that their concentrations are expected to increase with the increased applications of TiO2 and Ag engineered nanomaterials in consumer products.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.142017","usgsCitation":"Md. Mahmudun Nabi, Wang, J., Meyer, M., Croteau, M.N., Ismail, N., and Baalousha, M., 2020, Concentrations and size distribution of TiO2 and Ag engineered particles in five wastewater treatment plants in the United States: Science of the Total Environment, v. 753, 142017, 11 p., https://doi.org/10.1016/j.scitotenv.2020.142017.","productDescription":"142017, 11 p.","ipdsId":"IP-120434","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":455516,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.142017","text":"Publisher Index Page"},{"id":377935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Massachusetts, South Carolina","city":"Amherst, Columbia, Mt. Pleasant, Palo Alto","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.23251342773438,\n 37.38161597475995\n ],\n [\n -122.10067749023438,\n 37.38161597475995\n ],\n [\n -122.10067749023438,\n 37.52551993630741\n ],\n [\n -122.23251342773438,\n 37.52551993630741\n ],\n [\n -122.23251342773438,\n 37.38161597475995\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.55130767822266,\n 42.3643786536149\n ],\n [\n -72.49500274658203,\n 42.3643786536149\n ],\n [\n -72.49500274658203,\n 42.40317854182803\n ],\n [\n -72.55130767822266,\n 42.40317854182803\n ],\n [\n -72.55130767822266,\n 42.3643786536149\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.0897445678711,\n 33.951049661182104\n ],\n [\n -80.97610473632811,\n 33.951049661182104\n ],\n [\n -80.97610473632811,\n 34.0236404659703\n ],\n [\n -81.0897445678711,\n 34.0236404659703\n ],\n [\n -81.0897445678711,\n 33.951049661182104\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.90596771240234,\n 32.75306566002286\n ],\n [\n -79.80966567993164,\n 32.75306566002286\n ],\n [\n -79.80966567993164,\n 32.82738462221177\n ],\n [\n -79.90596771240234,\n 32.82738462221177\n ],\n [\n -79.90596771240234,\n 32.75306566002286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"753","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Md. Mahmudun Nabi","contributorId":239632,"corporation":false,"usgs":false,"family":"Md. Mahmudun Nabi","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":797436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Jingjing","contributorId":239635,"corporation":false,"usgs":false,"family":"Wang","given":"Jingjing","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":797437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meyer, Madeleine","contributorId":239638,"corporation":false,"usgs":false,"family":"Meyer","given":"Madeleine","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":797438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":797439,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ismail, Niveen","contributorId":239641,"corporation":false,"usgs":false,"family":"Ismail","given":"Niveen","affiliations":[{"id":47946,"text":"Smith College","active":true,"usgs":false}],"preferred":false,"id":797440,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baalousha, Mohammed","contributorId":239642,"corporation":false,"usgs":false,"family":"Baalousha","given":"Mohammed","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":797441,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212678,"text":"ofr20201078 - 2020 - Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17","interactions":[],"lastModifiedDate":"2020-08-26T15:51:06.049297","indexId":"ofr20201078","displayToPublicDate":"2020-08-26T10:30:00","publicationYear":"2020","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":"2020-1078","displayTitle":"Assessment of Dissolved-Selenium Concentrations and Loads in the Lower Gunnison River Basin, Colorado, as Part of the Selenium Management Program, 2011–17","title":"Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17","docAbstract":"The Gunnison Basin Selenium Management Program implemented a water-quality monitoring network in 2011 to measure concentrations of selenium in the lower Gunnison River Basin in Colorado. Selenium is a trace element that bioaccumulates in aquatic food chains. Selenium is essential for life, but elevated amounts can cause reproductive failure, deformities, and other harmful effects. The primary goal of the Selenium Management Program is to meet the State of Colorado water-quality standard of 4.6 micrograms per liter (µg/L) for dissolved selenium at the U.S. Geological Survey (USGS) streamflow-gaging station number 09152500—Gunnison River near Grand Junction, Colorado—herein referred to as “Whitewater.” The U.S. Geological Survey, in cooperation with the Bureau of Reclamation, has completed a review of dissolved-selenium data collected from the Selenium Management Program network during Water Year (WY) 2017 (October 1, 2016 through September 30, 2017) to further the understanding of the status and trends of selenium in the basin. This report presents the percentile values for selenium because regulatory agencies in Colorado make decisions based on the U.S. Environmental Protection Agency’s Clean Water Act section 303(d), which uses percentile values for concentrations. Also presented are dissolved-selenium loads at 14 sites in the lower Gunnison River Basin for WYs 2011–17. Annual dissolved-selenium loads were calculated for six sites with continuous U.S. Geological Survey streamflow-gaging stations. These six sites are referred to as “core” sites in this report. The remaining sites, which do not have streamflow-gaging stations, are referred to as “ancillary” sites in this report. During WY 2017, the loads calculated at the six core sites ranged from 306 pounds (lb) at Uncompahgre River at Colona to 12,600 lb at Whitewater, respectively.
By using discrete water-quality samples and the associated discharge measurements, instantaneous loads were calculated for 14 sites in WYs 2011–17 where discrete water-quality sampling took place. Median instantaneous loads ranged from 0.52 pounds per day (lb/d) at Uncompahgre River at Colona to 35.7 lb/d at Whitewater. Mean instantaneous loads ranged from 0.63 lb/d at Cummings Gulch at mouth to 35.5 lb/d at Whitewater. Most tributary sites in the basin had a median instantaneous dissolved-selenium load of less than 20.0 lb/d. In general, dissolved-selenium loads at Gunnison River main-stem sites showed an increase from upstream to downstream.
The State of Colorado’s water-quality standard for dissolved selenium of 4.6 µg/L was compared to the 85th percentiles for dissolved selenium at selected sites. Annual 85th percentiles for dissolved selenium were calculated by using estimated dissolved-selenium concentrations from linear regression models for the six core sites with U.S. Geological Survey streamflow-gaging stations. The 85th-percentile concentrations for WY 2017 based on this method ranged from 0.68 µg/L at Uncompahgre River at Colona to 140 µg/L at Loutzenhizer Arroyo at North River Road. The 85th percentiles for concentrations of dissolved selenium also were calculated from water-quality samples collected during WY 2017 from sites with sufficient data. The annual 85th-percentile concentrations based on the discrete samples ranged from 0.75 µg/L at Uncompahgre River at Colona to 106 µg/L at Loutzenhizer Arroyo at North River Road.
An analysis was completed for Whitewater to determine if an upward or downward trend exists for dissolved-selenium loads during two time periods. The first time period included all data at Whitewater, whereas the second time period focused on more recent data. The trend analysis indicates a decrease from 22,200 to 12,600 lb, which is a 43.1 percent (9,600 lb) reduction during the time period WY 1986 through WY 2017. The trend analysis for the annual dissolved-selenium load for WY 1995 through WY 2017 indicates a decrease of 6,600 lb per year, or 35.5 percent. An evaluation of laboratory bias was completed for selenium data which was used in the trend analysis. Findings indicated a potential positive bias of approximately 12 percent may exist in the data from October 2005 through August 2015.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201078","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Henneberg, M.F., 2020, Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17: U.S. Geological Survey Open-File Report 2020–1078, 21 p., https://doi.org/10.3133/ofr20201078","productDescription":"v, 21 p.","onlineOnly":"Y","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":377861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1078/ofr20201078.pdf","text":"Report","size":"1.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1078"},{"id":377860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1078/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.80584716796875,\n 39.01064750994083\n ],\n [\n -109.11895751953125,\n 38.8782049970615\n ],\n [\n -108.6328125,\n 38.10214399750345\n ],\n [\n -108.69598388671875,\n 37.77288579232439\n ],\n [\n -107.87750244140625,\n 37.309014074275915\n ],\n [\n -107.4462890625,\n 37.31338308990806\n ],\n [\n -107.1441650390625,\n 37.727280276860036\n ],\n [\n -107.18536376953125,\n 38.07620357665235\n ],\n [\n -107.26776123046875,\n 38.50304202775689\n ],\n [\n -107.50671386718749,\n 38.9380483825641\n ],\n [\n -107.6495361328125,\n 39.115144700901475\n ],\n [\n -108.80584716796875,\n 39.01064750994083\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Streamflow estimates for floods with an annual exceedance probability of 0.001 or lower are needed to accurately portray risks to critical infrastructure, such as nuclear powerplants and large dams. However, extrapolating flood-frequency curves developed from at-site systematic streamflow records to very low annual exceedance probabilities (less than 0.001) results in large uncertainties in the streamflow estimates. Traditionally, methods for statistically estimating flood frequency have relied on the systematic streamflow record, which provides a time series of annual maximum flood peaks, often including some historical peaks. However, most peak-flow records are less than 100 years, and uncertainties are large when trying to extrapolate magnitudes of very low annual exceedance probability events.
Other data may be available that extend the record beyond the systematic dataset. Historical data are defined as data from outside the period of systematic records but within the period of human records. Examples of historical information include flood estimates from other agencies and newspaper accounts that can be translated to flood magnitude point estimates, interval estimates, or perception thresholds (such as a statement that an 1880 flood was the largest since 1869). Paleoflood data, which may also extend the dataset, include a broad range of information about flood occurrence or magnitude from sources like sediment deposits or tree rings.
Several assumptions are made in flood-frequency analysis, and an understanding of whether the data conform to these assumptions is desired. A particularly difficult assumption to evaluate for flood-frequency analysis is the underlying assumption that the flood series is stationary—the assumption that a time series of peak flow varies around a constant mean within a particular range of values (constant variance). As the hydrologic community’s understanding of natural systems and anthropogenic effects on streamflows has evolved, the community has come to understand that many surface-water systems exhibit one or more forms of nonstationarity, and thus the stationarity assumption is often violated to some degree. However, there is currently (2020) no consensus among hydrologists regarding the most appropriate flood-frequency-analysis methods for nonstationary systems, and this topic remains an active area of research.
A literature review was completed to summarize the state of the science of flood frequency. The literature review highlights tools available to detect nonstationarities and identifies approaches that include external information to inform flood-frequency analysis. To demonstrate methods for initial data analysis and for incorporating historical and paleoflood information in flood-frequency analysis, five sites were selected: the Red River of the North at James Avenue Pumping Station, Winnipeg, Manitoba, Canada; lower reach, Rapid Creek, South Dakota; Spring Creek, South Dakota; Cherry Creek near Melvin, Colorado; and Escalante River near Escalante, Utah. The sites were chosen for the availability of published historical and paleoflood data and for their geographic diversity and unique characteristics, which highlighted issues such as autocorrelation, change points, trends, outlier peaks, or short periods of record.
An initial data analysis that involved examining records for autocorrelation, change points, and trends was completed for all sites. The flood-frequency analysis completed for this study used version 7.2 of the U.S. Geological Survey PeakFQ program. Multiple analyses were done on each site documenting the change in the flood-frequency curve when additional historical or paleoflood data were added. When other flood-frequency studies were available, their results were compared to the results here. The comparisons in some cases simply show the effect of additional years of data, whereas other comparisons show results from probability distributions or fitting methods other than those used in PeakFQ.
For the Red River of the North, flood-frequency analysis shows that paleoflood data appear necessary to reasonably estimate very low annual exceedance probabilities. For the analysis of the lower reach of Rapid Creek and Spring Creek, paleoflood information helped put a high outlier from the systematic period in context; however, very low annual exceedance probabilities at these sites still had extraordinarily large confidence bounds. These sites also showed that paleoflood information might be transferred from one site to another, with the caveat that this is a case where we had existing paleoflood data to test the transfer of paleoflood information—this is not the case at many sites, and transferring paleoflood information requires assumptions about the comparability of floods at the sites. The Cherry Creek analysis affirmed the result of an earlier study that showed that the generalized Pareto distribution was not a good distribution for estimating very low annual exceedance probabilities. The Escalante River analysis showed that adding paleoflood information might increase uncertainty for very low annual exceedance probabilities, compared to analysis with the systematic period of record information only, when the paleoflood peaks are of much larger magnitudes than the systematic record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205065","collaboration":"Prepared in cooperation with the U.S. Nuclear Regulatory Commission","usgsCitation":"Ryberg, K.R., Kolars, K.A., Kiang, J.E., and Carr, M.L., 2020, Flood-frequency estimation for very low annual exceedance probabilities using historical, paleoflood, and regional information with consideration of nonstationarity: U.S. Geological Survey Scientific Investigations Report 2020–5065, 89 p., https://doi.org/10.3133/sir20205065.","productDescription":"Report: xii, 89 p.; 5 Tables; Appendix; Dataset","numberOfPages":"105","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088812","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":377559,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_appendix.zip","text":"Appendix 1. Data, Settings, and Output for Each Site and Scenario","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5065 Appendix 1","linkHelpText":"— Each zipped file represents the analysis for a particular site and scenario"},{"id":377557,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_7.pdf","text":"Table 7","size":"114 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 7","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under two different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 06712500 Cherry Creek near Melvin, Colorado, with comparisons to other distributions and fitting methods."},{"id":377553,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065.pdf","text":"Report","size":"5.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065"},{"id":377554,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_4.pdf","text":"Table 4","size":"139 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 4","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under 10 different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 05OJ015 Red River of the North at James Avenue Pumping Station, Winnipeg, Manitoba, Canada, as well as results from flood-frequency studies by Burn and Goel (2001) and Harden (1999)."},{"id":377697,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":377555,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_5.pdf","text":"Table 5","size":"122 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 5","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for the lower reach of Rapid Creek, South Dakota, with comparisons to Harden and others (2011)."},{"id":377556,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_6.pdf","text":"Table 6","size":"112 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 6","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for Spring Creek, South Dakota, with comparisons to Harden and others (2011)."},{"id":377552,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5065/coverthb.jpg"},{"id":377558,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_8.pdf","text":"Table 8","size":"116 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 8","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 09337500 Escalante River near Escalante, Utah, with comparisons to Webb and others (1988), Webb and Rathburn (1988), and Kenney and others (2008)."}],"contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Flood-frequency (hereinafter frequency) estimates provide information used to design, operate, and maintain hydraulic structures such as bridges and dams. Failures of these structures could cause catastrophic loss of property, life, or both. In addition to frequency estimates that use annual peak streamflow, frequency estimates of flood durations are required to safely and effectively operate the numerous dams in the Columbia River Basin of the northwestern United States, and British Columbia, Canada. Frequency studies rely on U.S. Geological Survey Guidelines for Determining Flood Flow Frequency (Bulletin 17C, published in 2018). A major consideration in estimating frequencies is the use of skew coefficients, which measure the asymmetry of flood flow distributions. Large uncertainties are associated with estimating the at-site skew coefficients directly from streamflow records, which are limited in length. Skew also is sensitive to extreme events for limited record lengths. Bulletin 17C recommends using regional skew coefficients to weight with the at-site skew estimate for more reliable frequency estimates. In this study, streamflow records from 313 unregulated U.S. Geological Survey streamgage sites and 97 regulated sites with naturalized streamflow records provided by the U.S. Army Corps of Engineers were used to develop regional skew models for the Columbia River Basin. The naturalized streamflow records were synthesized by removing regulatory components such as withdrawals and reservoir storage. Skew models were developed for 1-, 3-, 7-, 10-, 15-, 30-, and 60-day flood durations and used to estimate regional skew coefficients for the Columbia River Basin.
This report used Bayesian statistical regression methods to develop and analyze regional skew models based on hydrologically important basin characteristics. After examining a suite of available basin characteristics, mean annual precipitation had the strongest correlation to skew across the flood durations. Regional skew regression models were fit using mean annual precipitation for selected subbasins in the Columbia River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205073","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Lind, G.D., Lamontagne, J.R., and Stonewall, A.J., 2020, Development of regional skew coefficients for selected flood durations in the Columbia River Basin, northwestern United States and British Columbia, Canada (ver. 1.1, October 2020): U.S. Geological Survey Scientific Investigations Report 2020–5073, 48 p., https://doi.org/10.3133/sir20205073.","productDescription":"Report: viii, 48 p.; 8 Tables; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109443","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":377840,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5073/sir20205073_table1.csv","text":"Table 1","size":"26 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2130 SW 5th Avenue
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Aquatic insects are poorly represented in water quality criteria, and previous studies have suggested a lack of sensitivity in acute toxicity tests despite observational studies demonstrating the contrary. Our objectives were to determine the toxicity of nickel (Ni) and zinc (Zn) to the mayfly Neocloeon triangulifer in fed acute (96-h) and chronic exposures to estimate aqueous effect concentrations while acknowledging the importance of dietary exposure for these insects. For the chronic tests, we conducted preliminary full–life cycle (~25–30 d) and subchronic (14 d) exposures to compare the relative sensitivity of the 2 test durations under similar conditions (i.e., feeding rates). Observing similar sensitivity, we settled on 14 d as the definitive test duration. Furthermore, we conducted experiments to determine how much food could be added to a given volume of water while minimally impacting dissolved metal recovery; a ratio of food dry mass to water volume (<0.005) achieved this. In the 14-d tests, we obtained a median lethal concentration and most sensitive chronic endpoint of 147 and 23 µg/L dissolved Ni (acute to chronic ratio [ACR] = 6.4), respectively, and 81 (mean value) and 10 µg/L dissolved Zn (ACR = 8.1), respectively. The acute values are orders of magnitude lower than previously published values for mayflies, probably most importantly due to the presence of dietary exposure but also potentially with some influence of organism age and test temperature.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.4683","usgsCitation":"Soucek, D.J., Dickinson, A., Schlekat, C.E., Van Genderen, E., and Hammer, E.J., 2020, Acute and chronic toxicity of nickel and zinc to a laboratory cultured mayfly (Neocloeon triangulifer) in aqueous but fed exposures: Environmental Toxicology and Chemistry, v. 39, no. 6, p. 1196-1206, https://doi.org/10.1002/etc.4683.","productDescription":"11 p.","startPage":"1196","endPage":"1206","ipdsId":"IP-132676","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":436813,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T75RNV","text":"USGS data release","linkHelpText":"Survival, reproduction, and weight of Neocloeon triangulifer after short and long-term exposures to nickel and zinc"},{"id":388543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"6","noUsgsAuthors":true,"publicationDate":"2020-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Soucek, David J. 0000-0002-7741-0193","orcid":"https://orcid.org/0000-0002-7741-0193","contributorId":224591,"corporation":false,"usgs":false,"family":"Soucek","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":821609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dickinson, Amy","contributorId":224592,"corporation":false,"usgs":false,"family":"Dickinson","given":"Amy","email":"","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":821610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schlekat, Christan E.","contributorId":139228,"corporation":false,"usgs":false,"family":"Schlekat","given":"Christan","email":"","middleInitial":"E.","affiliations":[{"id":12705,"text":"Nickel Producers Environmental Research Association, Durham, Nor","active":true,"usgs":false}],"preferred":false,"id":821611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Genderen, Eric","contributorId":242622,"corporation":false,"usgs":false,"family":"Van Genderen","given":"Eric","affiliations":[{"id":48485,"text":"International Zinc Association, Durham, NC","active":true,"usgs":false}],"preferred":false,"id":821612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammer, Edward J.","contributorId":150723,"corporation":false,"usgs":false,"family":"Hammer","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":18077,"text":"U. S. Environmental Protection Agency, Region 5, Water Quality Branch, Chicago, Illinois","active":true,"usgs":false}],"preferred":false,"id":821613,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212768,"text":"70212768 - 2020 - Reducing water scarcity by improving water productivity in the United States","interactions":[],"lastModifiedDate":"2020-08-27T16:59:15.03136","indexId":"70212768","displayToPublicDate":"2020-08-25T11:55:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Reducing water scarcity by improving water productivity in the United States","docAbstract":"Nearly one-sixth of U.S. river basins are unable to consistently meet societal water demands while also providing sufficient water for the environment. Water scarcity is expected to intensify and spread as populations increase, new water demands emerge, and climate changes. Improving water productivity by meeting realistic benchmarks for all water users could allow U.S. communities to expand economic activity and improve environmental flows. Here we utilize a spatially detailed database of water productivity to set realistic benchmarks for over 400 industries and products. We assess unrealized water savings achievable by each industry in each river basin within the conterminous U.S. by bringing all water users up to industry- and region-specific water productivity benchmarks. Some of the most water stressed areas throughout the U.S. West and South have the greatest potential for water savings, with around half of these water savings obtained by improving water productivity in the production of corn, cotton, and alfalfa. By incorporating benchmark-meeting water savings within a national hydrological model (WaSSI), we demonstrate that depletion of river flows across Western U.S. regions can be reduced on average by 6.2–23.2%, without reducing economic production. Lastly, we employ an environmentally extended input-output model to identify the U.S. industries and locations that can make the biggest impact by working with their suppliers to reduce water use 'upstream' in their supply chain. The agriculture and manufacturing sectors have the largest indirect water footprint due to their reliance on water-intensive inputs but these sectors also show the greatest capacity to reduce water consumption throughout their supply chains.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ab9d39","usgsCitation":"Marston, L., Lamsal, G., Ancona, Z.H., Caldwell, P.V., Richter, B., Ruddell, B., Rushforth, R., and Davis, K.F., 2020, Reducing water scarcity by improving water productivity in the United States: Environmental Research Letters, v. 15, no. 9, 094033, 13 p., https://doi.org/10.1088/1748-9326/ab9d39.","productDescription":"094033, 13 p.","ipdsId":"IP-114542","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":455531,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab9d39","text":"Publisher Index Page"},{"id":377942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": 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University","active":true,"usgs":false}],"preferred":false,"id":797433,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rushforth, Richard","contributorId":239630,"corporation":false,"usgs":false,"family":"Rushforth","given":"Richard","email":"","affiliations":[],"preferred":false,"id":797434,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Davis, Kyle F. 0000-0003-4504-1407","orcid":"https://orcid.org/0000-0003-4504-1407","contributorId":239631,"corporation":false,"usgs":false,"family":"Davis","given":"Kyle","email":"","middleInitial":"F.","affiliations":[{"id":47945,"text":"Department of Geography and Spatial Sciences & Department of Plant and Soil Sciences, University of Delaware","active":true,"usgs":false}],"preferred":false,"id":797435,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228386,"text":"70228386 - 2020 - Groundwater upwelling regulates thermal hydrodynamics and salmonid movements during high-temperature events at a montane 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We used integrated networks of temperature loggers, PIT tag antenna stations, and in situ temperature mapping to investigate the thermal hydrodynamics and associated movements of PIT-tagged salmonids at the confluence of Tenderfoot Creek, a major, unaltered coldwater tributary of the Smith River. Contrary to expectations, Tenderfoot Creek itself was not used as a thermal refuge by salmonids during periods of high water temperatures in Smith River; rather, its cool outflow plume into the main stem was used instead. Mean daily outflow water temperatures averaged 2.9°C lower than those of the Smith River during summer and ranged from 0.5°C to 6.1°C lower. Moreover, measured and estimated temperatures in the outflow were cooler (by up to 2.8°C) than in Tenderfoot Creek itself at times as a result of groundwater upwelling at the confluence. Detections of PIT-tagged fish in the thermal plume increased, especially at night, when daily mean water temperatures exceeded 20°C in the main-stem Smith River; more than four times as many PIT-tagged fish were detected in the plume (N = 52) than along the opposite bank (N = 12), which ostensibly afforded better cover. Coldwater tributary confluences may provide superior thermal refuges for salmonids—cooler than the tributaries themselves—when water temperatures in river main stems are stressful.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10259","usgsCitation":"Ritter, T.D., Zale, A.V., Grisak, G., and Lance, M.J., 2020, Groundwater upwelling regulates thermal hydrodynamics and salmonid movements during high-temperature events at a montane tributary confluence: Transactions of the American Fisheries Society, v. 149, no. 5, p. 600-619, https://doi.org/10.1002/tafs.10259.","productDescription":"20 p.","startPage":"600","endPage":"619","ipdsId":"IP-115228","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":455533,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10259","text":"Publisher Index Page"},{"id":395786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Smith River, Tenderfoot Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.2964391708374,\n 46.98804000472103\n ],\n [\n -111.25751495361327,\n 46.98804000472103\n ],\n [\n -111.25751495361327,\n 46.9993095934231\n ],\n [\n -111.2964391708374,\n 46.9993095934231\n ],\n [\n -111.2964391708374,\n 46.98804000472103\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ritter, Thomas David","contributorId":275611,"corporation":false,"usgs":false,"family":"Ritter","given":"Thomas","email":"","middleInitial":"David","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":834174,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zale, Alexander V. 0000-0003-1703-885X","orcid":"https://orcid.org/0000-0003-1703-885X","contributorId":244099,"corporation":false,"usgs":true,"family":"Zale","given":"Alexander","email":"","middleInitial":"V.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":834173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grisak, Grant","contributorId":275612,"corporation":false,"usgs":false,"family":"Grisak","given":"Grant","email":"","affiliations":[{"id":48627,"text":"mtfwp","active":true,"usgs":false}],"preferred":false,"id":834175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lance, Michael J.","contributorId":275613,"corporation":false,"usgs":false,"family":"Lance","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":834176,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212621,"text":"sim3459 - 2020 - Stratigraphic units of shallow unconsolidated deposits in Deadwood, South Dakota, delineated by real-time kinematic surveys","interactions":[],"lastModifiedDate":"2020-08-26T13:05:12.542236","indexId":"sim3459","displayToPublicDate":"2020-08-25T11:11:39","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3459","displayTitle":"Stratigraphic Units of Shallow Unconsolidated Deposits in Deadwood, South Dakota, Delineated by Real-Time Kinematic Surveys","title":"Stratigraphic units of shallow unconsolidated deposits in Deadwood, South Dakota, delineated by real-time kinematic surveys","docAbstract":"The City of Deadwood, South Dakota, has been working on a new archeological investigation in preparation for economic growth and expansion within the city limits, through the Deadwood Historic Preservation Office. During the excavation process, buried artifacts and historical features from the late 1800s have been uncovered. The stratigraphy of shallow unconsolidated deposits in the city of Deadwood, S. Dak., was surveyed on January 29, 2020, using real-time kinematic survey methods and described to identify variations in geologic material, thickness, and depth from the land surface in support of archeological studies by the city. The findings of the study will provide city managers and the public with reliable and impartial information for their use by advancing field or analytical methodology and understanding of hydrologic processes in the study area. The primary excavation site was surveyed, and stratigraphic units were delineated from changes in material properties or depositional environment. The primary excavation site consisted of nine stratigraphic units; however, some units were not consistent along the length of the excavation and pinched out along the cross section. Survey data points also were collected for artifacts and other sites of interest. The shallow surficial geology in the study area was affected by human construction, fires, and flooding.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3459","collaboration":"Prepared in cooperation with the City of Deadwood, South Dakota","usgsCitation":"Tatge, W.S., Medler, C.J., Eldridge, W.G., and Valder, J.F., 2020, Stratigraphic units of shallow unconsolidated deposits in Deadwood, South Dakota, delineated by real-time kinematic surveys: U.S. Geological Survey Scientific Investigations Map 3459, pamphlet 7 p., 1 sheet, https://dx.doi.org/10.3133/sim3459.","productDescription":"Pamphlet: vi, 7 p.; 1 Sheet: 42.75 x 35.40 inches; 1 Table","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119064","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":377805,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3459/sim3459_table1.csv","text":"Table 1","size":"32.7 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIM 3459 Table 1","linkHelpText":"— Survey points collected for delineation of selected stratigraphic units."},{"id":377804,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3459/sim3459_pamphlet.pdf","text":"Pamphlet","size":"2.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3459 Pamphlet"},{"id":377803,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3459/sim3459.pdf","text":"Sheet 1","size":"5.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3459","linkHelpText":"— Stratigraphic Units of Shallow Unconsolidated Deposits in Deadwood, South Dakota, Delineated by Real-Time Kinematic Surveys"},{"id":377802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3459/coverthb.jpg"}],"country":"United States","state":"South Dakota","city":"Deadwood","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.74698638916016,\n 44.364237624976326\n ],\n [\n -103.71814727783202,\n 44.364237624976326\n ],\n [\n -103.71814727783202,\n 44.38558741441454\n ],\n [\n -103.74698638916016,\n 44.38558741441454\n ],\n [\n -103.74698638916016,\n 44.364237624976326\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Continuous water-quality monitoring data and chemical analysis of surface-water and groundwater samples collected during 2017–19 in the recharge zone of the Edwards aquifer were used to develop a better understanding of the surface-water/groundwater connection in and around Bexar County in south-central Texas. This fact sheet is provided to inform water-resource managers, city planners, the scientific community, and the general public about the effects of urbanization on water quality in the Edwards aquifer recharge zone.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203028","collaboration":"Prepared in cooperation with the City of San Antonio","usgsCitation":"Opsahl, S.P., Musgrove, M., and Mecum, K.E., 2020, Effects of urbanization on water quality in the Edwards aquifer, San Antonio and Bexar County, Texas: U.S. Geological Survey Fact Sheet 2020–3028, 4 p., https://doi.org/10.3133/fs20203028.","productDescription":"Report: 4 p.; Companion Report","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-115922","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":376080,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3028/coverthb2.jpg"},{"id":376081,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3028/fs20203028.pdf","text":"Report","size":"1.27 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