Statistical analysis of the observational record from U.S. Geological Survey (USGS) streamgaging stations and other historical information provides an informative means of estimating flood flow frequency. Flood flow frequency is defined by values or quantiles of discharge for selected annual exceedance probabilities (AEPs) (England and others, 2018). The annual peak discharge data as part of systematic operation of a streamgaging station provides the foundation for a detailed analysis of peak discharge, but additional historical information pertaining to peak discharges also can be used. An annual peak discharge is defined as the maximum instantaneous discharge for a streamgaging station for a given water year, and annual peak discharge data for USGS streamgaging stations can be acquired through the USGS National Water Information System (NWIS) database (USGS, 2018). The statistical analyses are based on water-year increments. A water year is the 12-month period from October 1 of a given year through September 30 of the following year designated by the calendar year in which it ends.
For the statistical hydrology portion of the multi-layered analysis, InFRM team members from the USGS analyzed annual peak discharge records for the 15 USGS streamgaging stations (gages) shown on Figure A.1. Information on the period of record data for those USGS gages are listed in Table A.1.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"U.S. Army Corps of Engineers, Federal Emergency Management Agency","usgsCitation":"Wallace, D., 2022, Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix A: Statistical hydrology: Interagency Flood Risk Management Report, 64 p.","productDescription":"64 p.","ipdsId":"IP-101867","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":427112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396304,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/#ha"}],"country":"United States","state":"Texas","otherGeospatial":"Neches River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96,\n 32\n ],\n [\n -96,\n 30\n ],\n [\n -94,\n 30\n ],\n [\n -94,\n 32\n ],\n [\n -96,\n 32\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David S. 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":205198,"corporation":false,"usgs":true,"family":"Wallace","given":"David S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835668,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70262186,"text":"70262186 - 2022 - Broadscale population structure and hatchery introgression of Midwestern brook trout: Midwestern brook trout population genetics","interactions":[],"lastModifiedDate":"2025-01-15T17:52:54.665605","indexId":"70262186","displayToPublicDate":"2022-01-01T11:45:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Broadscale population structure and hatchery introgression of Midwestern brook trout: Midwestern brook trout population genetics","docAbstract":"Brook Trout Salvelinus fontinalis have faced significant declines throughout their native range and have been stocked in Midwestern waters since the late 1800s to offset such losses. Several studies have investigated the genetic effects of these stockings, but these efforts have been confined to relatively small spatial scales. In this study, we compiled 8,454 Brook Trout microsatellite genotypes from 188 wild Midwestern populations and 26 hatchery strains to provide novel insights of broadscale population structure, regional patterns of genetic diversity, and estimates of hatchery introgression for inland Wisconsin populations. Our results indicate high levels of differentiation among our study populations, a lack of hydrological population structuring, lower estimates of genetic diversity in the Driftless Area, and that hatchery introgression has been largely confined to regions of inland Wisconsin that have been heavily affected by anthropogenic disturbances (i.e., the Driftless Area). We also provide evidence that populations may be able to purge hatchery‐derived alleles, discuss possible mechanisms behind this phenomenon, and consider their relevance to accurate estimation of hatchery introgression. Collectively, these results summarize the genetic effects of over a century of anthropogenic disturbance on native Brook Trout populations and emphasize the importance of integrating historical data into contemporary genetic research of intensively managed species.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/tafs.10333","usgsCitation":"Bradley Erdman, Matthew G. Mitro, Joanna D.T. Griffin, David Rowe, Kazyak, D.C., Keith Turnquist, Michael Siepker, Loren Miller, Stott, W., Hughes, M., Sloss, B., Kinnison, M.T., and Larson, W., 2022, Broadscale population structure and hatchery introgression of Midwestern brook trout: Midwestern brook trout population genetics: Transactions of the American Fisheries Society, v. 151, no. 1, p. 81-99, https://doi.org/10.1002/tafs.10333.","productDescription":"19 p.","startPage":"81","endPage":"99","ipdsId":"IP-132403","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.62198592078825,\n 47.17494781150509\n ],\n [\n -95.43596059373883,\n 47.28169312893286\n ],\n [\n -95.46739097764518,\n 42.15136704567587\n ],\n [\n -86.50823833580571,\n 42.15136704567587\n ],\n [\n -86.62198592078825,\n 47.17494781150509\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"151","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley Erdman","contributorId":348382,"corporation":false,"usgs":false,"family":"Bradley Erdman","affiliations":[{"id":83360,"text":"University of Maine School of Biology and Ecology","active":true,"usgs":false}],"preferred":false,"id":923419,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matthew G. Mitro","contributorId":348383,"corporation":false,"usgs":false,"family":"Matthew G. Mitro","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923420,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Joanna D.T. Griffin","contributorId":348384,"corporation":false,"usgs":false,"family":"Joanna D.T. Griffin","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923421,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"David Rowe","contributorId":348385,"corporation":false,"usgs":false,"family":"David Rowe","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923422,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":923423,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keith Turnquist","contributorId":348386,"corporation":false,"usgs":false,"family":"Keith Turnquist","affiliations":[{"id":83363,"text":"College of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923424,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Michael Siepker","contributorId":348387,"corporation":false,"usgs":false,"family":"Michael Siepker","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923425,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Loren Miller","contributorId":348388,"corporation":false,"usgs":false,"family":"Loren Miller","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923426,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stott, Wendylee 0000-0002-5252-4901 wstott@usgs.gov","orcid":"https://orcid.org/0000-0002-5252-4901","contributorId":191249,"corporation":false,"usgs":true,"family":"Stott","given":"Wendylee","email":"wstott@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":923427,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hughes, Michael","contributorId":348579,"corporation":false,"usgs":false,"family":"Hughes","given":"Michael","affiliations":[],"preferred":false,"id":923611,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sloss, Brian","contributorId":191462,"corporation":false,"usgs":false,"family":"Sloss","given":"Brian","affiliations":[],"preferred":false,"id":923612,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kinnison, Michael T.","contributorId":169617,"corporation":false,"usgs":false,"family":"Kinnison","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":923613,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923418,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70230312,"text":"70230312 - 2022 - Response in the water level of Anvil Lake, Wisconsin, to changes in meteorological and climatic changes, Wisconsin","interactions":[],"lastModifiedDate":"2022-09-13T16:42:30.131021","indexId":"70230312","displayToPublicDate":"2022-01-01T11:39:31","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Response in the water level of Anvil Lake, Wisconsin, to changes in meteorological and climatic changes, Wisconsin","docAbstract":"Anvil Lake, a relatively shallow seepage lake in northern Wisconsin, USA, has experienced dramatic changes in water level since elevation records began in 1938 in response to changes in meteorological and climatic conditions (Figure 1. Robertson et al., 2018). Anvil Lake’s water level record shows a pronounced 10–15-yr cycle, with recurring highs and lows with a typical swing of over 1 m. Although experiencing large cycles in water levels, the long-term average levels were relatively stable until about 1987, when water level dropped dramatically by an additional 1 m (in 2016). Water levels then rebounded dramatically, reaching near “normal” water levels in 2020. At its lowest level, the lake had a maximum depth of 8.2 m (mean depth of 4.7 m) and an area of 128 ha.\nLike most long-term records, Anvil Lake’s water level record has been measured by several observers using various techniques. To verify the consistency of the various datums used throughout this period, historical photographs with the water’s edge identified were obtained, tied to NAVD 1988 using a Real Time Kinematic satellite global positioning system, and compared with the measured water levels (See Figure 1).\nTo determine the causes of the changes in water level, a complete water budget was estimated for Anvil Lake from 1980 to 2014. Water levels in Anvil Lake were simulated (Figure 1) using a hydrodynamic model (General Lake Model, GLM), with daily lake evaporation estimated by\nGLM, monthly lake/groundwater exchange estimated with a groundwater model (MODFLOW), daily precipitation from the North American Land Data Assimilation System (NLDAS), and stream inflow and outflow were set as zero because the lake has no inlets or outlet. Atmospheric fluxes (precipitation minus evaporation) primarily drove the lake-level fluctuations and trends, but sub-decadal fluctuations in net groundwater exchange (groundwater inflow minus lake seepage) either enhanced or reduced the lake level response to the atmospheric drivers.\nThe changes in water levels were shown to affect the extent of stratification and water quality in the lake (Robertson et al., 2018). During periods of lower precipitation and lower water levels, Anvil Lake was a polymictic lake, whereas during periods of higher precipitation and higher water levels the lake was a dimictic lake with stratification lasting throughout summer. During periods with higher water levels, the water quality in the lake was shown to improve slightly as a result of the nutrients being diluted in a larger volume of water. If precipitation increases in the future, as results from many General Circulation Models (GCMs) suggest (Robertson et al., 2016), and if that outweighs the effects of increased evaporation caused by increased air temperatures, water levels in Anvil Lake may be expected to fluctuate at a higher level. Higher water levels in Anvil Lake are expected to result in the lake becoming more strongly stratified and have slightly improved water quality (lower nutrient and algal concentrations and increased water clarity) (Robertson et al., 2018).","language":"English","publisher":"Wisconsin Department of Natural Resources","usgsCitation":"Robertson, D., 2022, Response in the water level of Anvil Lake, Wisconsin, to changes in meteorological and climatic changes, Wisconsin, 2 p.","productDescription":"2 p.","ipdsId":"IP-130734","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":406607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398290,"type":{"id":15,"text":"Index Page"},"url":"https://wicci.wisc.edu/water-resources-working-group/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","otherGeospatial":"Anvil Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.07800674438477,\n 45.93515431167519\n ],\n [\n -89.05139923095703,\n 45.93515431167519\n ],\n [\n -89.05139923095703,\n 45.9536560062781\n ],\n [\n -89.07800674438477,\n 45.9536560062781\n ],\n [\n -89.07800674438477,\n 45.93515431167519\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":839932,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70226897,"text":"70226897 - 2022 - Status and trends of the Lake Huron prey fish community, 1976-2020","interactions":[],"lastModifiedDate":"2022-04-08T16:11:32.829999","indexId":"70226897","displayToPublicDate":"2022-01-01T11:08:38","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Status and trends of the Lake Huron prey fish community, 1976-2020","docAbstract":"The USGS Great Lakes Science Center (GLSC) has assessed annual changes in the offshore prey fish community of Lake Huron since 1973. Assessments are based on a bottom trawl survey conducted in October and an acoustics-midwater trawl survey conducted in September-October. In 2020, USGS-GLSC vessels were not permitted to cross into Canada due to the COVID-19 pandemic, so prey fish surveys sampled only sites in U.S. (Michigan) waters of Lake Huron. This prevented USGS from providing information about the current status and trends of prey fish communities in Georgian Bay and the North Channel. Prey fish biomass in U.S. waters of Lake Huron in 2020 remained below levels observed prior to community-wide declines that began in the early to mid-1990s. Fish community biomass was dominated by two species, Bloater (Coregonus hoyi) and Rainbow Smelt (Osmerus mordax). While both surveys found Bloater biomass in the main basin had declined from levels observed in 2019, Bloater still comprised over three-quarters of prey fish biomass in Lake Huron in 2020. Biomass and abundance for other prey fish species were within the range observed over the past five years. Current low biomass of invasive species like Alewife (Alosa pseudoharengus) and Rainbow Smelt is consistent with fish community objectives focused on restoration of native fish communities. Reduced lake productivity, predation by a recovering piscivore community, and shifts in food web dynamics that favor fish production in nearshore environments may prevent prey fish biomass in offshore areas from returning to levels observed prior to the early 1990’s. However, the dominance of Bloater in bottom trawl catches and acoustic surveys suggests that current lake conditions are conducive to the recovery of some native species.","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Hondorp, D.W., O’Brien, T.P., Esselman, P., and Roseman, E., 2022, Status and trends of the Lake Huron prey fish community, 1976-2020, 31 p.","productDescription":"31 p.","ipdsId":"IP-134682","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":398391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398390,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org"}],"country":"Canada, United States","otherGeospatial":"Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.7705078125,\n 45.81348649679973\n ],\n [\n -84.4189453125,\n 45.5679096098613\n ],\n [\n -83.81469726562499,\n 45.390735154248894\n ],\n [\n -83.507080078125,\n 45.166547157856016\n ],\n [\n -83.38623046875,\n 44.73892994307368\n ],\n [\n -83.507080078125,\n 44.315987905196906\n ],\n [\n -83.9794921875,\n 44.02442151965934\n ],\n [\n -83.95751953125,\n 43.59630591596548\n ],\n [\n -83.60595703125,\n 43.61221676817573\n ],\n [\n -82.891845703125,\n 44.05601169578525\n ],\n [\n -82.46337890625,\n 42.94838139765314\n ],\n [\n -81.705322265625,\n 43.37311218382002\n ],\n [\n -81.683349609375,\n 44.11125397357155\n ],\n [\n -81.134033203125,\n 44.61393394730626\n ],\n [\n -81.49658203125,\n 45.205263456162385\n ],\n [\n -81.024169921875,\n 44.629573191951046\n ],\n [\n -79.95849609375,\n 44.41024041296011\n ],\n [\n -79.903564453125,\n 44.77793589631623\n ],\n [\n -79.56298828125,\n 44.72332018895825\n ],\n [\n -80.145263671875,\n 45.56021795715051\n ],\n [\n -80.804443359375,\n 46.03510927947334\n ],\n [\n -81.58447265624999,\n 46.18743678432541\n ],\n [\n -82.63916015625,\n 46.255846818480315\n ],\n [\n -84.122314453125,\n 46.39998810407942\n ],\n [\n -83.81469726562499,\n 46.17983040759436\n ],\n [\n -83.8916015625,\n 46.126556302418514\n ],\n [\n -83.968505859375,\n 45.98169518512228\n ],\n [\n -84.6826171875,\n 46.11132565729796\n ],\n [\n -84.7705078125,\n 45.81348649679973\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":828709,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Brien, Timothy P. 0000-0003-4502-5204 tiobrien@usgs.gov","orcid":"https://orcid.org/0000-0003-4502-5204","contributorId":2662,"corporation":false,"usgs":true,"family":"O’Brien","given":"Timothy","email":"tiobrien@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":828710,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":828711,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":828712,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256646,"text":"70256646 - 2022 - Agent-based modeling of movements and habitat selection by mid-continent mallards","interactions":[],"lastModifiedDate":"2024-09-09T16:11:44.295133","indexId":"70256646","displayToPublicDate":"2022-01-01T11:07:39","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"FWS/CSS-143-2022","title":"Agent-based modeling of movements and habitat selection by mid-continent mallards","docAbstract":"We found that the absence of existing conservation measures would reduce wintering mallard population size by ~70-80%, underlining the importance of current wetland easements for waterfowl foraging. Under standard conditions, the partial active flooding of easements later in the season and the upgrading of unmanaged wetlands to managed status resulted in greatest mallard populations, indicating that active flooding (stored water release) was able to considerably increase carrying capacity under strong drought conditions.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Weller, F., Webb, E.B., Beatty, W., Fogenburg, S., Kesler, D., Blenk, R., Eadie, J., Ringelman, K., and Miller, M.L., 2022, Agent-based modeling of movements and habitat selection by mid-continent mallards: Cooperator Science Series FWS/CSS-143-2022, ii, 102 p.","productDescription":"ii, 102 p.","ipdsId":"IP-138825","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":431977,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/agent-based-modeling-movements-and-habitat-selection-mid-continent-mallards"},{"id":433631,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weller, Florian G.","contributorId":341462,"corporation":false,"usgs":false,"family":"Weller","given":"Florian G.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beatty, William S. 0000-0003-0013-3113","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":224795,"corporation":false,"usgs":true,"family":"Beatty","given":"William S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":908465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fogenburg, Sean","contributorId":341463,"corporation":false,"usgs":false,"family":"Fogenburg","given":"Sean","affiliations":[],"preferred":false,"id":908466,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kesler, Dylan","contributorId":341464,"corporation":false,"usgs":false,"family":"Kesler","given":"Dylan","affiliations":[{"id":37290,"text":"The Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":908467,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Blenk, Robert H.","contributorId":341465,"corporation":false,"usgs":false,"family":"Blenk","given":"Robert H.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":908468,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eadie, John M.","contributorId":341466,"corporation":false,"usgs":false,"family":"Eadie","given":"John M.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":908469,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ringelman, Kevin","contributorId":341467,"corporation":false,"usgs":false,"family":"Ringelman","given":"Kevin","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908470,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Miller, Matt L.","contributorId":341468,"corporation":false,"usgs":false,"family":"Miller","given":"Matt","email":"","middleInitial":"L.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":908471,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70228833,"text":"70228833 - 2022 - Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses","interactions":[],"lastModifiedDate":"2024-03-27T15:12:27.207997","indexId":"70228833","displayToPublicDate":"2022-01-01T10:07:41","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses","docAbstract":"RiverWare is a river system modeling tool developed by CADSWES (Center of Advanced Decision Support for Water and Environmental Systems) that allows the user to simulate complex reservoir operations and perform period-of-record analyses for different scenarios. For the InFRM hydrology studies, RiverWare is used to generate a homogeneous regulated POR by simulating the basin as if the reservoirs and their current rule sets had been present in the basin for the entire time period. Statistical analyses can then be performed on the extended records at the gages. This report summarizes the RiverWare portion of the hydrologic analysis being completed for the InFRM Hydrology study of the Neches River Basin.
The RiverWare model described in this chapter presents development of the Neches River Basin hydrology, which mimics current operational conditions. The use of the RiverWare program allows for data extension to periods prior to dam construction. The utilization of longer streamgage record improves discharge frequency results and increases the confidence of the analysis being performed. The modeling evaluation criteria are: (1) evaluate output based on validating policies and functions, and (2) prioritize operation based on surcharge and flood control. A detailed explanation of the Neches River Basin POR hydrology will be in a later section.
Calibration results will also be shown that illustrate model performance since the Salt Water Barrier (SWB) construction was completed in 2005. The time window simulation run is for water year (WY) 2005 – WY 2018. This time window also captures the time when Hurricane Harvey occurred (late August of 2017). Each simulated water year was inspected individually to better validate the results.
After calibration, a general run for January 01, 1929 through WY 2018 was made. Historical pool elevations along with observed inflows and outflows were compared against the model simulated results. More emphasis was put on B.A. Steinhagen’s operations because the dam captures two major rivers (i.e. the Angelina and the Neches Rivers). Results were inspected closely for B.A. Steinhagen’s pool and releases, the simulated discharges at the Neches at Evadale gage, and the simulated discharges at the SWB at Beaumont, Texas.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"U.S. Army Corps of Engineers, Federal Emergency Management Agency","usgsCitation":"Wallace, D., 2022, Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses: Interagency Flood Risk Management Report, 66 p.","productDescription":"66 p.","ipdsId":"IP-113418","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":427144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396305,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/#ha"}],"country":"United States","state":"Texas","otherGeospatial":"Neches River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96,\n 32\n ],\n [\n -96,\n 30\n ],\n [\n -94,\n 30\n ],\n [\n -94,\n 32\n ],\n [\n -96,\n 32\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David S. 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":205198,"corporation":false,"usgs":true,"family":"Wallace","given":"David S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835669,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70229817,"text":"70229817 - 2022 - Relative bias in catch among long-term fish monitoring surveys within the San Francisco Estuary","interactions":[],"lastModifiedDate":"2022-03-18T14:34:13.706782","indexId":"70229817","displayToPublicDate":"2022-01-01T09:21:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Relative bias in catch among long-term fish monitoring surveys within the San Francisco Estuary","docAbstract":"Fish monitoring gears rarely capture all available fish, an inherent bias in monitoring programs referred to as catchability. Catchability is a source of bias that can be affected by numerous aspects of gear deployment (e.g., deployment speed, mesh size, and avoidance behavior). Thus, care must be taken when multiple surveys—especially those using different sampling methods—are combined to answer spatio-temporal questions about population and community dynamics. We assessed relative catchability differences among four long-term fish monitoring surveys from the San Francisco Estuary: the Bay Study Otter Trawl (BSOT), the Bay Study Midwater Trawl (BSMT), the Fall Midwater Trawl (FMWT), and the Suisun Marsh Otter Trawl (SMOT). We used generalized additive models with a spatio-temporal smoother and survey as a fixed effect to predict gear-specific estimates of catch for 45 different fish species within large and small size classes. We used estimates of the fixed effect coefficients for each survey (e.g., BSOT) relative to the reference gear (FMWT) to develop relative measures of catchability among taxa, surveys, and fish-size classes, termed the catch-ratio. We found higher relative catchability of 27%, 22%, and 57% of fish species in large size classes from the FMWT than in the BSMT, BSOT, or SMOT, respectively. In the small size class, relative catchability was higher in the FMWT than the BSMT, BSOT, or SMOT for 50%, 18%, and 25% of fish species, respectively. As expected, relative catchability of demersal species was higher in the otter trawls (BSOT, SMOT) while relative catchability of pelagic species was higher in the midwater trawls (FMWT, BSMT). Our results demonstrate that catchability is a source of bias among monitoring efforts within the San Francisco Estuary, and assuming equal catchability among surveys, species, and size classes could result in significant bias when describing spatio-temporal patterns in catch if ignored.
","language":"English","publisher":"University of California Davis","doi":"10.15447/sfews.2022v20iss1art3","usgsCitation":"Huntsman, B., Mahardja, B., and Bashevkin, S., 2022, Relative bias in catch among long-term fish monitoring surveys within the San Francisco Estuary: San Francisco Estuary and Watershed Science, v. 20, no. 1, 3, 17 p., https://doi.org/10.15447/sfews.2022v20iss1art3.","productDescription":"3, 17 p.","ipdsId":"IP-130127","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":449301,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2022v20iss1art3","text":"Publisher Index Page"},{"id":397305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.684326171875,\n 37.1165261849112\n ],\n [\n -121.2,\n 37.1165261849112\n ],\n [\n -121.2,\n 39.00211029922515\n ],\n [\n -122.684326171875,\n 39.00211029922515\n ],\n [\n -122.684326171875,\n 37.1165261849112\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Huntsman, Brock 0000-0003-4090-1949","orcid":"https://orcid.org/0000-0003-4090-1949","contributorId":223101,"corporation":false,"usgs":true,"family":"Huntsman","given":"Brock","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mahardja, Brian 0000-0003-0695-3745","orcid":"https://orcid.org/0000-0003-0695-3745","contributorId":288940,"corporation":false,"usgs":false,"family":"Mahardja","given":"Brian","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":838467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bashevkin, Samuel M.","contributorId":288941,"corporation":false,"usgs":false,"family":"Bashevkin","given":"Samuel M.","affiliations":[{"id":61910,"text":"Delta Science Program, Delta Stewardship Council","active":true,"usgs":false}],"preferred":false,"id":838468,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229656,"text":"70229656 - 2022 - San Francisco Estuary chlorophyll sensor and sample analysis intercomparison","interactions":[],"lastModifiedDate":"2022-03-11T15:26:53.094975","indexId":"70229656","displayToPublicDate":"2022-01-01T09:19:02","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":10383,"text":"Intercomparison Report","active":true,"publicationSubtype":{"id":3}},"title":"San Francisco Estuary chlorophyll sensor and sample analysis intercomparison","docAbstract":"This report presents an assessment of chlorophyll collection methods and anonymous results of field and laboratory comparisons in 2018 - 2019 by agencies in the San Francisco Estuary (SFE). The methods assessment and comparison exercises, with funding provided by the Delta Regional Monitoring Program and Bay Nutrient Management Strategy and in-kind contributions from participating agencies, are a first step to facilitate future comparisons and syntheses of data and inform best science practices in the region. In situ sonde comparison exercises found general agreement between two models of Yellow Springs Instrument (YSI) sensors, but the newer sensor (EXO v2 - total algae) measured higher chlorophyll fluorescence (fCHL) relative to the older YSI sensor (6-series 6025). Results may be attributed to the use of a two-point calibration and the fluorescence response of algal cultures in sensor development by the manufacturer. The laboratory comparison included participation by 12 distinct field - laboratory pairs (or groups), with one group analyzing filters using two analytical methods. Filters were collected in triplicate across three sampling events in 2018, and all sample results were pooled together. Results of statistical analyses indicated that nominal filter pore size, the grinding method associated with pigment extraction, and analytical methods do not introduce variability to the chlorophyll-a measurement (Chl-a). When Chl-a results were assessed by sample event, however, significant differences between nominal pore size and analytical methods existed; these differences could be attributed to the small sample size per event. Consistent reporting units and high-concentration calibration standards for field sensors among data collection agencies would improve the consistency and comparability of data collected in the SFE. More routine split sampling events, longer term sensor comparison exercises, and further processing and analytical comparisons that control for individual filterers may also enhance comparability in the region.
","language":"English","publisher":"Delta Regional Monitoring Program","usgsCitation":"Stumpner, E.B., Yin, J.S., Heberger, M., Wu, J., Wong, A., and Saraceno, J., 2022, San Francisco Estuary chlorophyll sensor and sample analysis intercomparison: Intercomparison Report, 61 p.","productDescription":"61 p.","ipdsId":"IP-123558","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":397022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397021,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://deltarmp.org/documents/"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.67608642578126,\n 37.36579146999664\n ],\n [\n -121.4,\n 37.36579146999664\n ],\n [\n -121.4,\n 38.348118547988065\n ],\n [\n -122.67608642578126,\n 38.348118547988065\n ],\n [\n -122.67608642578126,\n 37.36579146999664\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stumpner, Elizabeth B. 0000-0003-2356-2244 estumpner@usgs.gov","orcid":"https://orcid.org/0000-0003-2356-2244","contributorId":181854,"corporation":false,"usgs":true,"family":"Stumpner","given":"Elizabeth","email":"estumpner@usgs.gov","middleInitial":"B.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yin, Jamie S.","contributorId":288390,"corporation":false,"usgs":false,"family":"Yin","given":"Jamie","email":"","middleInitial":"S.","affiliations":[{"id":61747,"text":"San Francisco Estuary Institute - Aquatic Science Center","active":true,"usgs":false}],"preferred":false,"id":837826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heberger, Matthew","contributorId":288391,"corporation":false,"usgs":false,"family":"Heberger","given":"Matthew","email":"","affiliations":[{"id":61747,"text":"San Francisco Estuary Institute - Aquatic Science Center","active":true,"usgs":false}],"preferred":false,"id":837827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Jing","contributorId":191126,"corporation":false,"usgs":false,"family":"Wu","given":"Jing","email":"","affiliations":[],"preferred":false,"id":837828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wong, Adam","contributorId":288392,"corporation":false,"usgs":false,"family":"Wong","given":"Adam","affiliations":[{"id":61747,"text":"San Francisco Estuary Institute - Aquatic Science Center","active":true,"usgs":false}],"preferred":false,"id":837829,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Saraceno, John Franco 0000-0003-0064-1820","orcid":"https://orcid.org/0000-0003-0064-1820","contributorId":217534,"corporation":false,"usgs":false,"family":"Saraceno","given":"John Franco","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":837830,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240413,"text":"70240413 - 2022 - Workshops report for mesophotic and deep benthic community fish, mobile invertebrates, sessile invertebrates and infauna","interactions":[],"lastModifiedDate":"2023-02-08T11:59:25.77461","indexId":"70240413","displayToPublicDate":"2022-01-01T09:01:19","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":13296,"text":"DWH MDBC Summary Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"SR-22-01","title":"Workshops report for mesophotic and deep benthic community fish, mobile invertebrates, sessile invertebrates and infauna","docAbstract":"Two workshops with subject matter experts in the appropriate fields, were held in November and December 2021 to elicit guidance and feedback from the broader mesophotic and deep benthic scientific community. These workshops focused on best practices/approaches and identifying data gaps relative to habitat assessment and evaluation goals of the Mesophotic and Deep Benthic Community (MDBC) restoration portfolio. The first workshop was a combined effort of the Habitat Assessment and Evaluation (HAE) Project Team and the Deepwater Horizon (DWH) Program. Industrial Economics, Inc. (IEc) provided extensive workshop planning, organizing, execution, and facilitation support during all stages of the workshop. Based on a questionnaire sent to scientists in August, 2021, the workshop focused on fish and mobile invertebrate habitat associations, abundance trends, community metrics, and food web functionality. Topical presentations and discussions focused not only on demersal fish and mobile invertebrates that are directly associated with mesophotic and deep benthic habitats, but also considered water column species and communities that benefit from these habitats more broadly. The second workshop, intended to complement the first workshop, focused on identifying best practices and critical information gaps for key community metrics, larval dispersal modeling, connectivity, effects and variability of environmental parameters, and recovery trajectories of corals, infauna, and other sessile invertebrates. Through literature review, internal HAE scientists considered these topics to be critical for restoration success. Products from the literature review included topical summaries (see Appendix B) that summarized the current state-of-the-science and provided the framework for the workshop. Information generated from the workshops will assist the MDBC HAE Project, and more broadly the DWH Program, identify data gaps and develop a suite of best practices for restoration activities.","language":"English","publisher":"NOAA","doi":"10.25923/8ph6-j393","usgsCitation":"Bassett, R., Harter, S.L., Clark, R., Zink, I., Hornick, K., Hartman, J., Bliska, H., Carle, M., Sutton, T., Demopoulos, A., David, A., Benson, K., Bourque, J., Nizinski, M.S., Prouty, N.G., Sharuga, S.M., Caporaso, A., Le, J., Herting, J., Morrison, C., and Poti, M., 2022, Workshops report for mesophotic and deep benthic community fish, mobile invertebrates, sessile invertebrates and infauna: DWH MDBC Summary Report SR-22-01, 177 p., https://doi.org/10.25923/8ph6-j393.","productDescription":"177 p.","startPage":"1","endPage":"177","ipdsId":"IP-143984","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":412815,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bassett, Rachel","contributorId":302194,"corporation":false,"usgs":false,"family":"Bassett","given":"Rachel","email":"","affiliations":[{"id":65431,"text":"CSS Inc, under contract to NOAA/NOS","active":true,"usgs":false}],"preferred":false,"id":863705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harter, Stacey L.","contributorId":302195,"corporation":false,"usgs":false,"family":"Harter","given":"Stacey","email":"","middleInitial":"L.","affiliations":[{"id":62397,"text":"NOAA/NMFS","active":true,"usgs":false}],"preferred":false,"id":863706,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Randy","contributorId":218497,"corporation":false,"usgs":false,"family":"Clark","given":"Randy","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":863707,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zink, Ian","contributorId":289796,"corporation":false,"usgs":false,"family":"Zink","given":"Ian","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":863708,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hornick, Katherine","contributorId":302196,"corporation":false,"usgs":false,"family":"Hornick","given":"Katherine","email":"","affiliations":[{"id":65433,"text":"Earth Resources Technology, Inc. Under contract to NOAA/NMFS","active":true,"usgs":false}],"preferred":false,"id":863709,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartman, Jennifer","contributorId":265721,"corporation":false,"usgs":false,"family":"Hartman","given":"Jennifer","email":"","affiliations":[{"id":54777,"text":"Rogue Detection Teams, Rice, Washington, USA","active":true,"usgs":false}],"preferred":false,"id":863710,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bliska, Hanna","contributorId":302197,"corporation":false,"usgs":false,"family":"Bliska","given":"Hanna","email":"","affiliations":[{"id":65434,"text":"Industrial Economics, Inc","active":true,"usgs":false}],"preferred":false,"id":863711,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Carle, 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characteristics","interactions":[],"lastModifiedDate":"2022-11-09T15:12:18.689229","indexId":"70237985","displayToPublicDate":"2022-01-01T08:59:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12807,"text":"Proceedings of the North American Crane Workshop","active":true,"publicationSubtype":{"id":10}},"title":"Whooping crane stay length in relation to stopover site characteristics","docAbstract":"Whooping crane (Grus americana) migratory stopovers can vary in length from hours to more than a month. Stopover sites provide food resources and safety essential for the completion of migration. Factors such as weather, climate, demographics of migrating groups, and physiological condition of migrants influence migratory movements of cranes (Gruidae) to varying degrees. However, little research has examined the relationship between habitat characteristics and stopover stay length in cranes. Site quality may relate to stay length with longer stays that allow individuals to improve body condition, or with shorter stays because of increased foraging efficiency. We examined this question using habitat data collected at 605 use locations from 449 stopover sites throughout the United States Great Plains visited by 58 whooping cranes from the Aransas–Wood Buffalo Population tracked with platform transmitting terminals. Research staff compiled land cover (e.g., hectares of corn; landscape level) and habitat metric (e.g., maximum water depth; site level) data for day use and evening roost locations via site visits and geospatial mapping. We used Random Forest regression analyses to estimate importance of covariates for predicting stopover stay length. Site-level variables explained 9% of variation in stay length, whereas landscape-level variables explained 43%. Stay length increased with latitude and the proportion of land cover as open-water slough with emergent vegetation as well as alfalfa, whereas stay length decreased as open-water lacustrine wetland land cover increased. At the site-level, stopover duration increased with wetted width at riverine sites but decreased with wetted width at palustrine and lacustrine wetland sites. Stopover duration increased with mean distance to visual obstruction as well as where management had reduced the height of vegetation through natural (e.g., grazing) or mechanical (e.g., harvesting) means and decreased with maximum water depth. Our results suggest that stopover length increases with the availability of preferred land cover types for foraging. High quality stopover sites with abundant forage resources may help whooping cranes maintain fat reserves important to their annual life cycle.
","language":"English","publisher":"North American Crane Working Group","usgsCitation":"Caven, A.J., Pearse, A.T., Brandt, D.A., Harner, M.J., Wright, G.D., Baasch, D.M., Brinley Buckley, E.M., Metzger, K.L., Rabbe, M.R., and Lacy, A.E., 2022, Whooping crane stay length in relation to stopover site characteristics: Proceedings of the North American Crane Workshop, v. 15, p. 6-33.","productDescription":"28 p.","startPage":"6","endPage":"33","ipdsId":"IP-123212","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":409262,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":409261,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.nacwg.org/proceedings15.html","linkFileType":{"id":5,"text":"html"}}],"volume":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Caven, Andrew J.","contributorId":177586,"corporation":false,"usgs":false,"family":"Caven","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":856431,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":856432,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, David A. 0000-0001-9786-307X dbrandt@usgs.gov","orcid":"https://orcid.org/0000-0001-9786-307X","contributorId":149929,"corporation":false,"usgs":true,"family":"Brandt","given":"David","email":"dbrandt@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":856433,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harner, Mary J.","contributorId":177584,"corporation":false,"usgs":false,"family":"Harner","given":"Mary","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":856434,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Greg D.","contributorId":177585,"corporation":false,"usgs":false,"family":"Wright","given":"Greg","email":"","middleInitial":"D.","affiliations":[{"id":12957,"text":"Chippewa Ottawa Resource Authority","active":true,"usgs":false}],"preferred":false,"id":856435,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baasch, David M.","contributorId":147145,"corporation":false,"usgs":false,"family":"Baasch","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":16795,"text":"Headwaters Corp, Kearney, NE","active":true,"usgs":false}],"preferred":false,"id":856436,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brinley Buckley, Emma M.","contributorId":198370,"corporation":false,"usgs":false,"family":"Brinley Buckley","given":"Emma","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":856437,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Metzger, Kristine L.","contributorId":147144,"corporation":false,"usgs":false,"family":"Metzger","given":"Kristine","email":"","middleInitial":"L.","affiliations":[{"id":16794,"text":"USFWS, Div of Biol Serv, Albuquerque, NM","active":true,"usgs":false}],"preferred":false,"id":856438,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rabbe, Matthew R","contributorId":298794,"corporation":false,"usgs":false,"family":"Rabbe","given":"Matthew","email":"","middleInitial":"R","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":856439,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lacy, Anne E","contributorId":174362,"corporation":false,"usgs":false,"family":"Lacy","given":"Anne","email":"","middleInitial":"E","affiliations":[{"id":16606,"text":"International Crane Foundation","active":true,"usgs":false}],"preferred":false,"id":856440,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236142,"text":"70236142 - 2022 - Extensive droughts in the conterminous United States during multiple centuries","interactions":[],"lastModifiedDate":"2022-08-30T13:12:56.867999","indexId":"70236142","displayToPublicDate":"2022-01-01T08:09:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Extensive droughts in the conterminous United States during multiple centuries","docAbstract":"Extensive and severe droughts have substantial effects on water supplies, agriculture, and aquatic ecosystems. To better understand these droughts, we used tree-ring-based reconstructions of the Palmer drought severity index (PDSI) for the period 1475–2017 to examine droughts that covered at least 33% of the conterminous United States (CONUS). We identified 37 spatially extensive drought events for the CONUS and examined their spatial and temporal patterns. The duration of the extensive drought events ranged from 3 to 12 yr and on average affected 43% of the CONUS. The recent (2000–08) drought in the southwestern CONUS, often referred to as the turn-of-the-century drought, is likely one of the longest droughts in the CONUS during the past 500 years. A principal components analysis of the PDSI data from 1475 through 2017 resulted in three principal components (PCs) that explain about 48% of the variability of PDSI and are helpful to understand the temporal and spatial variability of the 37 extensive droughts in the CONUS. Analyses of the relations between the three PCs and well-known climate indices, such as indices of El Niño–Southern Oscillation, indicate statistically significant correlations; however, the correlations do not appear to be large enough (all with an absolute value less than 0.45) to be useful for the development of drought prediction models.
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Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":850242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":219213,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":850243,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225504,"text":"70225504 - 2022 - Impact of spectral resolution on quantifying cyanobacteria in lakes and reservoirs: A machine-learning assessment","interactions":[],"lastModifiedDate":"2024-05-17T17:00:12.08779","indexId":"70225504","displayToPublicDate":"2022-01-01T05:55:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9530,"text":"IEEE Transactions in Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Impact of spectral resolution on quantifying cyanobacteria in lakes and reservoirs: A machine-learning assessment","docAbstract":"Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large (N = 905) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( Rrs) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to Rrs spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between ~73% and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.
","language":"English","publisher":"IEEE","doi":"10.1109/TGRS.2021.3114635","usgsCitation":"Zolfaghari, K., Pahlevan, N., Binding, C., Gurlin, D., Simis, S.G., Verdu, A.R., Li, L., Crawford, C., VanderWoude, A., Errera, R., Zastepa, A., and Duguay, C.R., 2022, Impact of spectral resolution on quantifying cyanobacteria in lakes and reservoirs: A machine-learning assessment: IEEE Transactions in Geoscience and Remote Sensing, v. 60, 5515520, 20 p., https://doi.org/10.1109/TGRS.2021.3114635.","productDescription":"5515520, 20 p.","ipdsId":"IP-132686","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":449319,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/tgrs.2021.3114635","text":"Publisher Index Page"},{"id":390590,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zolfaghari, Kiana","contributorId":267804,"corporation":false,"usgs":false,"family":"Zolfaghari","given":"Kiana","email":"","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":825333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pahlevan, Nima","contributorId":267805,"corporation":false,"usgs":false,"family":"Pahlevan","given":"Nima","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":825334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Binding, Caren","contributorId":267806,"corporation":false,"usgs":false,"family":"Binding","given":"Caren","affiliations":[],"preferred":false,"id":825335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gurlin, Daniela","contributorId":267807,"corporation":false,"usgs":false,"family":"Gurlin","given":"Daniela","email":"","affiliations":[],"preferred":false,"id":825336,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Simis, Stefan G.H.","contributorId":267808,"corporation":false,"usgs":false,"family":"Simis","given":"Stefan","email":"","middleInitial":"G.H.","affiliations":[],"preferred":false,"id":825337,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Verdu, Antonio Ruiz","contributorId":267809,"corporation":false,"usgs":false,"family":"Verdu","given":"Antonio","email":"","middleInitial":"Ruiz","affiliations":[],"preferred":false,"id":825338,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Lin","contributorId":267810,"corporation":false,"usgs":false,"family":"Li","given":"Lin","email":"","affiliations":[],"preferred":false,"id":825339,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":825340,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"VanderWoude, Andrea","contributorId":267811,"corporation":false,"usgs":false,"family":"VanderWoude","given":"Andrea","email":"","affiliations":[],"preferred":false,"id":825341,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Errera, Reagan","contributorId":267812,"corporation":false,"usgs":false,"family":"Errera","given":"Reagan","email":"","affiliations":[],"preferred":false,"id":825342,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zastepa, Arthur","contributorId":267813,"corporation":false,"usgs":false,"family":"Zastepa","given":"Arthur","email":"","affiliations":[],"preferred":false,"id":825343,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Duguay, Claude R.","contributorId":267814,"corporation":false,"usgs":false,"family":"Duguay","given":"Claude","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":825344,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70227508,"text":"70227508 - 2022 - Nitrogen reductions have decreased hypoxia in the Chesapeake Bay: Evidence from empirical and numerical modeling","interactions":[],"lastModifiedDate":"2022-01-20T13:30:49.473216","indexId":"70227508","displayToPublicDate":"2021-12-31T07:30:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Nitrogen reductions have decreased hypoxia in the Chesapeake Bay: Evidence from empirical and numerical modeling","docAbstract":"Seasonal hypoxia is a characteristic feature of the Chesapeake Bay due to anthropogenic nutrient input from agriculture and urbanization throughout the watershed. Although coordinated management efforts since 1985 have reduced nutrient inputs to the Bay, oxygen concentrations at depth in the summer still frequently fail to meet water quality standards that have been set to protect critical estuarine living resources. To quantify the impact of watershed nitrogen reductions on Bay hypoxia during a recent period including both average discharge and extremely wet years (2016–2019), this study employed both statistical and three-dimensional (3-D) numerical modeling analyses. Numerical model results suggest that if the nitrogen reductions since 1985 had not occurred, annual hypoxic volumes (O2 < 3 mg L−1) would have been ~50–120% greater during the average discharge years of 2016–2017 and ~20–50% greater during the wet years of 2018–2019. The effect was even greater for O2 < 1 mg L−1, where annual volumes would have been ~80–280% greater in 2016–2017 and ~30–100% greater in 2018–2019. These results were supported by statistical analysis of empirical data, though the magnitude of improvement due to nitrogen reductions was greater in the numerical modeling results than in the statistical analysis. This discrepancy is largely accounted for by warming in the Bay that has exacerbated hypoxia and offset roughly 6–34% of the improvement from nitrogen reductions. Although these results may reassure policymakers and stakeholders that their efforts to reduce hypoxia have improved ecosystem health in the Bay, they also indicate that greater reductions are needed to counteract the ever-increasing impacts of climate change.
Process wastewaters from food, beverage, and feedstock facilities, although regulated, are an under-investigated environmental contaminant source. Food process wastewaters (FPWWs) from 23 facilities in 17 U.S. states were sampled and documented for a plethora of chemical and microbial contaminants. Of the 576 analyzed organics, 184 (32%) were detected at least once, with concentrations as large as 143 μg L–1 (6:2 fluorotelomer sulfonic acid), and as many as 47 were detected in a single FPWW sample. Cumulative per/polyfluoroalkyl substance concentrations up to 185 μg L–1 and large pesticide transformation product concentrations (e.g., methomyl oxime, 40 μg L–1; clothianidin TMG, 2.02 μg L–1) were observed. Despite 48% of FPWW undergoing disinfection treatment prior to discharge, bacteria resistant to third-generation antibiotics were found in each facility type, and multiple bacterial groups were detected in all samples, including total coliforms. The exposure–activity ratios and toxicity quotients exceeded 1.0 in 13 and 22% of samples, respectively, indicating potential biological effects and toxicity to vertebrates and invertebrates associated with the discharge of FPWW. Organic contaminant profiles of FPWW differed from previously reported contaminant profiles of municipal effluents and urban storm water, indicating that FPWW is another important source of chemical and microbial contaminant mixtures discharged into receiving surface waters.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.1c06821","usgsCitation":"Hubbard, L.E., Kolpin, D., Givens, C.E., Blackwell, B.D., Bradley, P., Gray, J., Lane, R.F., Masoner, J.R., McCleskey, R., Romanok, K., Sandstrom, M.W., Smalling, K., and Villeneuve, D., 2022, Food, beverage, and feedstock processing facility wastewater: A unique and underappreciated source of contaminants to U.S. streams: Environmental Science & Technology, v. 56, no. 2, p. 1028-1040, https://doi.org/10.1021/acs.est.1c06821.","productDescription":"13 p.","startPage":"1028","endPage":"1040","ipdsId":"IP-130265","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":449332,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9219000","text":"External Repository"},{"id":436021,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WJYI2H","text":"USGS data release","linkHelpText":"Concentrations of inorganic, organic, and microbial analytes from a national reconnaissance of wastewater from food, beverage, and feedstock facilities across the United States"},{"id":393860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, California, Idaho, Illinois, Iowa, Michigan, Minnesota, Nebraska, New York, Pennsylvania, Tennessee, Texas, 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"properties\":{\"name\":\"Alabama\",\"nation\":\"USA \"}}]}","volume":"56","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-12-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Hubbard, Laura E. 0000-0003-3813-1500 lhubbard@usgs.gov","orcid":"https://orcid.org/0000-0003-3813-1500","contributorId":4221,"corporation":false,"usgs":true,"family":"Hubbard","given":"Laura","email":"lhubbard@usgs.gov","middleInitial":"E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":205652,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Givens, Carrie E. 0000-0003-2543-9610","orcid":"https://orcid.org/0000-0003-2543-9610","contributorId":270741,"corporation":false,"usgs":true,"family":"Givens","given":"Carrie","middleInitial":"E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blackwell, Bradley D. 0000-0003-1296-4539","orcid":"https://orcid.org/0000-0003-1296-4539","contributorId":198381,"corporation":false,"usgs":false,"family":"Blackwell","given":"Bradley","email":"","middleInitial":"D.","affiliations":[{"id":18090,"text":"U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL","active":true,"usgs":false}],"preferred":false,"id":829924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829925,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gray, James L. 0000-0002-0807-5635","orcid":"https://orcid.org/0000-0002-0807-5635","contributorId":202726,"corporation":false,"usgs":true,"family":"Gray","given":"James L.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":829926,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lane, Rachael F. 0000-0001-9202-0612","orcid":"https://orcid.org/0000-0001-9202-0612","contributorId":222471,"corporation":false,"usgs":true,"family":"Lane","given":"Rachael","email":"","middleInitial":"F.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":829927,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Masoner, Jason R. 0000-0002-4829-6379 jmasoner@usgs.gov","orcid":"https://orcid.org/0000-0002-4829-6379","contributorId":3193,"corporation":false,"usgs":true,"family":"Masoner","given":"Jason","email":"jmasoner@usgs.gov","middleInitial":"R.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829928,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":829929,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829930,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":829931,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":214623,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829932,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Villeneuve, Daniel L. 0000-0003-2801-0203","orcid":"https://orcid.org/0000-0003-2801-0203","contributorId":219631,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel L.","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":829933,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70256773,"text":"70256773 - 2022 - Reservoir attributes display cascading spatial patterns along river basins","interactions":[],"lastModifiedDate":"2024-09-06T15:46:21.690394","indexId":"70256773","displayToPublicDate":"2021-12-28T10:43:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Reservoir attributes display cascading spatial patterns along river basins","docAbstract":"Considering reservoirs as linear fragments in a basin's river network could improve understanding, predictability, and management efficiency. We looked for general cascading spatial patterns across five categories of reservoir attributes: land cover, morphology and hydrology, fish habitat, fish assemblages, and fisheries. Attributes were pulled from various databases for large reservoirs (>100 ha) located in the United States. 16 widely distributed river basins, each including a minimum of 15 large reservoirs, were selected for analysis. Using analysis of covariance with basin as the class variable, we tested each attribute as a linear function of catchment area, which is an index of reservoir position in the basin. The majority of reservoir attributes displayed log-linear patterns as catchment area increased, indicating that reservoirs act as members of a larger network just as river reaches do. Several patterns were detected including attributes with no apparent lengthwise arrangement along the basin; cascading spatial patterns in which attributes increase or decrease from upstream to downstream within a basin; and attributes that increase with catchment area in some basins, decrease in others, or may simply remain constant throughout the basin. We conclude that each pattern may have different implications for management, and that the effectiveness with which most management activities influence reservoirs is likely to increase or decrease along river basins.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR029910","usgsCitation":"Faucheux, N., Sample, A., Aldridge, C., Norris, D., Owens, C., Starnes, V.R., VanderBloemen, S., and Miranda, L.E., 2022, Reservoir attributes display cascading spatial patterns along river basins: Water Resources Research, v. 58, no. 1, e2021WR029910, 14 p., https://doi.org/10.1029/2021WR029910.","productDescription":"e2021WR029910, 14 p.","ipdsId":"IP-119850","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433562,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Faucheux, N.M.","contributorId":341806,"corporation":false,"usgs":false,"family":"Faucheux","given":"N.M.","affiliations":[{"id":81792,"text":"Mississippi State Uni","active":true,"usgs":false}],"preferred":false,"id":908915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sample, A.R.","contributorId":341807,"corporation":false,"usgs":false,"family":"Sample","given":"A.R.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, C.A.","contributorId":275883,"corporation":false,"usgs":false,"family":"Aldridge","given":"C.A.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norris, D.M.","contributorId":341780,"corporation":false,"usgs":false,"family":"Norris","given":"D.M.","email":"","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":908918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Owens, C.","contributorId":341808,"corporation":false,"usgs":false,"family":"Owens","given":"C.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Starnes, Victoria R.","contributorId":343988,"corporation":false,"usgs":false,"family":"Starnes","given":"Victoria","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":908920,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"VanderBloemen, S.","contributorId":341810,"corporation":false,"usgs":false,"family":"VanderBloemen","given":"S.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908921,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908922,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70246520,"text":"70246520 - 2022 - Reconstructing the paleoceanographic and redox conditions responsible for variations in uranium content in North American Devonian black shales","interactions":[],"lastModifiedDate":"2023-07-07T12:17:22.507283","indexId":"70246520","displayToPublicDate":"2021-12-27T07:13:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2996,"text":"Palaeogeography, Palaeoclimatology, Palaeoecology","printIssn":"0031-0182","active":true,"publicationSubtype":{"id":10}},"title":"Reconstructing the paleoceanographic and redox conditions responsible for variations in uranium content in North American Devonian black shales","docAbstract":"The uranium (U) content, and more recently, the ratio between 238U and 235U in black shales are commonly applied as a proxy to determine redox conditions and infer organic-richness. Uranium contents typically display a linear relationship with total organic carbon (TOC) in shales. This relationship is due to the processes and mechanisms responsible for the incorporation of U into the sediment during the deposition and remineralization of organic matter. This U/TOC relationship can vary, however, and some shales display uncharacteristically low U content despite having high TOC content, while others show large enrichments of U relative to TOC. Here we examine the U to TOC ratios and U-isotope compositions of three Upper Devonian-Lower Mississippian shales: the Woodford Shale, the Cleveland Shale, and the Bakken Shale, with two study sites in Oklahoma, one site in eastern Kentucky, and three sites in eastern Montana and western North Dakota, respectively. The U/TOC ratios of each shale are distinct from one another exhibiting average ratios ranging from 3 in the Cleveland Shale, to over 10 in the Bakken Shale. The distinct geochemical composition of the three shales suggests that, although lithologically similar, each study site represents a markedly different and dynamic depositional environment. The low average U/TOC (~3) along with the relatively high δ238U values (~0.03‰) of the Cleveland Shale core suggests deposition along the basin margin under normal marine conditions with periods of reduced bottom water oxygenation, likely due to fluctuations in the location of the pycnocline. The Woodford Shale on the other hand, shows higher U/TOC ratios (~4, George core, ~9, Poe core) and δ238U (~0.02‰ average, George core, ~0.06‰ average, Poe core), which suggests an unrestricted setting with intermittent euxinic conditions. In contrast, high U/TOC ratios (2–15), and very high δ238U values (up to 0.55‰) in the Bakken Shale cores indicate intense metal draw-down into sediments under sulfidic waters. The results show that when the U/TOC ratios and U-isotopic compositions of each studied shale are compared to modern anoxic basins and upwelling areas, it allows for an enhanced understanding of the paleoenvironmental conditions such as basin restriction and redox state of waters within the Late Devonian epicontinental seas of North America.
Increased salinity is an emerging contaminant of concern for aquatic taxa. For amphibians exposed to salinity, there is scarce information about the physiological effects and changes in osmoregulatory hormones such as corticosterone (CORT) and aldosterone (ALDO). Recent studies have quantified effects of salinity on CORT physiology of amphibians based on waterborne hormone collection methods, but much less is known about ALDO in iono- and osmoregulation of amphibians. We re-assayed waterborne hormone samples from a previous study to investigate effects of salinity (sodium chloride, NaCl) and a glucocorticoid receptor antagonist (RU486) on ALDO of northern leopard frog (Rana pipiens) larvae. We also investigated relationships between ALDO and CORT. Waterborne ALDO marginally decreased with increasing salinity and was, unexpectedly, positively correlated with baseline and stress-induced waterborne CORT. Importantly, ALDO increased when larvae were exposed to RU486, suggesting that RU486 may also suppress mineralocorticoid receptors or that negative feedback of ALDO is mediated through glucocorticoid receptors. Alternatively, CORT increases with RU486 treatment and might be a substrate for ALDO synthesis, which could account for increases in ALDO with RU486 treatment and the correlation between CORT and ALDO. ALDO was negatively correlated with percent water, such that larvae secreting more ALDO retained less water. Although sample sizes were limited and further validation and studies are warranted, our findings expand our understanding of adrenal steroid responses to salinization in amphibians and proposes new hypotheses regarding the co-regulation of ALDO and CORT.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ygcen.2021.113972","usgsCitation":"Tornabene, B., Breuner, C., Hossack, B., and Crespi, E.J., 2022, Effects of salinity and a glucocorticoid antagonist, RU486, on waterborne aldosterone and corticosterone of northern leopard frog larvae: General and Comparative Endocrinology, v. 317, 113972, 6 p., https://doi.org/10.1016/j.ygcen.2021.113972.","productDescription":"113972, 6 p.","ipdsId":"IP-133771","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":449356,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ygcen.2021.113972","text":"Publisher Index Page"},{"id":397304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"317","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tornabene, Brian J.","contributorId":200041,"corporation":false,"usgs":false,"family":"Tornabene","given":"Brian J.","affiliations":[],"preferred":false,"id":838469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breuner, Creagh W","contributorId":241893,"corporation":false,"usgs":false,"family":"Breuner","given":"Creagh W","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":838470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":838471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crespi, Erica J","contributorId":260876,"corporation":false,"usgs":false,"family":"Crespi","given":"Erica","email":"","middleInitial":"J","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":838472,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227511,"text":"70227511 - 2022 - Phytoplankton community interactions and cyanotoxin mixtures in three recurring surface blooms within one lake","interactions":[],"lastModifiedDate":"2022-01-20T14:20:59.532904","indexId":"70227511","displayToPublicDate":"2021-12-24T08:15:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Phytoplankton community interactions and cyanotoxin mixtures in three recurring surface blooms within one lake","docAbstract":"Cyanobacteria can produce numerous secondary metabolites (cyanotoxins) with various toxicities, yet data on cyanotoxins in many lakes are limited. Moreover, little research is available on complex relations among cyanobacteria that produce toxins. Therefore, we studied cyanobacteria and 19 cyanotoxins at three sites with recurring blooms in Kabetogama Lake (USA). Seven of 19 toxins were detected in various combinations. Anabaenopeptin A and B were detected in every sample. Microcystin-YR was detected more frequently than microcystin-LR, unlike other lakes in the region. Microcystin-YR concentrations, however, generally were low; two samples exceeded drinking water guidelines and no samples exceeded recreational guidelines. Anabaenopeptins correlated with six cyanobacterial taxa, most of which lack available literature on peptide production. The potential toxin producing cyanobacteria, Microcystis, was significantly correlated to microcystin-YR. Pseudanabaena sp. and Synechococcus sp. had strong negative correlations with several toxins that may indicate competition or stress between organisms. Non-metric multidimensional scaling identified three cyanobacterial pairs that may reflect symbiotic or antagonistic relations. This study highlights interactions among cyanobacteria and multiple cyanotoxins and the methods used may be useful for uncovering additional patterns in cyanobacteria communities in other systems, leading to further understanding of how those interactions lead to toxin production.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2021.128142","usgsCitation":"Christensen, V., Olds, H., Norland, J.E., and Khan, E., 2022, Phytoplankton community interactions and cyanotoxin mixtures in three recurring surface blooms within one lake: Journal of Hazardous Materials, v. 427, 128142, 12 p., https://doi.org/10.1016/j.jhazmat.2021.128142.","productDescription":"128142, 12 p.","ipdsId":"IP-128039","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":394575,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Kabetogama Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.11805725097656,\n 48.4105166936892\n ],\n [\n -92.779541015625,\n 48.4105166936892\n ],\n [\n -92.779541015625,\n 48.537977131982025\n ],\n [\n -93.11805725097656,\n 48.537977131982025\n ],\n [\n -93.11805725097656,\n 48.4105166936892\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"427","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olds, Hayley T. 0000-0002-6701-6459 htemplar@usgs.gov","orcid":"https://orcid.org/0000-0002-6701-6459","contributorId":5002,"corporation":false,"usgs":true,"family":"Olds","given":"Hayley T.","email":"htemplar@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":false,"id":831206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Norland, Jack E.","contributorId":214257,"corporation":false,"usgs":false,"family":"Norland","given":"Jack","email":"","middleInitial":"E.","affiliations":[{"id":39001,"text":"School of Natural Resources Sciences, North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":831207,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Khan, Eakalak","contributorId":220550,"corporation":false,"usgs":false,"family":"Khan","given":"Eakalak","email":"","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":831208,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227357,"text":"70227357 - 2022 - Mapped predictions of manganese and arsenic in an alluvial aquifer using boosted regression trees","interactions":[],"lastModifiedDate":"2022-05-13T14:36:19.096668","indexId":"70227357","displayToPublicDate":"2021-12-24T07:09:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Mapped predictions of manganese and arsenic in an alluvial aquifer using boosted regression trees","docAbstract":"Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.
Research on fishes sometimes requires that individual fish be captured and subjected to invasive procedures multiple times over a relatively short time span. Electrofishing is one of the most common techniques used to capture fish, and it is known to cause injury to fish under certain circumstances. We evaluated the relationship of growth rates in Columbia River Redband Trout Oncorhynchus mykiss gairdneri to the number of times that they were captured via electrofishing and gastrically lavaged during the summer of 2018 in a mountainous, headwater stream. We captured fish between two and seven times over the course of 86 d using continuous (smooth) DC backpack electrofishing. We observed no relationship between the growth rate of Columbia River Redband Trout and the number of times that they were captured or gastrically lavaged. Although these findings contrast with hatchery electrofishing experiments, they may represent the greater resiliency of wild fish. It appears that researchers can use electrofishing and gastric lavage in cold waters at least once per month, and potentially up to twice per month, without greatly affecting the growth of wild Columbia River Redband Trout.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10728","usgsCitation":"Clancy, N.G., Dunnigan, J.L., and Budy, P., 2022, Relationship of trout growth to frequent electrofishing and diet collection in a headwater stream: North American Journal of Fisheries Management, v. 42, no. 1, p. 109-114, https://doi.org/10.1002/nafm.10728.","productDescription":"6 p.","startPage":"109","endPage":"114","ipdsId":"IP-133486","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":429871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Bear Creek, Libby Creek, Ramsey Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.6766783082558,\n 48.4467644853234\n ],\n [\n -115.6766783082558,\n 48.14538875631595\n ],\n [\n -115.30322838074561,\n 48.14538875631595\n ],\n [\n -115.30322838074561,\n 48.4467644853234\n ],\n [\n -115.6766783082558,\n 48.4467644853234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Clancy, Niall G.","contributorId":337769,"corporation":false,"usgs":false,"family":"Clancy","given":"Niall","email":"","middleInitial":"G.","affiliations":[{"id":52338,"text":"Montana Fish, Wildlife & Parks","active":true,"usgs":false}],"preferred":false,"id":902663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunnigan, James L.","contributorId":337770,"corporation":false,"usgs":false,"family":"Dunnigan","given":"James","email":"","middleInitial":"L.","affiliations":[{"id":52338,"text":"Montana Fish, Wildlife & Parks","active":true,"usgs":false}],"preferred":false,"id":902664,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902662,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227008,"text":"70227008 - 2022 - Salinity contributions from geothermal waters to the Rio Grande and shallow aquifer system in the transboundary Mesilla (United States)/Conejos-Médanos (Mexico) Basin","interactions":[],"lastModifiedDate":"2021-12-28T14:11:36.8632","indexId":"70227008","displayToPublicDate":"2021-12-23T08:30:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Salinity contributions from geothermal waters to the Rio Grande and shallow aquifer system in the transboundary Mesilla (United States)/Conejos-Médanos (Mexico) Basin","docAbstract":"Freshwater scarcity has raised concerns about the long-term availability of the water supplies within the transboundary Mesilla (United States)/Conejos-Médanos (Mexico) Basin in Texas, New Mexico, and Chihuahua. Analysis of legacy temperature data and groundwater flux estimates indicates that the region’s known geothermal systems may contribute more than 45,000 tons of dissolved solids per year to the shallow aquifer system, with around 8500 tons of dissolved solids being delivered from localized groundwater upflow zones within those geothermal systems. If this salinity flux is steady and eventually flows into the Rio Grande, it could account for 22% of the typical average annual cumulative Rio Grande salinity that leaves the basin each year—this salinity proportion could be much greater in times of low streamflow. Regional water level mapping indicates upwelling brackish waters flow towards the Rio Grande and the southern part of the Mesilla portion of the basin with some water intercepted by wells in Las Cruces and northern Chihuahua. Upwelling waters ascend from depths greater than 1 km with focused flow along fault zones, uplifted bedrock, and/or fractured igneous intrusions. Overall, this work demonstrates the utility of using heat as a groundwater tracer to identify salinity sources and further informs stakeholders on the presence of several brackish upflow zones that could notably degrade the quality of international water supplies in this developed drought-stricken region.
","language":"English","publisher":"MDPI","doi":"10.3390/w14010033","usgsCitation":"Pepin, J.D., Robertson, A.J., and Kelley, S.A., 2022, Salinity contributions from geothermal waters to the Rio Grande and shallow aquifer system in the transboundary Mesilla (United States)/Conejos-Médanos (Mexico) Basin: Water, v. 14, 33, 24 p., https://doi.org/10.3390/w14010033.","productDescription":"33, 24 p.","ipdsId":"IP-130212","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":449370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14010033","text":"Publisher Index Page"},{"id":393412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Chuhuahua, New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.60009765625,\n 30.372875188118016\n ],\n [\n -105.6884765625,\n 30.372875188118016\n ],\n [\n -105.6884765625,\n 32.9257074887604\n ],\n [\n -107.60009765625,\n 32.9257074887604\n ],\n [\n -107.60009765625,\n 30.372875188118016\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2021-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Pepin, Jeff D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeff","email":"","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robertson, Andrew J. 0000-0003-2130-0347 ajrobert@usgs.gov","orcid":"https://orcid.org/0000-0003-2130-0347","contributorId":4129,"corporation":false,"usgs":true,"family":"Robertson","given":"Andrew","email":"ajrobert@usgs.gov","middleInitial":"J.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, Shari A.","contributorId":216179,"corporation":false,"usgs":false,"family":"Kelley","given":"Shari","email":"","middleInitial":"A.","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":829165,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227402,"text":"70227402 - 2022 - Improving groundwater model calibration with repeat microgravity measurements","interactions":[],"lastModifiedDate":"2022-05-13T14:37:28.20751","indexId":"70227402","displayToPublicDate":"2021-12-23T06:52:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Improving groundwater model calibration with repeat microgravity measurements","docAbstract":"Groundwater-flow models depend on hydraulic head and flux observations for evaluation and calibration. A different type of observation—change in storage measured using repeat microgravity—can also be used for parameter estimation by simulating the expected change in gravity from a groundwater model and including the observation misfit in the objective function. The method is demonstrated using new software linked to MODFLOW input and output files and field data from the vicinity of the All American Canal in southeast California, USA. Over a 10-year period following lining of the previously highly permeable canal with concrete, gravity decreased by over 100 μGal (equivalent to about 2.5 m of free-standing water) at some locations as seepage decreased and the remnant groundwater mound dissipated into the aquifer or was removed by groundwater pumping. Simulated gravity from a MODFLOW model closely matched observations, and repeat microgravity data proved useful for constraining both hydraulic conductivity and specific yield estimates. Specific yield estimated using the infinite-horizontal slab approximation agreed well with model-derived values, and the departure from the linear, flat-water-table approximation was small, less than 2%, despite relatively large and dynamic water-table slope. First-order second-moment parameter uncertainty analysis shows reduction in uncertainty for all hydraulic conductivity and specific yield parameter estimates with the addition of repeat microgravity data, as compared to drawdown data alone.