Chemical-contaminant mixtures are widely reported in large stream reaches in urban/agriculture-developed watersheds, but mixture compositions and aggregate biological effects are less well understood in corresponding smaller headwaters, which comprise most of stream length, riparian connectivity, and spatial biodiversity. During 2014–2017, the U.S. Geological Survey (USGS) measured 389 unique organic analytes (pharmaceutical, pesticide, organic wastewater indicators) in 305 headwater streams within four contiguous United States (US) regions. Potential aquatic biological effects were evaluated for estimated maximum and median exposure conditions using multiple lines of evidence, including occurrence/concentrations of designed-bioactive pesticides and pharmaceuticals and cumulative risk screening based on vertebrate-centric ToxCast™ exposure-response data and on invertebrate and nonvascular plant aquatic life benchmarks. Mixed-contaminant exposures were ubiquitous and varied, with 78% (304) of analytes detected at least once and cumulative maximum concentrations up to more than 156,000 ng/L. Designed bioactives represented 83% of detected analytes. Contaminant summary metrics correlated strong-positive (rho (ρ): 0.569–0.719) to multiple watershed-development metrics, only weak-positive to point-source discharges (ρ: 0.225–353), and moderate- to strong-negative with multiple instream invertebrate metrics (ρ: −0.373 to −0.652). Risk screening indicated common exposures with high probability of vertebrate-centric molecular effects and of acute toxicity to invertebrates, respectively. The results confirm exposures to broad and diverse contaminant mixtures and provide convincing multiple lines of evidence that chemical contaminants contribute substantially to adverse multi-stressor effects in headwater-stream communities.
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and Sycamore Creek near Lansing, Michigan","title":"Hydrologic and hydraulic analyses of the Grand River, Red Cedar River, and Sycamore Creek near Lansing, Michigan","docAbstract":"The U.S. Geological Survey (USGS) completed hydrologic and hydraulic analyses for selected reaches of the Grand River, Red Cedar River, and Sycamore Creek near Lansing, Michigan, in cooperation with the city of Lansing. The study comprised a 3.1-mile reach of the Grand River, a 30.3-mile reach of the Red Cedar River, and a 12.0-mile reach of Sycamore Creek. The information produced from the study can be used to update and expand an existing Federal Emergency Management Agency Flood Insurance Study for Ingham County, Mich.
Historical streamflow data from USGS streamgages on Grand River at Lansing, Mich. (station number 04113000); Red Cedar River at East Lansing, Mich. (station number 04112500); Red Cedar River near Williamston, Mich. (station number 04111379); and Sycamore Creek at Holt Road near Holt, Mich. (station number 04112850) were used to estimate instantaneous peak streamflows for floods with 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities (AEPs) and a “1-percent plus” AEP.
The Hydrologic Engineering Center’s River Analysis System step-backwater model was used to determine water-surface elevation profiles for the 10-, 4-, 2-, 1-, and 0.2-percent AEP floods, the 1-percent plus AEP flood, and a regulatory floodway for each stream reach. The hydraulic models were calibrated based on stage-streamflow ratings at USGS streamgages. Flood-inundation boundaries for the 1- and 0.2-percent annual exceedance probability floods and regulatory floodway were created for each stream.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205144","collaboration":"Prepared in cooperation with the city of Lansing, Michigan","usgsCitation":"Whitehead, M.T., and Ostheimer, C.J., 2021, Hydrologic and hydraulic analyses of the Grand River, Red Cedar River, and Sycamore Creek near Lansing, Michigan: U.S. Geological Survey Scientific Investigations Report 2020–5144, \n17 p., https://doi.org/10.3133/sir2020–5144.","productDescription":"Report: iv, 17 p.; Data Realease","onlineOnly":"Y","ipdsId":"IP-118378","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":382823,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5144/coverthb.jpg"},{"id":382824,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5144/sir20205144.pdf","text":"Report","size":"3.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5144"},{"id":382825,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91CQ755","text":"USGS data release","linkHelpText":"Geospatial datasets and hydraulic models for the Grand River, Red Cedar River, and Sycamore Creek near Lansing, Michigan"}],"country":"United States","state":"Michigan","otherGeospatial":"Grand River, Red Cedar River, Sycamore Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.57550048828125,\n 42.48526384858916\n ],\n [\n -83.9959716796875,\n 42.48526384858916\n ],\n [\n -83.9959716796875,\n 42.76465818533266\n ],\n [\n -84.57550048828125,\n 42.76465818533266\n ],\n [\n -84.57550048828125,\n 42.48526384858916\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Science Center
U.S. Geological Survey
6460 Busch Blvd., Suite 100
Columbus, OH 43229
The factors that set range limits for animal populations can inform management plans aimed at maintaining regional biodiversity. We examine abiotic and biotic drivers of the distribution of finescale dace (Chrosomus neogaeus) in two Great Plains basins to identify limiting factors for a threatened freshwater fish population at the edge of their range.
Great Plains, Nebraska, South Dakota and Wyoming, USA.
We investigated abiotic and biotic factors influencing the contemporary distribution of finescale dace in the Belle Fourche and Niobrara River basins with Random Forests classification models using fish surveys from multiple agencies spanning 2008–2019 and GIS-derived environmental data.
In both basins, finescale dace occurrence exhibited a nonlinear response to mean August water temperature. Abiotic covariates, including streamflow, water temperature and channel slope, were important limiting factors in the final model fit with Belle Fourche River basin surveys (n = 131). In contrast, a biotic covariate, native minnow richness, was the most important predictor of finescale dace occurrence in the Niobrara River basin model (n = 27). In the Niobrara River, native minnow richness was lower at sites with non-native northern pike (Esox lucius).
Basin-specific analyses revealed context dependencies for species–environment relationships, which can inform targeted restoration actions. Similar relationships between water temperature and finescale dace occurrence across both basins suggest summer thermal habitat as a regional limiting factor. The importance of biotic interactions in the Niobrara River highlights an emergent threat from invasive predators to a distinct assemblage of native prairie fishes.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13227","usgsCitation":"Booher, E., and Walters, A.W., 2021, Biotic and abiotic determinants of finescale dace distribution at the southern edge of their range: Diversity and Distributions, v. 27, no. 4, p. 696-709, https://doi.org/10.1111/ddi.13227.","productDescription":"14 p.","startPage":"696","endPage":"709","ipdsId":"IP-119393","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":453583,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13227","text":"Publisher Index Page"},{"id":396435,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska, South Dakota, Wyoming","otherGeospatial":"Belle Fourche River, Niobara River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.8,\n 42.132858175814626\n ],\n [\n -102.80731201171875,\n 42.132858175814626\n ],\n [\n -102.80731201171875,\n 42.7\n ],\n [\n -104.8,\n 42.7\n ],\n [\n -104.8,\n 42.132858175814626\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.94140625,\n 44.44358514592119\n ],\n [\n -103.53240966796875,\n 44.44358514592119\n ],\n [\n -103.53240966796875,\n 45\n ],\n [\n -104.94140625,\n 45\n ],\n [\n -104.94140625,\n 44.44358514592119\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Booher, Evan C. J.","contributorId":280044,"corporation":false,"usgs":false,"family":"Booher","given":"Evan C. J.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":835937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":835936,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217813,"text":"cir1474 - 2021 - Yellowstone Volcano Observatory 2018 annual report","interactions":[],"lastModifiedDate":"2025-05-08T16:27:47.535453","indexId":"cir1474","displayToPublicDate":"2021-02-03T09:37:43","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1474","displayTitle":"Yellowstone Volcano Observatory 2018 Annual Report","title":"Yellowstone Volcano Observatory 2018 annual report","docAbstract":"The Yellowstone Volcano Observatory (YVO) monitors volcanic and hydrothermal activity associated with the Yellowstone magmatic system, conducts research into magmatic processes occurring beneath Yellowstone Caldera, and issues timely warnings and guidance related to potential future geologic hazards. This report summarizes the activities and findings of YVO during the year 2018, focusing on the Yellowstone magmatic system. The most noteworthy seismic activity of the year was a February swarm of hundreds of earthquakes in the same area as the 2017 Maple Creek earthquake swarm. The February 2018 activity is viewed as a continuation of the 2017 swarm. Ground deformation trends were mostly unchanged throughout the year, with uplift of the Norris Geyser Basin area and subsidence of the caldera.
Field work in 2018, conducted under research permits granted by the National Park Service, included routine maintenance visits to seismic and geodetic stations as well as deployment of a semipermanent Global Positioning System network during the summer months; installation of an eddy covariance system for tracking carbon dioxide emissions and heat flux near Norris Geyser Basin; deployment of nodal seismic arrays on Geyser Hill, near Steamboat Geyser, and around Yellowstone Lake; and collection of water and gas samples from the Bechler River area in the southwest part of Yellowstone National Park. In addition, examination of satellite thermal imagery resulted in the discovery of a new thermal area on the east side of the Sour Creek resurgent dome, near west Tern Lake. This thermal area appears to have started forming in the early 2000s; before then it was an area of healthy forest. The year might best be remembered, however, for some extraordinary geyser and hot spring activity, specifically at Steamboat Geyser and Ear Spring.
Yellowstone Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court, Suite 100
Vancouver, WA 98683
Email: yvowebteam@usgs.gov
","tableOfContents":"Due to increasing water demands globally, freshwater ecosystems are under constant pressure. Groundwater resources, as the main source of accessible freshwater, are crucially important for irrigation worldwide. Over‐abstraction of groundwater leads to declines in groundwater levels; consequently, the groundwater inflow to streams decreases. The reduction in base flow and alteration of the stream flow regime can potentially have an adverse impact on groundwater‐dependent ecosystems. A spatially distributed, coupled groundwater‐surface water model can simulate the impacts of groundwater abstraction on aquatic ecosystems. A constrained optimization algorithm and a simulation model in combination can provide an objective tool for the water practitioner to evaluate the interplay between economic benefits of groundwater abstractions and requirements to environmental flow. In this study, a holistic catchment‐scale groundwater abstraction optimization framework has been developed that allows for a spatially explicit optimization of groundwater abstraction, while fulfilling a pre‐defined maximum allowed reduction of stream flow (base flow (Q95) or median flow (Q50)) as constraint criteria for 1484 stream locations across the catchment. A balanced K‐Means clustering method was implemented to reduce the computational burden of the optimization. The model parameters and observation uncertainties calculated based on Bayesian linear theory allow for a risk assessment on the optimized groundwater abstraction values. The results from different optimization scenarios indicated that using the linear programming optimization algorithm in conjunction with integrated models provides valuable information for guiding the water practitioners in designing an effective groundwater abstraction plan with the consideration of environmental flow criteria important for the ecological status of the entire system.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13083","usgsCitation":"Danapour, M., Fienen, M., Hojberg, A.L., Jensen, K.H., and Stisen, S., 2021, Multi‐constrained catchment scale optimization of groundwater abstraction using linear programming: Groundwater, v. 59, no. 4, p. 503-516, https://doi.org/10.1111/gwat.13083.","productDescription":"14 p.","startPage":"503","endPage":"516","ipdsId":"IP-124860","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":383584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Danapour, Mehrdis 0000-0003-1877-0233","orcid":"https://orcid.org/0000-0003-1877-0233","contributorId":251915,"corporation":false,"usgs":false,"family":"Danapour","given":"Mehrdis","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":810824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hojberg, Anker Lajer","contributorId":251916,"corporation":false,"usgs":false,"family":"Hojberg","given":"Anker","email":"","middleInitial":"Lajer","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":810826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jensen, Karsten Hogh","contributorId":251917,"corporation":false,"usgs":false,"family":"Jensen","given":"Karsten","email":"","middleInitial":"Hogh","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":810827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stisen, Simon","contributorId":251920,"corporation":false,"usgs":false,"family":"Stisen","given":"Simon","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":810828,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218008,"text":"70218008 - 2021 - Body condition of wintering Pacific greater white-fronted geese","interactions":[],"lastModifiedDate":"2021-03-19T20:55:49.752814","indexId":"70218008","displayToPublicDate":"2021-02-02T13:22:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Body condition of wintering Pacific greater white-fronted geese","docAbstract":"Extreme changes to key waterfowl habitats in the Klamath Basin (KB) on the Oregon–California border and the Sacramento Valley (SV) in California, USA, have occurred since 1980. The spatial distribution of Pacific greater white‐fronted geese (Anser albifrons sponsa; geese) has likewise changed among these areas and population size has grown from 79,000 to >600,000 geese during the same period. To assess the effects of landscape changes and spatial‐temporal distribution of geese, we collected Pacific greater white‐fronted geese during winters of 2009–2010 and 2010–2011 in the KB and SV and compared their body condition to geese collected during 1979–1980 and 1980–1981. We modeled body and lipid mass to assess body condition for each sex independently and examined the influence of collection day, year, and region. Body condition of geese varied throughout the winter and within years in a nonlinear fashion. We detected an increase in body condition in both sexes during December and January in the SV, which corresponds with improved habitat conditions and increases seen in other species in the region. Body condition upon arrival in fall migration varied by year for females and by year and region for males. Males and females arrived in poorer body condition during 2010–2011 than all other study years and males in the KB during 2010–2011 had extremely low lipid mass, reflecting poor regional habitat conditions induced by drought. Body condition of females varied over spring, by year, and by region and regional effects were evident for males. Body condition was significantly higher for geese in the SV than in the KB during spring. Our results suggest that Pacific greater white‐fronted geese have adapted to a changing landscape and have adjusted historical spatial use patterns to take advantage of more favorable conditions in the SV between 1979 and 2010.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21997","usgsCitation":"Skalos, D., Eadie, J.M., Yparraguirre, D., Weaver, M.L., Oldenburger, S.L., Ely, C.R., Yee, J.L., and Fleskes, J., 2021, Body condition of wintering Pacific greater white-fronted geese: Journal of Wildlife Management, v. 85, no. 3, p. 484-497, https://doi.org/10.1002/jwmg.21997.","productDescription":"14 p.","startPage":"484","endPage":"497","ipdsId":"IP-116418","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":383221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath Basin, Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.44262695312501,\n 37.80544394934271\n ],\n [\n -120.794677734375,\n 37.80544394934271\n ],\n [\n -120.794677734375,\n 39.444677580473424\n ],\n [\n -122.44262695312501,\n 39.444677580473424\n ],\n [\n -122.44262695312501,\n 37.80544394934271\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.431640625,\n 41.51680395810118\n ],\n [\n -121.06933593749999,\n 41.51680395810118\n ],\n [\n -121.06933593749999,\n 42.52069952914966\n ],\n [\n -122.431640625,\n 42.52069952914966\n ],\n [\n -122.431640625,\n 41.51680395810118\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-02-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Skalos, Daniel A.","contributorId":250668,"corporation":false,"usgs":false,"family":"Skalos","given":"Daniel A.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":810205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eadie, John M.","contributorId":65219,"corporation":false,"usgs":false,"family":"Eadie","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":7082,"text":"University of California - Davis","active":true,"usgs":false}],"preferred":false,"id":810206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yparraguirre, Daniel R.","contributorId":250671,"corporation":false,"usgs":false,"family":"Yparraguirre","given":"Daniel R.","affiliations":[{"id":6952,"text":"California Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":810207,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weaver, Melanie L.","contributorId":250673,"corporation":false,"usgs":false,"family":"Weaver","given":"Melanie","email":"","middleInitial":"L.","affiliations":[{"id":6952,"text":"California Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":810208,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oldenburger, Shaun L.","contributorId":177598,"corporation":false,"usgs":false,"family":"Oldenburger","given":"Shaun","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":810209,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ely, Craig R. 0000-0003-4262-0892 cely@usgs.gov","orcid":"https://orcid.org/0000-0003-4262-0892","contributorId":3214,"corporation":false,"usgs":true,"family":"Ely","given":"Craig","email":"cely@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":810210,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":810211,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fleskes, Joseph P. 0000-0001-5388-6675","orcid":"https://orcid.org/0000-0001-5388-6675","contributorId":210345,"corporation":false,"usgs":false,"family":"Fleskes","given":"Joseph P.","affiliations":[],"preferred":false,"id":810212,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70217745,"text":"ofr20201112 - 2021 - Summary of fish communities along Underwood Creek, Milwaukee, Wisconsin, 2004–2019","interactions":[],"lastModifiedDate":"2021-02-03T12:36:09.475181","indexId":"ofr20201112","displayToPublicDate":"2021-02-02T12:50:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1112","displayTitle":"Summary of Fish Communities along Underwood Creek, Milwaukee, Wisconsin, 2004–2019","title":"Summary of fish communities along Underwood Creek, Milwaukee, Wisconsin, 2004–2019","docAbstract":"Beginning in 2010, sections of Underwood Creek in Milwaukee County, Wisconsin, have undergone reconstruction to allow for improved fish habitat and better management of storm flows. In addition, dam and drop structures were removed to help improve fish migration while reintroducing several native fish species. With the reconstruction of Underwood Creek underway, the Milwaukee Metropolitan Sewerage District sought to evaluate if these measures have resulted in improvements to the fish community in the upstream parts of the watershed. The U.S. Geological Survey began sampling fish communities in 2004 at the farthest downstream site on Underwood Creek (Reach A) which was reconstructed in 2017. Reach B, which is slightly upstream, had undergone reconstruction in 2010 and fish community sampling began in 2016. A third reach farther upstream near Elm Grove was schedule to begin reconstruction in 2019. To compare the fish before and after reconstruction at the Elm Grove Reach, a fish community survey was conducted in spring of 2019 at Elm Grove and Reach B. This document describes the fish community from this sampling in comparison to previous surveys. Before reconstruction, Elm Grove Reach contained fish species more indicative of a slower, stagnant, warmwater stream than the other two rehabilitated reaches. Although six of the eight species found in Elm Grove Reach have been found at the lower reaches, all but two of the species are considered tolerant. Reconstruction of Elm Grove Reach to a similar habitat as the lower reaches will likely support a more diverse fish community.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201112","collaboration":"Prepared in Cooperation with Milwaukee Metropolitan Sewerage District","usgsCitation":"Bell, A.H., Sullivan, D.J., and Scudder Eikenberry, B.C., 2021, Summary of fish communities along Underwood Creek, Milwaukee, Wisconsin, 2004–2019: U.S. Geological Survey Open-File Report 2020–1112, 14 p., https://doi.org/10.3133/ofr20201112.","productDescription":"Report: v, 14 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-117234","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":382828,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77W698B","text":"USGS data release","linkHelpText":"U.S. Geological Survey, n.d., BioData — aquatic bioassessment data for the Nation"},{"id":382826,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1112/coverthb.jpg"},{"id":382827,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1112/ofr20201112.pdf","text":"Report","size":"5.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1112"}],"country":"United States","state":"Wisconsin","city":"Milwaukee","otherGeospatial":"Underwood Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.13438415527344,\n 43.01594430071724\n ],\n [\n -88.01696777343749,\n 43.01669737169671\n ],\n [\n -88.01525115966797,\n 43.086441866511805\n ],\n [\n -88.13301086425781,\n 43.08594039080513\n ],\n [\n -88.13438415527344,\n 43.01594430071724\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Boreal peatland forests have relatively low species diversity and thus impacts of climate change on one or more dominant species could shift ecosystem function. Despite abundant soil water availability, shallowly rooted vascular plants within peatlands may not be able to meet foliar demand for water under drought or heat events that increase vapor pressure deficits while reducing near surface water availability, although concurrent increases in atmospheric CO2 could buffer resultant hydraulic stress. We assessed plant water relations of co‐occurring shrub (primarily Rhododendron groenlandicum and Chamaedaphne calyculata) and tree (Picea mariana and Larix laricina) species prior to, and in response to whole ecosystem warming (0 to +9°C) and elevated CO2 using 12.8‐m diameter open‐top enclosures installed within an ombrotrophic bog. Water relations (water potential [Ψ], turgor loss point, foliar and root hydraulic conductivity) were assessed prior to treatment initiation, then Ψ and peak sap flow (trees only) assessed after 1 or 2 years of treatments. Under the higher temperature treatments, L. laricina Ψ exceeded its turgor loss point, increased its peak sap flow, and was not able to recover Ψ overnight. In contrast, P. mariana operated below its turgor loss point and maintained constant Ψ and sap flow across warming treatments. Similarly, C. calyculata Ψ stress increased with temperature while R. groenlandicum Ψ remained at pretreatment levels. The more anisohydric behavior of L. laricina and C. calyculata may provide greater net C uptake with warming, while the more conservative P. mariana and R. groenlandicum maintained greater hydraulic safety. These latter species also responded to elevated CO2 by reduced Ψ stress, which may also help limit hydraulic failure during periods of extreme drought or heat in the future. Along with Sphagnum moss, the species‐specific responses of peatland vascular communities to drier or hotter conditions will shape boreal peatland composition and function in the future.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.15543","usgsCitation":"Warren, J.M., Jensen, A.M., Ward, E., Guha, A., Childs, J., Wullschleger, S.D., and Hanson, P.J., 2021, Divergent species‐specific impacts of whole ecosystem warming and elevated CO2 on vegetation water relations in an ombrotrophic peatland: Global Change Biology, v. 27, no. 9, p. 1820-1835, https://doi.org/10.1111/gcb.15543.","productDescription":"16 p.","startPage":"1820","endPage":"1835","ipdsId":"IP-120525","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":453597,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1779150","text":"Publisher Index Page"},{"id":384226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Marcell Experimental Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.53828430175781,\n 47.44341438795746\n ],\n [\n -93.45245361328125,\n 47.44341438795746\n ],\n [\n -93.45245361328125,\n 47.52461999690651\n ],\n [\n -93.53828430175781,\n 47.52461999690651\n ],\n [\n -93.53828430175781,\n 47.44341438795746\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Warren, Jeffrey M .","contributorId":198318,"corporation":false,"usgs":false,"family":"Warren","given":"Jeffrey","email":"","middleInitial":"M .","affiliations":[],"preferred":false,"id":811473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jensen, Anna M","contributorId":254940,"corporation":false,"usgs":false,"family":"Jensen","given":"Anna","email":"","middleInitial":"M","affiliations":[{"id":49394,"text":"Linnaeus University","active":true,"usgs":false}],"preferred":false,"id":811474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ward, Eric 0000-0002-5047-5464","orcid":"https://orcid.org/0000-0002-5047-5464","contributorId":218962,"corporation":false,"usgs":true,"family":"Ward","given":"Eric","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":811475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guha, Anirban","contributorId":254941,"corporation":false,"usgs":false,"family":"Guha","given":"Anirban","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":811476,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Childs, Joanne","contributorId":254942,"corporation":false,"usgs":false,"family":"Childs","given":"Joanne","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":811477,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wullschleger, Stan D.","contributorId":167343,"corporation":false,"usgs":false,"family":"Wullschleger","given":"Stan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":811478,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hanson, Paul J","contributorId":218965,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"J","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":811479,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236572,"text":"70236572 - 2021 - Sea state from single optical images: A methodology to derive wind-generated ocean waves from cameras, drones and satellites","interactions":[],"lastModifiedDate":"2022-09-12T14:24:33.012692","indexId":"70236572","displayToPublicDate":"2021-02-01T09:09:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Sea state from single optical images: A methodology to derive wind-generated ocean waves from cameras, drones and satellites","docAbstract":"Sea state is a key variable in ocean and coastal dynamics. The sea state is either sparsely\nmeasured by wave buoys and satellites or modelled over large scales. Only a few attempts have been devoted to sea state measurements covering a large domain; in particular its estimation from optical images. With optical technologies becoming omnipresent, optical images offer incomparable spatial resolution from diverse sensors such as shore-based cameras, airborne drones (unmanned aerial vehicles/UAVs), or satellites. Here, we present a standalone methodology to derive the water surface elevation anomaly induced by wind-generated ocean waves from optical imagery. The methodology was tested on drone and satellite images and compared against ground truth. The results show a clear dependence on the relative azimuth view angle in relation to the wave crest. A simple correction is proposed to overcome this bias. Overall, the presented methodology offers a practical way of estimating ocean waves for a wide range of applications.","language":"English","publisher":"MDPI","doi":"10.3390/rs13040679","usgsCitation":"Almar, R., Bergsma, E.W., Catalan, P.A., Cienfuegos, R., Suarez, L., Lucero, F., Lerma, A.N., Desmazes, F., Perugini, E., Palmsten, M.L., and Chickadel, C., 2021, Sea state from single optical images: A methodology to derive wind-generated ocean waves from cameras, drones and satellites: Remote Sensing, v. 13, no. 4, 679, 8 p., https://doi.org/10.3390/rs13040679.","productDescription":"679, 8 p.","ipdsId":"IP-126573","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13040679","text":"Publisher Index Page"},{"id":406532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Almar, Rafael","contributorId":296397,"corporation":false,"usgs":false,"family":"Almar","given":"Rafael","email":"","affiliations":[{"id":64029,"text":"LEGOS (CNRS/IRD/CNES/Université de Toulouse), Toulouse, France","active":true,"usgs":false}],"preferred":false,"id":851414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bergsma, Erwin W. 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J.","affiliations":[{"id":64031,"text":"Earth Observation Lab CNES (French Space Agency), Toulouse, France","active":true,"usgs":false}],"preferred":false,"id":851415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Catalan, Patrico A.","contributorId":296399,"corporation":false,"usgs":false,"family":"Catalan","given":"Patrico","email":"","middleInitial":"A.","affiliations":[{"id":64032,"text":"Departamento de Obras Civiles, Universidad Técnica Federico Santa María, Valparaiso 2390123, Chile","active":true,"usgs":false}],"preferred":false,"id":851416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cienfuegos, Rodrigo","contributorId":296400,"corporation":false,"usgs":false,"family":"Cienfuegos","given":"Rodrigo","email":"","affiliations":[{"id":64033,"text":"Departamento de Ingeniería Hidráulica y Ambiental, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile; r","active":true,"usgs":false}],"preferred":false,"id":851417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Suarez, Leandro","contributorId":296401,"corporation":false,"usgs":false,"family":"Suarez","given":"Leandro","email":"","affiliations":[{"id":64033,"text":"Departamento de Ingeniería Hidráulica y Ambiental, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile; r","active":true,"usgs":false}],"preferred":false,"id":851418,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lucero, Felipe","contributorId":296402,"corporation":false,"usgs":false,"family":"Lucero","given":"Felipe","email":"","affiliations":[{"id":64033,"text":"Departamento de Ingeniería Hidráulica y Ambiental, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile; r","active":true,"usgs":false}],"preferred":false,"id":851419,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lerma, Alexandre Nicolae","contributorId":296403,"corporation":false,"usgs":false,"family":"Lerma","given":"Alexandre","email":"","middleInitial":"Nicolae","affiliations":[{"id":64034,"text":"Bureau de Recherches Géologiques et Minières (BRGM), 33600 Pessac, France","active":true,"usgs":false}],"preferred":false,"id":851420,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Desmazes, Franck","contributorId":296404,"corporation":false,"usgs":false,"family":"Desmazes","given":"Franck","email":"","affiliations":[{"id":64034,"text":"Bureau de Recherches Géologiques et Minières (BRGM), 33600 Pessac, France","active":true,"usgs":false}],"preferred":false,"id":851421,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Perugini, Eleonora","contributorId":296405,"corporation":false,"usgs":false,"family":"Perugini","given":"Eleonora","email":"","affiliations":[{"id":64035,"text":"Department of DICEA, Università Politecnica delle Marche, 60131 Ancona, Italy","active":true,"usgs":false}],"preferred":false,"id":851422,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":851423,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Chickadel, Chris","contributorId":296406,"corporation":false,"usgs":false,"family":"Chickadel","given":"Chris","affiliations":[{"id":64036,"text":"Applied Physics Laboratory, University of Washington, Seattle, WA 98195, USA","active":true,"usgs":false}],"preferred":false,"id":851424,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70236991,"text":"70236991 - 2021 - Sap flow evidence of chilling injury and recovery in mangroves following a spring cold spell","interactions":[],"lastModifiedDate":"2022-09-27T13:50:33.036756","indexId":"70236991","displayToPublicDate":"2021-02-01T08:45:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3651,"text":"Trees: Structure and Function","active":true,"publicationSubtype":{"id":10}},"title":"Sap flow evidence of chilling injury and recovery in mangroves following a spring cold spell","docAbstract":"Mangroves are periodically influenced in negative ways by non-freezing temperatures across their global sub-tropical range. However, physiological and morphological evidence of chilling influences to non-freezing chilling events has not been measured in field settings. In this study, we measured sap flow (Js) during such a chilling (but non-freezing) event in southern China and documented the reductions in Js and the recovery that ensued. We calculated tree water use (TWU) from Js measurements taken from thermal dissipation sap flow sensors on two mangrove species (Sonneratia apetala and S. caseolaris). This chilling event significantly injured the mangrove trees in the form of leaf scorch and massive defoliation. Diurnal variations of stem Js of both species were altered significantly after chilling. On the day of the chilling event, Js of S. caseolaris was reduced from the daily maximum of 44.1 g H2O m−2 s−1 to 0 immediately after chilling, which lasted throughout the remainder of the day. In contrast, S. apetala showed a certain low-temperature tolerance, while still maintaining an adequate transpiration rate after chilling, indicative of a more resilient hydraulic transport system to low temperatures. The sap flow data collected revealed substantial evidence for acute water conservation during low-temperature events, perhaps ameliorating low-temperature damage. Hence, the responses of some mangrove species with high sensitivity to low, but non-freezing, temperature (such as S. caseolaris) may indicate that mangroves possess adaptive whole-tree strategies to cold temperature.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s00468-021-02089-9","usgsCitation":"Gu, X., Yang, C., Zhao, H., Hu, N., Krauss, K., Deng, C., and Chen, L., 2021, Sap flow evidence of chilling injury and recovery in mangroves following a spring cold spell: Trees: Structure and Function, v. 35, no. 3, p. 907-917, https://doi.org/10.1007/s00468-021-02089-9.","productDescription":"11 p.","startPage":"907","endPage":"917","ipdsId":"IP-114795","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":407398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 107.9296875,\n 22.228090416784486\n ],\n [\n 107.75390625,\n 21.616579336740603\n ],\n [\n 109.5556640625,\n 20.46818922264095\n ],\n [\n 108.19335937499999,\n 19.352610894378625\n ],\n [\n 108.4130859375,\n 18.271086109608877\n ],\n [\n 110.1708984375,\n 17.97873309555617\n ],\n [\n 111.3134765625,\n 19.642587534013032\n ],\n [\n 110.9619140625,\n 21.04349121680354\n ],\n [\n 113.6865234375,\n 21.861498734372567\n ],\n [\n 116.4111328125,\n 22.755920681486405\n ],\n [\n 120.0146484375,\n 25.12539261151203\n ],\n [\n 120.1904296875,\n 26.27371402440643\n ],\n [\n 121.37695312499999,\n 27.68352808378776\n ],\n [\n 122.56347656249999,\n 29.99300228455108\n ],\n [\n 121.728515625,\n 32.69486597787505\n ],\n [\n 120.5419921875,\n 32.65787573695528\n ],\n [\n 119.92675781249999,\n 29.458731185355344\n ],\n [\n 116.806640625,\n 25.363882272740256\n ],\n [\n 111.796875,\n 23.241346102386135\n ],\n [\n 107.9296875,\n 22.228090416784486\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Gu, Xiaoxuan","contributorId":296950,"corporation":false,"usgs":false,"family":"Gu","given":"Xiaoxuan","email":"","affiliations":[{"id":64251,"text":"College of the Environment and Ecology, Xiamen University","active":true,"usgs":false}],"preferred":false,"id":852951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yang, Chen","contributorId":219219,"corporation":false,"usgs":false,"family":"Yang","given":"Chen","email":"","affiliations":[{"id":39971,"text":"School of Earth Sciences, The Ohio State University, Columbus, Ohio","active":true,"usgs":false}],"preferred":false,"id":852952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhao, Hewei","contributorId":296951,"corporation":false,"usgs":false,"family":"Zhao","given":"Hewei","email":"","affiliations":[{"id":64251,"text":"College of the Environment and Ecology, Xiamen University","active":true,"usgs":false}],"preferred":false,"id":852953,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hu, Naxu","contributorId":296952,"corporation":false,"usgs":false,"family":"Hu","given":"Naxu","email":"","affiliations":[{"id":64251,"text":"College of the Environment and Ecology, Xiamen University","active":true,"usgs":false}],"preferred":false,"id":852954,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":852955,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deng, Chuanyuan","contributorId":296953,"corporation":false,"usgs":false,"family":"Deng","given":"Chuanyuan","email":"","affiliations":[{"id":64252,"text":"College of Landscape Architecture, Fujian Agriculture and Forestry University","active":true,"usgs":false}],"preferred":false,"id":852956,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chen, Luzhen","contributorId":194706,"corporation":false,"usgs":false,"family":"Chen","given":"Luzhen","email":"","affiliations":[],"preferred":false,"id":852957,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70222491,"text":"70222491 - 2021 - Time since burning and rainfall characteristics impact post-fire debris flow initiation and magnitude","interactions":[],"lastModifiedDate":"2021-07-30T13:00:46.620849","indexId":"70222491","displayToPublicDate":"2021-02-01T07:58:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9124,"text":"Environmental Engineering and Geology","active":true,"publicationSubtype":{"id":10}},"title":"Time since burning and rainfall characteristics impact post-fire debris flow initiation and magnitude","docAbstract":"The extreme heat from wildfire alters soil properties and incinerates vegetation, leading to changes in infiltration capacity, ground cover, soil erodibility, and rainfall interception. These changes promote elevated rates of runoff and sediment transport that increase the likelihood of runoff-generated debris flows. Debris flows are most common in the year immediately following wildfire, but temporal changes in the likelihood and magnitude of debris flows following wildfire are not well constrained. In this study, we combine measurements of soil-hydraulic properties with vegetation survey data and numerical modeling to understand how debris-flow threats are likely to change in steep, burned watersheds during the first 3 years of recovery. We focus on documenting recovery following the 2016 Fish Fire in the San Gabriel Mountains, California, and demonstrate how a numerical model can be used to predict temporal changes in debris-flow properties and initiation thresholds. Numerical modeling suggests that the 15-minute intensity-duration (ID) threshold for debris flows in post-fire year 1 can vary from 15 to 30 mm/hr, depending on how rainfall is temporally distributed within a storm. Simulations further demonstrate that expected debris-flow volumes would be reduced by more than a factor of three following 1 year of recovery and that the 15-minute rainfall ID threshold would increase from 15 to 30 mm/hr to greater than 60 mm/hr by post-fire year 3. These results provide constraints on debris-flow thresholds within the San Gabriel Mountains and highlight the importance of considering local rainfall characteristics when using numerical models to assess debris-flow and flood potential.
Endocrine-disrupting compounds (EDCs), specifically estrogenic endocrine-disrupting compounds, vary in concentration and composition in surface waters under the influence of different landscape sources and landcover gradients. Estrogenic activity in surface waters may lead to adverse effects in aquatic species at both individual and population levels, often observed through the presence of intersex and vitellogenin induction in male fish. In the Chesapeake Bay Watershed, located on the mid-Atlantic coast of the USA, intersex has been observed in several sub-watersheds where previous studies have identified specific landscape sources of EDCs in tandem with observed fish health effects. Previous work in the Potomac River Watershed (PRW), the largest basin within the Chesapeake Bay Watershed, was leveraged to build random forest regression models to predict estrogenic activity at unsampled reaches in both the Potomac River and larger Chesapeake Bay Watersheds (CBW). Model outputs including important variables, partial dependence plots, and predicted values of estrogenic activity at unsampled reaches provide insight into drivers of estrogenic activity at different seasons and scales. Using the US Environmental Protection Agency effects-based threshold of 1.0 ng/L 17 β-estradiol equivalents, catchments predicted to exceed this value were categorized as at risk for adverse effects from exposure to estrogenic compounds and evaluated relative to healthy watersheds and recreation access locations throughout the PRW. Results show immediate catchment scale models are more reliable than upstream models, and the best predictive variables differ by season and scale. A small percentage of healthy watersheds (< 13%) and public access sites were classified as at risk using the “Total” (annual) model in the CBW. This study is the first Potomac River Watershed assessment of estrogenic activity, providing a new foundation for future risk assessment and management design efforts, with additional context provided for the entire Chesapeake Bay Watershed.
As permafrost thaws in the Arctic, new subsurface pathways open for the transport of groundwater, energy, and solutes. We identify different ways that these subsurface changes are driving observed surface consequences, including the potential for increased contaminant transport, modification to water resources, and enhanced rates of infrastructure (e.g. buildings and roads) damage. Further, as permafrost thaws it allows groundwater to transport carbon, nutrients, and other dissolved constituents from terrestrial to aquatic environments via progressively deeper subsurface flow paths. Cryohydrogeology, the study of groundwater in cold regions, should be included in northern research initiatives to account for this hidden catalyst of environmental and societal change.
","language":"English","publisher":"Copernicus","doi":"10.5194/tc-15-479-2021","usgsCitation":"McKenzie, J.M., Kurylyk, B.L., Walvoord, M.A., Bense, V.F., Fortier, D., Spence, C., and Grenier, C., 2021, Invited perspective: What lies beneath a changing Arctic?: The Cryosphere, v. 15, p. 479-484, https://doi.org/10.5194/tc-15-479-2021.","productDescription":"6 p.","startPage":"479","endPage":"484","ipdsId":"IP-099776","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453634,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-15-479-2021","text":"Publisher Index Page"},{"id":382873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2021-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenzie, Jeffrey M.","contributorId":176299,"corporation":false,"usgs":false,"family":"McKenzie","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":809556,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kurylyk, Barret L.","contributorId":176296,"corporation":false,"usgs":false,"family":"Kurylyk","given":"Barret","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":809557,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":809558,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bense, Victor F.","contributorId":248636,"corporation":false,"usgs":false,"family":"Bense","given":"Victor","email":"","middleInitial":"F.","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":809559,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fortier, Daniel","contributorId":194641,"corporation":false,"usgs":false,"family":"Fortier","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":809560,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spence, Chris","contributorId":248637,"corporation":false,"usgs":false,"family":"Spence","given":"Chris","email":"","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":809561,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Grenier, Christophe","contributorId":248640,"corporation":false,"usgs":false,"family":"Grenier","given":"Christophe","email":"","affiliations":[{"id":49963,"text":"Université Paris-Saclay","active":true,"usgs":false}],"preferred":false,"id":809562,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70218232,"text":"70218232 - 2021 - Biological and chemical recovery of acidified Catskill Mountain streams in response to the Clean Air Act Amendments of 1990","interactions":[],"lastModifiedDate":"2021-02-19T17:52:29.548291","indexId":"70218232","displayToPublicDate":"2021-01-31T11:47:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":924,"text":"Atmospheric Environment","active":true,"publicationSubtype":{"id":10}},"title":"Biological and chemical recovery of acidified Catskill Mountain streams in response to the Clean Air Act Amendments of 1990","docAbstract":"Decades of acidic deposition have adversely affected aquatic and terrestrial ecosystems in acid-sensitive watersheds in parts of the eastern United States. The national Acid Rain Program (Title IV of the 1990 Clean Air Act Amendments - CAAA) helped reduce emissions of sulfur dioxide (SO2) and nitrogen oxides (NOx) and resulted in sharp decreases in the acidity of atmospheric deposition. The decrease in acidic deposition produced a steady decline in the acidity of streams in many poorly buffered waters across the western Adirondacks and parts of the Catskill Mountains of New York. Until recently, however, there has been little evidence of biological recovery in most acid-sensitive streams in both regions. Long-term deposition and stream-chemistry records and fish-community data from quantitative surveys done during 1991–93 and again during 2012–19 at 13 sites in the upper Neversink River and its tributaries were evaluated to determine if chemical and biological recovery were evident in this Catskill Mountain watershed and if they could be linked to regional declines in acidic deposition. Between 1991 and 2019, large decreases in sulfate and nitrate deposition in the basin mirrored declines in total nationwide SO2 and NOx emissions. There were corresponding decreases in sulfate and nitrate concentrations in deposition at a National Trends Network station at Frost Valley (NY68) and coincident declines in sulfate concentrations at four long-term monitoring sites in the Neversink River watershed. Mean acid neutralizing capacity and pH increased and inorganic aluminum (Ali) concentrations from routine summertime samples decreased significantly at most moderately to severely acidified sites between the two study periods. Richness, density, and biomass of fish communities increased at most sites, while the density and biomass of brook trout Salvelinus fontinalis populations increased at fewer sites that were undergoing chemical recovery. Although recovery is far from complete, trends in deposition chemistry, water quality, and fish assemblages in streams of the upper Neversink watershed indicate that the 1990 CAAA is having positive impacts on aquatic ecosystems in the Catskill Mountain region, New York.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.atmosenv.2021.118235","usgsCitation":"Baldigo, B.P., George, S.D., Winterhalter, D., and McHale, M., 2021, Biological and chemical recovery of acidified Catskill Mountain streams in response to the Clean Air Act Amendments of 1990: Atmospheric Environment, v. 249, 118235, 18 p., https://doi.org/10.1016/j.atmosenv.2021.118235.","productDescription":"118235, 18 p.","ipdsId":"IP-121887","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":453636,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.atmosenv.2021.118235","text":"Publisher Index Page"},{"id":383377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Neversink watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.63973999023438,\n 41.81175536180908\n ],\n [\n -74.53399658203125,\n 41.873139978873574\n ],\n [\n -74.4275665283203,\n 41.937019660425264\n ],\n [\n -74.33967590332031,\n 41.963064211132306\n ],\n [\n -74.28680419921875,\n 42.039094188385945\n ],\n [\n -74.34104919433594,\n 42.10382653879911\n ],\n [\n -74.40696716308594,\n 42.11859868281563\n ],\n [\n -74.45571899414062,\n 42.08395512413707\n ],\n [\n -74.62806701660156,\n 41.95080927751363\n ],\n [\n -74.70291137695312,\n 41.86700416724044\n ],\n [\n -74.67750549316406,\n 41.81021999190292\n ],\n [\n -74.63973999023438,\n 41.81175536180908\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"249","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winterhalter, Dylan R. 0000-0003-1774-8034","orcid":"https://orcid.org/0000-0003-1774-8034","contributorId":251765,"corporation":false,"usgs":true,"family":"Winterhalter","given":"Dylan R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McHale, Michael 0000-0003-3780-1816 mmchale@usgs.gov","orcid":"https://orcid.org/0000-0003-3780-1816","contributorId":177292,"corporation":false,"usgs":true,"family":"McHale","given":"Michael","email":"mmchale@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810548,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228368,"text":"70228368 - 2021 - Nuclear eDNA estimates population allele frequencies and abundance in experimental mesocosms","interactions":[],"lastModifiedDate":"2022-02-09T16:27:57.063348","indexId":"70228368","displayToPublicDate":"2021-01-31T10:16:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Nuclear eDNA estimates population allele frequencies and abundance in experimental mesocosms","docAbstract":"Advances in environmental DNA (eDNA) methodologies have led to improvements in the ability to detect species and communities in aquatic environments, yet the majority of studies emphasize biological diversity at the species level by targeting variable sites within the mitochondrial genome. Here, we demonstrate that eDNA approaches also have the capacity to detect intraspecific diversity in the nuclear genome, allowing for assessments of population-level genetic diversity and estimates of the number of genetic contributors in a sample. Using a panel of microsatellite loci, we evaluated intraspecific genetic diversity in the round goby (Neogobius melanostomus) using eDNA samples from experimental mesocosms. First, we tested the similarity between eDNA and individual tissue-based estimates of allele frequencies. Subsequently, we used a likelihood-based DNA mixture framework to estimate the number of unique genetic contributors in mesocosm eDNA samples and in simulated mixtures of alleles. Allele frequencies from eDNA accurately reflected allele frequencies from genotyped round goby tissue samples, indicating nuclear markers can be reliably amplified from water samples under controlled conditions. DNA mixture analyses were able to estimate the number of genetic contributors from eDNA samples and simulated mixtures of DNA from up to 58 individuals, with the degree of positive or negative bias dependent on the filtering scheme of low-frequency alleles. This study is the first to document the application of eDNA and multiple amplicon-based methods to obtain intraspecific nuclear genetic information and estimate the absolute abundance of a species in mesocosms. With proper validation, this approach has the potential to advance non-invasive survey methods to characterize populations and broadens the application of eDNA methodologies to inform population-level management objectives.","language":"English","publisher":"Wiley","doi":"10.1111/mec.15765","usgsCitation":"Andres, K.J., Sethi, S., Lodge, D., and Andres, J., 2021, Nuclear eDNA estimates population allele frequencies and abundance in experimental mesocosms: Molecular Ecology, v. 30, no. 3, p. 685-697, https://doi.org/10.1111/mec.15765.","productDescription":"13 p.","startPage":"685","endPage":"697","ipdsId":"IP-114574","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":453638,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/mec.15765","text":"External Repository"},{"id":395675,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Cayuga Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.87408447265625,\n 42.45588764197166\n ],\n [\n -76.42913818359375,\n 42.45588764197166\n ],\n [\n -76.42913818359375,\n 42.94234987312984\n ],\n [\n -76.87408447265625,\n 42.94234987312984\n ],\n [\n -76.87408447265625,\n 42.45588764197166\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-01-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Andres, Kara J.","contributorId":275314,"corporation":false,"usgs":false,"family":"Andres","given":"Kara","email":"","middleInitial":"J.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":833982,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833981,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lodge, David M.","contributorId":275315,"corporation":false,"usgs":false,"family":"Lodge","given":"David M.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":833983,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andres, Jose","contributorId":275316,"corporation":false,"usgs":false,"family":"Andres","given":"Jose","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":833984,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221208,"text":"70221208 - 2021 - Beware of spatial autocorrelation when applying machine learning algorithms to borehole geophysical logs","interactions":[],"lastModifiedDate":"2021-06-07T12:36:39.054303","indexId":"70221208","displayToPublicDate":"2021-01-31T07:34:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Beware of spatial autocorrelation when applying machine learning algorithms to borehole geophysical logs","docAbstract":"Although many of the algorithms now considered to be machine learning algorithms (MLAs) have existed for nearly a century (e.g., Rosenblatt 1958), interest in MLAs has recently increased exponentially for solving data-driven problems across a variety of fields due to the expanded availability of large, complex datasets that may be difficult to interrogate using other methods, increases in computing power, and a growing library of easily implemented machine learning tools. While MLAs are often similar to statistical methods, there are key differences in the approach to problem solving. Namely, statistical methods are more concerned with generating informative models from “long” data (i.e., many more observations than explanatory variables), whereas MLAs are typically concerned with generating accurate predictions from “wide” data (i.e., a large number of variables with relatively fewer observations, Bzdok et al. 2018). In hydrogeologic studies, such wide datasets may be available from boreholes, where various types of geophysical, geochemical, and lithological information may exist. Borehole datasets are therefore a tempting target for MLAs to reveal hidden relations among gathered data and parameters of interest (e.g., contaminant concentration), and as a method of parameter reduction (e.g., reduce costs by collecting fewer datasets).
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13081","usgsCitation":"Terry, N., Johnson, C., Day-Lewis, F., Parker, B.L., and Slater, L., 2021, Beware of spatial autocorrelation when applying machine learning algorithms to borehole geophysical logs: Groundwater, v. 59, no. 3, p. 315-319, https://doi.org/10.1111/gwat.13081.","productDescription":"5 p.","startPage":"315","endPage":"319","ipdsId":"IP-124633","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":436525,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TN8EC4","text":"USGS data release","linkHelpText":"Selected borehole geophysical logs from three contaminant sites in California, Wisconsin, and New Jersey"},{"id":386259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-02-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":817055,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Carole D. 0000-0001-6941-1578","orcid":"https://orcid.org/0000-0001-6941-1578","contributorId":245365,"corporation":false,"usgs":true,"family":"Johnson","given":"Carole D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":817056,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":817057,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parker, Beth L.","contributorId":209230,"corporation":false,"usgs":false,"family":"Parker","given":"Beth","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":817058,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Slater, Lee D. 0000-0003-0292-746X","orcid":"https://orcid.org/0000-0003-0292-746X","contributorId":192555,"corporation":false,"usgs":false,"family":"Slater","given":"Lee D.","affiliations":[],"preferred":false,"id":817059,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220677,"text":"70220677 - 2021 - Modern Mars' geomorphological activity, driven by wind, frost, and gravity","interactions":[],"lastModifiedDate":"2021-05-25T12:46:27.117445","indexId":"70220677","displayToPublicDate":"2021-01-30T07:38:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Modern Mars' geomorphological activity, driven by wind, frost, and gravity","docAbstract":"Extensive evidence of landform-scale martian geomorphic changes has been acquired in the last decade, and the number and range of examples of surface activity have increased as more high-resolution imagery has been acquired. Within the present-day Mars climate, wind and frost/ice are the dominant drivers, resulting in large avalanches of material down icy, rocky, or sandy slopes; sediment transport leading to many scales of aeolian bedforms and erosion; pits of various forms and patterned ground; and substrate material carved out from under subliming ice slabs. Due to the ability to collect correlated observations of surface activity and new landforms with relevant environmental conditions with spacecraft on or around Mars, studies of martian geomorphologic activity are uniquely positioned to directly test surface-atmosphere interaction and landform formation/evolution models outside of Earth. In this paper, we outline currently observed and interpreted surface activity occurring within the modern Mars environment, and tie this activity to wind, seasonal surface CO2 frost/ice, sublimation of subsurface water ice, and/or gravity drivers. Open questions regarding these processes are outlined, and then measurements needed for answering these questions are identified. In the final sections, we discuss how many of these martian processes and landforms may provide useful analogs for conditions and processes active on other planetary surfaces, with an emphasis on those that stretch the bounds of terrestrial-based models or that lack terrestrial analogs. In these ways, modern Mars presents a natural and powerful comparative planetology base case for studies of Solar System surface processes, beyond or instead of Earth.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2021.107627","usgsCitation":"Diniega, S., Bramson, A.M., Buratti, B.J., Buhler, P., Burr, D., Chojnacki, M., Conway, S.J., Dundas, C.M., Hansen, C.J., McEwen, A.S., Lapotre, M.G., Levy, J.S., McKeown, L., Piqueux, S., Portyankina, G., Swann, C., Titus, T.N., and Widmer, J., 2021, Modern Mars' geomorphological activity, driven by wind, frost, and gravity: Geomorphology, v. 380, 107627, 43 p., https://doi.org/10.1016/j.geomorph.2021.107627.","productDescription":"107627, 43 p.","ipdsId":"IP-121082","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":453646,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://hal.science/hal-03186543","text":"Publisher Index Page"},{"id":385915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"380","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diniega, Serina","contributorId":212017,"corporation":false,"usgs":false,"family":"Diniega","given":"Serina","email":"","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":816382,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bramson, Ali M 0000-0003-4903-0916","orcid":"https://orcid.org/0000-0003-4903-0916","contributorId":201618,"corporation":false,"usgs":false,"family":"Bramson","given":"Ali","email":"","middleInitial":"M","affiliations":[{"id":27205,"text":"U. 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Remote sensing-based methods are often used for synoptic and frequent monitoring of CyanoHABs. In this study, one such algorithm – a sub-component of the Cyanobacteria Index called the CIcyano, was validated for effectiveness in identifying lakes with toxin-producing blooms in 11 states across the contiguous United States over 11 bloom seasons (2005–2011, 2016–2019). A matchup data set was created using satellite data from MEdium Resolution Imaging Spectrometer (MERIS) and Ocean Land Colour Imager (OLCI), and nearshore, field-measured Microcystins (MCs) data as a proxy of CyanoHAB presence. While the satellite sensors cannot detect toxins, MCs are used as the indicator of health risk, and as a confirmation of cyanoHAB presence. MCs are also the most common laboratory measurement made by managers during CyanoHABs. Algorithm performance was evaluated by its ability to detect CyanoHAB ‘Presence’ or ‘Absence’, where the bloom is confirmed by the presence of the MCs. With same-day matchups, the overall accuracy of CyanoHAB detection was found to be 84% with precision and recall of 87 and 90% for bloom detection. Overall accuracy was expected to be between 77% and 87% (95% confidence) based on a bootstrapping simulation. These findings demonstrate that CIcyano has utility for synoptic and routine monitoring of potentially toxic cyanoHABs in lakes across the United States.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.145462","usgsCitation":"Mishra, S., Stumpf, R.P., Schaeffer, B., Werdell, P.J., Loftin, K.A., and Meredith, A., 2021, Evaluation of a satellite-based cyanobacteria bloom detection algorithm using field-measured microcystin data: Science for the Total Environment, v. 774, 145462, 12 p., https://doi.org/10.1016/j.scitotenv.2021.145462.","productDescription":"145462, 12 p.","ipdsId":"IP-124532","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":453647,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2021.145462","text":"Publisher Index Page"},{"id":387288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"774","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mishra, Sachidananda 0000-0001-6613-3103","orcid":"https://orcid.org/0000-0001-6613-3103","contributorId":222356,"corporation":false,"usgs":false,"family":"Mishra","given":"Sachidananda","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":819557,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stumpf, Richard P. 0000-0001-5531-6860","orcid":"https://orcid.org/0000-0001-5531-6860","contributorId":222357,"corporation":false,"usgs":false,"family":"Stumpf","given":"Richard","email":"","middleInitial":"P.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":819558,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schaeffer, Blake 0000-0001-9794-3977","orcid":"https://orcid.org/0000-0001-9794-3977","contributorId":245603,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":819559,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Werdell, P. Jeremy 0000-0002-3592-0152","orcid":"https://orcid.org/0000-0002-3592-0152","contributorId":222358,"corporation":false,"usgs":false,"family":"Werdell","given":"P.","email":"","middleInitial":"Jeremy","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":819560,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221964,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":819561,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meredith, Andrew 0000-0001-9651-7132","orcid":"https://orcid.org/0000-0001-9651-7132","contributorId":222359,"corporation":false,"usgs":false,"family":"Meredith","given":"Andrew","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":819562,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217730,"text":"sir20205132 - 2021 - Characterization of groundwater quality and discharge with emphasis on selenium in an irrigated agricultural drainage near Delta, Colorado, 2017–19","interactions":[],"lastModifiedDate":"2021-08-18T22:10:40.433467","indexId":"sir20205132","displayToPublicDate":"2021-01-29T13:45:00","publicationYear":"2021","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":"2020-5132","displayTitle":"Characterization of Groundwater Quality and Discharge with Emphasis on Selenium in an Irrigated Agricultural Drainage near Delta, Colorado, 2017–19","title":"Characterization of groundwater quality and discharge with emphasis on selenium in an irrigated agricultural drainage near Delta, Colorado, 2017–19","docAbstract":"Selenium is a water-quality constituent of concern for aquatic ecosystems in the lower Gunnison River Basin. Selenium is derived from bedrock of the Mancos Shale and is mobilized and transported to groundwater and surface water by application of irrigation water. Although it is recognized that groundwater contributes an appreciable amount of selenium to surface water, few studies have addressed interactions between the two. The U.S. Geological Survey in cooperation with the Colorado Water Conservation Board conducted a study during 2017–19 to characterize the quality and quantity of groundwater discharging to an agricultural drainage near Delta, Colorado, locally known as Sunflower Drain.
Water quality in the study area is characterized by high dissolved solids with elevated concentrations of selenium and nitrate resulting from dissolution of soluble salts in the Mancos Shale. Selenium concentrations have decreased by 50 percent since the early 2000s, possibly in response to irrigation system improvements. Stable water isotopes indicate streamflow is dominated by canal water during the irrigation season (April to October) and, during the nonirrigation season (November to March), is dominated by groundwater that has undergone some degree of evaporation. Pesticide and pharmaceutical compounds were infrequently detected, and results indicate they were derived from sources outside the study area such that they do not appear to be useful as tracers of groundwater sources. Stable isotopes of nitrate indicate that nitrate originates from the Mancos Shale, and the isotopic composition is enriched by denitrification in the groundwater system. Using a mass-balance approach, estimated groundwater discharge rates to Sunflower Drain ranged from 0.15 to 0.27 cubic feet per second per mile with one losing reach identified. Selenium, sulfate, and nitrate concentrations in groundwater estimated by mass-balance calculations were similar to concentrations measured in the Poly 17 observation well, located in a largely irrigated area in east tributary. One tributary reach had higher concentrations of selenium, sulfate, and nitrate likely reflecting localized inputs of more concentrated groundwater, similar to the concentrations in the Poly 7 observation well, which is downgradient from a residential area in the west tributary.
Three pilot studies were conducted, including fiber optic distributed temperature sensing to detect groundwater discharge zones in the stream channel, a passive seismic technique to estimate depth to bedrock, and use of radon-222 as a geochemical tracer of groundwater discharge. All three techniques show promise as additional approaches for investigating groundwater discharge surface-water systems in irrigated drainage areas on Mancos Shale.
The factors that affect groundwater movement mainly include when and where irrigation water is transported and applied, and the distribution of bedrock of the Mancos Shale and overlying alluvial deposits. The average groundwater recharge rate for the study area was estimated at 8.1 inches per year, based on mass balance calculations from synoptic survey data. Along the western tributary of Sunflower Drain, there was evidence that spills from the East Canal may recharge the groundwater aquifer adjacent to the stream channel. Groundwater movement to the stream channel may be controlled by the topography of the alluvial/bedrock interface or focused along human-made features, such as tile drains and ditches constructed around irrigated fields. On larger scales, the top of bedrock was also important, creating a topographic constriction that caused a zone of groundwater discharge. The groundwater system is complex, and further study could better define the system, possibly through application of a groundwater flow model and more extensive studies using some of the exploratory methods evaluated in this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205132","collaboration":"Prepared in cooperation with Colorado Water Conservation Board","usgsCitation":"Mast, M.A., 2021, Characterization of groundwater quality and discharge with emphasis on selenium in an irrigated agricultural drainage near Delta, Colorado, 2017–19: U.S. Geological Survey Scientific Investigations Report 2020–5132, 34 p., https://doi.org/10.3133/sir20205132.","productDescription":"Report: vi, 34 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119514","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":382809,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LKYX9H","text":"USGS data release","linkHelpText":"Near-surface geophysical data collected in the Sunflower Drain study area near Delta, Colorado, March 2018"},{"id":382805,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5132/coverthb.jpg"},{"id":382806,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5132/sir20205132.pdf","text":"Report","size":"5.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5132"}],"country":"United States","state":"Colorado","city":"Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.21945190429688,\n 38.638327308061875\n ],\n [\n -107.97019958496094,\n 38.638327308061875\n ],\n [\n -107.97019958496094,\n 38.82205601494022\n ],\n [\n -108.21945190429688,\n 38.82205601494022\n ],\n [\n -108.21945190429688,\n 38.638327308061875\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
In this study, we develop urban ecosystem accounts in the U.S., using the System of Environmental-Economic Accounting Experimental Ecosystem Accounting (SEEA EEA) framework. Most ecosystem accounts focus on regional and national scales, which are appropriate for many ecosystem services. However, ecosystems provide substantial services in cities, improving quality of life and contributing to resiliency for substantial parts of the population. Our models estimate energy savings for indoor cooling resulting from heat mitigated by trees and rainfall intercepted by trees. Both models cover major cities in the contiguous U.S. and report the results through physical supply and use tables for multiple accounting periods (2011 and 2016). Using conservative assumptions, urban trees provide substantial heat mitigation (4,098 and 4,229 GWh, valued at $523 and $539 million in 2011 and 2016, respectively) and rainfall interception (2,422 and 2,627 million m3, valued at $434 and $425 million for 2011 and 2016, respectively). Interannual differences largely reflect variations in weather patterns. Our work shows how Earth observation data can support urban ecosystem accounting. We provide model code within a public repository to facilitate model runs elsewhere, enabling the SEEA EEA and Earth observation user communities to reuse our models and provide feedback for improvement.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2020.101226","usgsCitation":"Heris, M., Bagstad, K.J., Rhodes, C., Troy, A., Middel, A., Hopkins, K.G., and Matuszak, J., 2021, Piloting urban ecosystem accounting for the United States: Ecosystem Services, v. 48, 101226, 18 p., https://doi.org/10.1016/j.ecoser.2020.101226.","productDescription":"101226, 18 p.","ipdsId":"IP-114089","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":453652,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoser.2020.101226","text":"Publisher Index Page"},{"id":436527,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QV182X","text":"USGS data release","linkHelpText":"Data 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0000-0002-4418-5030","orcid":"https://orcid.org/0000-0002-4418-5030","contributorId":248592,"corporation":false,"usgs":false,"family":"Heris","given":"Mehdi","affiliations":[{"id":12652,"text":"University of Colorado-Denver","active":true,"usgs":false}],"preferred":false,"id":809469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":809470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rhodes, Charles 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University","active":true,"usgs":false}],"preferred":false,"id":809473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809474,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Matuszak, John","contributorId":211869,"corporation":false,"usgs":false,"family":"Matuszak","given":"John","email":"","affiliations":[{"id":38336,"text":"U.S. Department of State","active":true,"usgs":false}],"preferred":false,"id":809475,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70219570,"text":"70219570 - 2021 - Comparison of detection limits estimated using single- and multi-concentration spike-based and blank-based procedures","interactions":[],"lastModifiedDate":"2021-05-27T13:23:08.289537","indexId":"70219570","displayToPublicDate":"2021-01-29T07:04:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3517,"text":"Talanta","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of detection limits estimated using single- and multi-concentration spike-based and blank-based procedures","docAbstract":"Spike- and blank-based procedures were applied to estimate the detection limits (DLs) for example analytes from inorganic and organic methods for water samples to compare with the U.S. Environmental Protection Agency's (EPA) Method Detection Limit (MDL) procedures (revisions 1.11 and 2.0). The multi-concentration spike-based procedures ASTM Within-laboratory Critical Level (DQCALC) and EPA's Lowest Concentration Minimum Reporting Level were compared in one application, with DQCALC further applied to many methods. The blank-based DLs, MDLb99 (99th percentile) or MDLbY (= mean blank concentration + s × t), estimated using large numbers (>100) of blank samples often provide DLs that better approach or achieve the desired ≤1% false positive risk level compared to spike-based DLs. For primarily organic methods that do not provide many uncensored blank results, spike-based DQCALC or MDL rev. 2.0 are needed to simulate the blank distribution and estimate the DL. DQCALC is especially useful for estimating DLs for multi-analyte methods having very different analyte response characteristics. Time series plots of DLs estimated using different procedures reveal that DLs are dependent on the applied procedure, should not be expected to be static over time, and seem best viewed as falling over a range versus being a single value. Use of both blank- and spike-based DL procedures help inform this DL range. Data reporting conventions that censor data at a threshold and report “less than” that threshold concentration as the reporting level have unknown and potentially high false negative risk. The U.S. Geological Survey National Water Quality Laboratory's Laboratory Reporting Level (LRL) convention (applied primarily to organic methods) attempts to simultaneously minimize both the false positive and false negative risk when <LRL is reported and data between DL and the higher LRL are allowed to be reported.
Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0–1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0245973","usgsCitation":"D’Acunto, L., Pearlstine, L.G., and Romanach, S., 2021, Joint species distribution models of Everglades wading birds to inform restoration planning: PLoS ONE, v. 16, no. 1, e0245973, 21 p.; Data Release, https://doi.org/10.1371/journal.pone.0245973.","productDescription":"e0245973, 21 p.; Data Release","ipdsId":"IP-119201","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":453665,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0245973","text":"Publisher Index Page"},{"id":383272,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418748,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P934K8T0","text":"EverWaders species distribution model development and output in the Greater Everglades from 2000-2009","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.419921875,\n 25.209911213827688\n ],\n [\n -80.474853515625,\n 25.27450351782018\n ],\n [\n -80.562744140625,\n 25.348990395713393\n ],\n [\n -80.562744140625,\n 25.55730943578312\n ],\n [\n -80.299072265625,\n 26.288490072961164\n ],\n [\n -80.1617431640625,\n 26.426308999847024\n ],\n [\n -80.26611328125,\n 26.711266913515747\n ],\n [\n -80.4473876953125,\n 26.716173757934094\n ],\n [\n -80.5133056640625,\n 26.554136386183785\n ],\n [\n -80.79345703125,\n 26.504988828743404\n ],\n [\n -81.0296630859375,\n 26.455820238459893\n ],\n [\n -81.00082397460938,\n 26.292183791046618\n ],\n [\n -81.47872924804688,\n 26.223215001067558\n ],\n [\n -81.64352416992188,\n 26.172694044887898\n ],\n [\n -81.78085327148438,\n 26.078987535225927\n ],\n [\n -81.74514770507812,\n 25.90617390922084\n ],\n [\n -81.6339111328125,\n 25.808545671771615\n ],\n [\n -81.38259887695312,\n 25.720735134412106\n ],\n [\n -81.24252319335938,\n 25.492868271257127\n ],\n [\n -81.18209838867188,\n 25.370086680063082\n ],\n [\n -81.17660522460938,\n 25.21488107113259\n ],\n [\n -81.03790283203125,\n 25.069429002821355\n ],\n [\n -80.58059692382812,\n 25.14652775303499\n ],\n [\n -80.419921875,\n 25.209911213827688\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"D’Acunto, Laura 0000-0001-6227-0143","orcid":"https://orcid.org/0000-0001-6227-0143","contributorId":215343,"corporation":false,"usgs":true,"family":"D’Acunto","given":"Laura","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":810303,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearlstine, Leonard G.","contributorId":34751,"corporation":false,"usgs":false,"family":"Pearlstine","given":"Leonard","email":"","middleInitial":"G.","affiliations":[{"id":12462,"text":"U.S. Department of the Interior, National Park Service","active":true,"usgs":false}],"preferred":false,"id":810304,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":223479,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":810305,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220311,"text":"70220311 - 2021 - The optical river bathymetry toolkit","interactions":[],"lastModifiedDate":"2021-05-04T12:12:56.975607","indexId":"70220311","displayToPublicDate":"2021-01-28T07:10:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"The optical river bathymetry toolkit","docAbstract":"Spatially distributed information on water depth is essential for many applications in river research and management and, under certain circumstances, can be inferred from remotely sensed data. Although fluvial remote sensing has emerged as a rapidly developing subdiscipline of the riverine sciences, more widespread adoption of these techniques has been hindered by a lack of accessible software. The Optical River Bathymetry Toolkit (ORByT) fills this void by providing a standalone package for mapping water depth from passive optical image data. The ORByT interface enables end users to import images and field‐based depth measurements, create and refine water masks, and perform spectrally based depth retrieval via an Optimal Band Ratio Analysis algorithm. The resulting bathymetric map can be exported as an image file, point cloud, and/or cross section; a thorough accuracy assessment also is incorporated into the workflow. In addition, image‐derived depth estimates can be subtracted from water surface elevations to obtain bed elevations suitable for input to a hydrodynamic model. Potential users of ORByT must bear in mind the inherent limitations of passive optical remote sensing: reliable bathymetry can only be inferred in clear‐flowing, shallow streams; this approach is not appropriate for more turbid, deeper rivers.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3773","usgsCitation":"Legleiter, C.J., 2021, The optical river bathymetry toolkit: River Research and Applications, v. 4, no. 37, p. 555-568, https://doi.org/10.1002/rra.3773.","productDescription":"14 p.","startPage":"555","endPage":"568","ipdsId":"IP-119553","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":453673,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.3773","text":"Publisher Index Page"},{"id":385444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"37","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":815120,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70219193,"text":"70219193 - 2021 - Channel response to a dam‐removal sediment pulse captured at high‐temporal resolution using routine gage data","interactions":[],"lastModifiedDate":"2021-06-01T17:29:08.413936","indexId":"70219193","displayToPublicDate":"2021-01-28T07:07:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7951,"text":"Earth Surfaces Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Channel response to a dam‐removal sediment pulse captured at high‐temporal resolution using routine gage data","docAbstract":"In this study, we captured how a river channel responds to a sediment pulse originating from a dam removal using multiple lines of evidence derived from streamflow gages along the Patapsco River, Maryland, USA. Gages captured characteristics of the sediment pulse, including travel times of its leading edge (~7.8 km yr−1) and peak (~2.6 km yr−1) and suggest both translation and increasing dispersion. The pulse also changed local hydraulics and energy conditions, increasing flow velocities and Froude number, due to bed fining, homogenization and/or slope adjustment. Immediately downstream of the dam, recovery to pre‐pulse conditions occurred within the year, but farther downstream recovery was slower, with the tail of the sediment pulse working through the lower river by the end of the study 7 years later.
The patterns and timing of channel change associated with the sediment pulse were not driven by large flow or suspended sediment‐transporting events, with change mostly occurring during lower flows. This suggests pulse mobility was controlled by process‐factors largely independent of high flow.
In contrast, persistent changes occurred to out‐of‐channel flooding dynamics. Stage associated with flooding increased during the arrival of the sediment pulse, 1 to 2 years after dam removal, suggesting persistent sediment deposition at the channel margins and nearby floodplain. This resulted in National Weather Service‐indicated flood stages being attained by 3–43% smaller discharges compared to earlier in the study period.
This study captured a two‐signal response from the sediment pulse: (1) short‐ to medium‐term (weeks to months) translation and dispersion within the channel, resulting in aggradation and recovery of bed elevations and changing local hydraulics; and (2) dispersion and persistent longer‐term (years) effects of sediment deposition on overbank surfaces. This study further demonstrated the utility of US Geological Survey gage data to quantify geomorphic change, increase temporal resolution, and provide insights into trajectories of change over varying spatial and temporal scales.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5083","usgsCitation":"Cashman, M.J., Gellis, A.C., Boyd, E.L., Collins, M.J., Anderson, S.W., Mcfarland, B.D., and Ryan, A.M., 2021, Channel response to a dam‐removal sediment pulse captured at high‐temporal resolution using routine gage data: Earth Surfaces Processes and Landforms, v. 46, no. 6, p. 1145-1159, https://doi.org/10.1002/esp.5083.","productDescription":"15 p.","startPage":"1145","endPage":"1159","ipdsId":"IP-113441","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":436533,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9REXNQ9","text":"USGS data release","linkHelpText":"Data for Specific Gage Analysis on the Patapsco River, 2010-2017"},{"id":384751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Patapsco River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.9317626953125,\n 39.38738660316804\n ],\n [\n -76.98257446289062,\n 39.35394512666976\n ],\n [\n -76.88438415527344,\n 39.31198794598777\n ],\n [\n -76.8218994140625,\n 39.29976783250087\n ],\n [\n -76.7999267578125,\n 39.26043647112078\n ],\n [\n -76.75666809082031,\n 39.216295294574024\n ],\n [\n -76.68937683105469,\n 39.21097520599528\n ],\n [\n -76.60697937011719,\n 39.22480659786848\n ],\n [\n -76.9317626953125,\n 39.38738660316804\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cashman, Matthew J. 0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":813165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Eric L. 0000-0002-1473-967X","orcid":"https://orcid.org/0000-0002-1473-967X","contributorId":256743,"corporation":false,"usgs":true,"family":"Boyd","given":"Eric","email":"","middleInitial":"L.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collins, Matthias J. 0000-0003-4238-2038","orcid":"https://orcid.org/0000-0003-4238-2038","contributorId":196365,"corporation":false,"usgs":false,"family":"Collins","given":"Matthias","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":813168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mcfarland, Brett Dare 0000-0002-2941-4966","orcid":"https://orcid.org/0000-0002-2941-4966","contributorId":256744,"corporation":false,"usgs":true,"family":"Mcfarland","given":"Brett","email":"","middleInitial":"Dare","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813170,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ryan, Ashley Mattie 0000-0001-5647-7447","orcid":"https://orcid.org/0000-0001-5647-7447","contributorId":256746,"corporation":false,"usgs":true,"family":"Ryan","given":"Ashley","email":"","middleInitial":"Mattie","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813171,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}