In-situ recovery (ISR) has been the only technique used to extract uranium from sandstone-hosted uranium deposits in the Pliocene Goliad Sand in the Texas Coastal Plain. Water plays a crucial role throughout the ISR lifecycle of production and groundwater restoration yet neither the water use nor other environmental footprints have been well documented. The goal of this study is to examine historical records for all six ISR operations completed in the Goliad Sand to identify and quantify parameters that indicate the surface and aquifer disturbances, water use, and radon emissions. Overall, the average mine area was 0.00023 ± 0.00006 acres per pound (ac/lb) U3O8. The average mine pore volume was 48.9 ± 50 gal/lb U3O8 with a minimum affected aquifer volume of 0.51 ± 0.08 cubic feet per pound (cu ft/lb) U3O8. An average of 258 ± 40 gallons (gal) of fluid were disposed per pound (lb) U3O8, with an average of 169 ± 26 gal/lb U3O8 attributed to restoration and 89 ± 36 gal/lb U3O8 attributed to the uranium production phase. The average radon emitted was 1.06 × 10−3 ± 7.4 × 10−4 curies per pound (Ci/lb) U3O8. Goodness-of-fit (R2) values are ≥0.79 for linear regressions of the amount of uranium produced versus mine area, mine pore volumes, mine aquifer volumes, water pumped, and total water disposed. The R2 value for radon emitted was 0.68. However, the water disposed only during the uranium production phase is more strongly correlated to the number of production days (R2 = 0.96) than to uranium production (R2 = 0.84), whereas the volume of water disposed during restoration is more strongly correlated to the “pore volume” (R2 = 0.97) than to uranium production (R2 = 0.90). Pore volume is an industry term used to describe the amount of fluid circulated through the aquifer during the uranium production period and stipulated in bond agreements in order to satisfy groundwater restoration requirements. Models constructed in this study can be used to estimate probable water use and the extent of surface and aquifer disturbances associated with ISR-amenable undiscovered uranium resources in the Goliad Sand. The historical perspective offered by the data compiled and correlations may prove useful to both industry and regulators.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/min12030369","usgsCitation":"Gallegos, T., Scott, A., Stengel, V.G., and Teeple, A., 2022, A methodology to assess the historical environmental footprint of in-situ recovery (ISR) of uranium: A demonstration in the Goliad Sand in the Texas Coastal Plain, USA: Minerals, v. 12, no. 3, 369, 29 p., https://doi.org/10.3390/min12030369.","productDescription":"369, 29 p.","ipdsId":"IP-132933","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":448350,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min12030369","text":"Publisher Index Page"},{"id":397704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Texas Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.69140625,\n 31.44741029142872\n ],\n [\n -96.328125,\n 31.22219703210317\n ],\n [\n -99.65698242187499,\n 27.61540601339959\n ],\n [\n -99.525146484375,\n 27.45953933271788\n ],\n [\n -99.60205078124999,\n 27.332735136859146\n ],\n [\n -99.503173828125,\n 27.225325836903373\n ],\n [\n -99.481201171875,\n 27.039556602163195\n ],\n [\n -99.2724609375,\n 26.686729520004036\n ],\n [\n -99.129638671875,\n 26.362342068998764\n ],\n [\n -98.843994140625,\n 26.303264239389534\n ],\n [\n -98.69018554687499,\n 26.165298896316042\n ],\n [\n -98.58032226562499,\n 26.194876675795218\n ],\n [\n -98.250732421875,\n 25.987674852384966\n ],\n [\n -97.85522460937499,\n 25.987674852384966\n ],\n [\n -97.3828125,\n 25.77021384896025\n ],\n [\n -97.174072265625,\n 26.007424156802212\n ],\n [\n -97.152099609375,\n 26.352497858154024\n ],\n [\n -97.31689453125,\n 26.755420897359123\n ],\n [\n -97.294921875,\n 27.01998400798257\n ],\n [\n -97.283935546875,\n 27.342494467201007\n ],\n [\n -97.03125,\n 27.819644755099446\n ],\n [\n -96.734619140625,\n 28.091366281406945\n ],\n [\n -95.965576171875,\n 28.565225490654658\n ],\n [\n -95.38330078125,\n 28.76765910569123\n ],\n [\n -95.108642578125,\n 29.084976575985912\n ],\n [\n -94.7021484375,\n 29.31514119318728\n ],\n [\n -93.98803710937499,\n 29.640320395351402\n ],\n [\n -93.812255859375,\n 29.640320395351402\n ],\n [\n -93.84521484375,\n 29.79298413547051\n ],\n [\n -93.702392578125,\n 29.964452850852005\n ],\n [\n -93.66943359374999,\n 30.07860131571654\n ],\n [\n -93.66943359374999,\n 30.287531589298727\n ],\n [\n -93.66943359374999,\n 30.552800413453546\n ],\n [\n -93.50463867187499,\n 30.911651004518244\n ],\n [\n -93.482666015625,\n 31.21280145833882\n ],\n [\n -93.69140625,\n 31.44741029142872\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":838857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scott, Annie 0000-0001-7286-3698 annescott@usgs.gov","orcid":"https://orcid.org/0000-0001-7286-3698","contributorId":223421,"corporation":false,"usgs":true,"family":"Scott","given":"Annie","email":"annescott@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":838858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stengel, Victoria G. 0000-0003-0481-3159 vstengel@usgs.gov","orcid":"https://orcid.org/0000-0003-0481-3159","contributorId":5932,"corporation":false,"usgs":true,"family":"Stengel","given":"Victoria","email":"vstengel@usgs.gov","middleInitial":"G.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838859,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teeple, Andrew 0000-0003-1781-8354 apteeple@usgs.gov","orcid":"https://orcid.org/0000-0003-1781-8354","contributorId":193061,"corporation":false,"usgs":true,"family":"Teeple","given":"Andrew","email":"apteeple@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838976,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230665,"text":"70230665 - 2022 - Local diversity in phenological responses of migratory lake sturgeon to warm winters","interactions":[],"lastModifiedDate":"2022-06-01T15:20:29.68924","indexId":"70230665","displayToPublicDate":"2022-03-28T09:12:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2939,"text":"Oikos","active":true,"publicationSubtype":{"id":10}},"title":"Local diversity in phenological responses of migratory lake sturgeon to warm winters","docAbstract":"Rich intraspecific diversity in traits that shape responses to environmental conditions implies that effects of climate change will differ within species or even populations. Nevertheless, few studies investigate how different groups within species respond to climatic fluctuations, and most risk assessments rely upon species-wide generalizations. We studied effects of among-year variation in air temperature on the spring migratory phenology of a metapopulation of lake sturgeon Acipenser fulvescens within waters connecting Lake Huron and Lake Erie of the Laurentian Great Lakes. Sturgeon here express multiple migratory phenotypes that all spawn in either the St. Clair River or Detroit River but differ in their use after spawning of more than 86 000 km2 of accessible lake and river habitat. Acoustic tracking over nine years (2012–2020) revealed mixed phenological responses to late-winter air temperatures, with three migratory groups arriving at rivers earlier during warm years and one whose arrival was consistent across years regardless of temperature. Notably, two groups that spawn in the same river but overwinter in different lakes entered the river with greater synchrony during warm years because one advanced its phenology while the other did not. The results indicated warm weather could alter the dynamics of the metapopulation and broader community, and exemplify the complexity hidden beneath broadscale generalizations of species' response to climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/oik.08977","usgsCitation":"Buchinger, T.J., Hondorp, D.W., and Krueger, C.C., 2022, Local diversity in phenological responses of migratory lake sturgeon to warm winters: Oikos, v. 2022, no. 6, e08977, 6 p., https://doi.org/10.1111/oik.08977.","productDescription":"e08977, 6 p.","ipdsId":"IP-137745","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":448353,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/oik.08977","text":"Publisher Index Page"},{"id":399398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Detroit River, Lake St. Clair, St. Clair River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.353271484375,\n 42.04113400940807\n ],\n [\n -82.2216796875,\n 42.04113400940807\n ],\n [\n -82.2216796875,\n 43.02472955416351\n ],\n [\n -83.353271484375,\n 43.02472955416351\n ],\n [\n -83.353271484375,\n 42.04113400940807\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2022","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Buchinger, Tyler J. 0000-0002-4590-341X","orcid":"https://orcid.org/0000-0002-4590-341X","contributorId":290501,"corporation":false,"usgs":false,"family":"Buchinger","given":"Tyler","email":"","middleInitial":"J.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":841114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":841115,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krueger, Charles C. 0000-0002-6735-5012","orcid":"https://orcid.org/0000-0002-6735-5012","contributorId":274493,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles","email":"","middleInitial":"C.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":841116,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230440,"text":"70230440 - 2022 - Sedimentary record of annual-decadal timescale reservoir dynamics: Anthropogenic stratigraphy of Lake Powell, Utah, U.S.A.","interactions":[],"lastModifiedDate":"2022-04-13T11:57:33.660983","indexId":"70230440","displayToPublicDate":"2022-03-28T06:54:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9134,"text":"The Sedimentary Record","active":true,"publicationSubtype":{"id":10}},"title":"Sedimentary record of annual-decadal timescale reservoir dynamics: Anthropogenic stratigraphy of Lake Powell, Utah, U.S.A.","docAbstract":"The tributaries of Lake Powell were impounded following construction of Glen Canyon Dam, resulting in deposition of reservoir sediment over a ∼650 km2 area since 1963. These units have been exposed through erosion as water storage in Lake Powell has decreased since 2000. This anthropogenic sedimentary record reflects the complex interplay among wet and dry periods of Colorado River runoff and the reservoir operating rules of Lake Powell. The relevant sedimentary exposures are mapped at reconnaissance level over 300 river-km above Glen Canyon Dam in canyons of the Colorado, San Juan, Escalante, and Dirty Devil Rivers. A detailed reference section measured in Calf Canyon, a tributary to the Colorado River, preserves more than 12 m of lacustrine, mainstem Colorado River, and local tributary sediment in an up-river location and elevation that is determined to have been inundated only during the highest reservoir level periods. At Calf Canyon, exposed reservoir sediment is comprised of cyclic sand-mud interbeds that record periods of deposition when reservoir level was at or above full pool. Six depositional cycles are identified in Calf Canyon, and each of these is interpreted to represent rapid sand deposition during Colorado River flood events (likely related to spring snowmelt runoff) followed by deposition of lacustrine mud while reservoir levels were high. The lacustrine mud units display significant pedogenic modification, indicating exposure and colonization dominated by tamarisk plants, prior to deposition of the next sand unit. High-precision elevation surveys of the 6 main lacustrine marker beds in Calf Canyon are correlated to multiple lake level highstands between 1975 and 2000. Preliminary observations suggest that age-equivalent strata are widespread within the reservoir-affected zones of all major tributaries including the Colorado and San Juan River arms as well as the Escalante and Dirty Devil Rivers. We predict that future map- ping in other Lake Powell side canyons will demonstrate strong local control on sediment provenance, dictated by side canyon lithology, as well as time-transgressive deposition (and erosion) moving up and down the main canyons.","language":"English","publisher":"Society for Sedimentary Geology","doi":"10.2110/sedred.2022.1.3","usgsCitation":"Johnson, C., Root, J.C., Hynek, S., and Schmidt, J., 2022, Sedimentary record of annual-decadal timescale reservoir dynamics: Anthropogenic stratigraphy of Lake Powell, Utah, U.S.A.: The Sedimentary Record, v. 20, no. 1, p. 15-29, https://doi.org/10.2110/sedred.2022.1.3.","productDescription":"15 p.","startPage":"15","endPage":"29","ipdsId":"IP-133199","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":448362,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2110/sedred.2022.1.3","text":"Publisher Index Page"},{"id":398629,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.73095703125,\n 37.01132594307015\n ],\n [\n -109.720458984375,\n 37.01132594307015\n ],\n [\n -109.720458984375,\n 38.16911413556086\n ],\n [\n -111.73095703125,\n 38.16911413556086\n ],\n [\n -111.73095703125,\n 37.01132594307015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Cari","contributorId":290196,"corporation":false,"usgs":false,"family":"Johnson","given":"Cari","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":840434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Root, Jonathan Casey 0000-0003-0537-4418","orcid":"https://orcid.org/0000-0003-0537-4418","contributorId":223107,"corporation":false,"usgs":true,"family":"Root","given":"Jonathan","email":"","middleInitial":"Casey","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":840435,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hynek, Scott 0000-0002-6885-0445","orcid":"https://orcid.org/0000-0002-6885-0445","contributorId":216634,"corporation":false,"usgs":true,"family":"Hynek","given":"Scott","email":"","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":840436,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, John (Jack) C.","contributorId":290197,"corporation":false,"usgs":false,"family":"Schmidt","given":"John (Jack) C.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":840437,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230415,"text":"70230415 - 2022 - Mapping aquifer salinity gradients and effects of oil field produced water disposal using geophysical logs: Elk Hills, Buena Vista and Coles Levee Oil Fields, San Joaquin Valley, California","interactions":[],"lastModifiedDate":"2022-04-12T11:46:20.251679","indexId":"70230415","displayToPublicDate":"2022-03-28T06:39:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Mapping aquifer salinity gradients and effects of oil field produced water disposal using geophysical logs: Elk Hills, Buena Vista and Coles Levee Oil Fields, San Joaquin Valley, California","docAbstract":"The effects of oil and gas production on adjacent groundwater quality are becoming a concern in many areas of the United States. As a result, it has become increasingly important to identify which aquifers require monitoring and protection. In this study, we map the extent of groundwater with less than 10,000 mg/L TDS both laterally and vertically near the Elk Hills, Buena Vista and Coles Levee Oil Fields in the San Joaquin Valley, California and note evidence of effects of produced water disposal on salinity within the Tulare aquifer. Subsurface maps showing the depth at which groundwater salinity is less than 10,000 mg/L (or Base 10K) in the Tulare aquifer are generated using geophysical logs and verified by comparison to water sample analyses. The depth to Base 10K ranges from 240 m (800 ft) in Elk Hills to 800 m (2650 ft) in the adjacent Buena Vista syncline and is 670 m (2,200 ft) deep in the Coles Levee area to the east. Log-calculated salinities show a relatively smooth increase with depth prior to disposal activities whereas salinities calculated from logs collected near and after disposal activities show a more variable salinity profile with depth. The effect of produced water injection is represented by log resistivity profiles that change from low resistivity at the base of the sand to higher resistivity near the top due to density differences between the saline produced water and the brackish groundwater within each sand. Continued post-disposal logging in new wells in the 18G disposal area on the south flank of Elk Hills shows that injected water has migrated approximately 1,200 m (4,000 ft) downdip (south) over a period of 20 years since the inception of disposal activity.
Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2590","usgsCitation":"Lofton, M., Brentrup, J.A., Beck, W.S., Zwart, J.A., Bhattacharya, R., Brighenti, L.S., Burnett, S.H., McCullough, I.M., Steele, B., Carey, C.C., Cottingham, K., Dietze, M., Ewing, H.A., Weathers, K.C., and LaDeau, S.L., 2022, Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density: Ecological Applications, v. 32, e2590, 24 p., https://doi.org/10.1002/eap.2590.","productDescription":"e2590, 24 p.","ipdsId":"IP-119852","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":448368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2590","text":"Publisher Index Page"},{"id":420889,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire","otherGeospatial":"Lake Sunapee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.10639790180213,\n 43.45916503807919\n ],\n [\n -72.10639790180213,\n 43.3077735355308\n ],\n [\n -72.01805843117764,\n 43.3077735355308\n ],\n [\n -72.01805843117764,\n 43.45916503807919\n ],\n [\n -72.10639790180213,\n 43.45916503807919\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","noUsgsAuthors":false,"publicationDate":"2022-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Lofton, Mary","contributorId":329783,"corporation":false,"usgs":false,"family":"Lofton","given":"Mary","email":"","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":883298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brentrup, Jennifer A.","contributorId":194457,"corporation":false,"usgs":false,"family":"Brentrup","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beck, Whitney S.","contributorId":268335,"corporation":false,"usgs":false,"family":"Beck","given":"Whitney","email":"","middleInitial":"S.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":883300,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":883301,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bhattacharya, Ruchi","contributorId":297412,"corporation":false,"usgs":false,"family":"Bhattacharya","given":"Ruchi","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":883302,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brighenti, Ludmila S","contributorId":317713,"corporation":false,"usgs":false,"family":"Brighenti","given":"Ludmila","email":"","middleInitial":"S","affiliations":[{"id":69135,"text":"Universidade do Estado de Minas Gerais","active":true,"usgs":false}],"preferred":false,"id":883303,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burnett, Sarah H.","contributorId":288140,"corporation":false,"usgs":false,"family":"Burnett","given":"Sarah","email":"","middleInitial":"H.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":883304,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCullough, Ian M.","contributorId":149952,"corporation":false,"usgs":false,"family":"McCullough","given":"Ian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":883305,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Steele, Bethel 0000-0003-4365-4103","orcid":"https://orcid.org/0000-0003-4365-4103","contributorId":329785,"corporation":false,"usgs":false,"family":"Steele","given":"Bethel","email":"","affiliations":[{"id":36248,"text":"Cary Institute of Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":883306,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Carey, Cayelan C.","contributorId":130969,"corporation":false,"usgs":false,"family":"Carey","given":"Cayelan","email":"","middleInitial":"C.","affiliations":[{"id":7185,"text":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":883307,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cottingham, Kathryn L","contributorId":329786,"corporation":false,"usgs":false,"family":"Cottingham","given":"Kathryn L","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":883308,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Dietze, Michael","contributorId":248349,"corporation":false,"usgs":false,"family":"Dietze","given":"Michael","affiliations":[],"preferred":false,"id":883309,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ewing, Holly A.","contributorId":191962,"corporation":false,"usgs":false,"family":"Ewing","given":"Holly","email":"","middleInitial":"A.","affiliations":[{"id":33413,"text":"Bates College","active":true,"usgs":false}],"preferred":false,"id":883310,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Weathers, Kathleen C.","contributorId":202417,"corporation":false,"usgs":false,"family":"Weathers","given":"Kathleen","email":"","middleInitial":"C.","affiliations":[{"id":36424,"text":"Cary Institute of Ecosystems Studies","active":true,"usgs":false}],"preferred":false,"id":883311,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"LaDeau, Shannon L.","contributorId":172640,"corporation":false,"usgs":false,"family":"LaDeau","given":"Shannon","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":883312,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70230098,"text":"70230098 - 2022 - Arsenic in private well water and birth outcomes in the United States","interactions":[],"lastModifiedDate":"2022-03-29T11:50:35.732821","indexId":"70230098","displayToPublicDate":"2022-03-26T06:46:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1523,"text":"Environment International","active":true,"publicationSubtype":{"id":10}},"title":"Arsenic in private well water and birth outcomes in the United States","docAbstract":"Prenatal exposure to drinking water with arsenic concentrations >50 μg/L is associated with adverse birth outcomes, with inconclusive evidence for concentrations ≤50 μg/L. In a collaborative effort by public health experts, hydrologists, and geologists, we used published machine learning model estimates to characterize arsenic concentrations in private wells—federally unregulated for drinking water contaminants—and evaluated associations with birth outcomes throughout the conterminous U.S.
Using several machine learning models, including boosted regression trees (BRT) and random forest classification (RFC), developed from measured groundwater arsenic concentrations of ∼20,000 private wells, we characterized the probability that arsenic concentrations occurred within specific ranges in groundwater. Probabilistic model estimates and private well usage data were linked by county to all live birth certificates from 2016 (n = 3.6 million). We evaluated associations with gestational age and term birth weight using mixed-effects models, adjusted for potential confounders and incorporated random intercepts for spatial clustering.
We generally observed inverse associations with term birth weight. For instance, when using BRT estimates, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 5 μg/L was associated with a −1.83 g (95% CI: −3.30, −0.38) lower term birth weight after adjusting for covariates. Similarly, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 10 μg/L was associated with a −2.79 g (95% CI: −4.99, −0.58) lower term birth weight. Associations with gestational age were null.
In this largest epidemiologic study of arsenic and birth outcomes to date, we did not observe associations of modeled arsenic estimates in private wells with gestational age and found modest inverse associations with term birth weight. Study limitations may have obscured true associations, including measurement error stemming from a lack of individual-level information on primary water sources, water arsenic concentrations, and water consumption patterns.
Sediment deposition on floodplains is essential for the development and maintenance of riparian ecosystems. Upstream erosion is known to influence downstream floodplain construction, but linking these disparate processes is challenging, especially over large spatial and temporal scales. Sediment fingerprinting is thus a robust tool to establish process linkages between downstream floodplain development and sediment production in distal headwater basins. Here we use sediment geochemistry to connect historical erosion in several tributaries of the Yampa River in Colorado and Wyoming, USA, to the construction of downstream floodplains on which extensive cottonwood forests established. Using a combination of conventional techniques and the relatively novel machine-learning random forest algorithm, we build multiple fingerprints of diagnostic geochemical tracers that are then input into a Bayesian mixing model to apportion provenance of floodplain sediment. Sediment samples for provenance analysis were collected from an excavated floodplain in Deerlodge Park on the Yampa River at the rooting surface of the surrounding cottonwood forest and dominantly comprised of very fine (4Φ) sand. Fingerprinting analysis of the 4Φ fraction of collected floodplain sink (n = 38) and tributary source (n = 218) samples revealed floodplain sediment to be dominantly sourced from the tributaries of Muddy Creek (45 ± 4%) and Sand Wash (42 ± 6%). Dendrochronology results moreover indicate the Deerlodge floodplain sediment was deposited in ∼1912, which falls squarely within the time (1880–1940) these tributaries were actively eroding. Taken together, study results indicate a demonstrable link between historical tributary erosion and downstream floodplain construction and concomitant forest establishment. Our findings suggest processes operating in tributary watersheds play an important role in the dynamics of large rivers and emphasize both the need for holistic, collaborative management of sediment as an essential resource and the potential to utilize sediment fingerprinting to inform and direct river ecosystem management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.catena.2022.106231","usgsCitation":"Kemper, J.T., Rathburn, S.L., Friedman, J.M., Nelson, J.M., Mueller, E., and Vincent, K.R., 2022, Fingerprinting historical tributary contributions to floodplain sediment using bulk geochemistry: Catena, v. 214, 106231, 16 p., https://doi.org/10.1016/j.catena.2022.106231.","productDescription":"106231, 16 p.","ipdsId":"IP-136718","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":397975,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Wyoming","otherGeospatial":"Little Snake River, Yampa River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.49822998046875,\n 40.42813291388417\n ],\n [\n -108.36639404296875,\n 40.42813291388417\n ],\n [\n -108.36639404296875,\n 40.48873742102282\n ],\n [\n -108.49822998046875,\n 40.48873742102282\n ],\n [\n -108.49822998046875,\n 40.42813291388417\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"214","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kemper, John T.","contributorId":270040,"corporation":false,"usgs":false,"family":"Kemper","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":839361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rathburn, Sara L.","contributorId":140606,"corporation":false,"usgs":false,"family":"Rathburn","given":"Sara","email":"","middleInitial":"L.","affiliations":[{"id":13539,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":839362,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":44495,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":839363,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, John M.","contributorId":83578,"corporation":false,"usgs":true,"family":"Nelson","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":839364,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mueller, Erich R. 0000-0001-8202-154X","orcid":"https://orcid.org/0000-0001-8202-154X","contributorId":207750,"corporation":false,"usgs":false,"family":"Mueller","given":"Erich R.","affiliations":[{"id":37626,"text":"Department of Geography, University of Wyoming, Laramie, WY, USA","active":true,"usgs":false}],"preferred":false,"id":839365,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vincent, Kirk R","contributorId":289578,"corporation":false,"usgs":false,"family":"Vincent","given":"Kirk","email":"","middleInitial":"R","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":839366,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230060,"text":"70230060 - 2022 - Murky waters: Divergent ways scientists, practitioners, and landowners evaluate beaver mimicry","interactions":[],"lastModifiedDate":"2022-03-28T11:31:27.159926","indexId":"70230060","displayToPublicDate":"2022-03-25T06:26:24","publicationYear":"2022","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":"Murky waters: Divergent ways scientists, practitioners, and landowners evaluate beaver mimicry","docAbstract":"From its rolling pastures to its forested Appalachian peaks, Kentucky’s scenery offers beauty along with contrast. Rivers, including the Mississippi and the Ohio, border much of the State, and more rivers and hundreds of lakes are inside its borders. Kentucky is also home to the world’s longest known cave system, Mammoth Cave National Park, and its residents maintain long-held traditions of coal mining, farming, horse racing, and bourbon making.
Although residents and visitors have a lot to explore up close, viewing Kentucky through a long lens—one extending into space—can offer even more information about its environmental and natural resources. Landsat satellite data can help State and Federal governments monitor the quality and health of Kentucky’s lands and waters.
Here is a closer look at some of the many ways that Landsat benefits Kentucky.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223017","usgsCitation":"U.S. Geological Survey, 2022, Kentucky and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2022–3017, 2 p., https://doi.org/10.3133/fs20223017.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-127727","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412029,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223017/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":411899,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3017/images"},{"id":411898,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3017/fs20223017.XML"},{"id":411897,"rank":3,"type":{"id":25,"text":"Version 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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Massachusetts is the seventh smallest U.S. State in land area, but its size is surpassed by its contributions to U.S. history and the economy, its academic and medical expertise, and its natural features. The Atlantic Ocean to the east gives the “Bay State” more than 1,500 miles of coastline that were important in past fishing and maritime trade industries and in the tourism industry of today for destinations such as Boston, Cape Cod, Nantucket, and Martha’s Vineyard. Forests cover roughly 60 percent of the State, which owns 315,000 acres of forests in parklands, reserves, and woodlands.
Massachusetts celebrates many strengths, but its strengths can be vulnerable to environmental change. A robust population of 7 million translates to a density of more than 800 people per square mile; in Boston, the density rises to more than 18,000 people per square mile. Urban temperature hotspots can increase health risks to residents.
Although the whims of wind and water have long reshaped coastlines, climate change-induced sea-level rise and severe storms can amplify coastal effects. In forests, changes in temperature or precipitation can provide more favorable conditions for invasive species.
State and local governments have been taking steps to address climate change. Here are several ways Landsat has benefited the residents of Massachusetts.
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\"}}]}","edition":"Version 1.0: March 24, 2022; Version 1.1: January 23, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Fluoride is a naturally occurring element in groundwater that supports bone and dental health at low concentrations but can cause health problems at elevated concentrations in drinking water. This study investigates spatial and temporal trends for fluoride concentrations in untreated groundwater from over 20,000 public supply wells in California. The presence of a significant temporal trend in fluoride concentrations in a well was assessed using the Mann-Kendall test and a spatial-weighting approach was used to identify the areal extent of the groundwater resources with significant trends. Less than 2% of the groundwater resources used for public supply in the state have concentrations above the California maximum contaminant level of 2 mg/L. Approximately 14% of the groundwater resource used for public supply show a significant recent trend (2000–2019), with decreasing trends occurring in 10% of the resource. Potential drivers for trends were evaluated in two of the areas in southern California with the greatest prevalence of trends but distinct climate and hydrogeological characteristics. Aquifers in the Mojave and Whitewater River watersheds, located in the desert region, and the Los Angeles Basin, located on the coast, have been replenished with imported and treated water over the last half century to maintain sustainable groundwater levels. The recharge of imported water with different chemistries has altered the geochemical conditions in the aquifers, driving changes in fluoride concentrations.
Despite several secretive marsh bird (SMB) species being listed as critically imperiled throughout the mid-continent of North America, limited information on SMB distribution and habitat use within primary migratory corridors results in uncertainty on contributions of wetlands in mid-latitude states toward their annual cycle needs. Our objectives were to quantify temporal patterns of SMB wetland occupancy during spring migration at a mid-latitude state and evaluate the relationships between SMB colonization probability and water-level management practices, and the resulting habitat conditions during spring migration. We conducted a 2-year, dynamic occupancy study (2013–2014) that included 6 rounds of repeated call-back surveys to detect the presence of 5 SMB species (i.e., Virginia rail [Rallus limicola], sora [Porzana carolina], king rail [R. elegans], least bittern [Ixobrychus exilis], and American bittern [Botaurus lentiginosus]) during spring (Apr–Jun) on 107 wetlands across 8 conservation areas and 4 national wildlife refuges throughout Missouri, USA. We detected sora most frequently, followed by least bittern, American bittern, Virginia rail, and king rail. Coefficient estimates indicated colonization probability for all species was positively associated with emergent vegetation cover and negatively associated with amount of open water. Open water was the only variable in the best supported model explaining American bittern site colonization, to which they were negatively associated. Virginia rail colonization had a strong positive association with vegetation height, whereas least bittern and sora site colonization were influenced positively by water depth and agriculture, respectively. Based on the habitat associations within and among SMB species identified in this study, wetland managers can tailor management strategies to optimize spring migration habitat for single- or multi-species objectives.
Vance Creek drains a 24 square mile area of the Olympic Mountains in western Washington. The lower 4 miles of the creek often go dry in discontinuous patches during the summer, limiting salmon rearing success. To better understand sediment transport dynamics in the creek and aid in potential restoration design, bedload and suspended-sediment concentration samples were collected for water years 2019–20 at a site about 2 miles upstream from the creek’s confluence with the South Fork Skokomish River.
Fifty bedload samples and 7 suspended-sediment concentration samples were collected over 7 sampling days. These samples were used to develop rating curves relating bedload flux or suspended-sediment concentration to discharge. Mean annual bedload flux was estimated to be 12,200 ± 2,300 tons per year for water years 1930–2020 period of record, based on application of the derived bedload rating curve to an extrapolated daily discharge record. The mean annual suspended-sediment load over the same period was estimated to be 9,000 tons per year with large, but unquantified, uncertainty. Bedload material was predominantly gravel from 0.08 to 2.5 inches (2 to 64 millimeters) in diameter. At the highest sampled discharges, approximately equivalent to a 50 percent annual exceedance probability (2-year peak-flow event), the bedload grain-size distribution was similar to that of the local channel bed. Bedload grain-size distributions generally coarsened as discharge increased. The suspended-sediment load was consistently one-half sand and one-half silt and clay, regardless of discharge. Bedload constituted about 60 percent of the total sediment flux (bedload plus suspended load). This is near the upper limit of values observed in a global compilation of long-term load partitioning data.
Sediment transport at the Vance Creek sampling site was compared with sediment-transport data from five other watersheds in the region. To facilitate comparisons, mean annual loads were divided by mean annual runoff volume to obtain an effective average sediment concentration. This normalization accounts for differences in both drainage area and mean runoff depth between the comparison watersheds. At the three comparison watershed sites with relatively complete sediment-transport data, mean bedload concentrations ranged from 44 to 109 milligrams per liter (mg/L) and mean suspended-sediment concentrations ranged from 139 to 374 mg/L; bedload constituted 21 to 29 percent of the total sediment load. The mean bedload concentration at the Vance Creek sampling site (69 mg/L) fell in the middle of the range observed in comparison watersheds, whereas the mean suspended-sediment concentration (50 mg/L) was markedly lower. Bedload samples at the Vance Creek sampling site also were generally less sand rich (sample-average sand fraction was 13 percent at Vance Creek versus 20 to 37 percent for comparison waters). Bedload transport rates at the Vance Creek sampling site appear relatively average for the region, given the drainage basin area and average runoff. In contrast, the supply and transport of finer material, both in the suspended load and the sand fraction of the bedload, are relatively low.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225019","collaboration":"Prepared in cooperation with the Mason Conservation District","usgsCitation":"Anderson, S.W., 2022, Bedload and suspended-sediment transport in lower Vance Creek, western Washington, water\nyears 2019–20: U.S. Geological Survey Scientific Investigations Report 2022–5019, 25 p., https://doi.org/10.3133/sir20225019.","productDescription":"vii, 25 p.","onlineOnly":"Y","ipdsId":"IP-119859","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":397071,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5019/sir20225019.XML"},{"id":397069,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5019/sir20225019.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5019"},{"id":397068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5019/coverthb.jpg"},{"id":397070,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5019/images"}],"country":"United States","state":"Washington","otherGeospatial":"Vance Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.2889,\n 47.3208\n ],\n [\n -123.2833,\n 47.3208\n ],\n [\n -123.2833,\n 47.325\n ],\n [\n -123.2889,\n 47.325\n ],\n [\n -123.2889,\n 47.3208\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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Understanding dispersion in rivers is critical for numerous applications, such as characterizing larval drift for endangered fish species and responding to spills of hazardous materials. Injecting a visible dye into the river can yield insight on dispersion processes, but conventional field instrumentation yields limited data on variations in dye concentration over time at a few, fixed points. Remote sensing can provide more detailed, spatially distributed information on the dye's motion, but this approach has only been tested in clear-flowing streams. The purpose of this study was to assess the potential of remote sensing to facilitate tracer studies in more turbid rivers. To pursue this objective, we injected Rhodamine WT dye into the Missouri River and collected field spectra from a boat, videos from a small unoccupied aircraft system (sUAS), and orthophotos from an airplane. Applying an optimal band ratio analysis (OBRA) algorithm to the field spectra revealed strong correlations (R2 = 0.936) between a spectrally based quantity and in situ concentration measurements. OBRA also performed well for broadband RGB (red, green, blue) images extracted from the sUAS-based videos; the resulting concentration maps were used to produce animations that captured movement of the dye pulse. Spectral mixture analysis of repeat orthophoto coverage yielded relative concentration estimates that provided a synoptic perspective on dispersion of the dye throughout the entire 13.8 km reach over the full 2.5-hr duration of the experiment. The results of this study demonstrate the potential to remotely sense tracer dye concentrations in large, highly turbid rivers.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR031396","usgsCitation":"Legleiter, C.J., Sansom, B.J., and Jacobson, R., 2022, Remote sensing of visible dye concentrations during a tracer experiment on a large, turbid river: Water Resources Research, v. 58, no. 4, e2021WR031396, 23 p., https://doi.org/10.1029/2021WR031396.","productDescription":"e2021WR031396, 23 p.","ipdsId":"IP-133418","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":448396,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr031396","text":"Publisher Index Page"},{"id":435912,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JDISO3","text":"USGS data release","linkHelpText":"Remotely sensed data and field measurements for mapping visible dye concentrations during a tracer experiment on the Missouri River near Columbia, MO, May 5, 2021"},{"id":398020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"Columbia","otherGeospatial":"Missouri River, Searcy's Bend","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.50162124633789,\n 38.856552783257754\n ],\n [\n -92.45372772216797,\n 38.856552783257754\n ],\n [\n -92.45372772216797,\n 38.91467806459576\n ],\n [\n -92.50162124633789,\n 38.91467806459576\n ],\n [\n -92.50162124633789,\n 38.856552783257754\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":839523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sansom, Brandon James 0000-0001-7999-9547","orcid":"https://orcid.org/0000-0001-7999-9547","contributorId":289636,"corporation":false,"usgs":true,"family":"Sansom","given":"Brandon","email":"","middleInitial":"James","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":839524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jacobson, R. 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Large herbivores are distributed differently on the landscape and therefore have different vulnerabilities as prey for lions. For instance, blue wildebeest (Connochaetes taurinus) occupy small grazing lawns at night but are difficult for lions to capture because open areas lack cover for stalking. African buffalo (Syncerus caffer) aggregate in large herds but are less available because these herds only intermittently enter the home ranges of individual lion prides. Unlike large herds of wildebeest and buffalo, plains zebra (Equus quagga) move widely in small herds while browsing greater kudus (Tragelaphus strepsiceros) and giraffes (Giraffa camelopardalis giraffa) generally occur in lower densities. We used spatial data derived from GPS collars to investigate several hypotheses regarding the movements of three lion prides in response to their prey. We found that lions were most active and moved longer distances during nighttime than during daytime. Lions remained within their core home ranges on 87% of nights and wandered to the outlying areas of the home ranges every second night. Lions visited grazing lawns, that is, area of short grass, where wildebeest herds resided every second night, and moved toward the direction of buffalo herds within 2 km of vicinity. Lions spent more time near riverbanks that provided dense woody cover at night than expected but concentrated only weakly near sites with surface water where herbivores drank in the dry season. Our study contributes to understanding how lions vary their movements in response to the spatial and temporal heterogeneity in the relative availability and vulnerability of multiple prey species.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyac025","usgsCitation":"Yiu, S., Owen-Smith, N., and Cain, J.W., 2022, How lions move at night when they hunt?: Journal of Mammalogy, v. 103, no. 4, p. 855-864, https://doi.org/10.1093/jmammal/gyac025.","productDescription":"10 p.","startPage":"855","endPage":"864","ipdsId":"IP-109457","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"South Africa","otherGeospatial":"Kruger National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 30.72137474888291,\n -22.35002959432397\n ],\n [\n 31.220577742146247,\n -25.52321749917516\n ],\n [\n 32.13088908280099,\n -25.529841746852213\n ],\n [\n 31.940017350082087,\n -23.896450066908073\n ],\n [\n 31.323354828993274,\n -22.370399291095367\n ],\n [\n 30.72137474888291,\n -22.35002959432397\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"103","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Yiu, Sze-Wing","contributorId":355799,"corporation":false,"usgs":false,"family":"Yiu","given":"Sze-Wing","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":938170,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Owen-Smith, Norman","contributorId":355800,"corporation":false,"usgs":false,"family":"Owen-Smith","given":"Norman","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":938171,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938169,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239153,"text":"70239153 - 2022 - Long-term hydrologic sustainability of calcareous fens along the Glacial Lake Agassiz beach ridges, northwestern Minnesota, USA","interactions":[],"lastModifiedDate":"2022-12-30T13:36:42.11684","indexId":"70239153","displayToPublicDate":"2022-03-22T07:25:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Long-term hydrologic sustainability of calcareous fens along the Glacial Lake Agassiz beach ridges, northwestern Minnesota, USA","docAbstract":"Calcareous fens are peat-accumulating wetlands fed by calcium-rich groundwater that support several threatened species of plants that thrive in these geochemical conditions. This investigation characterized the hydrology of two calcareous fens in the Glacial Lake Agassiz beach ridge complex in northwestern Minnesota, USA. Sandy surficial beach ridge aquifers and underlying buried glacial aquifers were considered as sources of groundwater to the fen. A combination of the two sources influenced by seasonal hydrology was also considered. Synchronous hydrologic responses to rainfall events and hydraulic gradients indicate the calcareous fens are well-connected to the beach-ridge aquifers. Chemistry of water discharging to the fens is calcium-magnesium-bicarbonate type similar to the beach ridge aquifers, and distinct from buried aquifers that have significant sodium and chloride. High tritium values and oxygen isotope signatures similar to the beach ridge aquifers characterized fen water. Beach ridge aquifer complexes are relatively thin (8–10 m) and overlie thick clay/clay loam till. These beach ridges exhibit high seasonal recharge and have permanent saturated zones, providing a continual source of calcium-rich water for the fens. Electrical resistivity profiles characterized the glacial stratigraphy and highlighted the well-developed physical connection between beach ridge aquifers and calcareous fens. The results of this study allow evaluation of the potential impacts of irrigation and aggregate quarrying on calcareous fens along sand and gravel beach ridges.
Groundwater withdrawals provide most public-water supplies and all private-domestic users in Fauquier County, Virginia, a fast-growing rural area southwest of Washington, D.C. Groundwater levels were measured in 129 wells during a county-wide synoptic survey from October 29 through November 2, 2018. Field measurements, combined with datapoints from the National Hydrography Dataset, were used to develop a county-wide groundwater-level contour map. Groundwater levels and withdrawals during the synoptic survey were near or slightly above long-term medians. Error analysis indicated that the estimated groundwater-level contours generally were lower than observed measurements, with a root-mean-squared error of 33.52 feet. Groundwater levels in Fauquier County are controlled largely by topography: low levels in the crystalline Blue Ridge aquifers in the northwestern part of the county contrast markedly with higher levels in the sedimentary Mesozoic Basin aquifers in the southeast. At current levels of groundwater withdrawal, and at the scale and scope of the synoptic survey, no cones of depression in the groundwater surface were detected. The Fauquier County groundwater-level contour map is available as a U.S. Geological Survey data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225014","collaboration":"Prepared in cooperation with the Fauquier County Board of Supervisors and the Virginia Department of Environmental Quality","usgsCitation":"Kearns, M.R., and McCoy, K.J., 2022, Groundwater-level contour map of Fauquier County, Virginia, October–November 2018: U.S. Geological Survey Scientific Investigations Report 2022–5014, 17 p., https://doi.org/10.3133/sir20225014.","productDescription":"Report: vi, 17 p.; Data release","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-131366","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":397299,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JM7GYZ","text":"USGS data release","linkHelpText":"Raster and vector geospatial data of interpolated groundwater level altitude associated with a groundwater-level map of Fauquier County, Virginia, October - 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U.S. Geological Survey
730 East Parham Road
Richmond, Virginia 23228
Lake trout (Salvelinus namaycush), collected from 13 remote lakes located in southwestern Alaska, were analyzed for carbon, nitrogen, and mercury (Hg) stable isotope values to assess the importance of migrating oceanic salmon, volcanic activity, and atmospheric deposition to fish Hg burden. Methylmercury (MeHg) bioaccumulation in phytoplankton (5.0–6.9 kg L–1) was also measured to quantify the basal uptake of MeHg to these aquatic food webs. Hg isotope values in lake trout revealed that while the extent of precipitation-delivered Hg was similar across the entire study area, volcanic Hg is likely an important additional source to lake trout in proximate lakes. In contrast, migratory salmon (Oncorhynchus nerka) deliver little MeHg to lake trout directly, although indirect delivery processes via decay could exist. A high level of variability in carbon, nitrogen, and Hg isotope values indicates niche partitioning in lake trout populations within each lake and that a complex suite of ecological interactions is occurring, complicating the conceptually linear assessment of the contaminant source to the receiving organism. Without connecting energy and contaminant isotope axes, we would not have understood why lake trout from these pristine lakes have highly variable Hg burdens despite consistently low water Hg and comparable age-length dynamics.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.estlett.2c00096","usgsCitation":"Lepak, R., Ogorek, J.M., Bartz, K.K., Janssen, S., Tate, M., Runsheng, Y., Hurley, J., Young, D.B., Eagles-Smith, C., and Krabbenhoft, D.P., 2022, Using carbon, nitrogen, and mercury isotope values to distinguish mercury sources to Alaskan lake trout: Environmental Science and Technology Letters, v. 9, no. 4, p. 312-319, https://doi.org/10.1021/acs.estlett.2c00096.","productDescription":"8 p.","startPage":"312","endPage":"319","ipdsId":"IP-132699","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":448420,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9171711","text":"External Repository"},{"id":435917,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UEP9C5","text":"USGS data release","linkHelpText":"Assessment of mercury sources in Alaskan lake food webs (version 1.1, September 2023)"},{"id":397405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Katmai National Park and preserve, Lake Clark Nation Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.325439453125,\n 60.31606836555203\n ],\n [\n -152.138671875,\n 60.8663124746226\n ],\n [\n -152.75390624999997,\n 60.94644199944748\n ],\n [\n -152.874755859375,\n 61.554109444927185\n ],\n [\n -153.643798828125,\n 61.543641475549954\n ],\n [\n -154.017333984375,\n 61.26495144723964\n ],\n [\n -154.368896484375,\n 61.079544234557304\n ],\n [\n -154.53369140625,\n 61.01040072727077\n ],\n [\n -154.44580078125,\n 60.71619779357714\n ],\n [\n 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58.78528524510292\n ],\n [\n -156.55517578125,\n 58.57398108438837\n ],\n [\n -156.412353515625,\n 58.56252272853734\n ],\n [\n -156.236572265625,\n 58.23438030939462\n ],\n [\n -155.7421875,\n 57.97315745102812\n ],\n [\n -155.0830078125,\n 57.80965135970151\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Lepak, Ryan F. 0000-0003-2806-1895","orcid":"https://orcid.org/0000-0003-2806-1895","contributorId":210990,"corporation":false,"usgs":false,"family":"Lepak","given":"Ryan F.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":838498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ogorek, Jacob M. 0000-0002-6327-0740 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A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":838506,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838507,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70229832,"text":"ofr20221010 - 2022 - Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019","interactions":[],"lastModifiedDate":"2026-03-27T19:46:42.747184","indexId":"ofr20221010","displayToPublicDate":"2022-03-21T10:33:31","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1010","displayTitle":"Documentation of Models Describing Relations Between Continuous Real-Time and Discrete Water-Quality Constituents in the Little Arkansas River, South-Central Kansas, 1998–2019","title":"Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019","docAbstract":"Data were collected at two monitoring sites along the Little Arkansas River in south-central Kansas that bracket most of the easternmost part of the Equus Beds aquifer. The data were used as part of the city of Wichita’s aquifer storage and recovery project to evaluate source water quality. The U.S. Geological Survey, in cooperation with the City of Wichita, has continued to monitor the water quality of these sites through 2019 to update previously published regression-based models using continuously measured physicochemical properties and discretely sampled water-quality constituents of interest. The purpose of this report is to provide an update of the previously published linear regression models that have been used to continuously compute estimates of water-quality constituent concentrations or densities at these two sites. Water-quality constituent model updates include those for dissolved and suspended solids, suspended-sediment concentration, hardness, alkalinity, primary ions (bicarbonate, calcium, sodium, chloride, and sulfate), nutrients (total Kjeldahl nitrogen and total phosphorus), total organic carbon, indicator bacteria (Escherichia coli and fecal coliform bacteria), a trace element (arsenic), and a pesticide (atrazine).
Regression analyses were used to develop surrogate models that related continuously measured physicochemical properties, streamflow, and seasonal components to discretely sampled water-quality constituent concentrations or densities. Specific conductance was an explanatory variable for dissolved solids, primary ions, and atrazine. Turbidity was an explanatory variable for total suspended solids and sediment, nutrients, total organic carbon, and indicator bacteria. Streamflow and water temperature were explanatory variables for dissolved arsenic. Seasonal components were included as explanatory variables for atrazine models. The amount of variance explained by most of the updated models was within 5 percent of previously published models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221010","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., and Klager, B.J., 2022, Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019: U.S. Geological Survey Open-File Report 2022–1010, 34 p., https://doi.org/10.3133/ofr20221010.","productDescription":"Report: vii, 34 p.; 2 Appendixes; Dataset","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-126572","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":397345,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221010/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397333,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397331,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010_appendix1.zip","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Model Archive Summaries for the Little Arkansas River at Highway 50 near Halstead, Kansas (Halstead Site; U.S. Geological Survey Station Number 07143672)"},{"id":501756,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112715.htm","linkFileType":{"id":5,"text":"html"}},{"id":397330,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1010/images"},{"id":397332,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010_appendix2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Model Archive Summaries for the Little Arkansas River near Sedgwick, Kansas (Sedgwick Site; U.S. Geological Survey Station Number 07144100)"},{"id":397329,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010.XML"},{"id":397328,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010.pdf","text":"Report","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1010"},{"id":397327,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1010/coverthb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.1667,\n 37.714244967649265\n ],\n [\n -97.1667,\n 37.714244967649265\n ],\n [\n -97.1667,\n 38.533333\n ],\n [\n -98.1667,\n 38.533333\n ],\n [\n -98.1667,\n 37.714244967649265\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Lake Powell is the second largest constructed water reservoir by storage capacity in the United States and represents a critical component in management of water resources in the Colorado River Basin. The reservoir provides hydroelectric power generation at Glen Canyon Dam, banks water storage for the Upper Colorado River Basin, stabilizes water commitments downstream, and buffers the Lower Colorado River Basin, including Lake Mead, against sedimentation and fluctuations in hydrological conditions. With completion of the dam in 1963, Lake Powell steadily filled with water before reaching full pool in 1980 and has become a popular destination for recreation, welcoming more than 4 million visitors per year. Since the early 2000s, severe drought and increases in water demand have resulted in a significant drop in reservoir elevation and stored water, prompting a heightened level of interest in the current state and future of Lake Powell.
Beginning in 2017, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, completed topobathymetric surveys of Lake Powell for the first update of elevation-area-capacity relationships since 1986. This report presents results of these surveys and comparisons with estimates from previous surveys. The storage volume and surface area, as of completion of the topobathymetric survey in spring 2018, are calculated at 0.33-foot (0.10-meter) increments for elevations ranging from 3,120.08 to 3,717.19 feet above the North American Vertical Datum of 1988 (NAVD 88). Between 0.33-foot increments, the storage volumes and areas were linearly interpolated at 0.01-foot intervals. Interpolation error in the 0.01-foot interval estimates was assessed at lower (3,160.00–3,161.00 feet above NAVD 88), middle (3,400.00–3,401.00 feet above NAVD 88), and upper (3,700.00–3,711.00 feet above NAVD 88) elevations. The interpolated storage capacity and area estimates are comparable to the measured values with differences ranging from 0.00 to 0.02 percent and from −0.01 to 0.03 percent, respectively.
Current storage capacity at full pool (3702.91 feet above NAVD 88) is 25,160,000 acre-feet. Compared to previously published estimates, this volume represents a 6.79 percent or 1,833,000-acre-foot decrease in storage capacity from 1963 to 2018 and a 4.00 percent or 1,049,000-acre-foot decrease from 1986 to 2018. Areal extent, as of spring 2018, at full pool is 159,200 acres, which represents a 1.33-percent decrease from 1963 to 2018 and a 0.96 percent decrease from 1986 to 2018.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225017","collaboration":"Prepared in cooperation with the Bureau of Reclamation","programNote":"Water Resources Mission Area","usgsCitation":"Root, J.C., and Jones, D.K., 2022, Elevation-area-capacity relationships of Lake Powell in 2018 and estimated loss of storage capacity since 1963: U.S. Geological Survey Scientific Investigations Report 2022–5017, 21 p., https://doi.org/10.3133/sir20225017.","productDescription":"Report: vii, 21 p.; 2 data releases","numberOfPages":"21","onlineOnly":"Y","ipdsId":"IP-120332","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":396681,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O3IPG3","text":"Elevation-area-capacity tables for Lake Powell, 2018","description":"Jones, D.K., and Root, J.C, 2022, Elevation-area-capacity tables for Lake Powell, 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9O3IPG3."},{"id":396680,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H60YCF","text":"Modified topobathymetric elevation data for Lake Powell","description":"Jones, D.K., and Root, J.C., 2021, Modified topobathymetric elevation data for Lake Powell: U.S. Geological Survey data release, https://doi.org/10.5066/P9H60YCF."},{"id":396679,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5017/images"},{"id":396678,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5017/sir20225017.xml"},{"id":396677,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5017/sir20225017.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":396676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5017/coverthb.jpg"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Colorado River, Glen Canyon Dam, Glen Canyon National Recreation Area, Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.06054687499999,\n 36.39033486213649\n ],\n [\n -109.566650390625,\n 36.39033486213649\n ],\n [\n -109.566650390625,\n 38.49229419236133\n ],\n [\n -112.06054687499999,\n 38.49229419236133\n ],\n [\n -112.06054687499999,\n 36.39033486213649\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
The U.S. Geological Survey (USGS), in cooperation with the Texas Water Development Board, collected streamflow and water-quality data at USGS monitoring stations in the lower Trinity River Basin from January 2016 to December 2019 to characterize streamflow, nutrients, and suspended sediment entering Galveston Bay from the Trinity River. Results from previous studies indicate that water from the main channel of the Trinity River is diverted into surrounding wetlands and water bodies and is stored or discharged directly into Galveston Bay through distributary channels in the delta. This study provides an assessment of the distribution of streamflow in the various channels that form the delta of the Trinity River to evaluate the effects of streamflow diversions on the eventual supply of freshwater, nutrients, and suspended sediment to Galveston Bay.
Instantaneous streamflow data and continuous streamflow records from USGS monitoring stations in the delta of the Trinity River were used to quantify freshwater inflow into Galveston Bay and assess the distribution of streamflow in the lowermost reaches of the Trinity River Basin. In this report, periods in which releases from Lake Livingston caused a rise in streamflow farther downstream at USGS station 08067000 Trinity River at Liberty, Tex. (hereinafter referred to as the “Liberty site”) that did not exceed 20,000 cubic feet per second (ft3/s) are referred to as “low-flow events,” and periods in which streamflow at the Liberty site exceeded 20,000 ft3/s are referred to as “high-flow events.”
During this study, it was estimated that only about 55 percent of the total water volume released from Lake Livingston was accounted for at USGS station 08067252 Trinity River at Wallisville, Tex. (hereinafter referred to as the “Wallisville site”), which is approximately 8 river miles upstream from where the Trinity River enters Galveston Bay. The difference in water volumes between what is released from Lake Livingston and what is measured at the Wallisville site is consistent with findings from previous studies and indicates that a large part of the volume released from Lake Livingston does not reach Galveston Bay through the main channel of the Trinity River.
To assess the distribution of streamflow and estimate the amount of water diverted from the main channel of the Trinity River into distributary channels, instantaneous streamflow measurements were made at USGS station 08067230 Old River Lake near Wallisville, Tex. (hereinafter referred to as the “Old River Lake site”) and the Wallisville site during a range of hydrologic conditions. Results indicate that a large portion of the freshwater inflow was likely delivered to Galveston Bay through pathways other than the main channel of the Trinity River, including Old River Lake. When streamflow at the Liberty site, located upstream from the Wallisville site, exceeded approximately 40,000 ft3/s, Old River Lake and its network of hydrologically connected channels likely became the primary pathway for freshwater inflow entering Galveston Bay.
Water quality was characterized from discrete samples collected during a range of hydrologic conditions at the Old River Lake site and the Wallisville site in order to evaluate the effects of streamflow diversions on the supply of suspended sediment and nutrients into Galveston Bay. Suspended-sediment concentrations were typically higher at the Wallisville site than at the Old River Lake site, likely because of lower water velocities at the Old River Lake site than at the Wallisville site; low water velocities allow suspended sediment to settle, thus reducing concentrations. Suspended-sediment loads were also typically higher at the Wallisville site than at the Old River Lake site during high-flow events. However, when streamflows at the Liberty site exceeded approximately 60,000 ft3/s, suspended-sediment loads were higher at the Old River Lake, which likely became the primary pathway for suspended-sediment delivery into Galveston Bay.
Suspended-sediment concentrations and loads were computed at the Wallisville and Liberty sites for the duration of 11 hydrologic events representing different streamflows by using the regression equations developed for each monitoring station. Overall, approximately 25 percent of the total sediment load measured during events at the Liberty site was measured at the Wallisville site, indicating that only a portion of the suspended-sediment load from the Liberty site reached Galveston Bay through the main channel of the Trinity River during the measured events. Based on data from discrete samples, some of this sediment load was diverted into Old River Lake and associated distributary channels.
Results from analysis of nutrient samples indicate that streamflow conditions affect the nitrogen concentrations in the delta of the Trinity River. At the Old River Lake site, nitrate plus nitrite and total dissolved nitrogen concentrations were typically lower during low-flow conditions than during high-flow events; low-flow conditions represent low-flow events or tidal-flow conditions (during low-flow conditions the streamflow at the Liberty site was less than 20,000 ft3/s). Lower concentrations of nitrate plus nitrite and total dissolved nitrogen at the Old River Lake site may be associated with various physical and biogeochemical processes, including the transformation and biological uptake of nitrate, nitrite, and other species of nitrogen resulting from extended water residence times and relatively small inputs of nitrogen from the upstream reaches of the Trinity River Basin. During high-flow events, the proportions of nitrogen species were similar among sites, indicating that the travel path through wetlands and channels surrounding Old River Lake likely does not affect the relative concentrations of the various nitrogen species present in freshwater inflow to Galveston Bay.
Results from analysis of nutrient samples also indicate that the pathways for nutrient delivery from the Trinity River into Galveston Bay are dependent on event magnitude. When streamflows at the Liberty site were low (approximately 20,000 ft3/s), the main channel of the Trinity River was the primary pathway for nitrogen and phosphorus entering Galveston Bay. Once streamflow at the Liberty site exceeded 20,000 ft3/s, however, the contribution of nutrient loading through Old River Lake to Galveston Bay increased proportionally to the nutrient loading in the main channel, and when streamflow at the Liberty site exceeded approximately 50,000 ft3/s, Old River Lake likely became the primary pathway for nutrient delivery into Galveston Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225015","collaboration":"Prepared in cooperation with the Texas Water Development Board","usgsCitation":"Lucena, Z., and Lee, M.T., 2022, Distribution of streamflow, sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, 2016–19: U.S. Geological Survey Scientific Investigations Report 2022–5015, 55 p., https://doi.org/10.3133/sir20225015.","productDescription":"Report: vi, 55 p.; Dataset","numberOfPages":"66","onlineOnly":"N","ipdsId":"IP-126129","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":397341,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225015/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397266,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397264,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5015/sir20225015.XML"},{"id":397265,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5015/images"},{"id":397263,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5015/sir20225015.pdf","text":"Report","size":"4.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5015"},{"id":397262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5015/coverthb.jpg"},{"id":502301,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112716.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Texas","otherGeospatial":"Galveston Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.86145019531249,\n 29.260044678228486\n ],\n [\n -94.50714111328125,\n 29.537619205973428\n ],\n [\n -94.71038818359375,\n 29.84302629154662\n ],\n [\n -95.03173828125,\n 29.752455480021393\n ],\n [\n -95.0592041015625,\n 29.59017705987947\n ],\n [\n -94.98504638671875,\n 29.489815619374962\n ],\n [\n -94.921875,\n 29.401319510041485\n ],\n [\n -94.8944091796875,\n 29.305561325527698\n ],\n [\n -94.86145019531249,\n 29.260044678228486\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Attempts to mitigate shoreline microbial contamination require a thorough understanding of pollutant sources, which often requires multiple years of data collection (e.g., point/nonpoint) and the interacting factors that influence water quality. Because restoration efforts can alter shoreline or beach morphology, revisiting source inputs is often necessary. Microbial source tracking (MST) using source-specific molecular markers, genomic community analyses, and physical modeling was used to identify contamination sources along three Lake Michigan beaches of the Laurentian Great Lakes with historically high fecal indicator bacteria (FIB, E. coli) concentrations. Genetic markers for human (Bacteroides HF183) and mixed gull species (Catellicoccus marimammalium) fecal sources were tested from water and sediment. Gene sequencing (16S rRNA) was used to identify similarities in bacterial communities in nearshore water, river inputs, sand, sediment, and groundwater. Synoptic surveys of water exchange were conducted to determine nearshore-offshore interactions of FIB. In addition to these MST studies, best management practices to mitigate FIB, including gull deterrence, slope grading, wetland establishment, and shoreline plantings, were reviewed for their effectiveness at reducing FIB concentrations over time. Using multiple tools for MST helped identify primary and secondary sources of FIB (gulls, stormwater inputs) and the physical processes that exacerbate FIB concentrations (onshore currents, limited circulation). Management actions were successful in the short-term at reducing FIB, but scope of success was temporally limited, with FIB concentrations often rebounding. Results highlight the usefulness of MST to inform best management practices and the need for a sustained adaptive approach that adjusts for changes in the coastal system.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.01.009","usgsCitation":"Nevers, M., Buszka, P.M., Byappanahalli, M., Cole, T., Corsi, S., Jackson, P.R., Kinzelman, J.L., Nakatsu, C.H., and Phanikumar, M.S., 2022, Microbial source tracking and evaluation of best management practices for restoring degraded beaches of Lake Michigan: Journal of Great Lakes Research, v. 48, no. 2, p. 441-454, https://doi.org/10.1016/j.jglr.2022.01.009.","productDescription":"14 p.","startPage":"441","endPage":"454","ipdsId":"IP-127246","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":448429,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2022.01.009","text":"Publisher Index Page"},{"id":397344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Wisconsin","city":"Chicago, Racine","otherGeospatial":"Jeorse Park Beach, Lake Michigan, North Beach, 63rd Street Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.9345703125,\n 41.60928183876483\n ],\n [\n -86.7095947265625,\n 41.60928183876483\n ],\n [\n -86.7095947265625,\n 42.84777884235988\n ],\n [\n -87.9345703125,\n 42.84777884235988\n ],\n [\n -87.9345703125,\n 41.60928183876483\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":838533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buszka, Paul M. 0000-0001-8218-826X pmbuszka@usgs.gov","orcid":"https://orcid.org/0000-0001-8218-826X","contributorId":1786,"corporation":false,"usgs":true,"family":"Buszka","given":"Paul","email":"pmbuszka@usgs.gov","middleInitial":"M.","affiliations":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":241924,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":838535,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cole, Travis 0000-0002-0935-381X","orcid":"https://orcid.org/0000-0002-0935-381X","contributorId":289099,"corporation":false,"usgs":false,"family":"Cole","given":"Travis","affiliations":[{"id":62047,"text":"Crane Environmental Services, LLC","active":true,"usgs":false}],"preferred":false,"id":838536,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838537,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838538,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kinzelman, Julie L.","contributorId":236944,"corporation":false,"usgs":false,"family":"Kinzelman","given":"Julie","email":"","middleInitial":"L.","affiliations":[{"id":37612,"text":"City of Racine Health Department","active":true,"usgs":false}],"preferred":false,"id":838539,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nakatsu, Cindy H 0000-0003-0663-180X","orcid":"https://orcid.org/0000-0003-0663-180X","contributorId":215593,"corporation":false,"usgs":false,"family":"Nakatsu","given":"Cindy","email":"","middleInitial":"H","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":838540,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Phanikumar, Mantha S.","contributorId":147924,"corporation":false,"usgs":false,"family":"Phanikumar","given":"Mantha","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":838541,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70240411,"text":"70240411 - 2022 - Evaluation of post-stocking dispersal and mortality of juvenile lake trout Salvelinus namaycush in Lake Ontario using acoustic telemetry","interactions":[],"lastModifiedDate":"2023-02-07T13:01:59.14599","indexId":"70240411","displayToPublicDate":"2022-03-20T06:58:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of post-stocking dispersal and mortality of juvenile lake trout Salvelinus namaycush in Lake Ontario using acoustic telemetry","docAbstract":"Wild reproduction by stocked lake trout Salvelinus namaycush in Lake Ontario has yet to produce a self-sustaining population, requiring a reliance on stocking. Once released, age-1 juvenile lake trout are not typically surveyed until age-2, creating a gap in knowledge of fine-scale post-release behaviors. A method to track fine-scale movements and estimate mortality of juvenile lake trout could complement standard survey methods and benefit management decisions regarding stocking locations. We used acoustic telemetry to estimate post-stocking mortality and observe fine-scale spatial and temporal movements of 38 hatchery-reared, age-1 lake trout from an offshore stocking site in the eastern basin of Lake Ontario from 2017 to 2018. Cumulative post-stocking mortality was estimated at 5.3%, 10.5%, and 26.3% after one week, one month and one year, respectively. The majority of lake trout (68.4%) emigrated from the stocking location within two months and entered deep water (∼50 m) once warm-water incursions at the stocking site exceeded lake trout thermal preferences (15 °C). Lake trout made large movements (i.e., median 1.9 km, maximum 12.4 km straight-line distance) within the first hour post-release and had an average swimming speed of 1.64 km‧hr−1over the first day. There was no statistically significant relationship between total distance traveled and time of day, although distance traveled tended to be greater during crepuscular and dark periods compared to daylight. Our results provide a conservative estimate of post-release mortality and reveal behaviors of hatchery-reared juvenile lake trout that may be helpful when selecting stocking locations beneficial to restoration program goals.
Saginaw Bay is a shallow, nutrient-rich embayment in Lake Huron that historically had a complex network of natural rocky reefs. These reef habitats were used as spawning and nursery areas for a variety of fish species, but decades of land-use related sedimentation caused many of these reefs to be degraded. Our study objectives were to analyze abiotic and biotic conditions on degraded and remnant reefs and describe spawning patterns of walleye (Sander vitreus) and lake whitefish (Coregonus clupeaformis) at these sites to determine the potential for increased utilization following reef restoration. During fall and spring 2014–2016, we evaluated water quality and egg predation at four sites with varying levels of reef degradation. Further, we documented reproductive utilization through capture of spawning adults and quantification of egg deposition. Walleye and lake whitefish utilized multiple sites for reproduction; however, densities of spawners and deposited eggs were low, suggesting that they were not utilizing study sites as major spawning locations. Walleye and lake whitefish eggs were eaten by multiple fish species, including larger fish such as channel catfish (Ictalurus punctatus). Dissolved oxygen levels were adequate (i.e., >7 mg 02 L−1) during spring walleye egg incubation; however, bottom dissolved oxygen levels became very low at some sites during winter ice cover, coinciding with lake whitefish egg incubation. As restoration of rocky reefs proceeds in the Bay, evidence of remnant reef spawning fish bodes well for long-term success, though potential limiting factors such as low dissolved oxygen, sedimentation, and egg predation require continued monitoring.