The Upper Jurassic (Kimmeridgian) mudstones of the Haynesville Formation in the Sabine Uplift, Louisiana and Texas, are widely considered to be a self-sourced natural gas reservoir; however, additional sources of gas may have charged the mudstones in the Louisiana portion of the uplift. Secondary migration of hydrocarbons into the Sabine Uplift from downdip, gas-generating Jurassic source rocks in the North Louisiana Salt Basin was quantitively modeled in this study. Jurassic source rocks include the Smackover, Haynesville, and Bossier Formations.
Thermodynamic equations of state were used to determine thermophysical properties of supercritical methane and water under reservoir conditions. A time-dependent derivation of Darcy’s Law for pressure-driven laminar fluid flow through porous media was used to model secondary migration at reservoir conditions. This study indicates secondary migration requires approximately 100,000 yr for pore fluids to migrate through 1.0 km of carrier beds having representative petrophysical, fluid, and reservoir properties of the Haynesville Formation. As an example migration pathway, the distance from the middle of the North Louisiana Salt Basin to the center of the Sabine Uplift is approximately 96 mi (155 km). Given migration velocities over this distance, 15.5 m.y. is required for hydrocarbons to migrate from the North Louisiana Salt Basin and charge the Haynesville Formation in the Sabine Uplift. This study also indicates supercritical water is 6 times more thermally conductive than methane under reservoir conditions; however, the relatively small volumes of migrated water likely did not transfer sufficient heat for the metagenesis of methane. Based on this study, a component of natural gas charging the Haynesville Formation of the Sabine Uplift area can reasonably be explained by lateral migration and hydrodynamic flow from thermally mature Jurassic source rocks located in adjacent basins.
","language":"English","publisher":"GCAGS","usgsCitation":"Burke, L.A., 2021, Quantitative modeling of secondary migration: Understanding the origin of natural gas charge of the Haynesville Formation in the Sabine Uplift area of Louisiana and Texas: GCAGS Journal, v. 10, p. 24-30.","productDescription":"7 p.","startPage":"24","endPage":"30","ipdsId":"IP-124868","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":396478,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396464,"type":{"id":15,"text":"Index Page"},"url":"https://www.gcags.org/Journal/GCAGS.Journal.Vol.10.html"}],"country":"United States","state":"Louisiana, Texas","otherGeospatial":"Sabine Uplift","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.416015625,\n 29.76437737516313\n ],\n [\n -91.318359375,\n 29.76437737516313\n ],\n [\n -91.318359375,\n 33.578014746143985\n ],\n [\n -96.416015625,\n 33.578014746143985\n ],\n [\n -96.416015625,\n 29.76437737516313\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burke, Lauri A. 0000-0002-2035-8048 lburke@usgs.gov","orcid":"https://orcid.org/0000-0002-2035-8048","contributorId":3859,"corporation":false,"usgs":true,"family":"Burke","given":"Lauri","email":"lburke@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":836018,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70225608,"text":"70225608 - 2021 - Hydrogeology and simulation of groundwater flow in Columbia County, Wisconsin","interactions":[],"lastModifiedDate":"2021-10-27T16:48:33.308605","indexId":"70225608","displayToPublicDate":"2021-10-01T08:15:46","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5959,"text":"Wisconsin Geological and NaturalHistory Survey Bulletin","active":true,"publicationSubtype":{"id":2}},"title":"Hydrogeology and simulation of groundwater flow in Columbia County, Wisconsin","docAbstract":"This report describes the regional hydrogeology and groundwater resources of Columbia County, Wisconsin, and documents a regional groundwater flow model developed for the county. Regional hydrostratigraphic units include the unlithified aquifer, the upper bedrock aquifer, and the Elk Mound aquifer.\n\nThe unlithified aquifer consists of deposits that range in composition from sand and gravel outwash and stream deposits to silty, sandy till. This aquifer is less than 25 ft thick in much of eastern Columbia County, but consists of permeable sand and gravel extending to over 250 ft in depth in the Wisconsin River valley bottom. \n\nThe upper bedrock aquifer consists of Ordovician and upper Cambrian sedimentary formations, including sandstone, siltstone and dolomitic strata. The upper bedrock aquifer underlies the unlithified aquifer in eastern portions of the County, but is absent to the west, where these formations are largely eroded. The contact between the Tunnel City Group and Wonewoc Formation (Top of Elk Mound Group) forms the base of the upper bedrock aquifer. Bedding plane fractures are common to this aquifer, although only a portion of the observed fractures appear to be hydraulically active. The upper bedrock aquifer is a significant source of groundwater at a regional scale. Measurements of hydraulic head showed a difference of several feet across the bottom of this aquifer to the underlying Wonewoc sandstone, indicating that the basal facies of the Tunnel City Group functions as an aquitard separating the upper bedrock aquifer from the Elk Mound aquifer. Conditions vary considerably within this aquifer, depending on the local lithostratigraphy. For example, where present, the St. Lawrence Fm. and fine-grained intervals of the Tunnel City Group may be locally-extensive aquitards. \nThe Elk Mound aquifer consists of Cambrian sandstone of the Wonewoc, Eau Claire, and Mount Simon Formations. It is thin to absent in several locations but ranges up to 600 ft in thickness over much of southern Columbia County. The variation in thickness is due in large part to the irregular topography of the underlying Precambrian crystalline rock, which generally serves as the base of the groundwater system. In neighboring counties, a fine-grained facies within the Eau Claire Fm. acts as a regionally extensive aquitard, referred to as the Eau Claire aquitard. Much of the data collected and compiled for this study suggest that shale or dolomite within the Eau Claire Fm., which is the equivalent of the Eau Claire aquitard, occurs only within southwestern Columbia County. There is little to no evidence of the Eau Claire aquitard over most of the county. Where the dolomite and shale are absent, the Elk Mound aquifer is relatively homogenous and does not include a mappable aquitard. \nA three-dimensional steady-state flow model presented here represents long-term, average conditions in the regional groundwater system since about 1970. The model was constructed with the U.S. Geological Survey’s MODFLOW-NWT code; it has six layers with a uniform grid of 300 ft x 300 ft cells. Layers 1 and 2 simulate the unlithified aquifer and layer 3 represents the upper bedrock aquifer. The Elk Mound aquifer is simulated by layers 4, 5 and 6, representing the Wonewoc, Eau Claire, and Mount Simon Formations, respectively. The model extends beyond the boundaries of Columbia County to ensure that hydrologic conditions simulated within the County are consistent with regional conditions. \nRecharge to the groundwater flow model is based on results from a GIS-based soil-water-balance model. Recharge was simulated with the unsaturated zone flow (UZF) package in MODFLOW. This approach is particularly useful for quantifying groundwater discharge to riparian wetlands because UZF tracks recharge that would lead to the simulated water table exceeding the land surface (represented by the top of model layer 1) and reroutes it to nearby stream segments. The model includes pumping from 256 wells, and 178 of these are located within Columbia County. Pumping totaled about 28 million gallons per day (mgd) on average since 1970, with 7.2 mgd of the withdrawal from within the County. Model calibration was performed with the PEST parameter estimation code. Calibration targets included approximately 3,900 head measurements and 91 stream flow measurements. Four vertical-head differences across hydrogeologic units, calculated from data collected during packer testing in wells in Columbia County, were also used in model calibration. \n\nResults from the calibrated model provide a groundwater balance for the region. About 83 percent of groundwater originates as recharge to the water table, 12 percent comes from leakage from streams, and about 5 percent of the groundwater flows into the model domain from surrounding areas. About 95 percent of the simulated groundwater discharges to steams and other surface water features, about 3 percent flows across model boundaries to surrounding areas of the groundwater system, and pumping accounts for 2 percent of discharge. Simulated flow paths are relatively local, from recharge in upland areas to discharge in nearby streams and wetlands. \n\nThe model has many potential applications, including simulation of the effects of existing or proposed high-capacity wells, estimating the zone of contribution for these wells, and understanding relationships between surface water and groundwater. Future refinements to the model, such as incorporating new information about the extent and hydraulic characteristics of the Tunnel City Group, will improve its utility in understanding advective flow between the upper bedrock and Elk Mound aquifers. If seasonal or annual variations in the groundwater system are of interest, this steady-state model could be brought into a transient mode.","language":"English","publisher":"Wisconsin Geological and Natural History Survey","usgsCitation":"Gotkowitz, M., Leaf, A.T., and Sellwood, S.M., 2021, Hydrogeology and simulation of groundwater flow in Columbia County, Wisconsin: Wisconsin Geological and NaturalHistory Survey Bulletin, 51 p.","productDescription":"51 p.","ipdsId":"IP-101440","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391000,"type":{"id":15,"text":"Index Page"},"url":"https://wgnhs.wisc.edu/catalog/publication/000985"}],"country":"United States","state":"Wisconsin","county":"Columbia County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.2453,43.643],[-89.127,43.6436],[-89.1271,43.6318],[-89.007,43.6332],[-89.0063,43.548],[-89.0044,43.4616],[-89.0038,43.3737],[-89.0088,43.3738],[-89.0094,43.286],[-89.1271,43.2827],[-89.246,43.2834],[-89.3624,43.2832],[-89.3617,43.2954],[-89.4819,43.2942],[-89.6008,43.2932],[-89.7209,43.2935],[-89.7235,43.2935],[-89.7292,43.3026],[-89.7279,43.3108],[-89.7254,43.3153],[-89.7229,43.3181],[-89.7185,43.3195],[-89.7129,43.3226],[-89.7078,43.3277],[-89.7028,43.3345],[-89.6909,43.3495],[-89.684,43.3573],[-89.6783,43.3586],[-89.6708,43.3582],[-89.6613,43.3577],[-89.6456,43.36],[-89.6311,43.3646],[-89.6166,43.371],[-89.6009,43.3806],[-89.6004,43.4688],[-89.5999,43.5544],[-89.6075,43.5603],[-89.6138,43.5626],[-89.6277,43.5617],[-89.6359,43.5603],[-89.6511,43.5621],[-89.658,43.5634],[-89.6643,43.5657],[-89.6707,43.5666],[-89.6783,43.5671],[-89.6877,43.5634],[-89.6934,43.5616],[-89.6991,43.562],[-89.706,43.5648],[-89.7187,43.5652],[-89.7288,43.5661],[-89.7351,43.5693],[-89.7364,43.5743],[-89.7326,43.5793],[-89.7288,43.5829],[-89.7244,43.587],[-89.7188,43.5929],[-89.7207,43.597],[-89.727,43.5979],[-89.7428,43.597],[-89.751,43.5997],[-89.7567,43.6029],[-89.7662,43.6029],[-89.7738,43.6092],[-89.7763,43.6161],[-89.7808,43.6215],[-89.7802,43.6274],[-89.7789,43.6343],[-89.784,43.6388],[-89.7866,43.6411],[-89.779,43.6411],[-89.7195,43.643],[-89.6,43.6427],[-89.4837,43.6423],[-89.3648,43.6427],[-89.2453,43.643]]]},\"properties\":{\"name\":\"Columbia\",\"state\":\"WI\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gotkowitz, Madeline","contributorId":268135,"corporation":false,"usgs":false,"family":"Gotkowitz","given":"Madeline","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":825890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leaf, Andrew T. 0000-0001-8784-4924 aleaf@usgs.gov","orcid":"https://orcid.org/0000-0001-8784-4924","contributorId":5156,"corporation":false,"usgs":true,"family":"Leaf","given":"Andrew","email":"aleaf@usgs.gov","middleInitial":"T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sellwood, Steven M.","contributorId":268136,"corporation":false,"usgs":false,"family":"Sellwood","given":"Steven","email":"","middleInitial":"M.","affiliations":[{"id":55571,"text":"TRC Companies, Inc.","active":true,"usgs":false}],"preferred":false,"id":825892,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224991,"text":"70224991 - 2021 - Mercury isotope fractionation by internal demethylation and biomineralization reactions in seabirds: Implications for environmental mercury science","interactions":[],"lastModifiedDate":"2021-11-01T16:05:38.186343","indexId":"70224991","displayToPublicDate":"2021-10-01T06:57:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Mercury isotope fractionation by internal demethylation and biomineralization reactions in seabirds: Implications for environmental mercury science","docAbstract":"A prerequisite for environmental and toxicological applications of mercury (Hg) stable isotopes in wildlife and humans is quantifying the isotopic fractionation of biological reactions. Here, we measured stable Hg isotope values of relevant tissues of giant petrels (Macronectes spp.). Isotopic data were interpreted with published HR-XANES spectroscopic data that document a stepwise transformation of methylmercury (MeHg) to Hg-tetraselenolate (Hg(Sec)4) and mercury selenide (HgSe) (Sec = selenocysteine). By mathematical inversion of isotopic and spectroscopic data, identical δ202Hg values for MeHg (2.69 ± 0.04‰), Hg(Sec)4 (−1.37 ± 0.06‰), and HgSe (0.18 ± 0.02‰) were determined in 23 tissues of eight birds from the Kerguelen Islands and Adélie Land (Antarctica). Isotopic differences in δ202Hg between MeHg and Hg(Sec)4 (−4.1 ± 0.1‰) reflect mass-dependent fractionation from a kinetic isotope effect due to the MeHg → Hg(Sec)4 demethylation reaction. Surprisingly, Hg(Sec)4 and HgSe differed isotopically in δ202Hg (+1.6 ± 0.1‰) and mass-independent anomalies (i.e., changes in Δ199Hg of ≤0.3‰), consistent with equilibrium isotope effects of mass-dependent and nuclear volume fractionation from Hg(Sec)4 → HgSe biomineralization. The invariance of species-specific δ202Hg values across tissues and individual birds reflects the kinetic lability of Hg-ligand bonds and tissue-specific redistribution of MeHg and inorganic Hg, likely as Hg(Sec)4. These observations provide fundamental information necessary to improve the interpretation of stable Hg isotope data and provoke a revisitation of processes governing isotopic fractionation in biota and toxicological risk assessment in wildlife.
“Bartram’s Bass” Micropterus sp. cf. coosae is endemic to the upper Savannah River basin of the southeastern United States and is threatened by hybridization with invasive Alabama Bass Micropterus henshalli. Bartram’s Bass have been functionally extirpated from reservoirs, and hybrid individuals have been detected in several tributaries. However, the extent of introgression in tributaries is currently unknown. Our objectives were to (1) assess the distribution of Bartram’s Bass, native Largemouth Bass M. salmoides, invasive Alabama Bass, and their hybrids in streams of the upper Savannah River basin and (2) quantify effects of abiotic variables on the distribution of each species. We sampled 154 locations in 2017 and 2018 and assigned genetic identity using hydrolysis probes and microsatellites. We used conditional inference trees to quantify variables affecting the occurrence of each species and hybrids. We observed widespread hybridization across the basin. Pure Bartram’s Bass were collected at 27% (42) of sites, among which only 12 sites contained pure Bartram’s Bass and no other congeners. Thirty sites where pure Bartram’s Bass were collected contained hybrids. In the montane Blue Ridge ecoregion, occurrence of pure Bartram’s Bass was negatively affected by low levels of local-scale developed land cover. In the lower-relief Piedmont ecoregion, pure Bartram’s Bass were positively associated with watershed-scale forest land cover and stream gradient. Distance from a reservoir was positively associated with occurrence of pure Bartram’s Bass in both ecoregions. Pure Bartram’s Bass are likely to occur with high probability in only 16% of nonimpounded stream segments; this represents a conservative estimate, and the true number is likely lower. However, future work accounting for incomplete detection of Bartram’s Bass will help to improve confidence in true extirpations. Conservation efforts may be more successful if implemented on stream segments farther from reservoirs or upstream of dispersal barriers preventing colonization of Alabama Bass.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10637","usgsCitation":"Peoples, B., Judson, E., Darden, T.L., Farrae, D.J., Kubach, K., Leitner, J., and Scott, M.C., 2021, Modeling distribution of endemic Bartram’s Bass Micropterus sp. cf. coosae: Disturbance and proximity to invasion source increase hybridization with invasive Alabama Bass: North American Journal of Fisheries Management, v. 41, no. 5, p. 1309-1321, https://doi.org/10.1002/nafm.10637.","productDescription":"13 p.","startPage":"1309","endPage":"1321","ipdsId":"IP-152697","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, North Carolina, South Carolina","otherGeospatial":"Savannah River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": 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Emily","contributorId":343368,"corporation":false,"usgs":false,"family":"Judson","given":"Emily","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":910837,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Darden, Tanya L.","contributorId":263425,"corporation":false,"usgs":false,"family":"Darden","given":"Tanya","email":"","middleInitial":"L.","affiliations":[{"id":53977,"text":"SC DNR","active":true,"usgs":false}],"preferred":false,"id":907846,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farrae, Daniel J.","contributorId":263426,"corporation":false,"usgs":false,"family":"Farrae","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":53977,"text":"SC DNR","active":true,"usgs":false}],"preferred":false,"id":907847,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kubach, Kevin","contributorId":341212,"corporation":false,"usgs":false,"family":"Kubach","given":"Kevin","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907848,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leitner, Jean","contributorId":177201,"corporation":false,"usgs":false,"family":"Leitner","given":"Jean","email":"","affiliations":[],"preferred":false,"id":910836,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scott, Mark C.","contributorId":341033,"corporation":false,"usgs":false,"family":"Scott","given":"Mark","email":"","middleInitial":"C.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":907849,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224620,"text":"sir20215065 - 2021 - Conceptual and numerical groundwater flow model of the Cedar River alluvial aquifer system with simulation of drought stress on groundwater availability near Cedar Rapids, Iowa, for 2011 through 2013","interactions":[],"lastModifiedDate":"2021-10-01T12:09:28.489755","indexId":"sir20215065","displayToPublicDate":"2021-09-30T21:14:22","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5065","displayTitle":"Conceptual and Numerical Groundwater Flow Model of the Cedar River Alluvial Aquifer System with Simulation of Drought Stress on Groundwater Availability near Cedar Rapids, Iowa, for 2011 through 2013","title":"Conceptual and numerical groundwater flow model of the Cedar River alluvial aquifer system with simulation of drought stress on groundwater availability near Cedar Rapids, Iowa, for 2011 through 2013","docAbstract":"Between July 2011 and February 2013, the City of Cedar Rapids observed water level declines in their horizontal collector wells approaching 11 meters. As a result, pumping from these production wells had to be halted, and questions were raised about the reliability of the alluvial aquifer under future drought conditions. The U.S. Geological Survey, in cooperation with the City of Cedar Rapids, completed a study to better understand the effects of drought stress on the Cedar River alluvial aquifer using a numerical groundwater flow model. Previously published groundwater flow models were combined with newly collected airborne, waterborne, down-hole, and land-based geophysical survey data and provided a detailed three-dimensional lithologic model of the Cedar River alluvial aquifer and surrounding area. An improved conceptual model for the groundwater flow system and a lithologic model were used to build and inform a numerical groundwater flow model capable of simulating water levels observed in the City of Cedar Rapids horizontal collector wells during the 2012 drought. Model performance was assessed primarily on the ability of the model to simulate water table elevation at six monitoring wells. Statistical tests were used to assess the numerical model during the calibration period, and results varied from satisfactory to unsatisfactory, likely because of stage changes in the Cedar River and production well withdrawal rates near monitoring wells. Simulated water levels during the 2012 drought indicated a depression near the horizontal collector wells, although simulated elevations at these locations and at monitoring wells were generally overestimated compared to measured values. The numerical groundwater flow model was modified to account for a decrease in seepage rate caused by low flow in the Cedar River and increased production. With seepage rate modification, model results improved; the simulated water table elevations were like those observed in horizontal collector and monitoring wells. Results demonstrated the ability of the model to simulate water levels observed in the horizontal collector wells during the 2012 drought when accounting for a decrease in infiltration from the Cedar River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215065","collaboration":"Prepared in cooperation with the City of Cedar Rapids","usgsCitation":"Haj, A.E., Ha, W.S., Gruhn, L.R., Bristow, E.L., Gahala, A.M., Valder, J.F., Johnson, C.D., White, E.A., and Sterner, S.P., 2021, Conceptual and numerical groundwater flow model of the Cedar River alluvial aquifer system with simulation of drought stress on groundwater availability near Cedar Rapids, Iowa, for 2011 through 2013: U.S. Geological Survey Scientific Investigations Report 2021–5065, 59 p., https://doi.org/10.3133/sir20215065.","productDescription":"Report: ix, 59 p.; Appendix; 3 Data Releases; Dataset","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-118762","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":390066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5065/coverthb.jpg"},{"id":390067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065.pdf","text":"Report","size":"8.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5065"},{"id":390069,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BS882S","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Airborne electromagnetic and magnetic survey data and inverted resistivity models, Cedar Rapids, Iowa, May 2017"},{"id":390070,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96CF4L5","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Cedar River alluvial aquifer near Cedar Rapids, Iowa"},{"id":390071,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YXJDHX","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Geophysical data collected in the Cedar River floodplain, Cedar Rapids, Iowa, 2015–2017"},{"id":390072,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":390068,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065_appendix.pdf","text":"Poster","size":"3.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5065 Appendix","linkHelpText":"— Geophysical methods used to better characterize surface water, alluvial aquifer, and bedrock aquifer interaction in the Cedar River Valley, Iowa"},{"id":390073,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065.xml","size":"367 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5065 xml"},{"id":390074,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5065/images"}],"country":"United States","state":"Iowa","city":"Cedar Rapids","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.76759719848633,\n 41.99139471889533\n ],\n [\n -91.69189453125,\n 41.99139471889533\n ],\n [\n -91.69189453125,\n 42.03565184193029\n ],\n [\n -91.76759719848633,\n 42.03565184193029\n ],\n [\n -91.76759719848633,\n 41.99139471889533\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The challenges associated with field measurements of turbidity are well known and result primarily from differences in reported values that depend on instrument design and the resulting need for reporting units that are specific to those designs. A critical challenge for making comparable turbidity measurements is the selection and use of appropriate turbidity standards for sensor calibration. The accepted primary standards for turbidity measurements use formazin made from scratch; all others should relate back to readings obtained using standard formazin. However, because turbidity is a qualitative property of water, comparing standards is not as simple as it is for many chemical measurements. The U.S. Geological Survey “National Field Manual for the Collection of Water-Quality Data” currently allows for the use of two standards, formazin and polymer beads, for the calibration of field turbidimeters. Another challenge for making comparable turbidity measurements is selection of turbidity sensors. A turbidity sensor commonly used in the U.S. Geological Survey, the Yellow Springs Instruments (YSI) 6136, has been replaced by the manufacturer with the YSI EXO turbidity sensor. Both sensors operate on the same principles but have slight design differences that result in readings that are not directly comparable on a 1:1 basis.
Differences in calibration standards and sensors are a cause of concern in ongoing studies that require switching calibration standards or sensor types, and for comparisons of data collected with sensors calibrated by using different calibration standards, different sensor types, or both. The objectives of this study were to evaluate the response of two YSI turbidity sensors in both formazin-based standards (StablCal) and polymer turbidity standards (in this case YSI brand; however, other brands are available) and to compare the performance of the YSI EXO and YSI 6136 turbidity sensors under similar laboratory and environmental (field) conditions. To quantify these differences, a series of laboratory and field side-by-side comparisons were conducted. Nine field comparisons of YSI EXO and YSI 6136 sensors were performed at site locations in Kansas and Virginia. Two field comparisons of StablCal and polymer calibration standards were performed in Kansas, both using YSI EXO turbidity sensors. Five laboratory comparisons between the YSI EXO and YSI 6136 turbidity sensors were performed, and seven laboratory comparisons between StablCal and polymer turbidity standards were performed using YSI EXO turbidity sensors. The results can help the USGS and others better understand how turbidity data can differ depending on the sensors and calibration standards used.
Key findings and conclusions include the following—
Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
A U.S. Geological Survey-led assessment of data gaps and science needs across the Great Lakes ecosystem indicated the following:
• Expanded data collection or monitoring would provide basic ecosystem, social, and public health data to manage the Great Lakes system and to develop and test models and decision support tools.
• New science and advanced technologies (for example, sensors and high-performance computing capability) would improve the understanding of critical threats, such as harmful algae blooms and high-water levels.
Although there is significant scientific knowledge in specific areas or for specific topics, managers could use improved models and decision support tools, strengthened by extensive data collection and developed at multiple scales, to better inform decision making in the future. Enhanced coordination of agency efforts and associated data collection across data types (for example, prey fish populations and water levels) is needed to effectively manage the Great Lakes.
This report highlights the data gaps; benefits of better, more structured coordination; and areas of concern specifically related to data collection/measurement and science efforts. It summarizes and analyzes stakeholder feedback and information from review of scientific literature. Finally, the report outlines steps necessary to create an integrated Great Lakes science plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211096","usgsCitation":"Carl, L.M., Hortness, J.E., and Strach, R.M., 2021, U.S. Geological Survey Great Lakes Science Forum—Summary of remaining data and science needs and next steps: U.S. Geological Survey Open-File Report 2021–1096, 4 p., https://doi.org/10.3133/ofr20211096.","productDescription":"iii, 4 p.","numberOfPages":"12","onlineOnly":"Y","ipdsId":"IP-133589","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":390007,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1096/ofr20211096.xml","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1096 xml"},{"id":390006,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1096/ofr20211096.pdf","text":"Report","size":"655 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1096"},{"id":390005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1096/coverthb.jpg"}],"contact":"Director, Midwest Regional Director’s Office
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Stocking of Rainbow Trout Oncorhynchus mykiss commonly provides seasonal or mitigation fisheries; however, these fish are usually small and ecosystem effects are spatially or temporally limited. Yet agencies receive requests to stock Rainbow Trout in relatively natural settings (i.e., not tailwater or mitigation fisheries), where introductions may have greater ecosystem consequences. The size of introduced fish is an important factor in determining biotic interactions with native species; therefore, our objectives were to assess the seasonal feeding ecology and microhabitat use of large (265–530 mm TL) nonnative Emmerson strain Rainbow Trout in a relatively unaltered, groundwater-influenced, warmwater stream of the Ozark Highlands. Rainbow Trout consumed a variety of prey; however, diets differed between cool (winter and spring) and warm (summer) seasons. Cool-season Rainbow Trout exhibited a mixed feeding strategy, with individual specialization on crayfishes and fishes and generalist feeding on Ephemeroptera and Diptera, but Gastropoda were the dominant prey. Feeding strategy in the warm season switched to individual specialization on numerous prey types. Overall, larger prey resources were important components of Rainbow Trout diets. Piscivory was relatively high in both seasons, and crayfishes were one of the most important prey types across seasons. Selection of coarse substrates and deeper-water microhabitats (>0.95 m) was similar between seasons. Rainbow Trout selected the lowest-velocity microhabitats available during the warm season and moderate velocities in the cool season. Rainbow Trout were five times more likely to be associated with cover in the warm season. Due to their higher temperature tolerance, Emmerson strain Rainbow Trout may persist in Ozark Highland streams, where they disrupt local food webs and occupy habitat otherwise selected by native fish, such as Neosho Smallmouth Bass Micropterus dolomieu velox. If native species conservation is a priority for agencies, then caution regarding Rainbow Trout stockings may be warranted.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10694","usgsCitation":"Rodger, A.W., Wolf, S.L., Starks, T.A., Burroughs, J.P., and Brewer, S.K., 2021, Seasonal diet and habitat use of large, introduced Rainbow Trout in an Ozark Highland stream: North American Journal of Fisheries Management, v. 41, no. 6, p. 1764-1780, https://doi.org/10.1002/nafm.10694.","productDescription":"17 p.","startPage":"1764","endPage":"1780","ipdsId":"IP-129494","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":397173,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Spavinaw Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.13336181640625,\n 36.22987352301491\n ],\n [\n -94.37393188476562,\n 36.22987352301491\n ],\n [\n -94.37393188476562,\n 36.46657630040234\n ],\n [\n -95.13336181640625,\n 36.46657630040234\n ],\n [\n -95.13336181640625,\n 36.22987352301491\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Rodger, A. W.","contributorId":288610,"corporation":false,"usgs":false,"family":"Rodger","given":"A.","email":"","middleInitial":"W.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolf, S. L.","contributorId":288613,"corporation":false,"usgs":false,"family":"Wolf","given":"S.","email":"","middleInitial":"L.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":838136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Starks, T. A.","contributorId":288616,"corporation":false,"usgs":false,"family":"Starks","given":"T.","email":"","middleInitial":"A.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838137,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burroughs, J. P.","contributorId":288619,"corporation":false,"usgs":false,"family":"Burroughs","given":"J.","email":"","middleInitial":"P.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838138,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838139,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229775,"text":"70229775 - 2021 - Laboratory infection rates and associated mortality of juvenile Chinook Salmon (Oncorhynchus tshawytscha) from parasitic copepod (Salmincola californiensis)","interactions":[],"lastModifiedDate":"2024-09-16T15:52:28.120247","indexId":"70229775","displayToPublicDate":"2021-09-30T10:39:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2286,"text":"Journal of Fish Diseases","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Laboratory infection rates and associated mortality of juvenile Chinook Salmon (Oncorhynchus tshawytscha) from parasitic copepod (Salmincola californiensis)","title":"Laboratory infection rates and associated mortality of juvenile Chinook Salmon (Oncorhynchus tshawytscha) from parasitic copepod (Salmincola californiensis)","docAbstract":"Pacific salmon (Oncorhynchus spp.) rearing in lakes and reservoirs above dams have been known to become heavily infected with an ectoparasitic copepod (Salmincola californiensis). Little is known about the factors that affect the parasite infection prevalence and intensity. However, previous research suggests that the parasite may negatively affect the fitness and survival of the host fish. The effect of water temperature, confinement and the density of the free-swimming infectious stage of S. californiensis, the copepodid, on infection prevalence and intensity was evaluated by experimentally exposing juvenile Chinook Salmon (O. tshawytscha). Infection rates observed in wild populations were achieved under warm water (15–16°C) and high copepodid density (150–300/L) treatment conditions. Infection prevalence and intensity were also significantly higher in larger fish. During the infection experiment, 4.5% of infected fish died within 54 days with mortality significantly related to copepod infection intensity. The potential for autoinfection was compared to cross-infection by cohabitation of infected fish with naïve fish. Previously infected fish had significantly greater infection intensity compared with naïve fish, indicating that infected fish can be reinfected and that they may be more susceptible than naïve fish.
","language":"English","publisher":"Wiley","doi":"10.1111/jfd.13450","usgsCitation":"Neal, T., Kent, M., Sanders, J., Schreck, C., and Peterson, J., 2021, Laboratory infection rates and associated mortality of juvenile Chinook Salmon (Oncorhynchus tshawytscha) from parasitic copepod (Salmincola californiensis): Journal of Fish Diseases, v. 44, no. 9, p. 1423-1434, https://doi.org/10.1111/jfd.13450.","productDescription":"12 p.","startPage":"1423","endPage":"1434","ipdsId":"IP-127527","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":397251,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Ohio","otherGeospatial":"Great Lakes region, Ottawa National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.5345458984375,\n 41.372686481864655\n ],\n [\n -82.9742431640625,\n 41.372686481864655\n ],\n [\n -82.9742431640625,\n 41.713930073371294\n ],\n [\n -83.5345458984375,\n 41.713930073371294\n ],\n [\n -83.5345458984375,\n 41.372686481864655\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.3258056640625,\n 43.09697190802465\n ],\n [\n -82.628173828125,\n 43.09697190802465\n ],\n [\n -82.628173828125,\n 43.54456658436357\n ],\n [\n -83.3258056640625,\n 43.54456658436357\n ],\n [\n -83.3258056640625,\n 43.09697190802465\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.473876953125,\n 41.934976500546604\n ],\n [\n -83.6993408203125,\n 41.934976500546604\n ],\n [\n -83.6993408203125,\n 42.48830197960227\n ],\n [\n -84.473876953125,\n 42.48830197960227\n ],\n [\n -84.473876953125,\n 41.934976500546604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Neal, Travis","contributorId":341105,"corporation":false,"usgs":false,"family":"Neal","given":"Travis","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":838241,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kent, Michael L.","contributorId":288715,"corporation":false,"usgs":false,"family":"Kent","given":"Michael L.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":838242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanders, Justin","contributorId":288718,"corporation":false,"usgs":false,"family":"Sanders","given":"Justin","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":838243,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schreck, Carl B.","contributorId":288720,"corporation":false,"usgs":false,"family":"Schreck","given":"Carl B.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":838244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838240,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226660,"text":"70226660 - 2021 - Accuracy of flowmeters measuring horizontal flow in fractured-rock simulators","interactions":[],"lastModifiedDate":"2021-12-02T16:33:07.928752","indexId":"70226660","displayToPublicDate":"2021-09-30T10:29:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1866,"text":"Groundwater Monitoring & Remediation","active":true,"publicationSubtype":{"id":10}},"title":"Accuracy of flowmeters measuring horizontal flow in fractured-rock simulators","docAbstract":"Laboratory evaluations of flowmeter response to flow in fractured-rock simulators are needed to improve understanding of data collected in field settings. The ability of flowmeters to accurately measure the velocity and direction of water flowing between parallel plates was used as a surrogate for instrument response in fractured-rock aquifers. A colloidal borescope flowmeter and a heat-pulse flowmeter were deployed in a fractured rock simulator with 4-inch and 6-inch inner-diameter, uncased wells with 0.39- and 1.0-inch fracture apertures and groundwater velocities from 35 to 975 ft/d. The colloidal borescope measurements and applied velocities were positively correlated in all wells and apertures (the coefficient of determination [r2] = 0.61–0.89) and most accurately measured direction at higher velocities. The mean directional error in colloidal borescope measurements was less than 17° in 6-inch wells and 31° in the 4-inch wells at velocities between 92 and 958 ft/d. Heat-pulse flowmeter measurements were 0.001 to 0.004 times less than applied rates and may indicate that water was moving around rather than through the instrument's integrated packer. The mean directional error of heat-pulse flowmeter measurements were about 18 and 42° in the 0.39- and 1.0-inch fractures, respectively, for groundwater velocities within the manufacturer's suggested range of application (0.5–100 ft/d). Measurements made at vertical increments and fracture positions in the well using the colloidal borescope indicate that laminar flow occurs within the central 50% of the fracture but measurements above or below are likely affected by eddy currents.
Relations between spectral absorbance and fluorescence properties of water and human-associated and fecal indicator bacteria were developed for facilitating field sensor applications to estimate wastewater contamination in waterways. Leaking wastewater conveyance infrastructure commonly contaminates receiving waters. Methods to quantify such contamination can be time consuming, expensive, and often nonspecific. Human-associated bacteria are wastewater specific but require discrete sampling and laboratory analyses, introducing latency. Human sewage has fluorescence and absorbance properties different than those of natural waters. To assist real-time field sensor development, this study investigated optical properties for use as surrogates for human-associated bacteria to estimate wastewater prevalence in environmental waters. Three spatial scales were studied: Eight watershed-scale sites, five subwatershed-scale sites, and 213 storm sewers and open channels within three small watersheds (small-scale sites) were sampled (996 total samples) for optical properties, human-associated bacteria, fecal indicator bacteria, and, for selected samples, human viruses. Regression analysis indicated that bacteria concentrations could be estimated by optical properties used in existing field sensors for watershed and subwatershed scales. Human virus occurrence increased with modeled human-associated bacteria concentration, providing confidence in these regressions as surrogates for wastewater contamination. Adequate regressions were not found for small-scale sites to reliably estimate bacteria concentrations likely due to inconsistent local sanitary sewer inputs.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.1c02644","usgsCitation":"Corsi, S., DeCicco, L.A., Hansen, A., Lenaker, P.L., Bergamaschi, B.A., Pellerin, B., Dila, D., Bootsma, M., Spencer, S., Borchardt, M.A., and McLellan, S.L., 2021, Optical properties of water for prediction of wastewater contamination, human-associated bacteria, and fecal indicator bacteria in surface water at three watershed scales: Environmental Science and Technology, v. 55, no. 20, p. 13770-13782, https://doi.org/10.1021/acs.est.1c02644.","productDescription":"13 p.","startPage":"13770","endPage":"13782","ipdsId":"IP-132758","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":450607,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.1c02644","text":"Publisher Index Page"},{"id":436178,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9I0SPDQ","text":"USGS data release","linkHelpText":"Optical signals of water for prediction of wastewater contamination, human-associated bacteria, and fecal indicator bacteria in surface water of Great Lake tributaries from 2011 to 2016"},{"id":390964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, New York, Ohio, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.176513671875,\n 45.706179285330855\n ],\n [\n -88.11035156249999,\n 46.63435070293566\n ],\n [\n -89.219970703125,\n 46.37725420510028\n ],\n [\n -89.176025390625,\n 45.84410779560204\n ],\n [\n 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]\n}","volume":"55","issue":"20","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"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":825732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeCicco, Laura A. 0000-0002-3915-9487 ldecicco@usgs.gov","orcid":"https://orcid.org/0000-0002-3915-9487","contributorId":174716,"corporation":false,"usgs":true,"family":"DeCicco","given":"Laura","email":"ldecicco@usgs.gov","middleInitial":"A.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water 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Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825735,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825736,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pellerin, Brian A. 0000-0003-3712-7884","orcid":"https://orcid.org/0000-0003-3712-7884","contributorId":204324,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian A.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":825737,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dila, Debra","contributorId":268031,"corporation":false,"usgs":false,"family":"Dila","given":"Debra","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":825738,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bootsma, Melinda","contributorId":268032,"corporation":false,"usgs":false,"family":"Bootsma","given":"Melinda","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":825739,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Spencer, Susan","contributorId":268033,"corporation":false,"usgs":false,"family":"Spencer","given":"Susan","affiliations":[{"id":38162,"text":"United States Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":825740,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":151033,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":825741,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McLellan, Sandra L. 0000-0003-3283-1151","orcid":"https://orcid.org/0000-0003-3283-1151","contributorId":210968,"corporation":false,"usgs":false,"family":"McLellan","given":"Sandra","email":"","middleInitial":"L.","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":825742,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70228198,"text":"70228198 - 2021 - Using the California Waterfowl Tracker to assess proximity of waterfowl to commercial poultry in the Central Valley of California","interactions":[],"lastModifiedDate":"2022-02-07T15:35:39.438342","indexId":"70228198","displayToPublicDate":"2021-09-30T09:30:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Using the California Waterfowl Tracker to assess proximity of waterfowl to commercial poultry in the Central Valley of California","docAbstract":"Migratory waterfowl are the primary reservoir of avian influenza viruses (AIV) which can be spread to commercial poultry. Surveillance efforts that track the location and abundance of wild waterfowl and link those data to inform assessments of risk and sampling for AIV currently do not exist. To assist surveillance and minimize poultry exposure to AIV, here we explored the utility of remotely sensed MODerate Resolution Imaging Spectroradiometer (MODIS) satellite imagery in combination with land-based climate measurements (e.g., temperature and precipitation) to predict waterfowl location and abundance in near real-time in the California Central Valley (CCV), where both wild waterfowl and domestic poultry are densely located. Specifically, remotely collected MODIS and climate data were integrated into a previously developed Boosted Regression Tree (BRT) model to predict and visualize waterfowl distributions across the CCV. Daily model-based predictions are publicly available during the winter as part of the dynamic California Waterfowl Tracker (CWT) web-app hosted on the University of California’s Cooperative Extension webpage. In this study, we analyzed 52 days of model predictions and produced daily spatio-temporal maps of waterfowl concentrations near the 605 commercial poultry farms in the CCV during January and February of 2019. Exposure of each poultry farm to waterfowl during each day was classified as “high”, “medium”, “low”, or “none” depending on the density of waterfowl within 4 km of a farm. Results indicated that farms were at substantially greater risk of “exposure” in January, when CCV waterfowl populations peak, than in February. For example, during January, 33% (199/605) of the farms were exposed ≥ 1 day to “high” waterfowl density versus 19% (115/605) of the farms in February. In addition to demonstrating the overall variability of waterfowl location and density, these data demonstrate how remote sensing can be used to better triage AIV surveillance and biosecurity efforts via the utilization of a functional web-app based tool. The ability to leverage remote sensing is an integral advancement toward improving AIV surveillance in waterfowl in close proximity to commercial poultry. Expansion of these types of remote sensing methods linked to a user-friendly web-tool could be further developed across the continental U.S. The BRT model incorporated into the CWT reflects a first attempt to give an accurate representation of waterfowl distribution and density relative to commercial poultry.","language":"English","publisher":"American Association of Avian Pathologists","doi":"10.1637/aviandiseases-D-20-00137","usgsCitation":"Acosta, S., Kelman, T., Feirer, S., Matchett, E., Smolinsky, J.A., Pitesky, M.E., and Buler, J.J., 2021, Using the California Waterfowl Tracker to assess proximity of waterfowl to commercial poultry in the Central Valley of California: Avian Diseases, v. 65, no. 3, p. 483-492, https://doi.org/10.1637/aviandiseases-D-20-00137.","productDescription":"10 p.","startPage":"483","endPage":"492","ipdsId":"IP-125341","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":395530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley of California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.958984375,\n 40.44694705960048\n ],\n [\n -122.51953124999999,\n 38.95940879245423\n ],\n [\n -121.70654296874999,\n 37.54457732085582\n ],\n [\n -120.08056640625,\n 35.92464453144099\n ],\n [\n -119.20166015625,\n 35.15584570226544\n ],\n [\n -118.43261718749999,\n 35.38904996691167\n ],\n [\n -119.0478515625,\n 36.73888412439431\n ],\n [\n -120.89355468749999,\n 38.238180119798635\n ],\n [\n -122.3876953125,\n 40.29628651711716\n ],\n [\n -122.958984375,\n 40.44694705960048\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Acosta, Sarai","contributorId":274842,"corporation":false,"usgs":false,"family":"Acosta","given":"Sarai","email":"","affiliations":[{"id":56669,"text":"Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA USA","active":true,"usgs":false}],"preferred":false,"id":833381,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelman, Todd","contributorId":274843,"corporation":false,"usgs":false,"family":"Kelman","given":"Todd","email":"","affiliations":[{"id":56669,"text":"Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA USA","active":true,"usgs":false}],"preferred":false,"id":833382,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Feirer, Shane","contributorId":274844,"corporation":false,"usgs":false,"family":"Feirer","given":"Shane","email":"","affiliations":[{"id":56670,"text":"Hopland Research & Extension Center, UC-Agriculture and Natural Resources. Hopland, CA USA","active":true,"usgs":false}],"preferred":false,"id":833383,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matchett, Elliott 0000-0001-5095-2884 ematchett@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-2884","contributorId":5541,"corporation":false,"usgs":true,"family":"Matchett","given":"Elliott","email":"ematchett@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833384,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smolinsky, Jaclyn A.","contributorId":202723,"corporation":false,"usgs":false,"family":"Smolinsky","given":"Jaclyn","email":"","middleInitial":"A.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":833385,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pitesky, Maurice E.","contributorId":176920,"corporation":false,"usgs":false,"family":"Pitesky","given":"Maurice","email":"","middleInitial":"E.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":833386,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Buler, Jeffrey J.","contributorId":194648,"corporation":false,"usgs":false,"family":"Buler","given":"Jeffrey","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":833387,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224634,"text":"70224634 - 2021 - Machine learning can assign geologic basin to produced water samples using major ion geochemistry","interactions":[],"lastModifiedDate":"2021-11-16T15:48:00.581979","indexId":"70224634","displayToPublicDate":"2021-09-30T08:16:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning can assign geologic basin to produced water samples using major ion geochemistry","docAbstract":"Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO3), Ca, Na, Cl, Mg, and sulfate (SO4). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO3, Ca, Na, Cl, Mg, SO4, pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.
","language":"English","publisher":"Springer Link","doi":"10.1007/s11053-021-09949-8","usgsCitation":"Shelton, J., Jubb, A., Saxe, S., Attanasi, E., Milkov, A., Engle, M.A., Freeman, P., Shaffer, C., and Blondes, M., 2021, Machine learning can assign geologic basin to produced water samples using major ion geochemistry: Natural Resources Research, v. 30, p. 4147-4163, https://doi.org/10.1007/s11053-021-09949-8.","productDescription":"17 p.","startPage":"4147","endPage":"4163","ipdsId":"IP-126045","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":450614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11053-021-09949-8","text":"Publisher Index Page"},{"id":390110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Shelton, Jenna L. 0000-0002-1377-0675 jlshelton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":5025,"corporation":false,"usgs":true,"family":"Shelton","given":"Jenna L.","email":"jlshelton@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saxe, Samuel 0000-0003-1151-8908","orcid":"https://orcid.org/0000-0003-1151-8908","contributorId":215753,"corporation":false,"usgs":true,"family":"Saxe","given":"Samuel","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":824456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Milkov, Alexei","contributorId":266160,"corporation":false,"usgs":false,"family":"Milkov","given":"Alexei","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":824458,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Engle, Mark A 0000-0001-5258-7374","orcid":"https://orcid.org/0000-0001-5258-7374","contributorId":228981,"corporation":false,"usgs":false,"family":"Engle","given":"Mark","email":"","middleInitial":"A","affiliations":[{"id":41535,"text":"The University of Texas at El Paso, Department of Geological Sciences, El Paso, TX 79968","active":true,"usgs":false}],"preferred":false,"id":824459,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":206294,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824460,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shaffer, Christopher","contributorId":266161,"corporation":false,"usgs":false,"family":"Shaffer","given":"Christopher","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":824461,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824462,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70237248,"text":"70237248 - 2021 - Loss of ice cover, shifting phenology, and more extreme events in Northern Hemisphere lakes","interactions":[],"lastModifiedDate":"2022-10-05T11:57:36.953598","indexId":"70237248","displayToPublicDate":"2021-09-30T06:54:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Loss of ice cover, shifting phenology, and more extreme events in Northern Hemisphere lakes","docAbstract":"Long-term lake ice phenological records from around the Northern Hemisphere provide unique sensitive indicators of climatic variations, even prior to the existence of physical meteorological measurement stations. Here, we updated ice phenology records for 60 lakes with time-series ranging from 107–204 years to provide the first re-assessment of Northern Hemispheric ice trends since 2004 by adding 15 additional years of ice phenology records and 40 lakes to our study. We found that, on average, ice-on was 11.0 days later, ice-off was 6.8 days earlier, and ice duration was 17.0 days shorter per century over the entire record for each lake. Trends in ice-on and ice duration were six times faster in the last 25-year period (1992–2016) than previous quarter centuries. More extreme events in recent decades, including late ice-on, early ice-off, shorter periods of ice cover, or no ice cover at all, contribute to the increasing rate of lake ice loss. Reductions in greenhouse gas emissions could limit increases in air temperature and abate losses in lake ice cover that would subsequently limit ecological, cultural, and socioeconomic consequences, such as increased evaporation rates, warmer water temperatures, degraded water quality, and the formation of toxic algal blooms.
The US Environmental Protection Agency's short-term freshwater effluent test methods include a fish (Pimephales promelas), a cladoceran (Ceriodaphnia dubia), and a green alga (Raphidocelis subcapitata). There is a recognized need for additional taxa to accompany the three standard species for effluent testing. An appropriate additional taxon is unionid mussels because mussels are widely distributed, live burrowed in sediment and filter particles from the water column for food, and exhibit high sensitivity to a variety of contaminants. Multiple studies were conducted to develop a relevant and robust short-term test method for mussels. We first evaluated the comparative sensitivity of two mussel species (Villosa constricta and Lampsilis siliquoidea) and two standard species (P. promelas and C. dubia) using two mock effluents prepared by mixing ammonia and five metals (cadmium, copper, nickel, lead, and zinc) or a field-collected effluent in 7-day exposures. Both mussel species were equally or more sensitive (more than two-fold) to effluents compared with the standard species. Next, we refined the mussel test method by first determining the best feeding rate of a commercial algal mixture for three age groups (1, 2, and 3 weeks old) of L. siliquoidea in a 7-day feeding experiment, and then used the derived optimal feeding rates to assess the sensitivity of the three ages of juveniles in a 7-day reference toxicant (sodium chloride [NaCl]) test. Juvenile mussels grew substantially (30%–52% length increase) when the 1- or 2-week-old mussels were fed 2 ml twice daily and the 3-week-old mussels were fed 3 ml twice daily. The 25% inhibition concentrations (IC25s) for NaCl were similar (314–520 mg Cl/L) among the three age groups, indicating that an age range of 1- to 3-week-old mussels can be used for a 7-day test. Finally, using the refined test method, we conducted an interlaboratory study among 13 laboratories to evaluate the performance of a 7-day NaCl test with L. siliquoidea. Eleven laboratories successfully completed the test, with more than 80% control survival and reliable growth data. The IC25s ranged from 296 to 1076 mg Cl/L, with a low (34%) coefficient of variation, indicating that the proposed method for L. siliquoidea has acceptable precision. Environ Toxicol Chem 2021;40:3392–3409. © 2021 SETAC
Water resources in the Big Lost River Basin, Idaho are vital to irrigated agriculture, domestic, municipal and other uses but declining groundwater levels, diminished streamflows, and concern about drought motivated an evaluation of water resources in the basin. This multichapter volume documents the findings of a hydrogeologic investigation of the Big Lost River Basin that was jointly conducted by the U.S. Geological Survey, Idaho Department of Water Resources, and Idaho Geological Survey from 2018 through 2021. Chapter A (Zinsser, 2021) describes the hydrogeologic framework of the Big Lost River Basin. The framework presents a conceptual definition of four hydrogeologic units, a three-dimensional hydrogeologic framework model representing the spatial occurrence of the hydrogeologic units, and a description of groundwater occurrence and movement. Chapter B (Dudunake and Zinsser, 2021) describes streamflow gains from and losses to groundwater in the Big Lost River between Mackay Reservoir and south of Arco. Streamflow losses and gains were estimated from a series of four measurement events completed during spring and fall conditions from 2019 to 2021. Chapter C (Clark, 2022) describes budgets for the Big Lost River Basin from 2000 to 2019. The groundwater budgets provide annual estimates for aquifer inflows and outflows and include representations of average, wet, and dry conditions. Collectively, these reports present a characterization of water resources in the Big Lost River Basin that will help address current challenges in water-resources management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215078","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Zinsser, L.M., ed., Characterization of water resources in the Big Lost River Basin, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2021–5078, https://doi.org/10.3133/sir20215078.","onlineOnly":"Y","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":389977,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5078/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Big Lost River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.16992187499999,\n 43.22919511396498\n ],\n [\n -112.82958984374999,\n 43.22919511396498\n ],\n [\n -112.82958984374999,\n 44.18220395771566\n ],\n [\n -114.16992187499999,\n 44.18220395771566\n ],\n [\n -114.16992187499999,\n 43.22919511396498\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
The Big Lost River of south-central Idaho interacts with the underlying aquifer by gaining and losing streamflow throughout various areas in the Big Lost River Valley. Surface-water and groundwater resources are used throughout the valley to sustain domestic, agricultural, and livestock needs. The U.S. Geological Survey, in cooperation with the Idaho Department of Water Resources, evaluated streamflow gains and losses by differential streamgaging in the lower Big Lost River, Idaho, during four measurement events: March 27–28, 2019; October 16–17, 2019; October 6–7, 2020; and March 30, 2021. This report presents and analyzes streamflow measurement and uncertainty data from each measurement event to describe surface-water/groundwater interactions. This report is the second chapter of a multi-chapter volume that characterizes water resources in the Big Lost River Basin.
During the four measurement events, 100 streamflow measurements were made at 46 unique sites on the Big Lost River, James Creek, and diversions or tributaries between Mackay Reservoir near Mackay and Arco, Idaho. Aquifer lithology and dimensions affected spatial patterns of streamflow gains and losses between the upper, middle, and lower reaches; changes in water supply, groundwater levels, and surface-water management affected seasonal differences within reaches. In the upper reach of the Big Lost River, streamflow losses and gains were greater during the wetter 2019 events and lesser during the drier 2020 and 2021 events. The middle reach includes the largest losses from the Big Lost River to groundwater; these losses occurred in the Darlington Sinks where 42 percent or more of streamflow was lost as the aquifer widens and groundwater deepens. These results suggest that changing surface-water supply, irrigation use, and recharge affect interannual groundwater levels and, in turn, affect patterns of streamflow gains and losses in the middle reach. Finally, surface-water management is the primary control on surface-water/groundwater interactions in the lower reach. Overall patterns of streamflow gains and losses in this study generally were consistent with previous reports. However, paired with the related hydrogeologic framework and water budget, this investigation provides new insights into how hydrogeologic conditions and interannual variability in water supply, groundwater levels, and surface-water management affect surface-water/groundwater interactions in the Big Lost River Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215078B","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Dudunake, T.J., and Zinsser, L.M., 2021, Surface-water and groundwater interactions in the Big Lost River, south-central Idaho, chap. B of Zinsser, L.M., ed., Characterization of water resources in the Big Lost River Basin, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2021–5078–B, 33 p., https://doi.org/10.3133/sir20215078B.","productDescription":"vii, 33 p.","onlineOnly":"Y","ipdsId":"IP-125229","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":409272,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5078/b/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2021-5078B Version History"},{"id":396946,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5078/b/images"},{"id":396945,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5078/b/sir20215078B.XML"},{"id":390009,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5078/b/sir20215078B.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5078B"},{"id":390008,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5078/b/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Big Lost River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.16992187499999,\n 43.22919511396498\n ],\n [\n -112.82958984374999,\n 43.22919511396498\n ],\n [\n -112.82958984374999,\n 44.18220395771566\n ],\n [\n -114.16992187499999,\n 44.18220395771566\n ],\n [\n -114.16992187499999,\n 43.22919511396498\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
Biogenic methane is estimated to account for one-fifth of the natural gas worldwide and there is great interest in controlling methane from different sources. Biogenic coalbed methane (CBM) production relies on syntrophic associations between fermentative bacteria and methanogenic archaea to anaerobically degrade recalcitrant coal and produce methanogenic substrates. However, very little is known about how differences in geochemistry, hydrology, and microbial community composition influence subsurface carbon utilization and CBM production. The addition of an amendment consisting of microalgal biomass has previously been shown to increase CBM production while providing the possibility of a closed-loop fossil system where waste (production water) is used to grow algae to ultimately produce energy (methane). However, the efficiency of enhancing CBM production under different redox conditions remains unresolved. In this study, we focused on the U.S. Geological Survey's Birney test site (Montana, USA) that has nine wells vertically accessing four coal seams with varying geochemistry (low and high sulfate (SO42−)) and methane production rates. We used organic matter (OM) in the form of algal biomass to discern the effect of this amendment on OM degradation and microbially enhanced CBM production potential under different geochemical constraints. We tracked changes in community composition, OM composition, organic carbon (OC) concentration, methane production, and nutrients in batch systems over six months. Methane production was detected only in microcosms from low SO42− wells (168 to 800 μg methane per gram of coal). The OC consumption varied across time for all wells and the variation was greatest for the low SO42− wells. Different groups of syntrophic bacteria were associated with net‑carbon consuming microcosms, and specifically Syntrophorhabdus was identified with several different statistical methods as a potentially important coal degrader. Results from this study provide insight into potential coal-degraders, the compositional changes in some of the different OM fractions, and trends in carbon consumption related to methane production across coal seams along the vertical SO42− gradient.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2021.103860","usgsCitation":"Smith, H.J., Schweitzer, H.S., Barnhart, E.P., Orem, W.H., Gerlach, R., and Fields, M.W., 2021, Effect of an algal amendment on the microbial conversion of coal to methane at different sulfate concentrations from the Powder River Basin, USA: International Journal of Coal Geology, v. 248, 103860, 16 p., https://doi.org/10.1016/j.coal.2021.103860.","productDescription":"103860, 16 p.","ipdsId":"IP-106713","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":450630,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/10037/24259","text":"External Repository"},{"id":391509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Powder River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.75439453125,\n 41.409775832009565\n ],\n [\n -104.765625,\n 41.83682786072714\n ],\n [\n -104.23828125,\n 44.59046718130883\n ],\n [\n -104.9853515625,\n 46.649436163350245\n ],\n [\n -106.58935546875,\n 46.7549166192819\n ],\n [\n -108.1494140625,\n 46.51351558059737\n ],\n [\n -108.12744140625,\n 45.38301927899065\n ],\n [\n -106.41357421875,\n 43.6599240747891\n ],\n [\n -105.99609375,\n 41.83682786072714\n ],\n [\n -105.75439453125,\n 41.409775832009565\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"248","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Heidi J.","contributorId":268344,"corporation":false,"usgs":false,"family":"Smith","given":"Heidi","email":"","middleInitial":"J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":826465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schweitzer, Hannah S.","contributorId":268345,"corporation":false,"usgs":false,"family":"Schweitzer","given":"Hannah","email":"","middleInitial":"S.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":826466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":826468,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gerlach, Robin","contributorId":203247,"corporation":false,"usgs":false,"family":"Gerlach","given":"Robin","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":826469,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fields, Matthew W.","contributorId":172391,"corporation":false,"usgs":false,"family":"Fields","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":826470,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224534,"text":"ofr20211080 - 2021 - Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making","interactions":[],"lastModifiedDate":"2021-09-29T11:36:22.700641","indexId":"ofr20211080","displayToPublicDate":"2021-09-28T09:20:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1080","displayTitle":"Optimization of Salt Marsh Management at the Rachel Carson National Wildlife Refuge, Maine, Through Use of Structured Decision Making","title":"Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making","docAbstract":"Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop an example of a prototype tool for optimizing tidal marsh management decisions for selected marsh management units at the Rachel Carson National Wildlife Refuge in Maine. The goal was to create a prototype that could be available for future implementation. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of seven marsh management units within the refuge and estimated the outcomes of each action in terms of regional performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale.
Management costs were estimated using limited available information, and estimated costs of individual management actions reflected relative differences among actions rather than actual expected expenditures. Results from this prototype showed how, for the objectives, actions, and estimated outcomes used for this example, total management benefits may increase consistently up to a certain estimated cost, and may continue to increase, at a lower rate, with further expenditures. Potential management actions in optimal portfolios at moderate total estimated costs included breaching or removing dikes, roads, or embankments; planting Spartina alterniflora (smooth cordgrass); and digging runnels, or shallow creeks, on the marsh platform to improve surface-water drainage. Potential management actions in optimal portfolios at high estimated costs (for example, up to $550,000) included breaching embankments to restore tidal exchange followed by planting salt marsh vegetation. The potential management benefits were derived from predicted increases in the numbers of tidal marsh obligate birds and spiders (as an indicator of trophic health), and expected improvement in the capacity of marsh elevation to keep pace with sea-level rise and reduced duration of marsh-surface inundation. The prototype presented here does not resolve current management decisions; rather, it provides a framework for decision making at the Rachel Carson National Wildlife Refuge that can be updated for implementation as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211080","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., O’Brien, K.M., Benvenuti, B., and Kleinert, R., 2021, Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1080, 35 p., https://doi.org/10.3133/ofr20211080.","productDescription":"vi, 35 p.","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126540","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":389743,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1080/coverthb.jpg"},{"id":389744,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1080/ofr20211080.pdf","text":"Report","size":"4.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1080"},{"id":389737,"rank":1,"type":{"id":9,"text":"Database"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/121918","text":"U.S. Fish and Wildlife Service database","linkHelpText":"- Salt marsh integrity and Hurricane Sandy vegetation, bird and nekton data"},{"id":389746,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1080/images/"},{"id":389747,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1080/ofr20211080.XML"}],"country":"United States","state":"Maine","otherGeospatial":"Rachel Carson National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.63796997070312,\n 43.20417480788432\n ],\n [\n -70.61325073242188,\n 43.153101551466385\n ],\n [\n -70.477294921875,\n 43.257205668363206\n ],\n [\n -70.43472290039062,\n 43.38508989465156\n ],\n [\n -70.53634643554688,\n 43.393073720674415\n ],\n [\n -70.63796997070312,\n 43.31418735795809\n ],\n [\n -70.63796997070312,\n 43.20417480788432\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
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The Los Angeles Coastal Plain (LACP) covers about 580 square miles and is the largest coastal plain of semiarid southern California. The LACP is heavily developed with mostly residential, commercial, and industrial land uses that rely heavily on groundwater for water supply. In 2010, the LACP was home to about 14 percent of California’s population, or about 5.4 million residents. The LACP is also a major commercial and industrial hub with industries including manufacturing, aerospace, entertainment, and tourism.
There has been a heavy reliance on groundwater from the LACP for many years. An average of 305,000 acre-feet per year (acre-ft/yr) of groundwater was used annually from the LACP from 1971 to 2015. The need to replenish the groundwater basins within the LACP was recognized as far back as the 1930s, when spreading grounds were first used to replenish groundwater basins and store water underground during times of water surplus to meet demands in times of shortage. Seawater intrusion resulting from freshwater pumping was first observed in the 1940s. As a result, injection of imported water through wells at what is now the West Coast Basin Barrier Project began on an experimental basis in 1951. Managed aquifer recharge from the spreading grounds and barrier wells is now a substantial component of the LACP’s groundwater supply. The average annual recharge from water spreading from 1971 to 2015 was about 120,000 acre-ft/yr, and the average annual injection into the barrier wells was about 33,000 acre-ft/yr. Other inflows include areal recharge, underflow from San Gabriel and San Fernando Valleys, and onshore flow from the ocean. The average annual recharge from these sources was 100,000 acre-feet (acre-ft) from 1971 to 2015. Additionally, cross-boundary flow from Orange County into the western Orange County subareas of the LACP was simulated as 48,000 acre-ft from 1971 to 2015.
This study, conducted in cooperation with the Water Replenishment District of Southern California (WRD), involved an assessment of the historical and present status of groundwater resources in the LACP and the development of tools to better understand the groundwater system. These efforts were built upon results from previous studies and incorporate new information and developments in modeling capabilities to provide a more detailed analysis of the aquifer systems.
This study includes a comprehensive compilation of geologic and hydrologic data (Chapter A), development of a chronostratigraphic model that provides a detailed description of the LACP aquifer systems (Chapter B), characterization of the groundwater hydrology of the LACP, including a down-hole analysis of grain size using lithologic and geophysical logs (Chapter C), and development and application of the Los Angeles Coastal Plain Groundwater-flow Model (LACPGM) to simulate past groundwater conditions, estimate groundwater-budget components and flow paths, and approximate future groundwater conditions under different scenarios (Chapter D).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215088","collaboration":"Prepared in cooperation with the Water Replenishment District of Southern California","usgsCitation":"Paulinski, S., ed., 2021, Development of a groundwater-simulation model in the Los Angeles Coastal Plain, Los Angeles County, California (ver. 1.1, May 2023): U.S. Geological Survey Scientific Investigations Report 2021-5088, 489 p., https://doi.org/10.3133/sir20215088.","productDescription":"Report: xiii, 489 p.; Data Release","numberOfPages":"489","onlineOnly":"Y","ipdsId":"IP-023155","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":389755,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H15ZAX","linkHelpText":"MODFLOW-USG model used to evaluate water management issues in the Los Angeles Coastal Plain, California"},{"id":389754,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5088/sir20215088_v1.1.pdf","text":"Report","size":"66 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":389753,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5088/covrthb_.jpg"},{"id":416877,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5088/versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"}},{"id":436182,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TJD4IE","text":"USGS data release","linkHelpText":"MODFLOW-6 model to update and extend the Los Angeles Coastal Plain Groundwater Model"},{"id":500446,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_111785.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","county":"Los Angeles County","otherGeospatial":"Los Angeles Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.90802001953125,\n 33.59860671494885\n ],\n [\n -117.59490966796875,\n 33.876116579321206\n ],\n [\n -117.82012939453125,\n 34.14249823152873\n ],\n [\n -118.20327758789062,\n 34.23337699755914\n ],\n [\n -118.53973388671874,\n 34.03672867489511\n ],\n [\n -118.41476440429686,\n 33.80083235326659\n ],\n [\n -118.24722290039061,\n 33.72776616734189\n ],\n [\n -117.90802001953125,\n 33.59860671494885\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: September 2021; Version 1.1: May 2023","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Schistosome parasites infect more than 200 million people annually, mostly in sub-Saharan Africa, where people may be co-infected with more than one species of the parasite. Infection risk for any single species is determined, in part, by the distribution of its obligate intermediate host snail. As the World Health Organization reprioritizes snail control to reduce the global burden of schistosomiasis, there is renewed importance in knowing when and where to target those efforts, which could vary by schistosome species. This study estimates factors associated with schistosomiasis risk in 16 villages located in the Senegal River Basin, a region hyperendemic for Schistosoma haematobium and S. mansoni. We first analyzed the spatial distributions of the two schistosomes’ intermediate host snails (Bulinus spp. and Biomphalaria pfeifferi, respectively) at village water access sites. Then, we separately evaluated the relationships between human S. haematobium and S. mansoni infections and (i) the area of remotely-sensed snail habitat across spatial extents ranging from 1 to 120 m from shorelines, and (ii) water access site size and shape characteristics. We compared the influence of snail habitat across spatial extents because, while snail sampling is traditionally done near shorelines, we hypothesized that snails further from shore also contribute to infection risk. We found that, controlling for demographic variables, human risk for S. haematobium infection was positively correlated with snail habitat when snail habitat was measured over a much greater radius from shore (45 m to 120 m) than usual. S. haematobium risk was also associated with large, open water access sites. However, S. mansoni infection risk was associated with small, sheltered water access sites, and was not positively correlated with snail habitat at any spatial sampling radius. Our findings highlight the need to consider different ecological and environmental factors driving the transmission of each schistosome species in co-endemic landscapes.
A numerical groundwater flow model of the Hualapai Valley Basin in northwestern Arizona was developed to assist water-resource managers in understanding the potential effects of projected groundwater withdrawals on groundwater levels in the basin. The Hualapai Valley Hydrologic Model (HVHM) simulates the hydrologic system for the years 1935 through 2219, including future withdrawal scenarios that simulate large-scale agricultural expansion with and without enhanced groundwater recharge from potential new infiltration basin projects. HVHM is a highly parameterized model (75,586 adjustable parameters) capable of simulating grid-scale variability in aquifer properties (for example, conductivity, specific yield, and specific storage) and system stresses (for instance, natural recharge and groundwater withdrawals). Parameter estimation and uncertainty quantification were performed using an iterative ensemble smoother software (PESTPP-IES) to produce an ensemble of models fit to historical data. Results via the future withdrawal scenario from this ensemble indicate that mean groundwater level will decline at wells in the Kingman subbasin 87 to 128 feet by the year 2050 and 204 to 241 feet by the year 2080. Mean groundwater level is expected to decline at wells in the Hualapai subbasin between 44 and 210 feet by 2050 and between 107 and 350 feet by 2080. The enhanced recharge scenario results show potential for these declines to be partially mitigated in the Kingman subbasin by between 8 and 23 feet in 2050 and between 23 and 43 feet in 2080. The enhanced recharge scenario has no simulated effect on groundwater levels in the Hualapai subbasin. All planned enhanced infiltration projects are located in the Kingman subbasin, which is simulated to become hydraulically disconnected from the Hualapai subbasin owing to groundwater-level declines before 2050. Mean depth to water in the Kingman subbasin as simulated in the future withdrawal scenario will exceed 1,200 feet between the years 2155 and 2214 (median year 2171). In the future withdrawal plus enhanced recharge scenario, mean depth to water in the Kingman subbasin exceeds 1,200 feet between the years 2163 and 2207 (median year 2180), except for one model realization in which the subbasin does not reach an mean depth to water of 1,200 feet by the end of forecast simulation (year 2220). Simulated dewatering of the basin margins reduces scenario pumping rates by as much as 7 percent in 2029 and 12 percent in 2079 below specified rates. Forecasts of groundwater-level declines are based on the reduced simulated pumping rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215077","collaboration":"Prepared in cooperation with Mohave County and the City of Kingman","usgsCitation":"Knight, J.E., Gungle, B., and Kennedy, J.R., 2021, Assessing potential groundwater-level declines from future withdrawals in the Hualapai Valley, northwestern Arizona: U.S. Geological Survey Scientific Investigations Report, 63 p., https://doi.org/10.3133/sir20215077.","productDescription":"Report: vii, 63 p.; Data Release","numberOfPages":"63","onlineOnly":"Y","ipdsId":"IP-118946","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":436183,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MJRMSQ","text":"USGS data release","linkHelpText":"Repeat microgravity data from the Hualapai Valley, Mohave County, Arizona, 2008-2019"},{"id":389758,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20125275","text":"Scientific Investigations Report 2012-5275","linkHelpText":"— Hydrogeologic framework and estimates of groundwater storage for the Hualapai Valley, Detrital Valley, and Sacramento Valley basins, Mohave County, Arizona"},{"id":389739,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5077/sir20215077.pdf","text":"Report","size":"26 MB"},{"id":389759,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20135122","text":"Scientific Investigations Report 2013-5122","linkHelpText":"— Preliminary groundwater flow model of the basin-fill aquifers in Detrital, Hualapai, and Sacramento Valleys, Mohave County, northwestern Arizona"},{"id":389740,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9017DI9","linkHelpText":"Data release for transient groundwater model of the Hualapai Valley Groundwater Basin, Mohave County, Arizona"},{"id":389738,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5077/covrthb.jpg"},{"id":389756,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20075182","text":"Scientific Investigations Report 2007-5182","linkHelpText":"— Ground-Water Occurrence and Movement, 2006, and Water-Level Changes in the Detrital, Hualapai, and Sacramento Valley Basins, Mohave County, Arizona"},{"id":389757,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20115159","text":"Scientific Investigations Report 2011-5159","linkHelpText":"— Groundwater budgets for Detrital, Hualapai, and Sacramento Valleys, Mohave County, Arizona, 2007-08"}],"country":"United States","state":"Arizona","otherGeospatial":"Hualapai Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.5,\n 36\n ],\n [\n -113.5,\n 36\n ],\n [\n -113.5,\n 35\n ],\n [\n -114.5,\n 35\n ],\n [\n -114.5,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
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In regions where water resources are scarce and in high demand, it is important to safeguard against contamination of groundwater aquifers by oil-field fluids (water, gas, oil). In this context, the geochemical characterisation of these fluids is critical so that anthropogenic contaminants can be readily identified. The first step is characterising pre-development geochemical fluid signatures (i.e., those unmodified by hydrocarbon resource development) and understanding how these signatures may have been perturbed by resource production, particularly in the context of enhanced oil recovery (EOR) techniques. Here, we present noble gas isotope data in fluids produced from oil wells in several water-stressed regions in California, USA, where EOR is prevalent. In oil-field systems, only casing gases are typically collected and measured for their noble gas compositions, even when oil and/or water phases are present, due to the relative ease of gas analyses. However, this approach relies on a number of assumptions (e.g., equilibrium between phases, water-to-oil ratio (WOR) and gas-to-oil ratio (GOR) in order to reconstruct the multiphase subsurface compositions. Here, we adopt a novel, more rigorous approach, and measure noble gases in both casing gas and produced fluid (oil-water-gas mixtures) samples from the Lost Hills, Fruitvale, North and South Belridge (San Joaquin Basin, SJB) and Orcutt (Santa Maria Basin) Oil Fields. Using this method, we are able to fully characterise the distribution of noble gases within a multiphase hydrocarbon system. We find that measured concentrations in the casing gases agree with those in the gas phase in the produced fluids and thus the two sample types can be used essentially interchangeably.
EOR signatures can readily be identified by their distinct air-derived noble gas elemental ratios (e.g., 20Ne/36Ar), which are elevated compared to pre-development oil-field fluids, and conspicuously trend towards air values with respect to elemental ratios and overall concentrations. We reconstruct reservoir 20Ne/36Ar values using both casing gas and produced fluids and show that noble gas ratios in the reservoir are strongly correlated (r2 = 0.88–0.98) to the amount of water injected within ~500 m of a well. We suggest that the 20Ne/36Ar increase resulting from injection is sensitive to the volume of fluid interacting with the injectate, the effective water-to-oil ratio, and the composition of the injectate. Defining both the pre-development and injection-modified hydrocarbon reservoir compositions are crucial for distinguishing the sources of hydrocarbons observed in proximal groundwaters, and for quantifying the transport mechanisms controlling this occurrence.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2021.120540","usgsCitation":"Tyne, R.L., Barry, P.H., Karolytė, R., Bryne, D.J., Kulongoski, J.T., Hillegonds, D., and Ballentine, C.J., 2021, Investigating the effect of enhanced oil recovery on the noble gas signature of casing gases and produced waters from selected California oil fields: Chemical Geology, v. 584, 120540, 10 p., https://doi.org/10.1016/j.chemgeo.2021.120540.","productDescription":"120540, 10 p.","ipdsId":"IP-126638","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":450655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2021.120540","text":"Publisher Index Page"},{"id":393752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"Fruitvale, Lost Hills and North and South Belridge Oil Fields","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.73150634765625,\n 34.4069096565206\n ],\n [\n -119.24011230468749,\n 34.4069096565206\n ],\n [\n -119.24011230468749,\n 35.85566574217861\n ],\n [\n -120.73150634765625,\n 35.85566574217861\n ],\n [\n -120.73150634765625,\n 34.4069096565206\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"584","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tyne, R. L.","contributorId":205891,"corporation":false,"usgs":false,"family":"Tyne","given":"R.","email":"","middleInitial":"L.","affiliations":[{"id":37187,"text":"Department of Earth Sciences, University of Oxford, Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":829842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barry, P. H.","contributorId":270728,"corporation":false,"usgs":false,"family":"Barry","given":"P.","email":"","middleInitial":"H.","affiliations":[{"id":56200,"text":"Dept. of Marine Chem. and Geochem., Woods Hole Oceanographic Institution, Woods Hole, MA, USA","active":true,"usgs":false}],"preferred":false,"id":829843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karolytė, R.","contributorId":270729,"corporation":false,"usgs":false,"family":"Karolytė","given":"R.","affiliations":[{"id":56201,"text":"Dept. of Earth Sci., University of Oxford, Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":829844,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bryne, D. J.","contributorId":270730,"corporation":false,"usgs":false,"family":"Bryne","given":"D.","email":"","middleInitial":"J.","affiliations":[{"id":56201,"text":"Dept. of Earth Sci., University of Oxford, Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":829845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829846,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hillegonds, D.J.","contributorId":205892,"corporation":false,"usgs":false,"family":"Hillegonds","given":"D.J.","email":"","affiliations":[{"id":37187,"text":"Department of Earth Sciences, University of Oxford, Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":829847,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ballentine, C. J.","contributorId":224737,"corporation":false,"usgs":false,"family":"Ballentine","given":"C.","email":"","middleInitial":"J.","affiliations":[{"id":40928,"text":"Oxford University","active":true,"usgs":false}],"preferred":false,"id":829848,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}