Of the emerging contaminant types thought to threaten freshwater biota, per- and polyfluoroalkyl substances appear to be particularly widespread, and limited studies conducted with these compounds thus far indicate insects may be particularly sensitive to them. This study investigated the short- and long-term effects of two commonly detected compounds on the laboratory-reared mayfly Neocloeon triangulifer in water only exposures. In acute tests, the mayfly was approximately 85-fold more sensitive to perfluorooctanesulfonate (PFOS) and 7-fold more sensitive to perfluorooctanoic acid (PFOA) than the next most sensitive species reported in the literature. In 14 day and full-life chronic PFOS toxicity tests, the lowest 10% effect concentration was 0.272 μg of PFOS/L, which is lower than any previous reports to the best of our knowledge, but consistent in demonstrating the sensitivity of insects to this compound. Conversely, N. triangulifer was not particularly chronically sensitive to PFOA. This study demonstrates the risks of environmentally relevant concentrations of PFOS to a freshwater insect and suggests that investigation of the toxicity of more compounds with different carbon-chain lengths and functional groups to freshwater insects is needed.
Improved simulations of streamflow and base flow for selected sites within and adjacent to the Mississippi River Alluvial Plain area are important for modeling groundwater flow because surface-water flows have a substantial effect on groundwater levels. One method for simulating streamflow and base flow, random forest (RF) models, was developed from the data at gaged sites and, in turn, was used to make monthly mean streamflow and base-flow predictions at 162 ungaged sites in the study area. Daily streamflow observations and computed base flow from 247 streamgages were used as the basis for the development of these RF models. RF models were constructed from basin and climatic characteristics and related to observed monthly mean streamflow values; models were used to compute monthly base-flow estimates from selected streamgages in and adjacent to the Mississippi River Alluvial Plain extent, which includes streamflows from parts of Alabama, Arkansas, Colorado, Florida, Illinois, Indiana, Kansas, Kentucky, Louisiana, Mississippi, Missouri, New Mexico, Tennessee, and Texas. The explanatory variables for the models were selected to represent physical characteristics and climatic time series for the contributing drainage basins to the streamgages and ungaged locations of interest. The Nash-Sutcliffe efficiency between observed and simulated monthly mean streamflow was greater than 0.80 for 155 of the 247 streamgages, with a median Nash-Sutcliffe efficiency value of 0.83. The streamflow and base-flow simulations can be used to improve inflow values and to verify the Mississippi River Alluvial Plain groundwater flow model. The statistical model, input data, and response data (simulated monthly mean streamflows) are available as a U.S. Geological Survey software release and a U.S. Geological Survey data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225079","programNote":"Water Availability and Use Science Program","usgsCitation":"Dietsch, B.J., Asquith, W.H., Breaker, B.K., Westenbroek, S.M., and Kress, W.H., 2023, Simulation of monthly mean and monthly base flow of streamflow using random forests for the Mississippi River Alluvial Plain, 1901 to 2018: U.S. Geological Survey Scientific Investigations Report 2022–5079, 17 p., https://doi.org/10.3133/sir20225079.","productDescription":"Report: v, 17 p.; Tables: 4; Data Release; Dataset; Software Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105480","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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Because tension infiltrometers apply water through a disk of finite size, the infiltrated water moves laterally as well as downward. Only the vertical component of this flow is indicative of the hydraulic conductivity K, so the algorithm for computing K must include a way of isolating that component from the total flow. Some commonly used formulas correct for the multidimensional effects by subtracting an estimate of the laterally spreading flow. For disks smaller than about 200 mm in diameter, however, lateral spreading constitutes so much of the total flow that these subtractive formulas lose considerable accuracy, and sometimes overcorrect so severely as to produce a negative number for K. Other methods rely on empiricisms that are not completely consistent with unsaturated-flow theory and that require prior knowledge of certain soil properties. We developed a new formula that uses a multiplicative factor instead of a subtracted term to achieve the needed correction. For testing we conducted numerical experiments with synthetic data produced by solving the Richardson-Richards equation using the code VS2DRTI, for diverse media and a range of disk sizes, including the widely used 45-mm diameter. We compared K values calculated from our formula to the actual K used to generate the simulated data, as well as to results from other published formulas. This comparison shows that our method provides an algorithm based in unsaturated-flow theory that produces more reliable values for small disks without requiring prior knowledge of soil properties.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022WR032475","usgsCitation":"Nimmo, J.R., and Voss, P.R., 2023, Improved calculation of hydraulic conductivity for small-disk tension infiltrometers: Water Resources Research, v. 59, no. 3, e2022WR032475, 16 p., https://doi.org/10.1029/2022WR032475.","productDescription":"e2022WR032475, 16 p.","ipdsId":"IP-144414","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":499247,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022wr032475","text":"Publisher Index Page"},{"id":418222,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":875702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voss, Paige R.","contributorId":310446,"corporation":false,"usgs":false,"family":"Voss","given":"Paige","email":"","middleInitial":"R.","affiliations":[{"id":67192,"text":"USGS, now at University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":875703,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241614,"text":"70241614 - 2023 - Status and trends in the Lake Superior fish community, 2022","interactions":[],"lastModifiedDate":"2023-03-30T16:36:41.863826","indexId":"70241614","displayToPublicDate":"2023-03-01T11:26:29","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Status and trends in the Lake Superior fish community, 2022","docAbstract":"In 2022, the Lake Superior fish community was sampled with daytime bottom and surface trawls at 71 nearshore locations in May-June and 35 offshore locations in July, and at 51 Coordinated Science and Monitoring Initiative (CSMI) locations in July-October with bottom trawls, surface trawls, mid-water trawls and acoustics that were previously sampled in 2011 and 2016. Nearshore bottom trawls collected 11,603 fish from 25 species or morphotypes. Nearshore mean biomass was 1.6 kg per ha which was one of the lowest biomass estimates over survey’s 45-year history. Offshore bottom trawls collected 13,876 fish from 11 species or morphotypes. Offshore mean biomass was 5.1 kg per ha, which was less than the annual average since 2011 of 6.5 kg per ha. Recruitment, as measured by age-1 densities, was near zero for Bloater (Coregonus hoyi), Cisco (C. artedi), and Kiyi (C. kiyi), 2 age-1 fish per ha for Lake Whitefish (C. clupeaformis) and 77 age-1 fish for Rainbow Smelt (Osmerus mordax). All were less than the long-term averages. Sampling at the CSMI locations collected 26 species and morphotypes. The most abundant species’ lakewide were Deepwater Sculpin (all years), young-of-year ciscoe (Bloater, Cisco, and Kiyi, 2022), and Rainbow Smelt (2011 and 2016). Cisco had the highest estimated lakewide biomass in 2011 and 2022 and siscowet Lake Trout had the highest estimated lakewide biomass in 2016. Native species were more abundant than invasive species by numbers (80, 65, and 92%) and biomass (94, 93, 96%) in 2011, 2016, and 2022, respectively.
Total lakewide benthic fish biomass declined from 47 thousand metric tons in 2011 to 29 thousand metric tons in 2016 and increased to 33 thousand metric tons in 2022. Total lakewide pelagic fish biomass declined from 61 thousand metric tons in 2011 to 25 thousand metric tons in 2016 and increased to 54 thousand metric tons in 2022. The most unexpected result from our sampling in 2022 was the 2 billion age-0 ciscoe estimate from the mid-water trawl and acoustic sampling in August-October. These fish were broadly distributed across the lake, being collected at 53 of the 54 locations, and their population estimates were highest in the depths >100 m. The factors underlying the survival of these ciscoes into late summer in 2022 as compared to previous years have not been identified, but our annual population surveys of larval ciscoes suggests that lake conditions in June and July may have differed from previous years and enhanced survival. In 2022, ciscoe larval densities in May were lower than average (likely due to a cold winter and spring that delayed hatching), June densities were similar to previous years, and July density estimates were more than double that of any previous year’s estimate.
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The aim of this study is to investigate how longer periods of open water (defined as < 15% ice cover), decreased sea ice cover, and changes in ocean and atmospheric conditions might affect wave and storm surge conditions, sediment transport patterns, and coastal erosion rates within Foggy Island Bay as well as the modeled influence of the offshore artificial island on sediment transport patterns.","language":"English","publisher":"Bureau of Ocean and Energy Management (BOEM)","usgsCitation":"Erikson, L.H., Nederhoff, C.M., Engelstad, A.C., Kasper, J., and Bieniek, P.A., 2023, Central Beaufort Sea Wave and Hydrodynamic Modeling Study; Report 2: Modeled waves, hydrodynamics, and sediment transport within Foggy Island Bay: OCS Study BOEM 2022-079, 64 p.","productDescription":"64 p.","ipdsId":"IP-147575","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491591,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://espis.boem.gov/final%20reports/BOEM_2022-079.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":491739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Foggy Island Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.9566685211152,\n 70.37889977965969\n ],\n [\n -147.9566685211152,\n 70.1704960051022\n ],\n [\n -147.22144502523884,\n 70.1704960051022\n ],\n [\n -147.22144502523884,\n 70.37889977965969\n ],\n [\n -147.9566685211152,\n 70.37889977965969\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nederhoff, Cornelis M. 0000-0003-0552-3428","orcid":"https://orcid.org/0000-0003-0552-3428","contributorId":265889,"corporation":false,"usgs":false,"family":"Nederhoff","given":"Cornelis","email":"","middleInitial":"M.","affiliations":[{"id":33886,"text":"Deltares USA","active":true,"usgs":false}],"preferred":true,"id":941726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engelstad, Anita C 0000-0002-0211-4189","orcid":"https://orcid.org/0000-0002-0211-4189","contributorId":268303,"corporation":false,"usgs":true,"family":"Engelstad","given":"Anita","email":"","middleInitial":"C","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kasper, Jeremy L. 0000-0003-0975-6114","orcid":"https://orcid.org/0000-0003-0975-6114","contributorId":208630,"corporation":false,"usgs":false,"family":"Kasper","given":"Jeremy L.","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":941728,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bieniek, Peter A.","contributorId":210907,"corporation":false,"usgs":false,"family":"Bieniek","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":941729,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240306,"text":"70240306 - 2023 - Graton Pesticides (GRAPE) Study: Exposure potential from groundwater and air in California wine country, results","interactions":[],"lastModifiedDate":"2024-04-05T16:26:40.087279","indexId":"70240306","displayToPublicDate":"2023-03-01T10:24:31","publicationYear":"2023","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"title":"Graton Pesticides (GRAPE) Study: Exposure potential from groundwater and air in California wine country, results","docAbstract":"No abstract available.
","language":"English","publisher":"California Breast Cancer Research Program","usgsCitation":"Warwick, N., Sellen, J., Reynolds, P., Hladik, M.L., Kolpin, D.W., Von Behren, J., and Burton, J., 2023, Graton Pesticides (GRAPE) Study: Exposure potential from groundwater and air in California wine country, results, 20 p.","productDescription":"20 p.","ipdsId":"IP-140201","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":425730,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.sonomasass.org/grape","linkFileType":{"id":5,"text":"html"}},{"id":425731,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.89461176291844,\n 38.459583811589795\n ],\n [\n -122.89461176291844,\n 38.41918676480765\n ],\n [\n -122.84009216486707,\n 38.41918676480765\n ],\n [\n -122.84009216486707,\n 38.459583811589795\n ],\n [\n -122.89461176291844,\n 38.459583811589795\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Revised March 2023","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Warwick, N.","contributorId":302026,"corporation":false,"usgs":false,"family":"Warwick","given":"N.","email":"","affiliations":[{"id":65399,"text":"SASS","active":true,"usgs":false}],"preferred":false,"id":863325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sellen, J.","contributorId":302027,"corporation":false,"usgs":false,"family":"Sellen","given":"J.","email":"","affiliations":[{"id":65400,"text":"Pesticide Reform","active":true,"usgs":false}],"preferred":false,"id":863326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reynolds, P.","contributorId":191901,"corporation":false,"usgs":false,"family":"Reynolds","given":"P.","email":"","affiliations":[],"preferred":false,"id":863327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221229,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":863328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kolpin, Dana W. 0000-0002-3629-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3629-6505","contributorId":302028,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":863329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Von Behren, J.","contributorId":302029,"corporation":false,"usgs":false,"family":"Von Behren","given":"J.","affiliations":[{"id":49956,"text":"University of California San Francisco","active":true,"usgs":false}],"preferred":false,"id":863330,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burton, J.","contributorId":302030,"corporation":false,"usgs":false,"family":"Burton","given":"J.","email":"","affiliations":[{"id":65401,"text":"Breast Cancer Action","active":true,"usgs":false}],"preferred":false,"id":863331,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70265046,"text":"70265046 - 2023 - Using public litigation records to identify priority science needs for managing public lands","interactions":[],"lastModifiedDate":"2025-03-31T15:28:46.250573","indexId":"70265046","displayToPublicDate":"2023-03-01T10:24:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Using public litigation records to identify priority science needs for managing public lands","docAbstract":"Relevant science is essential for effective natural resource decision making, including on public lands managed by the United States Department of the Interior (DOI) Bureau of Land Management (BLM), that cover 1/10th of the United States. Most of the BLM’s management decisions require analyses under the National Environmental Policy Act, and the use of science in these decisions is often challenged. Using coproduction, we assembled an interagency team of scientists and resource managers to develop a method for using public litigation to identify priority science needs for the BLM. We searched publicly available case documents finalized from 2015–2019 in Wyoming, Colorado, Utah, and New Mexico within federal courts and the DOI Office of Hearings and Appeals, and identified 108 case documents that involved challenges to the BLM’s use of science. We retained 48 case documents that contained at least one challenge about the BLM’s use of science for a specific resource. We categorized all challenges in each case document according to the proposed action, affected resource, type of science challenged (data about resources, science relevant to potential impacts, methods for analyzing potential impacts, and mitigation actions), and specific nature of the challenge (e.g., challenging direct effects analysis). We identified priority science needs based on the frequency of challenges, the number of states where similar challenges occurred, whether the BLM lost the challenge, and whether the case was remanded. Top needs related to oil and gas development actions and included science about effects on air quality and climate, water, and socioeconomics; data for air quality and climate; and methods for analyzing potential impacts to cultural resources and air quality and climate. The BLM can use this information to prioritize actions (e.g., funding new research or science syntheses) to strengthen its science foundation for decision-making.
","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ES-13708-280111","usgsCitation":"Foster, A.C., Carter, S.K., Haby, T.S., Espy, L., and Barton, M., 2023, Using public litigation records to identify priority science needs for managing public lands: Ecology and Society, v. 28, no. 1, 11, 27 p., https://doi.org/10.5751/ES-13708-280111.","productDescription":"11, 27 p.","ipdsId":"IP-130700","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":488927,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/es-13708-280111","text":"Publisher Index Page"},{"id":484021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico, Utah, Wyoming","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-104.053249,41.001406],[-102.051718,41.002377],[-102.051569,39.849805],[-102.045388,38.813392],[-102.044644,38.045532],[-102.041574,37.680436],[-102.04224,36.993083],[-102.698142,36.995149],[-102.75986,37.000019],[-102.979613,36.998549],[-103.002199,37.000104],[-103.002434,36.500397],[-103.041924,36.500439],[-103.040824,36.055231],[-103.043531,34.018014],[-103.064625,32.999899],[-103.064423,32.000518],[-105.428582,32.0006],[-106.125534,32.002533],[-106.618486,32.000495],[-106.619448,31.994733],[-106.623568,31.990999],[-106.631182,31.989809],[-106.636492,31.985719],[-106.639529,31.980348],[-106.638186,31.97682],[-106.630114,31.971258],[-106.626466,31.97069],[-106.623216,31.97291],[-106.619569,31.971578],[-106.619371,31.964777],[-106.624299,31.961054],[-106.625123,31.954531],[-106.622819,31.952891],[-106.614702,31.956],[-106.616136,31.948439],[-106.623659,31.94551],[-106.622529,31.934863],[-106.629747,31.92657],[-106.628663,31.923614],[-106.623933,31.925335],[-106.611846,31.920003],[-106.633668,31.90979],[-106.645479,31.89867],[-106.645646,31.895649],[-106.6429,31.892933],[-106.633927,31.889184],[-106.629197,31.883717],[-106.634873,31.874478],[-106.635926,31.866235],[-106.627808,31.860593],[-106.625763,31.856276],[-106.614637,31.84649],[-106.605845,31.846305],[-106.602045,31.844405],[-106.605267,31.827912],[-106.602727,31.825024],[-106.593826,31.824901],[-106.589045,31.822706],[-106.577244,31.810406],[-106.570944,31.810206],[-106.566844,31.813306],[-106.547144,31.807305],[-106.535843,31.798607],[-106.533043,31.791907],[-106.527943,31.790507],[-106.528543,31.784407],[-108.208394,31.783599],[-108.208573,31.333395],[-109.050044,31.332502],[-109.045363,34.785406],[-109.046796,35.363606],[-109.045223,36.999084],[-110.47019,36.997997],[-110.490908,37.003566],[-110.50069,37.00426],[-111.278286,37.000465],[-114.0506,37.000396],[-114.052827,37.103961],[-114.051405,37.233854],[-114.052962,37.592783],[-114.051728,37.745997],[-114.048473,37.809861],[-114.050423,37.999961],[-114.050485,38.499955],[-114.049465,38.874949],[-114.048054,38.878693],[-114.046178,40.398313],[-114.040231,41.49169],[-114.041152,41.850595],[-114.039648,41.884816],[-114.041723,41.99372],[-113.893261,41.988057],[-113.249159,41.996203],[-112.709375,42.000309],[-112.192976,42.001167],[-112.173352,41.996568],[-111.046689,42.001567],[-111.046801,42.504946],[-111.043564,42.722624],[-111.044168,43.189244],[-111.046515,43.908376],[-111.047349,43.999921],[-111.049077,44.020072],[-111.048974,44.474072],[-111.055208,44.624927],[-111.056888,44.866658],[-111.055199,45.001321],[-110.785008,45.002952],[-110.705272,44.992324],[-110.552433,44.992237],[-110.402927,44.99381],[-110.362698,45.000593],[-110.28677,44.99685],[-110.199503,44.996188],[-110.110103,45.003905],[-109.75073,45.001605],[-109.103445,45.005904],[-109.08301,44.99961],[-107.351441,45.001407],[-107.13418,45.000109],[-107.084939,44.996599],[-106.263586,44.993788],[-105.928184,44.993647],[-105.913382,45.000941],[-104.057698,44.997431],[-104.055914,44.874986],[-104.052583,42.650062],[-104.053249,41.001406]]]},\"properties\":{\"name\":\"Colorado\",\"nation\":\"USA \"}}]}","volume":"28","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Foster, Alison C. 0000-0002-6659-2120","orcid":"https://orcid.org/0000-0002-6659-2120","contributorId":260599,"corporation":false,"usgs":true,"family":"Foster","given":"Alison","email":"","middleInitial":"C.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":932404,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":932405,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haby, Travis S. 0000-0003-2204-9967","orcid":"https://orcid.org/0000-0003-2204-9967","contributorId":138831,"corporation":false,"usgs":false,"family":"Haby","given":"Travis","email":"","middleInitial":"S.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":932406,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Espy, Leigh","contributorId":329383,"corporation":false,"usgs":false,"family":"Espy","given":"Leigh","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":932407,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barton, Malia K.","contributorId":352909,"corporation":false,"usgs":false,"family":"Barton","given":"Malia K.","affiliations":[],"preferred":false,"id":932408,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240983,"text":"70240983 - 2023 - Influences of water hardness on chronic toxicity of potassium chloride to a unionid mussel (Lampsilis siliquoidea)","interactions":[],"lastModifiedDate":"2023-05-01T15:53:42.899318","indexId":"70240983","displayToPublicDate":"2023-03-01T09:59:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Influences of water hardness on chronic toxicity of potassium chloride to a unionid mussel (Lampsilis siliquoidea)","title":"Influences of water hardness on chronic toxicity of potassium chloride to a unionid mussel (Lampsilis siliquoidea)","docAbstract":"Elevated concentrations of potassium (K) often occur in effluents from wastewater treatment plants, oil and gas production operations, mineral extraction processes, and from other anthropogenic sources. Previous studies have demonstrated that freshwater mussels are highly sensitive to K in acute and chronic exposures, and acute toxicity of K decreases with increasing water hardness. However, little is known about the influence of hardness on the chronic toxicity of K. The objective of this study was to evaluate the chronic toxicity of K (tested as KCl) to a commonly tested unionid mussel (fatmucket, Lampsilis siliquoidea) at five hardness levels (25, 50, 100, 200, 300 mg/L as CaCO3) representing most surface waters in the United States. Chronic 28-d K toxicity tests were conducted with 3-week-old juvenile fatmucket in the five hardness waters using an ASTM standard method. The maximum acceptable toxicant concentrations (geometric mean of the no-observed-effect concentration and the lowest-observed-effect concentration) increased from 15.1 to 69.3 mg K/L for survival and from 15.1 to 35.8 mg K/L for growth (length and dry weight) and biomass when water hardness was increased from 25 mg/L (soft) to 300 mg/L (very hard). These results provided evidence to support water hardness influence on chronic K toxicity to juvenile fatmucket. However, the chronic effect concentrations based on the more sensitive endpoint (growth or biomass) increased only 2.4-fold from the soft water to the very hard water, indicating that water hardness had limited influence on the chronic toxicity of K to the mussels. These results can be used to establish chronic toxicity thresholds for K across a broad range of water hardness and to derive environmental guideline values for K to protect freshwater mussels and other organisms.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5598","usgsCitation":"Wang, N., Dorman, R.A., Kunz, J.L., Cleveland, D.M., Steevens, J.A., Dunn, S., and Martinez, D., 2023, Influences of water hardness on chronic toxicity of potassium chloride to a unionid mussel (Lampsilis siliquoidea): Environmental Toxicology and Chemistry, v. 42, no. 5, p. 1085-1093, https://doi.org/10.1002/etc.5598.","productDescription":"9 p.","startPage":"1085","endPage":"1093","ipdsId":"IP-146694","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":500023,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5598","text":"Publisher Index Page"},{"id":435428,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R8YGM2","text":"USGS data release","linkHelpText":"Survival, growth, and chemical data from a study on influences of water hardness on chronic toxicity of potassium chloride to a Unionid mussel (Lampsilis siliquoidea)"},{"id":413665,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":865606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dorman, Rebecca A. 0000-0002-5748-7046","orcid":"https://orcid.org/0000-0002-5748-7046","contributorId":28522,"corporation":false,"usgs":true,"family":"Dorman","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":865607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kunz, James L. 0000-0002-1027-158X jkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-1027-158X","contributorId":3309,"corporation":false,"usgs":true,"family":"Kunz","given":"James","email":"jkunz@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":865608,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":865609,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":865610,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dunn, Suzanne","contributorId":279599,"corporation":false,"usgs":false,"family":"Dunn","given":"Suzanne","email":"","affiliations":[{"id":57309,"text":"US Fish Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":865611,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Martinez, David","contributorId":279598,"corporation":false,"usgs":false,"family":"Martinez","given":"David","email":"","affiliations":[{"id":57309,"text":"US Fish Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":865612,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70245590,"text":"70245590 - 2023 - Results of validation exercise for Marine Benthic Index","interactions":[],"lastModifiedDate":"2023-06-26T14:21:41.468174","indexId":"70245590","displayToPublicDate":"2023-03-01T08:59:30","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesNumber":"23-03-009","title":"Results of validation exercise for Marine Benthic Index","docAbstract":"Marine benthic invertebrates (benthos) are key components of the Puget Sound ecosystem. Because of their direct association living in, and sometimes consuming, sediments, benthos can be valuable sentinels of ecosystem health. Therefore, indicators of benthic invertebrate community health can serve as direct measures of sediment and water quality.
In 2021, the Puget Sound Partnership funded development of a Marine Benthic Index. The Marine Benthic Index thus developed uses a novel approach that accounts for habitat preferences of the benthic invertebrate species. This report describes the design and results of the exercise conducted to validate the Marine Benthic Index.
The goals of the validation exercise were to determine (a) how well the Marine Benthic Index matches more standard ways of assessing community health and (b) how finely it is possible to distinguish between levels of disturbance. A controlled experiment was devised in which simulated benthic communities were generated to correspond to predetermined levels of disturbance, and experts in benthic ecology determined which communities reflected the more-disturbed conditions. In this way, the index was directly compared to traditional methods of assessing benthic communities.
The results provide strong evidence that the “latent disturbance” model used to derive the Marine Benthic Index is identifying effects that benthic experts recognize as disturbance. Not only did the model agree with the experts overall, but also the probability of agreement strongly increased with increasing difference in disturbance level.
The validation exercise results indicate that the Marine Benthic Index is a reliable method of determining disturbance without the necessity of assuming a priori knowledge of the disturbance. Furthermore, the numerical approach embodied in the Marine Benthic Index has the advantage of being able to find patterns beyond the capability of individual experts to know the effects of human disturbances for all species under all environmental conditions.
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Starting from a base year of 2001, NLCD releases a land cover database every 2–3-years. The recent release of NLCD2019 extends the database to 18 years. We implemented a stratified random sample to collect land cover reference data for the 2016 and 2019 components of the NLCD2019 database at Level II and Level I of the classification hierarchy. For both dates, Level II land cover overall accuracies (OA) were 77.5% ± 1% (± value is the standard error) when agreement was defined as a match between the map label and primary reference label only, and increased to 87.1% ± 0.7% when agreement was defined as a match between the map label and either the primary or alternate reference label. At Level I of the classification hierarchy, land cover OA was 83.1% ± 0.9% for both 2016 and 2019 when agreement was defined as a match between the map label and primary reference label only, and increased to 90.3% ± 0.7% when agreement also included the alternate reference label. The Level II and Level I OA for the 2016 land cover in the NLCD2019 database were 5% higher compared to the 2016 land cover component of the NLCD2016 database when agreement was defined as a match between the map label and primary reference label only. No improvement was realized by the NLCD2019 database when agreement also included the alternate reference label. User’s accuracies (UA) for forest loss and grass gain were>70% when agreement included either the primary or alternate label, and UA was generally<50% for all other change themes. Producer’s accuracies (PA) were>70% for grass loss and gain and water gain and generally<50% for the other change themes. We conducted a post-analysis review for map-reference agreement to identify patterns of disagreement, and these findings are discussed in the context of potential adjustments to mapping and reference data collection procedures that may lead to improved map accuracy going forward.
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Basin","interactions":[],"lastModifiedDate":"2023-06-01T14:08:40.055086","indexId":"70244090","displayToPublicDate":"2023-03-01T08:44:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Unravelling the influence of landscape alteration from flow alteration on benthic macroinvertebrate assemblage response in the Delaware River Basin","docAbstract":"Quantifying the effects of streamflow alteration on assemblage response is central to understanding the role humans play in shaping aquatic environments. These changes represent a level of complexity that impedes developing quantitative links between flow and ecological response because stream hydrology is strongly intertwined with natural and anthropogenic factors. Better management outcomes require disentangling these linkages. Benthic macroinvertebrate data were combined with GIS-derived natural and anthropogenic basin characteristics to identify factors associated with changes in flow processes and assemblage characteristics. Models linking streamflow metrics and macroinvertebrate response at basin and subregion scales were developed using boosted regression tree (BRT) analysis. Basin-scale BRT analyses revealed that links between macroinvertebrate response and flow metrics were often obscured, whereas more homogeneous subregions were better able to discern relations with flow. Urban land cover was the primary factor accounting for changes in flow characteristics. Elevation, land cover, and high flow frequency were the principal variables driving changes in assemblage structure within subregions. Assemblage metrics and traits were equally useful for building response models and were affected similarly by streamflow alteration. Results indicate that response models should be developed based on upland and coastal subregions. However, when defining subregions, care should be taken to maintain data sufficiency. Developing practical flow-protection standards that support a balance between human water requirements and ecological integrity requires models that reduce uncertainty and identify management-relevant drivers. However, effective management often differs by location and models developed at the subregion level may be more applicable to management and stakeholder interests.","language":"English","publisher":"Wiley","doi":"10.1002/eco.2508","usgsCitation":"Kennen, J., and Cuffney, T.F., 2023, Unravelling the influence of landscape alteration from flow alteration on benthic macroinvertebrate assemblage response in the Delaware River Basin: Ecohydrology, v. 16, no. 2, e2508, 41 p., https://doi.org/10.1002/eco.2508.","productDescription":"e2508, 41 p.","ipdsId":"IP-128360","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":498861,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2508","text":"Publisher Index Page"},{"id":417646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.07997541667648,\n 38.70906787639316\n ],\n [\n -74.80531721355018,\n 39.00778043156808\n ],\n [\n -74.33839826823872,\n 40.4450386444411\n ],\n [\n -73.72865705730113,\n 40.994591290300974\n ],\n [\n -73.7835886979261,\n 42.478350475454334\n ],\n [\n -75.40956526042564,\n 42.295772510663625\n ],\n [\n -75.42055158855078,\n 41.8349594674406\n ],\n [\n -76.34340315105082,\n 40.43667721449637\n ],\n [\n -75.78859358073888,\n 39.713504216020766\n ],\n [\n -75.76662092448927,\n 39.578152174338356\n ],\n [\n -75.66225080730156,\n 39.41283383409595\n ],\n [\n -75.50294904948888,\n 39.22584914314203\n ],\n [\n -75.47548322917642,\n 39.042631522344635\n ],\n [\n -75.33266096355176,\n 38.846103881559685\n ],\n [\n -75.07997541667648,\n 38.70906787639316\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-01-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Kennen, Jonathan G. 0000-0002-5426-4445 jgkennen@usgs.gov","orcid":"https://orcid.org/0000-0002-5426-4445","contributorId":574,"corporation":false,"usgs":true,"family":"Kennen","given":"Jonathan G.","email":"jgkennen@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cuffney, Thomas F. 0000-0003-1164-5560 tcuffney@usgs.gov","orcid":"https://orcid.org/0000-0003-1164-5560","contributorId":517,"corporation":false,"usgs":true,"family":"Cuffney","given":"Thomas","email":"tcuffney@usgs.gov","middleInitial":"F.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874462,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70248778,"text":"70248778 - 2023 - Indicators of the effects of climate change on freshwater ecosystems","interactions":[],"lastModifiedDate":"2023-09-21T12:06:36.333278","indexId":"70248778","displayToPublicDate":"2023-03-01T07:03:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1246,"text":"Climate Change","onlineIssn":"1573-1480","printIssn":"0165-0009","active":true,"publicationSubtype":{"id":10}},"title":"Indicators of the effects of climate change on freshwater ecosystems","docAbstract":"Freshwater ecosystems, including lakes, streams, and wetlands, are responsive to climate change and other natural and anthropogenic stresses. These ecosystems are frequently hydrologically and ecologically connected with one another and their surrounding landscapes, thereby integrating changes throughout their watersheds. The responses of any given freshwater ecosystem to climate change depend on the magnitude of climate forcing, interactions with other anthropogenic and natural changes, and the characteristics of the ecosystem itself. Therefore, the magnitude and manner in which freshwater ecosystems respond to climate change are difficult to predict a priori. We present a conceptual model to elucidate how freshwater ecosystems are altered by climate change. We identify eleven indicators that describe the response of freshwater ecosystems to climate change, discuss their potential value and limitations, and describe supporting measurements. Indicators are organized in three interrelated categories: hydrologic, water quality, and ecosystem structure and function. The indicators are supported by data sets with a wide range of temporal and spatial coverage, and they inform important scientific and management needs. Together, these indicators improve the understanding and management of the effects of climate change on freshwater ecosystems.
Within the western United States, increasingly severe and frequent wildfires may alter the magnitude, timing, and quality of water exported from burned areas by streams. Post-fire hydrologic studies often focus on peak stream flow responses to shifts in runoff generation or on annual streamflow yield response to changes in evapotranspiration following fire. However, the magnitude and duration of wildfire effects on groundwater recharge, changes in subsurface routing, and consequences for stream low flows sourced predominately by baseflow are poorly understood. Here, we demonstrate an approach using the amplitude and phase of paired annual air and stream water temperature signals to broadly identify changes in watershed subsurface flow contributions after fire. Watersheds were classified using pre-fire temperature data, as having air-coupled (i.e., reduced apparent groundwater signature), deep groundwater, or shallow groundwater stream temperature signals. Changes in pre- and post-fire paired air and stream water temperature metrics were compared for locations (n = 17) spanning a large range of physiographic and climatic conditions across the western United States. Pre- and post-fire comparisons were computed by quantile using bootstrapped confidence intervals (ci = 95), as well as in aggregate using Kruskal-Wallis and post-hoc Dunn tests. Statistical comparisons of pre- and post-fire temperature metrics suggest that overall, watersheds classified as having minimal groundwater influence are the most likely to experience fire-induced subsurface hydrologic change. More specifically, watersheds classified as having air-coupled or shallow groundwater signals experienced increases in the magnitude of groundwater discharge, with more stable annual thermal regimes post-fire that are less-coupled to ambient air temperature. These findings form the basis of a conceptual framework for watershed resistance to subsurface hydrologic change following fire that can be broadly applied as a first approximation for water management, impacts on aquatic habitat, and post-wildfire response planning.
Long waves play an important role in coastal inundation and shoreline and dune erosion, requiring a detailed understanding of their evolution in nearshore regions and interaction with shorelines. While their generation and dissipation mechanisms are relatively well understood, there are fewer studies describing how reflection processes govern their propagation in the nearshore. We propose a new approach, accounting for partial reflections, which leads to an analytical solution to the free wave linear shallow-water equations at the wave-group scale over general varying bathymetry. The approach, supported by numerical modeling, agrees with the classic Bessel standing solution for a plane sloping beach but extends the solution to arbitrary alongshore uniform bathymetry profiles and decomposes it into incoming and outgoing wave components, which are a combination of successively partially reflected waves lagging each other. The phase lags introduced by partial reflections modify the wave amplitude and explain why Green’s law, which describes the wave growth of free waves with decreasing depth, breaks down in very shallow water. This reveals that the wave amplitude at the shoreline is highly dependent on partial reflections. Consistent with laboratory and field observations, our analytical model predicts a reflection coefficient that increases and is highly correlated with the normalized bed slope (bed slope relative to wave frequency). Our approach shows that partial reflections occurring due to depth variations in the nearshore are responsible for the relationship between the normalized bed slope and the amplitude of long waves in the nearshore, with direct implications for determining long-wave amplitudes at the shoreline and wave runup.
Co-occurring species with similar resource requirements often partition ecological niches at different spatial and temporal scales. In the Northwest Atlantic (NWA), federally endangered roseate terns Sterna dougallii nest almost exclusively in coastal island colonies alongside common terns S. hirundo. Roseate terns are prey specialists compared to common terns, which are opportunistic generalists; however, the 2 species forage on similar resources during the breeding season. The degree to which these species overlap in their adult foraging ecologies is not well understood. We compared the isotopic niches of nesting adult roseate and common terns by analyzing stable carbon (δ13C) and nitrogen (δ15N) isotopes in eggshell membrane tissues collected in 2018 and 2019 from 10 colonies that span their NWA breeding range. Our aim was to characterize interspecific patterns in δ13C and δ15N values, isotopic niche breadth, and isotope niche overlap. We additionally examined interannual and subregional differences between ‘cold-water’ colonies in the Gulf of Maine and ‘warm-water’ colonies in Southern New England and Long Island Sound. At the range-wide scale, there was a high degree of overlap in the overall isotopic niches of the 2 species; however, more variable patterns were observed at the colony scale, ranging from nearly complete overlap to complete separation. The isotopic niches of roseate terns were generally narrower than those of common terns, consistent with their respective specialist/generalist tendencies. While the influence of isotopic baselines limits our interpretation of interannual and subregional differences, isotopic niche breadths and overlap suggest consistency of relative foraging ecologies across these scales.
The gray bat (Myotis grisescens) is a cave-obligate species that has been listed as federally endangered since 1976, following population declines from human disturbance at hibernation and maternity caves. However, with cave protection, most gray bat populations have increased. As part of a project examining bat use of transportation structures as day-roosts, we continuously acoustically monitored 12 riparian sites within the Clinch River Watershed of southwest Virginia from March through November, 2018–2020. We used 15 different landscape and weather-related variables in generalized linear mixed models to determine factors influencing gray bat presence and activity. Seasonal activity patterns were similar among years, but the number of nightly gray bat calls increased with each passing year, consistent with positive population trends observed at winter hibernacula. Year and average nightly temperatures were positively correlated with gray bat activity, as was, unexpectedly, average nightly wind speed. Total nightly precipitation, distance to the nearest hibernaculum in Tennessee, percent forested area within 2 km of a detector, mean elevation within 2 km of a detector, detector type, and amount of urban development within 2 km of a detector were negatively correlated with gray bat activity. Our findings show where and when gray bat presence is likely in southwest Virginia, thereby helping managers avoid negative impacts from activities such as bridge repair or replacement and planning of future monitoring to track population trends.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Taylor, H., Powers, K., Orndorff, W., Reynolds, R., Hallerman, E.M., and Ford, W., 2023, Sources of yearly variation in gray bat activity in the Clinch River watershed, Virginia: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 10, p. 107-113.","productDescription":"7 p.","startPage":"107","endPage":"113","ipdsId":"IP-142692","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481149,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/sources-yearly-variation-gray-bat-activity-clinch-river-watershed-virginia","linkFileType":{"id":5,"text":"html"}},{"id":481151,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Clinch River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.6898822712355,\n 37.244895334911206\n ],\n [\n -82.48049233355506,\n 36.59887550386596\n ],\n [\n -81.6898822712355,\n 36.59053043712693\n ],\n [\n -81.38353788654133,\n 36.697772476555386\n ],\n [\n -81.0769253267201,\n 37.244895334911206\n ],\n [\n -81.6898822712355,\n 37.244895334911206\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, H.","contributorId":195324,"corporation":false,"usgs":false,"family":"Taylor","given":"H.","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":924942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Powers, K.","contributorId":349843,"corporation":false,"usgs":false,"family":"Powers","given":"K.","affiliations":[{"id":34752,"text":"Radford University","active":true,"usgs":false}],"preferred":false,"id":924943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orndorff, W.","contributorId":349845,"corporation":false,"usgs":false,"family":"Orndorff","given":"W.","affiliations":[{"id":56188,"text":"Virginia Department of Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":924944,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reynolds, Rick","contributorId":267215,"corporation":false,"usgs":false,"family":"Reynolds","given":"Rick","email":"","affiliations":[{"id":55446,"text":"Virginia Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":925006,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hallerman, E. M.","contributorId":280251,"corporation":false,"usgs":false,"family":"Hallerman","given":"E.","email":"","middleInitial":"M.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":924945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":924946,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262166,"text":"70262166 - 2023 - Environmental correlates of walleye spawning movements in an Appalachian hydropower reservoir","interactions":[],"lastModifiedDate":"2025-01-15T20:01:12.656734","indexId":"70262166","displayToPublicDate":"2023-03-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Environmental correlates of walleye spawning movements in an Appalachian hydropower reservoir","docAbstract":"Understanding walleye (Sander vitreus) spawning behavior is important for managing walleye fisheries, but such information is limited for Appalachian reservoirs. We assessed spawning movements and spawning locations for a reestablished walleye population in Cheat Lake, West Virginia. We tagged fifty-two walleye with acoustic telemetry transmitters to evaluate environmental correlates associated with pre-spawn movements and to deter- mine spawning locations. Using an information-theoretic approach, we compared candidate logistic regression models to determine which environmental variables best explained upstream movements to spawning areas. The two models with the most support both included additive effects of year and water temperature, with sex also included in the second of these models. Water temperature had a significant positive relationship with pre-spawn movements in each model. Other environmental covariates such as river discharge and water elevation were not significant predictors of upstream pre-spawn move- ments. Walleye made pre-spawn upstream movements in late winter/early spring to spawning areas in the headwaters of Cheat Lake during periods of el- evated water temperatures (75 % of movement events occurred at water temperatures >4.1 C) where spawning occurred in shallow (<1.5 m), rocky habitat. Male walleye generally made upstream pre-spawn movements earlier than females. Our results also suggested the timing of walleye spawning with respect to water-level fluctuations could influence reproductive success due to stranding of eggs or reducing suitable spawning habitat. Knowledge of pre-spawn movement patterns and spawning locations could aid management of this recovering population. Benefits to management may include the prediction of spawning timing and locations for broodstock surveys and influences of water-level fluctuations and other environmental stressors on spawning success.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Smith, D., Welsh, S.A., and Hilling, C.D., 2023, Environmental correlates of walleye spawning movements in an Appalachian hydropower reservoir: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 10, p. 36-44.","productDescription":"9 p.","startPage":"36","endPage":"44","ipdsId":"IP-145288","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":466453,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/environmental-correlates-walleye-spawning-movements-appalachian-hydropower-reservoir"}],"country":"United States","state":"Pennsylvania, West Virginia","otherGeospatial":"Cheat Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.90059403320035,\n 39.7348398274502\n ],\n [\n -79.90059403320035,\n 39.665062724725885\n ],\n [\n -79.82498808610146,\n 39.665062724725885\n ],\n [\n -79.82498808610146,\n 39.7348398274502\n ],\n [\n -79.90059403320035,\n 39.7348398274502\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Dustin M.","contributorId":272979,"corporation":false,"usgs":false,"family":"Smith","given":"Dustin M.","affiliations":[{"id":56173,"text":"West Virginia DNR","active":true,"usgs":false}],"preferred":false,"id":923331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":1483,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart","email":"swelsh@usgs.gov","middleInitial":"A.","affiliations":[{"id":205,"text":"Cooperative Research Units","active":false,"usgs":true}],"preferred":false,"id":923332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hilling, Corbin David 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":298946,"corporation":false,"usgs":true,"family":"Hilling","given":"Corbin","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":923333,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240897,"text":"fs20233001 - 2023 - Flood warning toolset for the Sabinal River near Utopia, Texas","interactions":[],"lastModifiedDate":"2026-02-05T14:42:08.024436","indexId":"fs20233001","displayToPublicDate":"2023-02-28T12:30:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3001","displayTitle":"Flood Warning Toolset for the Sabinal River Near Utopia, Texas","title":"Flood warning toolset for the Sabinal River near Utopia, Texas","docAbstract":"Floods are one of the most frequent and expensive natural disasters that occur across the United States. Rapid, high-water events that occur in local areas—flash floods—are especially difficult for emergency managers to predict and provide advance warning to the public, and insufficient data can hamper postflood recovery efforts. Central Texas is hilly, and it is known as a “flash flood alley” because of its high-intensity rains, shallow soils, and steep terrain, all of which combined can result in loss of life and property damage. For example, the flash flood event during July 2002 claimed 12 lives in central Texas, including 1 in the town of Utopia, which is on the east bank of the Sabinal River in a flash-flood-prone area along the Balcones Escarpment. During the flood event, the peak discharge recorded on July 5, 2002, at U.S. Geological Survey (USGS) streamgage 08198000 Sabinal River near Sabinal, Tex. (hereinafter referred to as the “Sabinal gage”), was 108,000 cubic feet per second (corresponding to a stream stage [also called gage height] of 33.74 feet). To put the 2002 flood into context, during a typical year the median daily discharge in the Sabinal River at the Sabinal gage is only about 23 cubic feet per second. In 2021, the USGS, in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, developed a flood warning toolset for the Sabinal River near Utopia. This study builds on earlier USGS flood work on the Medina River in Bandera County. The newly developed toolset consists of a newly installed USGS streamgage to collect continuous stream stage data (streamgage 08197970 Sabinal River at Utopia, Tex.; hereinafter referred to as the “Utopia gage”) 13 miles upstream from the Sabinal gage, a hydraulic model developed for the Sabinal River near Utopia, and an online library of digital flood-inundation maps referenced to the stream stage at the Utopia gage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233001","issn":"2327-6932 (online)","collaboration":"Prepared in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board","usgsCitation":"Choi, N., 2023, Flood warning toolset for the Sabinal River near Utopia, Texas: U.S. Geological Survey Fact Sheet 2023–3001 (ver. 2.0, September 2023), 4 p., https://doi.org/10.3133/fs20233001.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-136337","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":421082,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2023/3001/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":499562,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114429.htm","linkFileType":{"id":5,"text":"html"}},{"id":421081,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20235001","text":"Scientific Investigations Report 2023–5001","description":"SIR 2023-5001","linkHelpText":"- Flood-Inundation Maps Created Using a Synthetic Rating Curve for a 10-Mile Reach of the Sabinal River and a 7-Mile Reach of the West Sabinal River Near Utopia, Texas, 2021"},{"id":421080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3001/fs20233001.pdf","text":"Report","size":"1.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3001"},{"id":414773,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3001/coverthb.jpg"}],"country":"United States","state":"Texas","city":"Utopia","otherGeospatial":"Sabinal River, West Sabinal River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.6333,\n 29.75\n ],\n [\n -99.6333,\n 29.6\n ],\n [\n -99.5,\n 29.6\n ],\n [\n -99.5,\n 29.75\n ],\n [\n -99.6333,\n 29.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: February 2023; Version 2.0: September 2023","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Overview
Creation of Flood Warning Toolset
References Cited
The sensitivity of wildland plants to temperature can be directly measured using experimental manipulations of temperature in situ. We show that soil surface temperature and plant density (per square meter) have a significant impact on the germination, growth, and phenology of Bromus tectorum L., cheatgrass, a short-statured invasive winter-annual grass, and assess a new experimental temperature manipulation method: the application of black and white gravel to warm and cool the soil surface.
We monitored height, seed production, and phenological responses of cheatgrass, seeded into colored gravel at low and high densities at two sites in the western USA: Boise, ID and Cheyenne, WY. Soil surface temperature and volumetric water content were measured to assess treatment effects on soil surface microclimate.
Black gravel increased mean temperatures of the surface soil by 1.6 and 2.6 °C compared to white gravel in Cheyenne and Boise, respectively, causing 21–24 more days with soil temperatures > 0 °C, earlier cheatgrass germination, and up to 2.8-fold increases in cheatgrass height. Higher seeding density of cheatgrass led to 1.4-fold taller plants on black gravel plots at both sites, but not white gravel at the Boise site, indicating a possible thermal benefit or reduction of water demand due to plant clustering in warmer treatments.
Manipulating soil-surface albedo altered the soil microclimate and thus growth and phenology of cheatgrass, whose life history and growth form confer a strong dependency on soil-surface conditions.
","language":"English","publisher":"Springer","doi":"10.1007/s11104-023-05929-4","usgsCitation":"Maxwell, T.M., Germino, M., Romero, S., Porensky, L., Blumenthal, D.M., Brown, C., and Adler, P.B., 2023, Experimental manipulation of soil-surface albedo alters phenology and growth of Bromus tectorum (cheatgrass): Plant and Soil, v. 487, p. 325-339, https://doi.org/10.1007/s11104-023-05929-4.","productDescription":"15 p.","startPage":"325","endPage":"339","ipdsId":"IP-141275","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":413664,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming","city":"Boise, Cheyenne","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.35917072312003,\n 43.73889472078221\n ],\n [\n -116.35917072312003,\n 43.464686153948634\n ],\n [\n -116.05823287588329,\n 43.464686153948634\n ],\n [\n -116.05823287588329,\n 43.73889472078221\n ],\n [\n -116.35917072312003,\n 43.73889472078221\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.67448200616478,\n 41.2486838873686\n ],\n [\n -104.94665093511199,\n 41.2486838873686\n ],\n [\n -104.94665093511199,\n 41.04211139242014\n ],\n [\n -104.67448200616478,\n 41.04211139242014\n ],\n [\n -104.67448200616478,\n 41.2486838873686\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"487","noUsgsAuthors":false,"publicationDate":"2023-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Maxwell, Toby M. 0000-0001-5171-0705","orcid":"https://orcid.org/0000-0001-5171-0705","contributorId":302845,"corporation":false,"usgs":false,"family":"Maxwell","given":"Toby","email":"","middleInitial":"M.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":865613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":865614,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romero, Seth","contributorId":302846,"corporation":false,"usgs":false,"family":"Romero","given":"Seth","email":"","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":865615,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Porensky, Lauren M.","contributorId":264925,"corporation":false,"usgs":false,"family":"Porensky","given":"Lauren M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":865616,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blumenthal, Dana M.","contributorId":203896,"corporation":false,"usgs":false,"family":"Blumenthal","given":"Dana","email":"","middleInitial":"M.","affiliations":[{"id":36745,"text":"USDA-ARS Rangeland Resources Research Unit","active":true,"usgs":false}],"preferred":false,"id":865617,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Cynthia S.","contributorId":302847,"corporation":false,"usgs":false,"family":"Brown","given":"Cynthia S.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":865618,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Adler, Peter B.","contributorId":64789,"corporation":false,"usgs":false,"family":"Adler","given":"Peter","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":865619,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70240728,"text":"sir20225125 - 2023 - Modeling flow and water quality in reservoir and river reaches of the Mahoning River Basin, Ohio","interactions":[],"lastModifiedDate":"2026-02-23T20:55:47.151064","indexId":"sir20225125","displayToPublicDate":"2023-02-27T16:09:05","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5125","displayTitle":"Modeling Flow and Water Quality in Reservoir and River Reaches of the Mahoning River Basin, Ohio","title":"Modeling flow and water quality in reservoir and river reaches of the Mahoning River Basin, Ohio","docAbstract":"The U.S. Army Corps of Engineers (USACE) is considering changes to the management of water surface elevation in four lakes in the Mahoning River Basin. These changes would affect the timing and amounts of water released to the Mahoning River and could affect the water quality of those releases. To provide information on possible water-quality effects from these operational changes, flow and water-quality models were constructed for Berlin Lake, Lake Milton, Michael J Kirwan Reservoir, Mosquito Creek Lake, Mosquito Creek, and the Mahoning River from the dams downstream to Lowellville, Ohio.
The models were calibrated for two calendar years each, with model years selected depending on the availability of water-quality data. Models were developed with CE-QUAL-W2 version 4.2 (Wells, S.A., 2020, CE-QUAL-W2—A two-dimensional, laterally averaged, hydrodynamic and water quality model [version 4.2]: Portland State University, variously paged), a two-dimensional, laterally averaged hydrodynamic and water-quality model. Modeled constituents included flow, velocity, ice cover, water temperature, total dissolved solids (TDS), sulfate, chloride, inorganic suspended sediment, nitrate, ammonia, total Kjeldahl nitrogen, orthophosphate, total phosphorus, dissolved and particulate organic matter, algae, and dissolved oxygen. Iron was included for the lake models, but not the river.
A whole-basin model, with the four lake models and river model, was used to run model scenarios to examine the effects of altered lake water surface elevations on flow and water quality in the lakes, the lake outflows, and the Mahoning River. The initial whole-basin model, with calendar year 2013 hydrology and measured or typical water quality, was designated as scenario 0. Mahoning River flows for calendar year 2013 were close to a 20-year median flow. Four additional scenarios were constructed based on reservoir operations model (RES-SIM) model water surface elevations for the four lakes as provided by USACE. Scenario 1 was the RES-SIM base case, scenario 2 kept Berlin Lake water surface elevations higher in summer, scenario 3 allowed 25 percent of summer flood storage to extend the guide curve, and scenario 4 allowed more flexibility in lake management by removing any downstream Mahoning River minimum flow requirements. The Mahoning River model was not changed in any scenarios but received altered flows from the lakes. Significant findings from this study include the following:
Director, Oregon Water Science Center
U.S. Geological Survey
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Portland, OR 97204