The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement (
The distribution and relative abundance of Westslope Cutthroat Trout (WCT) Oncorhynchus clarkii lewisi in relation to habitat characteristics remain unknown across large portions of the species’ range. The goals of this research were to provide a foundational understanding of WCT distribution and relative abundance related to habitat characteristics in tributaries of the St. Maries River, Idaho—a highly altered watershed. The basin drains an area of approximately 1,863 km2 and has a longitudinal elevation difference of about 207 m. Backpack electrofishing and habitat assessments were conducted at 68 reaches in 35 different tributaries of the St. Maries River in 2017 and 2018. Habitat was measured at small (reach-level) and large (watershed-level) scales. A total of 652 WCT was sampled from 52 of 68 total reaches. Habitat characteristics varied by age-class, but most WCT were estimated to be age 0 and age 1. Logistic regression models indicated that the presence of age-0 WCT was positively related to stream gradient and elevation, but negatively related to water temperature, road density, fine substrate, stream depth, and the presence of Brook Trout (BKT) Salvelinus fontinalis. The relative abundance of age-0 WCT was positively associated with road density and inversely related to wetted width, canopy cover, and elevation. The presence of age-1 and older (age-1+) WCT was positively related to gradient, canopy cover, and elevation, but negatively associated with road density, temperature, stream depth, and the presence of BKT. Relative abundance of age-1+WCT was positively associated with gradient, large substrate, canopy cover, and road density. Conversely, the relative abundance of age-1+WCT was inversely related to wetted width and elevation. This research indicates that WCT populations can persist in response to altered landscapes when suitable habitat exists. However, unmitigated threats, such as nonnative species competition (e.g., BKT), hybridization with Rainbow Trout O. mykiss, habitat loss, and habitat fragmentation, pose persistent complications to WCT abundance in locations where populations appear robust but their actual abundance is unknown.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10450","usgsCitation":"Heckel, J.W., Quist, M.C., Watkins, C.J., and Dux, A.M., 2020, Distribution and abundance of Westslope Cutthroat Trout in relation to habitat characteristics at multiple spatial scales: North American Journal of Fisheries Management, v. 40, no. 4, p. 893-909, https://doi.org/10.1002/nafm.10450.","productDescription":"17 p,","startPage":"893","endPage":"909","ipdsId":"IP-107683","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":393744,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"St. Maries River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.707763671875,\n 46.878968335076856\n ],\n [\n -115.631103515625,\n 46.878968335076856\n ],\n [\n -115.631103515625,\n 47.372314620566925\n ],\n [\n -116.707763671875,\n 47.372314620566925\n ],\n [\n -116.707763671875,\n 46.878968335076856\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Heckel, John W","contributorId":270716,"corporation":false,"usgs":false,"family":"Heckel","given":"John","email":"","middleInitial":"W","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":829822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":829821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watkins, Carson J.","contributorId":171708,"corporation":false,"usgs":false,"family":"Watkins","given":"Carson","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":829823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dux, Andrew M.","contributorId":212798,"corporation":false,"usgs":false,"family":"Dux","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":829824,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211909,"text":"70211909 - 2020 - Millennial-scale climate and human drivers of environmental change and fire activity in a dry, mixed-conifer forest of northwestern Montana","interactions":[],"lastModifiedDate":"2020-08-11T18:31:53.806671","indexId":"70211909","displayToPublicDate":"2020-04-17T13:25:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5860,"text":"Frontiers in Forests and Global Change","active":true,"publicationSubtype":{"id":10}},"title":"Millennial-scale climate and human drivers of environmental change and fire activity in a dry, mixed-conifer forest of northwestern Montana","docAbstract":"Warm summer temperatures and longer fire seasons are promoting larger, and in some cases, more fires that are severe in low- and mid-elevation, dry mixed-conifer forests of the Northern Rocky Mountains (NRM). Long-term historical fire conditions and human influence on past fire activity are not well understood for these topographically and biophysically heterogeneous forests. We developed reconstructions of millennial-scale fire activity, vegetation change, and human presence at Black Lake, a small closed-basin lake on the Flathead Indian Reservation in the Mission Valley, Northwestern Montana, United States. Fossil pollen, charcoal, and biomarkers associated with human presence were used to evaluate the interaction between climate variability, fire activity, vegetation change and human activity for the past 7000 years. Comparisons among multiple proxies suggest climate variability acted as the primary control on fire activity and vegetation change from the early Holocene until the late Holocene when records suggest fire activity and climate variability decoupled. Specific biomarkers (5β-stanols including coprostanol and epi-coprostanol) associated with human presence indicate humans were present within the Black Lake watershed for thousands of years, although the inferred intensity of human presence is highly variable. A strong relationship between climate variability and fire activity during the early and mid-Holocene weakens during the last few thousand years, suggesting possible increased influence of humans in mediating fire activity in recent millennia, and/or a shift in the interaction between the distribution and abundance of woody fuel and fire severity. Human-set fires during the cooler and wetter late Holocene may have been aimed at maintaining important cultural resources associated with the heterogeneous mosaic of mixed conifer forests within the Black Lake watershed. The paleoenvironmental reconstruction at Black Lake corroborates archeological records that show humans were present within the Black Lake watershed for over 7000 years. Further research is needed to evaluate the evidence for this continuous presence and the possible role that people played in shaping fire regimes and vegetation within low- to mid-elevation mixed-conifer ecosystems of the NRM.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffgc.2020.00044","usgsCitation":"McWethy, D.B., Alt, M., Argiriadis, E., Battistel, D., Everett, R.G., and Pederson, G.T., 2020, Millennial-scale climate and human drivers of environmental change and fire activity in a dry, mixed-conifer forest of northwestern Montana: Frontiers in Forests and Global Change, v. 3, 44, 16 p., https://doi.org/10.3389/ffgc.2020.00044.","productDescription":"44, 16 p.","ipdsId":"IP-113284","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":457040,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2020.00044","text":"Publisher Index Page"},{"id":377364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Black Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.33162689208984,\n 47.85907299252274\n ],\n [\n -114.31686401367188,\n 47.85907299252274\n ],\n [\n -114.31686401367188,\n 47.868631827737\n ],\n [\n -114.33162689208984,\n 47.868631827737\n ],\n [\n -114.33162689208984,\n 47.85907299252274\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2020-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"McWethy, David B.","contributorId":207232,"corporation":false,"usgs":false,"family":"McWethy","given":"David","email":"","middleInitial":"B.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":795763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alt, Mio","contributorId":237993,"corporation":false,"usgs":false,"family":"Alt","given":"Mio","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":795764,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Argiriadis, Elana","contributorId":237994,"corporation":false,"usgs":false,"family":"Argiriadis","given":"Elana","email":"","affiliations":[{"id":47673,"text":"Ca’ Foscari University of Venice","active":true,"usgs":false}],"preferred":false,"id":795765,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Battistel, Dario","contributorId":205865,"corporation":false,"usgs":false,"family":"Battistel","given":"Dario","email":"","affiliations":[{"id":37181,"text":"Department of Environmental Science, Informatics and Statistics, Ca' Foscari University of Venice, Italy","active":true,"usgs":false}],"preferred":false,"id":795766,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Everett, Richard G.","contributorId":221184,"corporation":false,"usgs":false,"family":"Everett","given":"Richard","email":"","middleInitial":"G.","affiliations":[{"id":37636,"text":"Salish Kootenai College","active":true,"usgs":false}],"preferred":false,"id":795767,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":795768,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220209,"text":"70220209 - 2020 - Seasonal manganese transport in the hyporheic zone of a snowmelt-dominated river (East River, Colorado)","interactions":[],"lastModifiedDate":"2021-04-27T17:16:33.696458","indexId":"70220209","displayToPublicDate":"2020-04-17T12:10:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal manganese transport in the hyporheic zone of a snowmelt-dominated river (East River, Colorado)","docAbstract":"Manganese (Mn) plays a critical role in river-water quality because Mn-oxides serve as sorption sites for contaminant metals. The aim of this study is to understand the seasonal cycling of Mn in an alpine streambed that experiences large spring snowmelt events and the potential responses to changes in snowmelt timing and magnitude. To address this goal, annual variations in river-water/groundwater interaction and Mn(aq) transport were measured and modeled in the bed of East River, Colorado, USA. In observations and numerical models, oxygenated river water containing dissolved organic carbon (DOC) mixes with groundwater rich in Mn(aq) in the streambed. The mixing depth increases during spring snowmelt when river discharge increases, leading to a greater DOC supply to the hyporheic zone and net respiration of Mn-oxides, despite an enhanced supply of oxygen. As groundwater upwelling resumes during the subsequent baseflow period, Mn(aq)-rich groundwater mixes with oxygenated river water, resulting in net accumulation of Mn-oxides until the bed freezes in winter. To explore potential responses of Mn transport to different climate-induced hydrological regimes, three hydrograph scenarios were numerically modeled (historic, low-snow, and storm) for the Rocky Mountain region. In a warming climate, Mn(aq) export to the river decreases, and Mn(aq) oxidation is favored in the upper streambed sediments over more of the year. One important implication is that the streambed may have an increased sorption capacity for metals over more of the year, leading to potential changes in river-water quality.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-020-02146-6","usgsCitation":"Bryant, S., Sawyer, A., Briggs, M., Saup, C., Nelson, A.R., Wilkins, M.J., Christensen, J.R., and Williams, K.H., 2020, Seasonal manganese transport in the hyporheic zone of a snowmelt-dominated river (East River, Colorado): Hydrogeology Journal, v. 28, p. 1323-1341, https://doi.org/10.1007/s10040-020-02146-6.","productDescription":"19 p.","startPage":"1323","endPage":"1341","ipdsId":"IP-115069","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":385333,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"East River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.95238709449768,\n 38.92190699243362\n ],\n [\n -106.94936156272888,\n 38.92190699243362\n ],\n [\n -106.94936156272888,\n 38.923893566458055\n ],\n [\n -106.95238709449768,\n 38.923893566458055\n ],\n [\n -106.95238709449768,\n 38.92190699243362\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2020-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Bryant, S.","contributorId":222764,"corporation":false,"usgs":false,"family":"Bryant","given":"S.","email":"","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":814777,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sawyer, A.","contributorId":222761,"corporation":false,"usgs":false,"family":"Sawyer","given":"A.","email":"","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":814778,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":814779,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saup, C.","contributorId":222763,"corporation":false,"usgs":false,"family":"Saup","given":"C.","email":"","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":814780,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nelson, A. R","contributorId":193402,"corporation":false,"usgs":false,"family":"Nelson","given":"A.","email":"","middleInitial":"R","affiliations":[],"preferred":false,"id":814781,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilkins, M. J.","contributorId":176779,"corporation":false,"usgs":false,"family":"Wilkins","given":"M.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":814782,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Christensen, J. R.","contributorId":204686,"corporation":false,"usgs":false,"family":"Christensen","given":"J.","email":"","middleInitial":"R.","affiliations":[{"id":36974,"text":"U.S. Environmental Protection Agency, National Exposure Research Laboratory, Las Vegas, NV","active":true,"usgs":false}],"preferred":false,"id":814783,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Williams, K. H.","contributorId":176777,"corporation":false,"usgs":false,"family":"Williams","given":"K.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":814784,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70208957,"text":"sim3451 - 2020 - Geologic map of the Homestake Reservoir 7.5′ quadrangle, Lake, Pitkin, and Eagle Counties, Colorado","interactions":[],"lastModifiedDate":"2020-04-30T13:51:00.671496","indexId":"sim3451","displayToPublicDate":"2020-04-17T11:50:00","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":"3451","title":"Geologic map of the Homestake Reservoir 7.5′ quadrangle, Lake, Pitkin, and Eagle Counties, Colorado","docAbstract":"The Homestake Reservoir 7.5' quadrangle lies at the northwestern end of the Upper Arkansas Valley, and headwaters of the Arkansas River, and the Roaring Fork, Fryingpan, and Eagle Rivers of the Colorado River system. The quadrangle lies within tectonic provinces of the 1.4 giga-annum (Ga) Picuris orogeny and includes the late Paleozoic Ancestral Rockies, Late Cretaceous-Paleocene Laramide orogeny, Oligocene-to-Miocene and Pliocene? volcanism, and Miocene to the present Rio Grande rift extensional tectonics. In the eastern half of the quadrangle, high-angle, east-dipping, Neogene normal faults displace Proterozoic rocks, and locally Miocene-to-Pliocene? volcanic rocks. Many quartz veins and hydrothermally altered zones are exposed along the eastern flank of the quadrangle, indicative of the multiple tectonic episodes the region has experienced. The main intent of the map is to unravel the structural complexity by partitioning the structures and volcanism within the appropriate geologic interval. This ultimately permits accurate identification of geomorphic features suitable for chronologies related to landscape evolution studies, seismic and other natural hazard identification, ground and surface water modeling, and paleoclimatic studies. Within the western half of the quadrangle, Mesoproterozoic and Paleoproterozoic igneous and metamorphic rocks of 1.4 Ga St. Kevin Granite and 1.8–1.7 Ga Biotite gneiss and schist, respectively, are uplifted along the generally east-dipping, high-angle Sawatch fault system. In the northwest portion of the quadrangle, strands of the Homestake shear zone have been mapped, dated and assigned to the 1.4 Ga Picuris orogeny of northern New Mexico. 10Be and 26Al cosmogenic nuclide ages of the youngest glacial deposits indicate a last glacial maximum age of about 22–21 kilo-annum (ka) and complete deglaciation by about 14 kilo-annum, supported by chronologic studies in adjacent drainages. The Turquoise Lake impounding lateral and terminal moraine complex was deposited during late Pleistocene glacial maximum ~22–21 ka. No late Pleistocene tectonic activity is apparent within the quadrangle.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sim3451","collaboration":"","usgsCitation":"Ruleman, C.A., Frothingham, M.G., Brandt, T.R., Shaw, C.A., Caffee, M.W., Brugger, K.A., and Goehring, B.M., 2020, Geologic map of the Homestake Reservoir 7.5' quadrangle, Lake, Pitkin, and Eagle Counties, Colorado: U.S. Geological Survey Scientific Investigations Map 3451, scale 1:24,000, https://doi.org/10.3133/sim3451.","productDescription":"3 Sheets: 54.00 x 48.50 inches; Read Me; Data Release","onlineOnly":"Y","ipdsId":"IP-088811","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":373759,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3451/coverthb.jpg"},{"id":373763,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3451/sim3451_ReadMe.txt","text":" Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3451 read me"},{"id":373762,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3451/sim3451_georeferenced.pdf","text":"Geologic Map of the Homestake Reservoir 7.5' Quadrangle, Eagle, Lake, and Pitkin Counties, Colorado","size":"165 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3451 georeferenced","linkHelpText":"(interactive georeferenced map with the shaded relief and topographic base layers)"},{"id":373764,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ON6QBE","text":"USGS data release","linkHelpText":"Data release for Geologic Map of the Homestake Reservoir 7.5' quadrangle, Lake, Pitkin, and Eagle Counties, Colorado"},{"id":373760,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3451/sim3451.pdf","text":"Geologic Map of the Homestake Reservoir 7.5' Quadrangle, Eagle, Lake, and Pitkin Counties, Colorado","size":"72.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3451 print quality","linkHelpText":"(print quality)"},{"id":373761,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3451/sim3451_hillshade_base.pdf","text":"Geologic Map of the Homestake Reservoir 7.5' Quadrangle, Eagle, Lake, and Pitkin Counties, Colorado","size":"54.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3451 hillshade base","linkHelpText":"(map with the shaded relief base)"}],"country":"United States","state":"Colorado","county":"Eagle County, Lake County, Pitkin County","otherGeospatial":"Homestake Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.5,\n 39.375\n ],\n [\n -106.375,\n 39.375\n ],\n [\n -106.375,\n 39.25\n ],\n [\n -106.5,\n 39.25\n ],\n [\n -106.5,\n 39.375\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
Global hydroclimatic conditions have been substantially altered over the past century by anthropogenic influences that arise from the warming global climate and from local/regional anthropogenic disturbances. Traditionally, studies have used coupling of multiple models to understand how land-surface water fluxes vary due to changes in global climatic patterns and local land-use changes. We argue that because the basis of the Budyko framework relies on the supply and demand concept, the framework could be effectively adapted and extended to quantify the role of drivers – both changing climate and local human disturbances – in altering the land-surface response across the globe. We review the Budyko framework, along with these potential extensions, with the intent of furthering the applicability of the framework to emerging hydrologic questions. Challenges in extending the Budyko framework over various spatio-temporal scales and the use of global datasets to evaluate the water balance at these various scales are also discussed.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-24-1975-2020","usgsCitation":"Sankarasubramanian, A., Wang, D., Archfield, S.A., Reitz, M., Vogel, R., Mazrooei, A., and Mukhopadhyaya, S., 2020, HESS opinions: Beyond the long-term water balance: Evolving Budyko's supply–demand framework for the Anthropocene towards a global synthesis of land-surface fluxes under natural and human-altered watersheds: Hydrology and Earth System Sciences, v. 24, p. 1975-1984, https://doi.org/10.5194/hess-24-1975-2020.","productDescription":"10 p.","startPage":"1975","endPage":"1984","ipdsId":"IP-116284","costCenters":[{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":457044,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-24-1975-2020","text":"Publisher Index Page"},{"id":378673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","noUsgsAuthors":false,"publicationDate":"2020-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Sankarasubramanian, A. 0000-0002-7668-1311","orcid":"https://orcid.org/0000-0002-7668-1311","contributorId":241034,"corporation":false,"usgs":false,"family":"Sankarasubramanian","given":"A.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":799382,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Dingbao","contributorId":166993,"corporation":false,"usgs":false,"family":"Wang","given":"Dingbao","email":"","affiliations":[{"id":18879,"text":"University of Central Florida","active":true,"usgs":false}],"preferred":false,"id":799383,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":799384,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reitz, Meredith 0000-0001-9519-6103 mreitz@usgs.gov","orcid":"https://orcid.org/0000-0001-9519-6103","contributorId":196694,"corporation":false,"usgs":true,"family":"Reitz","given":"Meredith","email":"mreitz@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":799385,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vogel, Richard M","contributorId":241035,"corporation":false,"usgs":false,"family":"Vogel","given":"Richard M","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":799386,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mazrooei, Amirhossein","contributorId":241036,"corporation":false,"usgs":false,"family":"Mazrooei","given":"Amirhossein","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":799387,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mukhopadhyaya, Sudarshana","contributorId":241037,"corporation":false,"usgs":false,"family":"Mukhopadhyaya","given":"Sudarshana","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":799388,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209245,"text":"gip197 - 2020 - PFAS in the environment","interactions":[],"lastModifiedDate":"2020-04-17T13:05:13.346934","indexId":"gip197","displayToPublicDate":"2020-04-17T09:10:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"197","displayTitle":"PFAS in the Environment","title":"PFAS in the environment","docAbstract":"The U.S. Geological Survey (USGS) is working with Federal, State, and local partners to monitor and evaluate perfluoroalkyl and polyfluoroalkyl substances (PFAS) in the State’s groundwater and surface waters. PFAS are synthetic chemicals with widespread commercial and industrial use that can take a very long time to break down in the environment and may affect human health. The USGS in New York is working with experts across the Nation to develop and implement rigorous and innovative techniques to detect PFAS at concentrations as low as parts per trillion to help the public understand the spatial distribution and magnitude of PFAS contamination within the environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip197","collaboration":"","usgsCitation":"U.S. Geological Survey, 2020, PFAS in the environment: U.S. Geological Survey General Information Product 197, 2 p., https://doi.org/10.3133/gip197.","productDescription":"Postcard","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-114421","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":373952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/197/coverthb.jpg"},{"id":374090,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/197/gip197.pdf","text":"Report","size":"400 KB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The U.S. Geological Survey (USGS) is working with Federal, State, and local partners to monitor and evaluate microplastics in our lakes, rivers, and coastal waters. Microplastics are very small pieces of plastic, some-times so small that they cannot be seen with the naked eye. The USGS is taking an active role in monitoring and assessing our natural resources in New York and throughout the Nation. To support microplastics research, the USGS has created two laboratories to identify the type and quantity of microplastic particles in water, air, sediment, and animal life. These results will help decisionmakers learn about the presence and potential bioavailability of microplastics in ecosystems and public water supplies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip196","collaboration":"","usgsCitation":"U.S. Geological Survey, 2020, Microplastics: U.S. Geological Survey General Information Product 196, 2 p., https://doi.org/10.3133/gip196.","productDescription":"Postcard","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-114413","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":373951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/196/coverthb.jpg"},{"id":374089,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/196/gip196.pdf","text":"Report","size":"330 KB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Sea-level rise will radically redefine the coastline of the 21st century. For many coastal regions, projections of global sea-level rise by the year 2100 (e.g., 0.5–2 meters) are comparable in magnitude to today’s extreme but short-lived increases in water level due to storms. Thus, the 21st century will see significant changes to coastal flooding regimes (where present-day, extreme-but-rare events become common), which poses a major risk to the safety and sustainability of coastal communities worldwide. So far, estimates of future coastal flooding frequency focus on endpoint scenarios, such as the increase in flooding by 2050 or 2100. Here, we investigate the continuous shift in coastal flooding regimes by quantifying continuous rates of increase in the occurrence of extreme water-level events due to sea-level rise. We find that the odds of exceeding critical water-level thresholds increases exponentially with sea-level rise, meaning that fixed amounts of sea-level rise of only ~1–10 cm in areas with a narrow range of present-day extreme water levels can double the odds of flooding. Combining these growth rates with established sea-level rise projections, we find that the odds of extreme flooding double approximately every 5 years into the future. Further, we find that the present-day 50-year extreme water level (i.e., 2% annual chance of exceedance, based on historical records) will be exceeded annually before 2050 for most (i.e., 70%) of the coastal regions in the United States. Looking even farther into the future, the present-day 50-year extreme water level will be exceeded almost every day during peak tide (i.e., daily mean higher high water) before the end of the 21st century for 90% of the U.S. coast. Our findings underscore the need for immediate planning and adaptation to mitigate the societal impacts of future flooding.
Groundwater samples have been collected in California as part of statewide investigations of groundwater quality conducted by the U.S. Geological Survey for the Groundwater Ambient Monitoring and Assessment (GAMA) Priority Basin Project (PBP) since 2004. The GAMA-PBP is being conducted in cooperation with the California State Water Resources Control Board to assess and monitor the quality of groundwater resources used for public and domestic drinking-water supply and to improve public knowledge of groundwater quality in California. Quality-control samples (including but not limited to field, equipment, and source-solution blanks) were collected to evaluate and quantify the quality of the groundwater sample results.
The GAMA-PBP previously determined study reporting levels (SRLs) for trace-element results based primarily on field blanks collected in California from May 2004 through March 2013. SRLs are raised reporting levels used to reduce the likelihood of reporting false detections attributable to contamination bias. The purpose of this report is to identify any changes in the pattern or magnitude of concentrations or detections in field blanks since the last evaluation that would require changing or ending the use of SRLs implemented in October 2009. Constituents analyzed were aluminum, antimony, arsenic, barium, beryllium, boron, cadmium, chromium, hexavalent chromium, cobalt, copper, iron, lead, lithium, manganese, molybdenum, nickel, selenium, silver, strontium, thallium, uranium, vanadium, and zinc.
For this review, data from 167 field blanks collected from October 2009 through October 2018 by the GAMA-PBP for trace elements were compiled. Based on a consistent pattern of decreasing cobalt and manganese concentrations in field blanks from 2009 to 2013, the GAMA-PBP decided to reevaluate all trace-element SRLs, effectively setting an end date for previously defined SRLs. Beginning October 2013, SRLs would be determined from field-blank data collected through October 2018. The detection frequency and upper limit of potential contamination bias (BD-90/90) were determined from field blanks for each trace element. The BD-90/90, that is, the upper 90-percent confidence limit of the 90th percentile concentration of potential extrinsic contamination, was calculated by assuming the binomial probability distribution. These results were compared to each constituent’s detection limit to determine whether an SRL was necessary to minimize the potential for detections in the groundwater samples, attributed principally to contamination bias. Results of the evaluation were used to set SRLs for trace-element data collected by the GAMA-PBP between October 2013 and October 2018. Trace elements prescribed an SRL based on this review were hexavalent chromium, cobalt, copper, lead, and zinc. This review also resulted in the removal of SRLs from iron, manganese, molybdenum, and nickel. Although an SRL for hexavalent chromium could not be evaluated in the earlier reviews because the data were not collected regularly until 2015, one was established herein as 0.34 micrograms per liter (µg/L). The SRL for cobalt, as previously implemented, had been to reject all results; it was changed to 0.16 µg/L following a reduction in cobalt field-blank detection frequency resulting from mitigation steps, starting in 2014, aimed at reducing contamination bias introduced by high-capacity capsule filters used during sample collection. The SRL for copper did not change, and the SRL for lead changed very little based on this review. Lastly, the SRL for zinc was lowered from 6.2 µg/L to 3.9 µg/L.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205034","collaboration":"A product of the California Groundwater Ambient Monitoring and Assessment ProgramDirector, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Passive groundwater sampling is defined as the collection of a water sample from a well without the use of purging by a pump or retrieval by a bailer (Interstate Technology and Regulatory Council [ITRC], 2006; American Society for Testing and Materials [ASTM], 2014). No purging means that advection of water is not involved in collecting the water sample from the well. Passive samplers rely on diffusion as the primary process that drives their collection of chemical constituents. Diffusion is the transport of chemicals caused by the presence of a chemical gradient. Chemicals tend to move or diffuse from areas of higher concentration to areas of lower concentration to reach an average or equilibrium concentration. Passive sampling of groundwater relies on the ambient exchange of groundwater in the formation with water in the screened or open interval of a well. In this report, the term formation is used to describe all saturated hydrogeologic units that yield water to a well. If the well opening is unclogged and free of a film of deposits from physical turbidity or chemical precipitation, then the exchange of groundwater is likely adequate, and the water in the open interval will be representative of water in the formation. In some cases, the passive sample from the well opening can be more representative of groundwater from the formation than a sample collected by pumping if pumping induces mixing of water in the open interval with stagnant casing water that has undergone chemical alteration (Harte and others, 2018). In most cases, passive sampling will better represent the ambient groundwater chemistry flowing through the open interval of a well because pumping may capture water of different chemistry from downgradient or lateral areas that would not normally pass through the well. Three basic types of passive samplers are discussed in this report. The first type of passive sampler is the equilibrium-membrane type, which includes a semi-permeable membrane through which chemicals diffuse or permeate. Permeation is simply the process of water or chemicals moving through openings in the membrane. The authors contend that permeation is dominated by diffusion for many of the passive samplers discussed in this report. Some passive equilibrium-membrane-type samplers allow most types of chemical constituents through, whereas others allow the diffusion of only selected groups of chemicals. Once the chemical constituents are inside the membrane, they are retained by the equilibration of concentrations inside the sampler with those outside the sampler. The second type of passive sampler is an equilibrium-thief type, which has no semi-permeable membrane. Chemical constituents simply move through the openings in the body of the sampler either initially through advection and dispersion or over time primarily by diffusion. Chemical constituents reach equilibrium between the water in the sampler and the water in the well and are captured in the sampler when the sampler is closed. The third type of passive sampler is an accumulation-type sampler that contains sorptive media. Selected chemical constituents are sorbed onto the media that the sampler contains for later extraction and analysis. Although passive samplers have been available for more than 15 years (from present [2020]), their use by U.S. Geological Survey (USGS) hydrologists and hydrologic technicians to monitor groundwater quality largely has been limited to selected research studies. The authors believe that this may be the result of (1) a lack of exposure of most USGS personnel to passive samplers and the uses of these samplers and (2) the lack of a USGS-approved protocol for the proper use of these samplers by USGS personnel. This report is an effort to fill those two needs. The focus of this report is on hydraulic, hydrologic, and chemical considerations in the application of passive samplers and interpretation of groundwater chemistry results obtained using passive samplers in wells. This report describes the differences between purging and passive sampling methods in groundwater and explains how and why passive samplers work. The report points out the advantages and limitations of passive samplers in general and for each particular type of passive sampler. Important considerations to be taken into account prior to the use of passive samplers are discussed, such as defining the data-quality objectives, the water-quality constituents to be sampled, sample volumes required for analysis, well construction of the sampling network, and the geologic formations that will be sampled. Potential applications of passive samplers also are discussed, such as chemical-vertical profiling of wells. A general field protocol for the deployment, recovery, and sample collection using these devices is described, and some overall guidance for the practitioner with application examples is given. Comparison methods used to evaluate results from passive sampling versus purge sampling also are discussed.
","largerWorkTitle":"","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm1D8","collaboration":"","usgsCitation":"Imbrigiotta, T.E., and Harte, P.T., 2020, Passive sampling of groundwater wells for determination of water chemistry: U.S. Geological Survey Techniques and Methods, chap. 8, section D, book 1, 80 p., https://doi.org/10.3133/tm1d8.\n","productDescription":"ix, 80 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-082895","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":373983,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/01/d8/coverthb.jpg"},{"id":373984,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/01/d8/tm1d8.pdf","text":"Report","size":"4.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 1-D8"}],"publicComments":"This report is Chapter 8 of Section D: Water quality in Book 1: Collection of water data by direct measurement","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Nutrient enrichment has degraded many of the world’s estuaries by amplifying algal production, leading to hypoxia/anoxia, loss of vascular plants and fish/shellfish habitat, and expansion of harmful blooms (HABs). Policies to protect coastal waters from the effects of nutrient enrichment require information to determine if a water body is impaired by nutrients and if regulatory actions are required. We compiled information to inform these decisions for San Francisco Bay (SFB), an urban estuary where the best path toward nutrient management is not yet clear. Our results show that SFB has high nutrient loadings, primarily from municipal wastewater; there is potential for high algal production, but that production is not fully realized; and SFB is not impaired by hypoxia or recurrent HABs. However, our assessment includes reasons for concern: nitrogen and phosphorus concentrations higher than those in other estuaries impaired by nutrient pollution, chronic presences of multiple algal toxins, a recent increase of primary production, and projected future hydroclimatic conditions that could increase the magnitude and frequency of algal blooms. Policymakers thus face the challenge of determining the appropriate protective policy for SFB. We identify three crucial next steps for meeting this challenge: (1) new research to determine if algal toxins can be reduced through nutrient management, (2) establish management goals as numeric targets, and (3) determine the magnitude of nutrient load reduction required to meet those targets. Our case study illustrates how scientific information can be acquired and communicated to inform policymakers about the status of nutrient pollution, its risks, and strategies for minimizing those risks.
","language":"English","publisher":"SpringerLink","doi":"10.1007/s12237-020-00737-w","usgsCitation":"Cloern, J.E., Schraga, T., Nejad, E., and Martin, C.A., 2020, Nutrient status of San Francisco Bay and its management implications: Estuaries and Coasts, v. 43, p. 1299-1317, https://doi.org/10.1007/s12237-020-00737-w.","productDescription":"19 p.","startPage":"1299","endPage":"1317","ipdsId":"IP-109047","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":457064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-020-00737-w","text":"Publisher Index Page"},{"id":387060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.76971435546874,\n 38.013476231041935\n ],\n [\n -121.76971435546874,\n 38.03078569382294\n ],\n [\n -121.8109130859375,\n 38.06539235133249\n ],\n [\n -121.871337890625,\n 38.08485140639173\n ],\n [\n -121.91253662109376,\n 38.05674222065296\n ],\n [\n -121.9207763671875,\n 38.08701320402273\n ],\n [\n -121.97296142578124,\n 38.07620357665235\n ],\n [\n -122.00317382812499,\n 38.10646650598286\n ],\n [\n -121.98669433593749,\n 38.12591462924157\n ],\n [\n -122.0306396484375,\n 38.13887716726548\n ],\n [\n -122.11029052734374,\n 38.06971703320484\n ],\n [\n -122.14599609375001,\n 38.052416771864834\n ],\n [\n -122.22290039062499,\n 38.0934982133674\n ],\n [\n -122.310791015625,\n 38.1151107557172\n ],\n [\n -122.39318847656249,\n 38.151837403006766\n ],\n [\n -122.4920654296875,\n 38.11078875872392\n ],\n [\n -122.50030517578124,\n 38.028622234587964\n ],\n [\n -122.47009277343749,\n 38.00049145082287\n ],\n [\n -122.4920654296875,\n 37.96801944035648\n ],\n [\n -122.51129150390625,\n 37.90736658145496\n ],\n [\n -122.50030517578124,\n 37.85100126460795\n ],\n [\n -122.464599609375,\n 37.79676317682161\n ],\n [\n -122.40966796874999,\n 37.80544394934271\n ],\n [\n -122.38494873046875,\n 37.74465712069939\n ],\n [\n -122.3822021484375,\n 37.58594229860422\n ],\n [\n -122.15698242187499,\n 37.49229399862877\n ],\n [\n -122.0416259765625,\n 37.39634613318923\n ],\n [\n -121.94549560546875,\n 37.41816326969145\n ],\n [\n -121.90979003906249,\n 37.45741810262938\n ],\n [\n -121.95098876953125,\n 37.48793540168987\n ],\n [\n -122.07733154296875,\n 37.51190453731693\n ],\n [\n -122.08282470703124,\n 37.58594229860422\n ],\n [\n -122.13775634765625,\n 37.592471511019085\n ],\n [\n -122.20367431640624,\n 37.73379707124429\n ],\n [\n -122.2613525390625,\n 37.72510788462094\n ],\n [\n -122.2613525390625,\n 37.76854362092148\n ],\n [\n -122.32452392578125,\n 37.803273851858656\n ],\n [\n -122.3052978515625,\n 37.8553385894982\n ],\n [\n -122.34924316406251,\n 37.913867495923746\n ],\n [\n -122.398681640625,\n 37.94852933714952\n ],\n [\n -122.26409912109375,\n 38.03294908916503\n ],\n [\n -122.13775634765625,\n 38.01131226070673\n ],\n [\n -122.05261230468751,\n 38.05025395161289\n ],\n [\n -121.915283203125,\n 38.028622234587964\n ],\n [\n -121.76971435546874,\n 38.013476231041935\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","noUsgsAuthors":false,"publicationDate":"2020-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Cloern, James E. 0000-0002-5880-6862 jecloern@usgs.gov","orcid":"https://orcid.org/0000-0002-5880-6862","contributorId":1488,"corporation":false,"usgs":true,"family":"Cloern","given":"James","email":"jecloern@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":818878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schraga, Tara 0000-0002-2108-5846 tschraga@usgs.gov","orcid":"https://orcid.org/0000-0002-2108-5846","contributorId":1118,"corporation":false,"usgs":true,"family":"Schraga","given":"Tara","email":"tschraga@usgs.gov","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":818879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nejad, Erica 0000-0001-8204-6368 enejad@usgs.gov","orcid":"https://orcid.org/0000-0001-8204-6368","contributorId":260812,"corporation":false,"usgs":true,"family":"Nejad","given":"Erica","email":"enejad@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Charles A. 0000-0003-3576-2585 camartin@usgs.gov","orcid":"https://orcid.org/0000-0003-3576-2585","contributorId":4860,"corporation":false,"usgs":true,"family":"Martin","given":"Charles","email":"camartin@usgs.gov","middleInitial":"A.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":818882,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209108,"text":"sir20205029 - 2020 - Benthic vertical hydraulic gradients in Upper Klamath Lake, Oregon, 2017","interactions":[],"lastModifiedDate":"2020-04-16T11:32:36.564056","indexId":"sir20205029","displayToPublicDate":"2020-04-15T12:29:12","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5029","displayTitle":"Benthic Vertical Hydraulic Gradients in Upper Klamath Lake, Oregon, 2017","title":"Benthic vertical hydraulic gradients in Upper Klamath Lake, Oregon, 2017","docAbstract":"Groundwater piezometers and lake stilling wells were deployed as paired sets at 10 locations in Upper Klamath Lake in south-central Oregon from May to October 2017 to measure hydraulic heads in and beneath the lake. Continuous water-level data from piezometers and stilling wells were then used to calculate the vertical hydraulic gradient (VHG) across the sediment-water interface to determine the direction and relative magnitude of the movement of water between the lake and underlying sediments. Over the study period, heads in lake-bed sediments closely tracked lake levels, both decreasing from spring into autumn. Instantaneous VHG was highly dynamic at all sites and exhibited high-frequency (less than 1 day to less than 1 hour) variations in magnitude and direction. Instantaneous and weekly mean VHG values often exceeded, but were commonly within, the range of measurement uncertainty (VHG less than +0.009 foot per foot [ft/ft] and greater than -0.009 ft/ft). 63 percent of instantaneous VHG values and 66 percent of weekly mean VHG values were within this range. Study period mean VHG was within measurement uncertainty at seven of the nine sites that had continuous water-level data, but two littoral sites (LC03 and LS01) had positive (upward) values greater than measurement uncertainty and are likely locations of vertical groundwater seepage. Data collected in this study provide new information about the hydraulic conditions at the sediment-water interface in UKL and demonstrate that sediment-groundwater exchange in UKL is spatially and temporally heterogeneous.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205029","collaboration":"","usgsCitation":"Corson-Dosch, N.T., 2020, Benthic vertical hydraulic gradients in Upper Klamath Lake, Oregon, 2017: U.S. Geological Survey Scientific Investigations Report 2020–5029, 22 p., https://doi.org/10.3133/sir20205029.","productDescription":"Report: v, 22 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-102002","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":374017,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7668CGD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Depth-to-water data and calculated vertical hydraulic gradient at the sediment-water interface in Upper Klamath Lake, Oregon, 2017"},{"id":374016,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5029/sir20205029.pdf","text":"Report","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5029"},{"id":374015,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5029/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.08694458007812,\n 42.218347726793304\n ],\n [\n -121.77246093750001,\n 42.218347726793304\n ],\n [\n -121.77246093750001,\n 42.61273829368574\n ],\n [\n -122.08694458007812,\n 42.61273829368574\n ],\n [\n -122.08694458007812,\n 42.218347726793304\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The North Carolina Turnpike Authority plans to improve transportation in the Currituck Sound area by constructing a two-lane bridge—the Mid-Currituck Bridge—across Currituck Sound from the mainland to the Outer Banks, North Carolina. The results of the final environmental impact statement for the project indicate potential water-quality and habitat effects for Currituck Sound associated with the bridge and roadway improvements.
The primary objective of this study is to characterize water-quality conditions and bed-sediment chemistry in the vicinity of the planned Mid-Currituck Bridge, providing a baseline for evaluating the potential effects of bridge construction and bridge deck runoff on environmental conditions in Currituck Sound. From August 2011 through January 2018, water-quality and bed-sediment samples were collected from five sampling stations along the planned bridge alignment. Samples were analyzed for numerous characteristics, including physical properties and constituents that are associated with bridge deck stormwater runoff and are important to estuarine waters. The analyzed characteristics included dissolved oxygen, pH, specific conductance, turbidity, suspended solids, metals, nutrients, semi-volatile organic compounds, bacteria, chlorophyll a, cyanotoxins, and phytoplankton abundance. The most common constituents with concentrations above applicable State and Federal water-quality thresholds included chlorophyll a, pH, turbidity, Enterococci, and pentachlorophenol. Few bed-sediment samples had constituent concentrations that exceeded applicable sediment-quality guidelines.
Results indicated that water sampled along the planned bridge alignment was well mixed vertically and horizontally but varied temporally. Seasonal changes in water quality best explained the variations in water-quality conditions in Currituck Sound during the study. Wind conditions also influenced water levels and water-quality conditions. Turbidity and concentrations of particle-associated constituents tended to be higher when water levels were lower, possibly reflecting the increased resuspension of bottom materials from wind-driven wave action.
Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Inland fishes and fisheries make substantial contributions to individuals, society, and the environment in a changing global landscape that includes climate, water allocations, and societal changes. However, current limitations to valuing the services provided by inland fish and their fisheries often leaves them out of key decision‐making discussions. InFish is a voluntary professional network with over 120 members from over 50 organizations in over 20 countries that seeks to address challenges facing inland fish through novel approaches and international collaborations. InFish fosters opportunities to share knowledge, pursue proposals, publications, and conference‐related events focused on inland fisheries. InFish has become a source of inland fisheries expertise, working collectively towards global conservation and sustainable use of inland fish through informing scientifically sound management practices. As such, InFish may serve as a model network for other natural resource challenges now and into the future.
","language":"English","publisher":"Wiley","doi":"10.1002/fsh.10419","usgsCitation":"Lynch, A.J., Bartley, D.M., Beard, Bunnell, D., Cooke, S.J., Cowx, I.G., Funge-Smith, S., Paukert, C.P., Rogers, M.W., and Taylor, W., 2020, InFish: A professional network to promote global conservation and responsible use of inland fish: Fisheries Magazine, v. 45, no. 6, p. 319-326, https://doi.org/10.1002/fsh.10419.","productDescription":"8 p.","startPage":"319","endPage":"326","ipdsId":"IP-110120","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":381134,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Lynch, Abigail J 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":245521,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","email":"","middleInitial":"J","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":806362,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartley, Devin M.","contributorId":15913,"corporation":false,"usgs":false,"family":"Bartley","given":"Devin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":806363,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beard, Jr. 0000-0003-2632-2350 dbeard@usgs.gov","orcid":"https://orcid.org/0000-0003-2632-2350","contributorId":169459,"corporation":false,"usgs":true,"family":"Beard","suffix":"Jr.","email":"dbeard@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":806364,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bunnell, David 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":245523,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":806365,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooke, Steve J.","contributorId":220492,"corporation":false,"usgs":false,"family":"Cooke","given":"Steve","email":"","middleInitial":"J.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":806366,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cowx, Ian. G.","contributorId":220479,"corporation":false,"usgs":false,"family":"Cowx","given":"Ian.","email":"","middleInitial":"G.","affiliations":[{"id":40174,"text":"University of Hull","active":true,"usgs":false}],"preferred":false,"id":806367,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Funge-Smith, Simon","contributorId":197466,"corporation":false,"usgs":false,"family":"Funge-Smith","given":"Simon","affiliations":[],"preferred":false,"id":806368,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":806369,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":806370,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Taylor, William W.","contributorId":49735,"corporation":false,"usgs":false,"family":"Taylor","given":"William W.","affiliations":[],"preferred":false,"id":806371,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70208713,"text":"sir20205018 - 2020 - Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River between Kansas City and St. Louis, Missouri, May 22–31, 2017","interactions":[],"lastModifiedDate":"2020-04-15T11:29:32.465392","indexId":"sir20205018","displayToPublicDate":"2020-04-14T12:41:15","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5018","displayTitle":"Bathymetric and Velocimetric Surveys at Highway Bridges Crossing the Missouri River between Kansas City and St. Louis, Missouri, May 22–31, 2017","title":"Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River between Kansas City and St. Louis, Missouri, May 22–31, 2017","docAbstract":"Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 10 bridges at 9 highway crossings of the Missouri River between Kansas City and St. Louis, Missouri, from May 22 to 31, 2017. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches ranging from 1,550 to 1,840 feet longitudinally and generally extending laterally across the active channel from bank to bank during moderate flood flow conditions. These surveys indicate the channel conditions at the time of the surveys and provide characteristics of scour holes that may be useful in the development of predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood flow comparison to help assess the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every pier that was in water, except those at the edge of water, and scour holes were observed at most surveyed piers. Occasionally, the scour hole near a pier was difficult to discern from nearby bed features. The observed scour holes at the surveyed bridges were generally examined with respect to shape and depth.
Although exposure of parts of substructural support elements was observed at several piers, at most sites the exposure likely can be considered minimal compared to the overall substructure that remains buried in bed material at these piers. The notable exceptions are piers 4 and 5 at structure K0999 on Missouri State Highway 41 at Miami, Mo.; piers 2 and 3 at structure G0069 on Missouri State Highway 240 at Glasgow, Mo.; and pier 5 at structure A4574 on Missouri State Highway 5 at Boonville, Mo. At these structures, the bed-material thickness between the bottom of the scour hole and bedrock was less than 6 feet.
Pier size, nose shape, and alignment to flow had a profound effect on the size of the scour hole observed for a given pier. Narrow piers having round or sharp noses that were aligned with flow often had scour holes that were difficult to discern from nearby bed features, whereas piers having wide or blunt noses resulted in larger, deeper scour holes. Several structures had piers that were skewed to primary approach flow, and scour holes near these piers generally indicated deposition on the leeward side of the pier and greater depth on the side of the pier with impinging flow. A riprap blanket constructed in 2015 around pier 4 of structures L0550 and A4497 on U.S. Highway 54 at Jefferson City, Mo., effectively mitigates the scour observed near those piers in previous surveys.
Previous bathymetric surveys exist for all the sites examined in this study. Bathymetric surfaces from a nonflood survey in 2013 and a flood survey in July 2011 at most of the sites are compared to the 2017 survey surfaces. The average channel-bed elevation at structure A4574 was remarkably similar in all three surveys and higher than what might be implied by a trendline along the reach between Kansas City and St. Louis, which may indicate this site is at or near a local feature that controls sediment deposition and scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205018","collaboration":"Prepared in cooperation with the Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2020, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River between Kansas City and St. Louis, Missouri, May 22–31, 2017: U.S. Geological Survey Scientific Investigations Report 2020–5018, 104 p., https://doi.org/10.3133/sir20205018.\n","productDescription":"Report: x, 104 p.; Data Releases","numberOfPages":"118","onlineOnly":"Y","ipdsId":"IP-110170","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":373939,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L6GW57","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri River in Kansas City, Missouri, March 2010 through May 2017"},{"id":372633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5018/coverthb.jpg"},{"id":373938,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5018/sir20205018.pdf","text":"Report","size":"23.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5018"},{"id":373940,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94M4US7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri River between Kansas City and St. Louis, Missouri, January 2010 through May 2017"}],"country":"United States","state":"Missouri","city":"Kansas City, St. Louis","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.142822265625,\n 38.805470223177466\n ],\n [\n -91.12060546875,\n 38.92522904714054\n ],\n [\n -92.16430664062499,\n 39.08743603215884\n ],\n [\n -93.2958984375,\n 39.04478604850143\n ],\n [\n -94.119873046875,\n 39.12153746241925\n ],\n [\n -94.68017578125,\n 39.198205348894795\n ],\n [\n -94.63623046875,\n 38.91668153637508\n ],\n [\n -94.04296874999999,\n 38.865374851611634\n ],\n [\n -93.109130859375,\n 38.79690830348427\n ],\n [\n -92.274169921875,\n 38.85682013474361\n ],\n [\n -91.91162109375,\n 38.81403111409755\n ],\n [\n -91.29638671875,\n 38.69408504756833\n ],\n [\n -90.648193359375,\n 38.659777730712534\n ],\n [\n -90.186767578125,\n 38.57393751557591\n ],\n [\n -90.142822265625,\n 38.805470223177466\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Sea level rise (SLR) is threatening coastal marshes, leading to large‐scale marsh loss in several micro‐tidal systems. Early recognition of marsh vulnerability to SLR is critical in these systems to aid managers to take appropriate restoration or mitigation measures. However, it is not clear if current marsh vulnerability indicators correctly assess long‐term stability of the marsh system. In this study, two indicators of marsh stress were studied: (i) the skewness of the marsh elevation distribution, and (ii) the abundance of codominant species in mixtures. We combined high‐precision elevation measurements (GPS), LiDAR imagery, vegetation surveys and water level measurements to study these indicators in an organogenic micro‐tidal system (Blackwater River, Maryland, USA), where large‐scale historical conversion from marshes to shallow ponds resulted in a gradient of increasing marsh loss. The two indicators reveal increasingly stressed marshes along the marsh loss gradient, but suggest that the field site with the most marsh loss seems to experience less stress. For the latter site, previous research indicates that wind waves generated on interior marsh ponds contribute to lateral erosion of surrounding marsh edges and hence marsh loss. The eroded marsh sediment might temporarily provide the remaining marshes with the necessary sediment to keep up with relative SLR. However, this is only a short‐term alleviation, as lateral marsh edge erosion and sediment export lead to severe marsh loss in the long term. Our findings indicate that marsh elevation skewness and the abundance of codominant species in mixtures can be used to supplement existing marsh stress indicators, but that additional indices such as fetch length and the sediment budget should be included to account for lateral marsh erosion and sediment export and to correctly assess long‐term stability of micro‐tidal marshes.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.4869","usgsCitation":"Schepers, L., Kirwan, M.L., Guntenspergen, G.R., and Temmerman, S., 2020, Evaluating indicators of marsh vulnerability to sea level rise along a historical marsh loss gradient: Earth Surface Processes and Landforms, v. 45, no. 9, p. 2107-2117, https://doi.org/10.1002/esp.4869.","productDescription":"11 p.","startPage":"2107","endPage":"2117","ipdsId":"IP-099311","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457093,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.4869","text":"Publisher Index Page"},{"id":375464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Blackwater marshes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.32476806640625,\n 38.40033474910393\n ],\n [\n -76.13868713378906,\n 38.40033474910393\n ],\n [\n -76.13868713378906,\n 38.49820570027114\n ],\n [\n -76.32476806640625,\n 38.49820570027114\n ],\n [\n -76.32476806640625,\n 38.40033474910393\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Schepers, Lennert","contributorId":189203,"corporation":false,"usgs":false,"family":"Schepers","given":"Lennert","email":"","affiliations":[],"preferred":false,"id":790511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kirwan, Matt L.","contributorId":189205,"corporation":false,"usgs":false,"family":"Kirwan","given":"Matt","middleInitial":"L.","affiliations":[],"preferred":false,"id":790512,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":790513,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Temmerman, Stijn","contributorId":189204,"corporation":false,"usgs":false,"family":"Temmerman","given":"Stijn","email":"","affiliations":[],"preferred":false,"id":790514,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209228,"text":"sir20205031 - 2020 - Effects of legacy sediment removal and effects on nutrients and sediment in Big Spring Run, Lancaster County, Pennsylvania, 2009–15","interactions":[],"lastModifiedDate":"2020-04-14T14:16:58.478936","indexId":"sir20205031","displayToPublicDate":"2020-04-14T09:30:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5031","displayTitle":"Effects of Legacy Sediment Removal on Nutrients and Sediment in Big Spring Run, Lancaster County, Pennsylvania, 2009–15","title":"Effects of legacy sediment removal and effects on nutrients and sediment in Big Spring Run, Lancaster County, Pennsylvania, 2009–15","docAbstract":"Big Spring Run is a 1.68-square mile watershed underlain by mostly carbonate rock in a mixed land-use setting (part agricultural and part developed) in Lancaster County, Pennsylvania. Big Spring Run is a subwatershed of Mill Creek, a tributary to the Conestoga River. These watersheds are known contributors of nutrient and sediment loads to the Chesapeake Bay and several stream reaches are on the Pennsylvania impaired waters list. Big Spring Run is listed as impaired and was selected by the Pennsylvania Department of Environmental Protection to evaluate a novel best management practice to restore natural aquatic ecosystems by removing legacy sediment. The study was designed to quantify sediment and nutrient contributions in pre- and postrestoration periods (water years 2009–11 and 2012–15, respectively) using an intensive monitoring approach at three surface-water sites within the watershed. Instrumentation at each site continuously measured (15-minute intervals) streamflow, water temperature, and turbidity. Water-quality samples were collected routinely (generally monthly and during selected storms); sampling frequency varied by site and constituent at the three monitoring sites.
Effects of legacy sediment removal and restoration on nutrient concentrations varied in surface water samples depending on the form (particulate, dissolved, organic, inorganic). For example, total phosphorus concentrations at the downstream site decreased from a median of 0.19 milligram per liter (mg/L) to 0.04 mg/L, pre- and postrestoration periods, respectively. Concentrations of orthophosphate, the dissolved form of phosphorus, were not significantly different pre- to postrestoration at the downstream site. Similarly, nitrate concentrations, the dominant form of nitrogen in Big Spring Run surface-water samples (92.3 percent of total nitrogen) were not significantly different in the pre- compared to the postrestoration periods.
Legacy sediment removal and restoration had significant effects on suspended-sediment concentrations and loads. Median suspended-sediment concentrations at the downstream site decreased from 556 mg/L prerestoration to 74 mg/L postrestoration even though streamflow hydrographs during the two periods were similar. In the postrestoration period, the mean annual suspended-sediment load conveyed to the restoration area from the upstream sites was 839 tons, whereas mean annual suspended-sediment load at the downstream site was reduced to 242 tons.
Streamflow during storms transports a large proportion of the suspended-sediment load; there were a total of 320 storms over the study period. In Big Spring Run, a single storm event can transport more than 25 percent of the annual suspended-sediment load. The greatest single-storm contribution to suspended-sediment load was 38 percent in water year 2015 at the downstream site. Although streamflow magnitudes during storms varied greatly over the study period, median streamflow was 17.5 cubic feet per second and median duration was about 3 hours and 24 minutes.
Results observed for this study using the newly proposed best management practice were compared with other best management practices intended to reduce sediment. For example, during a previous study, statistically significant reductions in suspended-sediment concentration were observed when streambank fencing was implemented in an adjacent watershed; however, suspended-sediment reductions were an order of magnitude less than the reductions observed in the current study. Median suspended-sediment concentration at the downstream site was reduced by 482 mg/L in the current study compared to only 30 to 46 mg/L as a result of streambank fencing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205031","collaboration":"Prepared in cooperation with the Pennsylvania Department of Environmental Protection, and in collaboration with Franklin and Marshall College and the U.S. Environmental Protection Agency","usgsCitation":"Langland, M.J., Duris, J.W., Zimmerman, T.M., and Chaplin, J.J., 2020, Effects of legacy sediment removal and effects on nutrients and sediment in Big Spring Run, Lancaster County, Pennsylvania, 2009–15: U.S. Geological Survey Scientific Investigations Report 2020-5031, 28 p., https://doi.org/10.3133/sir20205031.","productDescription":"Report: viii, 28 p.; 3 Data Releases","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084985","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":373895,"rank":5,"type":{"id":30,"text":"Data 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Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
The Fort Berthold Reservation is in west-central North Dakota and home to the Three Affiliated Tribes. The primary water-resources concerns on the Fort Berthold Reservation are associated with the different types of land uses from agricultural activities and the rapid development of oil and gas resources in western North Dakota. The Three Affiliated Tribes Environmental Department identified the need for long-term water-quality monitoring throughout the Fort Berthold Reservation to better understand the potential effects on surface-water and groundwater quality and to determine if water quality is changing with time. The U.S. Geological Survey, in cooperation with the Three Affiliated Tribes, identified surface-water sites and groundwater wells that represent the water resources in major drainages and the most utilized aquifers on the reservation. A water-quality monitoring program was designed to address data gaps and provide consistent long-term data that can be used to identify potential effects on water quality. During 2014–17, the initial water-quality sampling efforts associated with this program were completed. The efforts provide a current (2019) characterization of water-quality conditions in surface water and groundwater and can assist in establishing a long-term water-quality monitoring program
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205020","collaboration":"Prepared in cooperation with the Three Affiliated Tribes","usgsCitation":"Lundgren, R.F., and Iorio, M.J., 2020, Characterization of surface-water and groundwater quality on the Fort Berthold Reservation, North Dakota, 2014–17: U.S. Geological Survey Scientific Investigations Report 2020–5020, 37 p., https://doi.org/10.3133/sir20205020.","productDescription":"Report: vii, 37 p.; 1 Table; 6 Appendix Tables","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112739","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":373821,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5020/sir20205020.pdf","text":"Report","size":"8.60 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5020"},{"id":373822,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5020/sir20205020_table2.xlsx","text":"Table 2","size":"16.1 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5020 Table 2","linkHelpText":"– Site information for groundwater wells sampled on Fort Berthold Reservation, North Dakota, 2014–17"},{"id":373824,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5020/sir20205020_appendix_table1.2.xlsx","text":"Appendix Table 1.2","size":"30.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5020 Appendix Table 1.2","linkHelpText":"– Quality-assurance data collected for additional constituents on Fort Berthold Reservation, North Dakota, 2014–17"},{"id":373825,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5020/sir20205020_appendix_table1.3.xlsx","text":"Appendix Table 1.3","size":"30.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5020 Appendix Table 1.3","linkHelpText":"– Summary statistics for water-quality constituents analyzed but not selected for additional discussion in surface water on Fort Berthold Reservation, 2014–17"},{"id":373826,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5020/sir20205020_appendix_table1.4.xlsx","text":"Appendix Table 1.4","size":"21.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5020 Appendix Table 1.4","linkHelpText":"– Summary statistics for historical water-quality constituents at surface-water sites on Fort Berthold Reservation, August 1966 through April 2014"},{"id":373827,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5020/sir20205020_appendix_table1.5.xlsx","text":"Appendix Table 1.5","size":"31.1 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5020 Appendix Table 1.5","linkHelpText":"– Summary statistics for water-quality constituents analyzed but not selected for additional discussion in groundwater on Fort Berthold Reservation, 2014–17"},{"id":373828,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5020/sir20205020_appendix_table2.1.xlsx","text":"Appendix Table 2.1","size":"593 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5020 Appendix Table 2.1","linkHelpText":"– Summary statistics for historical water-quality constituents in major aquifers on Fort Berthold Reservation, North Dakota"},{"id":373823,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5020/sir20205020_appendix_table1.1.xlsx","text":"Appendix Table 1.1","size":"18.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5020 Appendix Table 1.1","linkHelpText":"– Quality-assurance data collected for selected constituents on Fort Berthold Reservation, North Dakota, 2014–17"},{"id":373820,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5020/coverthb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":" Fort Berthold Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.79632568359374,\n 47.428087261714275\n ],\n [\n -101.84326171874999,\n 47.42622912485741\n ],\n [\n -101.84875488281249,\n 47.68018294648414\n ],\n [\n -101.87896728515624,\n 48.004625021133904\n ],\n [\n -102.79632568359374,\n 48.01381248943335\n ],\n [\n -102.79632568359374,\n 47.428087261714275\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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Streamflow observations can be used to understand, predict, and contextualize hydrologic, ecological, and biogeochemical processes and conditions in streams. Stream gages are point measurements along rivers where streamflow is measured, and are often used to infer upstream watershed‐scale processes. When stream gages read zero, this may indicate that the stream has dried at this location; however, zero‐flow readings can also be caused by a wide range of other factors. Our ability to identify whether or not a zero‐flow gage reading indicates a dry fluvial system has far reaching environmental implications. Incorrect identification and interpretation by the data user can lead to inaccurate hydrologic, ecological, and/or biogeochemical predictions from models and analyses. Here, we describe several causes of zero‐flow gage readings: frozen surface water, flow reversals, instrument error, and natural or human‐driven upstream source losses or bypass flow. For these examples, we discuss the implications of zero‐flow interpretations. We also highlight additional methods for determining flow presence, including direct observations, statistical methods, and hydrologic models, which can be applied to interpret causes of zero‐flow gage readings and implications for reach‐ and watershed‐scale dynamics. Such efforts are necessary to improve our ability to understand and predict surface flow activation, cessation, and connectivity across river networks. Developing this integrated understanding of the wide range of possible meanings of zero‐flows will only attain greater importance in a more variable and changing hydrologic climate.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1436","usgsCitation":"Zimmer, M., Kaiser, K.E., Blaszczak, J., Zipper, S., Hammond, J., Fritz, K.M., Costigan, K., Hosen, J.D., Godsey, S., Allen, G.H., Kampf, S.K., Burrow, R., Krabbenhoft, C., Dodds, W., Hale, R., Olden, J., Shanafield, M., DelVecchia, A., Ward, A.S., Mims, M.C., Datry, T., Bogan, M.A., Boersma, K., Busch, M., Jones, N.M., Burgin, A., and Allen, D., 2020, Zero or not? Causes and consequences of zero-flow stream gage readings: WIREs Water, v. 7, no. 3, e1436, 25 p., https://doi.org/10.1002/wat2.1436.","productDescription":"e1436, 25 p.","ipdsId":"IP-112480","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":457103,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/wat2.1436","text":"External Repository"},{"id":437027,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R84W5K","text":"USGS data release","linkHelpText":"Contiguous US and Global streamflow gages measuring zero flow"},{"id":437026,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AB3KL9","text":"USGS data release","linkHelpText":"Sub-annual streamflow responses to rainfall and snowmelt inputs in snow-dominated watersheds of the western 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survey and sedimentation analysis of Lago Carite, Puerto Rico, January 2018","interactions":[],"lastModifiedDate":"2020-05-01T12:40:28.919954","indexId":"sim3454","displayToPublicDate":"2020-04-13T06:33:03","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":"3454","displayTitle":"Bathymetric Survey and Sedimentation Analysis of Lago Carite, Puerto Rico, January 2018","title":"Bathymetric survey and sedimentation analysis of Lago Carite, Puerto Rico, January 2018","docAbstract":"During January 23–30, 2018, the U.S. Geological Survey, in cooperation with the Puerto Rico Electric Power Authority, conducted a bathymetric survey of Lago Carite primarily to update estimates of the contemporary reservoir storage capacity and sedimentation rate. Previously designated transect lines were surveyed by using a depth sounder coupled to a differential Global Positioning System to generate a bottom contour map and, ultimately, the stage-storage relation for Lago Carite. Survey results indicated that the storage capacity was 10.0 million cubic meters in 2018; no substantial sedimentation has occurred since the last survey in 1999 and the annual capacity loss is about 0.20 percent of the original reservoir capacity. The useful life of Lago Carite is projected to be 397 years, ending in 2415.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3454","collaboration":"Prepared in cooperation with the Puerto Rico Electric Power Authority","usgsCitation":"Gómez-Fragoso, J.M., 2020, Bathymetric survey and sedimentation analysis of Lago Carite, Puerto Rico, January 2018: U.S. Geological Survey Scientific Investigations Map 3454, 1 sheet, https://doi.org/10.3133/sim3454.","productDescription":"1 Sheet: 36.00 inches x 41.80 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-102375","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":373840,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3454/sim3454.pdf","text":"Sheet","size":"3.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3454"},{"id":373841,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98B49LM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for bathymetric survey and sedimentation analysis of Lago Carite, Puerto Rico, January 2018"},{"id":373198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3454/coverthb.jpg"}],"country":"United States","state":"Puerto Rico","otherGeospatial":"Lago Carite","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.1107873916626,\n 18.060925083057324\n ],\n [\n -66.08997344970703,\n 18.060925083057324\n ],\n [\n -66.08997344970703,\n 18.086178958119767\n ],\n [\n -66.1107873916626,\n 18.086178958119767\n ],\n [\n -66.1107873916626,\n 18.060925083057324\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Since the early 2000s, Lake Erie has been experiencing annual cyanobacterial blooms that often cover large portions of the western basin and even reach into the central basin. These blooms have affected several ecosystem services provided by Lake Erie to surrounding communities (notably drinking water quality). Several modeling efforts have identified the springtime total bioavailable phosphorus (TBP) load as a major driver of maximum cyanobacterial biomass in western Lake Erie, and on this basis, international water management bodies have set a phosphorus (P) reduction goal. This P reduction goal is intended to reduce maximum cyanobacterial biomass, but there has been very limited effort to identify the specific locations within the western basin of Lake Erie that will likely experience the most benefits. Here, we used pixel‐specific linear regression to identify where annual variation in spring TBP loads is most strongly associated with cyanobacterial abundance, as inferred from satellite imagery. Using this approach, we find that annual TBP loads are most strongly associated with cyanobacterial abundance in the central and southern areas of the western basin. At the location of the Toledo water intake, the association between TBP load and cyanobacterial abundance is moderate, and in Maumee Bay (near Toledo, Ohio), the association between TBP and cyanobacterial abundance is no better than a null model. Both of these locations are important for the delivery of specific ecosystem services, but this analysis indicates that P load reductions would not be expected to substantially improve maximum annual cyanobacterial abundance in these locations. These results are preliminary in the sense that only a limited set of models were tested in this analysis, but these results illustrate the importance of identifying whether the spatial distribution of management benefits (in this case P load reduction) matches the spatial distribution of management goals (reducing the effects of cyanobacteria on important ecosystem services).
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6160","usgsCitation":"Larson, J.H., Hlavacek, E., De Jager, N.R., Evans, M.A., and Wynne, T., 2020, Preliminary analysis to estimate the spatial distribution of benefits of P load reduction: Identifying the spatial influence of phosphorus loading from the Maumee River (USA) in western Lake Erie: Ecology and Evolution, v. 10, no. 9, p. 3968-3976, https://doi.org/10.1002/ece3.6160.","productDescription":"9 p.","startPage":"3968","endPage":"3976","ipdsId":"IP-111658","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":457106,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.6160","text":"Publisher Index Page"},{"id":377436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Ohio","otherGeospatial":"Lake Erie, Maumee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.177490234375,\n 42.00848901572399\n ],\n [\n -83.33404541015625,\n 41.920672548686824\n ],\n [\n -83.47412109375,\n 41.76721469421018\n ],\n [\n -83.47686767578125,\n 41.69547509615208\n ],\n [\n -83.3587646484375,\n 41.67086022030498\n ],\n [\n -83.1610107421875,\n 41.62160222224564\n ],\n [\n -83.056640625,\n 41.582579601430346\n ],\n [\n -82.96875,\n 41.52502957323801\n ],\n [\n -82.957763671875,\n 41.96153247330561\n ],\n [\n -83.177490234375,\n 42.00848901572399\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-04-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":796024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hlavacek, Enrika 0000-0002-9872-2305 ehlavacek@usgs.gov","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":149114,"corporation":false,"usgs":true,"family":"Hlavacek","given":"Enrika","email":"ehlavacek@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":796025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":796026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":149358,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":796027,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wynne, Timothy","contributorId":147819,"corporation":false,"usgs":false,"family":"Wynne","given":"Timothy","affiliations":[{"id":16942,"text":"National Oceanic and Atmospheric Administration, Silver Spring, Maryland","active":true,"usgs":false}],"preferred":false,"id":796028,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}