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
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230 Collins Road
Boise, Idaho 83702-4520
The State of Hawai'i passed legislation to be carbon neutral by 2045, a goal that will partly depend on carbon sequestration by terrestrial ecosystems. However, there is considerable uncertainty surrounding the future direction and magnitude of the land carbon sink in the Hawaiian Islands. We used the Land Use and Carbon Scenario Simulator (LUCAS), a spatially explicit stochastic simulation model that integrates landscape change and carbon gain-loss, to assess how projected future changes in climate and land use will influence ecosystem carbon balance in the Hawaiian Islands under all combinations of two radiative forcing scenarios (RCPs 4.5 and 8.5) and two land use scenarios (low and high) over a 90 year timespan from 2010 to 2100. Collectively, terrestrial ecosystems of the Hawaiian Islands acted as a net carbon sink under low radiative forcing (RCP 4.5) for the entire 90 year simulation period, with low land use change further enhancing carbon sink strength. In contrast, Hawaiian terrestrial ecosystems transitioned from a net sink to a net source of CO2 to the atmosphere under high radiative forcing (RCP 8.5), with high land use accelerating this transition and exacerbating net carbon loss. A sensitivity test of the CO2 fertilization effect on plant productivity revealed it to be a major source of uncertainty in projections of ecosystem carbon balance, highlighting the need for greater mechanistic understanding of plant productivity responses to rising atmospheric CO2. Long-term model projections such as ours that incorporate the interactive effects of land use and climate change on regional ecosystem carbon balance will be critical to evaluating the potential of ecosystem-based climate mitigation strategies.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ac2347","usgsCitation":"Selmants, P., Sleeter, B.M., Liu, J., Wilson, T., Trauernicht, C., Frazier, A.G., and Asner, G.P., 2021, Ecosystem carbon balance in the Hawaiian Islands under different scenarios of future climate and land use change: Environmental Research Letters, v. 16, 104020, 14 p., https://doi.org/10.1088/1748-9326/ac2347.","productDescription":"104020, 14 p.","ipdsId":"IP-119813","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":450627,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac2347","text":"Publisher Index Page"},{"id":436181,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AWLFKZ","text":"USGS data release","linkHelpText":"Land change and carbon balance projections for the Hawaiian 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University","active":true,"usgs":false}],"preferred":false,"id":824905,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70268411,"text":"70268411 - 2021 - Understanding genetics for successful conservation and restoration of resilient Chesapeake Bay brook trout populations","interactions":[],"lastModifiedDate":"2025-06-25T14:13:55.200306","indexId":"70268411","displayToPublicDate":"2021-09-29T09:12:29","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Understanding genetics for successful conservation and restoration of resilient Chesapeake Bay brook trout populations","docAbstract":"Traditionally, fisheries management has focused on the abundance, distribution, and size structure of populations. Although these factors remain key aspects of management, a large and growing body of evidence highlights the importance of genetics in conserving wild populations, especially when populations are small and isolated (Frankham et al. 2017). Local adaptations are very common among fishes and help populations cope with specific conditions in their local environment (Fraser et al. 2011). The field of conservation genetics and genomics is highly technical and has advanced rapidly in recent years, offering a wealth of information to support brook trout conservation and restoration. A major impediment to successfully incorporating these advances into conservation outcomes is that most fisheries managers have only a basic understanding of fish genetics and its relevance to their management decisions.","language":"English","publisher":"STAC","usgsCitation":"Kazyak, D.C., Hallerman, E.M., Maloney, L., Faulkner, S., Welsh, A., Coombs, J., Whiteley, A., Rash, J., White, S.L., Bartron, M.L., Kulp, M.A., and Meek, M., 2021, Understanding genetics for successful conservation and restoration of resilient Chesapeake Bay brook trout populations, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-173712","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":491275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":491274,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.chesapeake.org/stac/events/understanding-genetics-for-successful-conservation-and-restoration-of-resilient-chesapeake-bay-brook-trout-populations/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.71040420312731,\n 39.694677079680844\n ],\n [\n -76.93796273435163,\n 39.694677079680844\n ],\n [\n -76.93796273435163,\n 37.158701995783204\n ],\n [\n -75.71040420312731,\n 37.158701995783204\n ],\n [\n -75.71040420312731,\n 39.694677079680844\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":941248,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hallerman, E. M.","contributorId":280251,"corporation":false,"usgs":false,"family":"Hallerman","given":"E.","email":"","middleInitial":"M.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":941251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maloney, Lori","contributorId":357372,"corporation":false,"usgs":false,"family":"Maloney","given":"Lori","affiliations":[],"preferred":false,"id":941304,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faulkner, Stephen 0000-0001-5295-1383 faulkners@usgs.gov","orcid":"https://orcid.org/0000-0001-5295-1383","contributorId":146152,"corporation":false,"usgs":true,"family":"Faulkner","given":"Stephen","email":"faulkners@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":941305,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Welsh, Amy","contributorId":287823,"corporation":false,"usgs":false,"family":"Welsh","given":"Amy","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":941252,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coombs, Jason","contributorId":205478,"corporation":false,"usgs":false,"family":"Coombs","given":"Jason","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":941306,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Whiteley, Andrew","contributorId":299020,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","affiliations":[{"id":48908,"text":"U Montana","active":true,"usgs":false}],"preferred":false,"id":941307,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rash, Jake","contributorId":357373,"corporation":false,"usgs":false,"family":"Rash","given":"Jake","affiliations":[],"preferred":false,"id":941308,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":941249,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bartron, Meredith L.","contributorId":149109,"corporation":false,"usgs":false,"family":"Bartron","given":"Meredith","email":"","middleInitial":"L.","affiliations":[{"id":26874,"text":"USFWS, Lamar, PA","active":true,"usgs":false},{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":941250,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kulp, Matt A.","contributorId":196801,"corporation":false,"usgs":false,"family":"Kulp","given":"Matt","email":"","middleInitial":"A.","affiliations":[{"id":35484,"text":"National Park Service, Great Smoky Mountains National Park","active":true,"usgs":false}],"preferred":false,"id":941309,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Meek, Mariah","contributorId":260835,"corporation":false,"usgs":false,"family":"Meek","given":"Mariah","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":941310,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70225742,"text":"70225742 - 2021 - Effect of an algal amendment on the microbial conversion of coal to methane at different sulfate concentrations from the Powder River Basin, USA","interactions":[],"lastModifiedDate":"2021-11-09T14:39:08.788372","indexId":"70225742","displayToPublicDate":"2021-09-29T08:34:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Effect of an algal amendment on the microbial conversion of coal to methane at different sulfate concentrations from the Powder River Basin, USA","docAbstract":"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":70224919,"text":"70224919 - 2021 - Compositional evolution of organic matter in Boquillas Shale across a thermal gradient at the single particle level","interactions":[],"lastModifiedDate":"2021-10-05T12:42:29.156695","indexId":"70224919","displayToPublicDate":"2021-09-29T07:40:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Compositional evolution of organic matter in Boquillas Shale across a thermal gradient at the single particle level","docAbstract":"The molecular composition of petroliferous organic matter and its compositional evolution throughout thermal maturation provides insight for understanding petroleum generation. This information is critical for understanding hydrocarbon resources in unconventional reservoirs, as source rock organic matter is highly dispersed, in contact with the surrounding mineral matrix, and may occur as multiple organic matter maceral types. Here, Raman spectroscopy and optical microscopy approaches were applied to a marginally mature (vitrinite reflectance ~0.5%) sample of the Late Cretaceous Boquillas Shale before and after hydrous pyrolysis (HP) at 300 °C and 330 °C for 72 h. This analytical approach allowed for correlative examination of micro-scale changes in organic matter compositional properties (e.g., aromaticity) for a variety of organic matter macerals across a thermal gradient (from marginally mature into the late oil/wet gas window) at the single particle level. Results indicate that while the examined amorphous organic matter, solid bitumen, and vitrinite particles exhibit different aromatic signatures in the unheated shale, they effectively progress along a similar trend through composition space with thermal maturation. Examined inertinite fragments were generally insensitive to the applied thermal stress, reinforcing the idea that reservoir temperature may be secondary for dictating the molecular composition of inertinite. Additional analysis of Raman spectra for individual organic matter macerals was performed using multivariate curve resolution (MCR) and correlation of standard Raman and reflectance-derived thermal maturity proxies against MCR parameters shows consistent trends. This trend suggests that MCR may be a fast and statistically robust method for extracting compositional information from Raman spectra of sedimentary organic matter, and can be used to construct thermal maturity relationships. These findings inform our understanding of how different petroliferous organic matter maceral types evolve throughout thermal reactions and further demonstrate that Raman spectroscopy combined with petrographic analysis can provide complementary estimates of organic matter composition and thermal maturity.
Climate change threatens global biodiversity. Many species vulnerable to climate change are important to humans for nutritional, cultural, and economic reasons. Polar bears Ursus maritimus are threatened by sea-ice loss and represent a subsistence resource for Indigenous people. We applied a novel population modeling-management framework that is based on species life history and accounts for habitat loss to evaluate subsistence harvest for the Chukchi Sea (CS) polar bear subpopulation. Harvest strategies followed a state-dependent approach under which new data were used to update the harvest on a predetermined management interval. We found that a harvest strategy with a starting total harvest rate of 2.7% (˜85 bears/yr at current abundance), a 2:1 male-to-female ratio, and a 10-yr management interval would likely maintain subpopulation abundance above maximum net productivity level for the next 35 yr (approximately three polar bear generations), our primary criterion for sustainability. Plausible bounds on starting total harvest rate were 1.7–3.9%, where the range reflects uncertainty due to sampling variation, environmental variation, model selection, and differing levels of risk tolerance. The risk of undesired demographic outcomes (e.g., overharvest) was positively related to harvest rate, management interval, and projected declines in environmental carrying capacity; and negatively related to precision in population data. Results reflect several lines of evidence that the CS subpopulation has been productive in recent years, although it is uncertain how long this will last as sea-ice loss continues. Our methods provide a template for balancing trade-offs among protection, use, research investment, and other factors. Demographic risk assessment and state-dependent management will become increasingly important for harvested species, like polar bears, that exhibit spatiotemporal variation in their response to climate change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2461","usgsCitation":"Regehr, E.V., Runge, M.C., Von Duyke, A.L., Wilson, R., Polasek, L., Rode, K.D., Hostetter, N.J., and Converse, S.J., 2021, Demographic risk assessment for a harvested species threatened by climate change: Polar bears in the Chukchi Sea: Ecological Applications, v. 31, no. 8, e02461, 13 p., https://doi.org/10.1002/eap.2461.","productDescription":"e02461, 13 p.","ipdsId":"IP-119837","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450636,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eap.2461","text":"External Repository"},{"id":429876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","otherGeospatial":"Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.44241992486556,\n 73.07923094153199\n ],\n [\n -179.9,\n 73.07923094153199\n ],\n [\n -179.9,\n 66.33440002284189\n ],\n [\n -157.44241992486556,\n 66.33440002284189\n ],\n [\n -157.44241992486556,\n 73.07923094153199\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":902940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":902941,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Von Duyke, Andrew L.","contributorId":214208,"corporation":false,"usgs":false,"family":"Von Duyke","given":"Andrew","email":"","middleInitial":"L.","affiliations":[{"id":38995,"text":"North Slope Borough Department of Wildlife Management","active":true,"usgs":false}],"preferred":false,"id":903129,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Ryan R. ","contributorId":222456,"corporation":false,"usgs":false,"family":"Wilson","given":"Ryan R. ","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":903130,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Polasek, Lori","contributorId":338318,"corporation":false,"usgs":false,"family":"Polasek","given":"Lori","email":"","affiliations":[],"preferred":false,"id":903131,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":902942,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":903132,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902943,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"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
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Using available data, the U.S. Geological Survey estimated that 306 billion cubic feet of recoverable helium is presently within the known geologic natural gas reservoirs of the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215085","usgsCitation":"Brennan, S.T., Rivera, J.L., Varela, B.A., and Park, A.J., 2021, National assessment of helium resources within known natural gas reservoirs: U.S. Geological Survey Scientific Investigations Report 2021–5085, 5 p., https://doi.org/10.3133/sir20215085.","productDescription":"Report: vi, 5 p.; Data Release","numberOfPages":"5","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112618","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":389388,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215085/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5085"},{"id":389060,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92QL79J","text":"USGS data release","linkHelpText":"Dataset of helium concentrations in United States wells"},{"id":389058,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5085/coverthb.jpg"},{"id":389059,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5085/sir20215085.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5085"},{"id":389386,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5085/images/"},{"id":389385,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5085/sir20215085.XML"}],"contact":"Director, Energy Resources Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
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
Some avian species have developed the capacity to leverage resource subsidies associated with human manipulated landscapes to increase population densities in habitats with naturally low carrying capacities. Elevated corvid densities and new territory establishment have led to an unsustainable increase in depredation pressure on sympatric native wildlife prey populations as well as in crop damage. Yet, subsidized predator removal programs aimed at reducing densities are likely most effective longer-term when conducted in tandem with subsidy control, habitat management, and robust assessment monitoring programs. We developed decision support software that leverages stage structured Lefkovitch population matrices to compare and identify treatment strategies that reduce subsidized avian predator densities most efficiently, in terms of limiting both cost and take levels. The StallPOPd (Version 4; available at https://doi.org/10.7298/sk2e-0c38.4) software enables managers to enter the area of their management stratum and the demographic properties (vital rates) of target bird population(s) of interest to evaluate strategies to decrease or curtail further population growth. Strategies explicitly include the reduction in fertility (i.e., eggs hatched) and/or the culling of hatchlings, non-breeders and/or breeders, but implicitly comprise reduction in survival or reproduction through subsidy denial. We illustrate the utilities of the software with examples using common ravens (Corvus corax; ravens) in the Mojave Desert of California, USA. Unfortunately, the survival and reproduction effects of each unit of a particular subsidy in that system have remained elusive, though this is the priority of current research. Because the software leverages a life history representation that is known to characterize hundreds of wildlife species in addition to ravens, the work expands the suite of tools available to wildlife managers and agricultural industry specialists to abate bird damage and impacts on sensitive wildlife in habitats with persistent human subsidies.
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
Tucson, AZ 85719
Reproductive strategies are an essential component of invasion ecology that influence invasion success and rates of population growth. Burmese Pythons (Python bivittatus) are large constrictor snakes that were introduced to the Greater Everglades Ecosystem of southern Florida, USA, from Asia. Since their introduction, these giant constrictors have spread throughout wetlands of southern Florida while increasing in abundance and causing declines in the native species upon which they prey. Multiple paternity in reproduction could facilitate invasion success by increasing the genetic diversity produced within each reproductive event. We used Diversity Arrays Technology genome-wide genotyping to assess multiple paternity in the progeny of wild Burmese Pythons in Florida. We analyzed >4,000 single nucleotide polymorphisms from 153 neonates belonging to 4 clutches collected in southwestern Florida. Complementary hierarchical and K-means clustering analyses of the genetic distances within clutches revealed that three clutches were each fertilized by two sires, with a fourth fertilized by a single sire. The proportions of offspring attributable to each sire within multiple paternity clutches ranged from nearly even to highly skewed. Analysis of multivariate dispersion showed significantly increased genetic variability in the multiple paternity clutches. These results improve our understanding of the reproductive strategy and invasion potential of a giant constrictor with significant ecological impacts.
Most studies of the ecological effects of climate change consider only a limited number of weather drivers that could affect populations, though we know that multiple weather drivers can simultaneously affect population growth rate. Multiple drivers could simultaneously increase/decrease one vital rate, or one may increase a vital rate while another decreases the same vital rate. Considering the impact of multiple weather drivers on vital rates is particularly important in a changing climate, in which correlations among drivers may not be preserved in the future. We used a long-term dataset on the endangered red-cockaded woodpecker (Dryobates borealis) to understand how multiple weather drivers jointly affect survival and reproductive vital rates and then assessed the contributions of individual weather drivers to historical trends in vital rates over time. We found that vital rates were often influenced by more than one weather driver and that weather drivers most commonly exerted opposing effects. For instance, some weather drivers increased vital rates over time, while others acted in the opposite direction, decreasing vital rates over time. Importantly, the historical correlations among weather drivers are almost always projected to change in the future climate, such that future trends in vital rates may not match historical trends. For example, we do not find historical trends in adult survival, but changing correlations among weather drivers could generate future trends in this vital rate. Our work provides an example of how multiple weather drivers can control a variety of vital rates and also illustrates how changes in the correlation structure of weather drivers through time might substantially affect future trends in individual and population performance.
Alewife (Alosa pseudoharengus) and blueback herring (Alosa aestivalis) are iteroparous anadromous fish found throughout the East Coast of North America. The phenology of anadromous fish migrations is important for fitness, and the duration of spawning migrations has been compressed in recent years in response to climate change. Anthropogenic barriers to movement, such as dams and culverts at road-stream crossings, can further disrupt migration phenology by delaying movement and increasing predation risk. We used passive integrated transponder (PIT) telemetry to quantify upstream and downstream migratory delay at five road-stream-crossing culverts on the Herring River (MA, USA). Groundspeeds were reduced at all culverts in both directions, confirming that the culverts impede movement despite high passage proportions. The cumulative delay of the culverts on the upstream migration was sufficient to more than double the amount of time required to traverse the river if the culverts had been absent. Furthermore, the presence of snapping turtles (Chelydra serpentina) ambushing river herring within one of the culverts resulted in reduced passage rates beyond the reduction in movement caused by the physical structure itself. This highlights that physical barriers can create cascading ecological consequences and the importance of taking a holistic approach to understanding barrier effects.
Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for stream temperature (Ts) temporal prediction (training in one period and predicting in another period at the same sites). However, spatial extrapolation is a well-known challenge to modelling Ts and it is uncertain how an LSTM-based daily Ts model will perform in unmonitored or dammed basins. Here we compiled a new benchmark dataset consisting of >400 basins across the contiguous United States in different data availability groups (DAG, meaning the daily sampling frequency) with and without major dams, and studied how to assemble suitable training datasets for predictions in basins with or without temperature monitoring. For prediction in unmonitored basins (PUB), LSTM produced a root-mean-square error (RMSE) of 1.129°C and an R2 of 0.983. While these metrics declined from LSTM's temporal prediction performance, they far surpassed traditional models' PUB values, and were competitive with traditional models' temporal prediction on calibrated sites. Even for unmonitored basins with major reservoirs, we obtained a median RMSE of 1.202°C and an R2 of 0.984. For temporal prediction, the most suitable training set was the matching DAG that the basin could be grouped into (for example, the 60% DAG was most suitable for a basin with 61% data availability). However, for PUB, a training dataset including all basins with data was consistently preferred. An input-selection ensemble moderately mitigated attribute overfitting. Our results indicate there are influential latent processes not sufficiently described by the inputs (e.g., geology, wetland covers), but temporal fluctuations can still be predicted well, and LSTM appears to be a highly accurate Ts modelling tool even for spatial extrapolation.
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}]}} ,{"id":70224530,"text":"70224530 - 2021 - A simplified method for rapid estimation of emergency water supply needs after earthquakes","interactions":[],"lastModifiedDate":"2022-12-23T17:20:52.03908","indexId":"70224530","displayToPublicDate":"2021-09-25T09:53:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"A simplified method for rapid estimation of emergency water supply needs after earthquakes","docAbstract":"Researchers are investigating the problem of estimating households with potable water service outages soon after an earthquake. Most of these modeling approaches are computationally intensive, have large proprietary data collection requirements or lack precision, making them unfeasible for rapid assessment, prioritization, and allocation of emergency water resources in large, complex disasters. This study proposes a new simplified analytical method—performed without proprietary water pipeline data—to estimate water supply needs after earthquakes, and a case study of its application in the HayWired earthquake scenario. In the HayWired scenario—a moment magnitude (Mw) 7.0 Hayward Fault earthquake in the San Francisco Bay Area, California (USA)—an analysis of potable water supply in two water utility districts was performed using the University of Colorado Water Network (CUWNet) model. In the case study, application of the simplified method extends these estimates of household water service outage to the nine counties adjacent to the San Francisco Bay, aggregated by a ~250 m2 (nine-arcsecond) grid. The study estimates about 1.38 million households (3.7 million residents) out of 7.6 million residents (2017, ambient, nighttime population) with potable water service outage soon after the earthquake—about an 8% increase from the HayWired scenario estimates.
","language":"English","publisher":"MDPI","doi":"10.3390/w13192635","usgsCitation":"Toland, J.C., and Wein, A., 2021, A simplified method for rapid estimation of emergency water supply needs after earthquakes: Water, v. 13, 2635, 27 p., https://doi.org/10.3390/w13192635.","productDescription":"2635, 27 p.","ipdsId":"IP-132813","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":450658,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13192635","text":"Publisher Index Page"},{"id":389813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.79968261718749,\n 37.24782120155428\n ],\n [\n -121.55273437499999,\n 37.24782120155428\n ],\n [\n -121.55273437499999,\n 38.324420427006544\n ],\n [\n -122.79968261718749,\n 38.324420427006544\n ],\n [\n -122.79968261718749,\n 37.24782120155428\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2021-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Toland, Joseph Charles 0000-0002-0092-0320","orcid":"https://orcid.org/0000-0002-0092-0320","contributorId":265976,"corporation":false,"usgs":true,"family":"Toland","given":"Joseph","email":"","middleInitial":"Charles","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":823911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wein, Anne 0000-0002-5516-3697 awein@usgs.gov","orcid":"https://orcid.org/0000-0002-5516-3697","contributorId":589,"corporation":false,"usgs":true,"family":"Wein","given":"Anne","email":"awein@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":823912,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252830,"text":"70252830 - 2021 - Age-0 Silver Carp otolith microchemistry and microstructure reveal multiple early life environments and protracted spawning in the upper Mississippi River","interactions":[],"lastModifiedDate":"2024-09-18T15:42:36.007312","indexId":"70252830","displayToPublicDate":"2021-09-25T06:43:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Age-0 Silver Carp otolith microchemistry and microstructure reveal multiple early life environments and protracted spawning in the upper Mississippi River","docAbstract":"Silver Carp Hypophthalmichthys molitrix are highly mobile and fecund planktivorous cyprinids that have invaded much of the Mississippi River and are known to alter food webs and compete with native planktivores. In 2016, for the first time, an abundance of age-0 Silver Carp (n = 12,208; 16–231 mm) were captured at many (n = 11) sites upstream of Lock and Dam 19 on the upper Mississippi River. Previous reports were of a few individuals at a few locations; however, effort to capture juveniles of this size was likely less in previous years. Determining the origin, frequency, and timing of the reproductive events that led to this large year-class is important for determining control strategies. We used otolith microstructure and microchemistry from age-0 Silver Carp to estimate timing and frequency of spawning and early life environments of these fish. Hatch dates were determined from the lapillus otoliths of 190 age-0 Silver Carp (16–231 mm), and early life environments were identified from otolith microchemistry for 124 of these fish (64–231 mm). Age-0 Silver Carp were collected from Pools 18 and 19 during July–October 2016 by using a variety of sampling gears. We identified 10 cohorts with hatch dates ranging from May to August 2016 and with main-stem Mississippi River (75%) and tributary (23%) early life signatures. Tributary otolith chemistry signatures were present in all cohorts between May and July (n = 8) but were absent from the August cohorts (n = 2). Our results indicate that tributaries and small tributary streams, in addition to the main-stem river, play an important role in Silver Carp recruitment in areas near the reproductive front, where management actions (e.g., contract removal and deterrents) are often targeted.
Nutrients, harmful algal blooms, and synthetic chemicals like per- and polyfluoroalkyl substances (PFAS) and 1,4-dioxane threaten Long Island’s water resources by affecting the quality of drinking water and ecologically sensitive habitats that support the diverse wildlife throughout the island. Understanding the occurrence, fate, and transport of these potentially harmful chemicals is critical to protect these vital resources. The U.S. Geological Survey (USGS) is collecting and analyzing data to support informed water-resource management decisions. This fact sheet introduces ongoing efforts and future areas of study aimed to help water professionals develop a comprehensive science strategy to address contamination of the Long Island aquifer system, the sole source of drinking water for nearly 3 million people. These studies include surface and groundwater collection and groundwater flow modeling. Funding for the data collection has been provided by the USGS, New York State Department of Environmental Conservation, New York City Department of Environmental Protection, Suffolk County Water Authority, Nassau County Department of Public Works, State and local agencies, and Tribal and Federal partners. Without the foresight and long-term commitment of these funding partners, evaluating sustainability and planning for future water needs would not be possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213044","usgsCitation":"Breault, R.F., Masterson, J.P., Schubert, C.E., and Herdman, L.M., 2021, Managing water resources on Long Island, New York, with integrated, multidisciplinary science: U.S. Geological Survey Fact Sheet 2021–3044, 4 p., https://doi.org/10.3133/fs20213044.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-131602","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":389579,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3044/fs20213044.pdf","text":"Report","size":"14.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3044"},{"id":389578,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3044/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0478515625,\n 40.538851525354666\n ],\n [\n -73.7677001953125,\n 40.538851525354666\n ],\n [\n -73.1304931640625,\n 40.60561205826018\n ],\n [\n -72.5537109375,\n 40.76806170936614\n ],\n [\n -71.9549560546875,\n 40.97575093157534\n ],\n [\n -71.83959960937499,\n 41.10832999732831\n ],\n [\n -72.1417236328125,\n 41.24064190269475\n ],\n [\n -72.8338623046875,\n 41.10005163093046\n ],\n [\n -73.5205078125,\n 40.96330795307353\n ],\n [\n -73.883056640625,\n 40.83043687764923\n ],\n [\n -74.06982421875,\n 40.713955826286046\n ],\n [\n -74.0863037109375,\n 40.61812224225511\n ],\n [\n -74.0478515625,\n 40.538851525354666\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The Columbia River Gorge is the Columbia River’s long-held yet evolving passage through the volcanic arc of the Cascade Range. The globally unique setting of a continental-scale river bisecting an active volcanic arc at the leading edge of a major plate boundary creates a remarkable setting where dynamic volcanic and tectonic processes interact with diverse and energetic fluvial processes. This three-day field trip explores several elements of the gorge and its remarkable geologic history—cast here as a contest between regional tectonic and volcanic processes building and displacing landscapes, and the relentless power of the Columbia River striving to maintain a smooth passage to the sea.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From terranes to terrains: Geologic field guides on the construction and destruction of the Pacific Northwest","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.0062(05)","usgsCitation":"O'Connor, J., Wells, R., Bennett, S.E., Cannon, C.M., Staisch, L.M., Anderson, J.L., Pivarunas, A.F., Gordon, G.W., Blakely, R.J., Stelten, M.E., and Evarts, R.C., 2021, Arc versus river: The geology of the Columbia River Gorge, chap. of From terranes to terrains: Geologic field guides on the construction and destruction of the Pacific Northwest, v. 62, p. 131-186, https://doi.org/10.1130/2021.0062(05).","productDescription":"56 p.","startPage":"131","endPage":"186","ipdsId":"IP-130012","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science 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lstaisch@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-5994","contributorId":167068,"corporation":false,"usgs":true,"family":"Staisch","given":"Lydia","email":"lstaisch@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":884311,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, James L","contributorId":330199,"corporation":false,"usgs":false,"family":"Anderson","given":"James","email":"","middleInitial":"L","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":884312,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pivarunas, Anthony Francis 0000-0002-0003-2059","orcid":"https://orcid.org/0000-0002-0003-2059","contributorId":301014,"corporation":false,"usgs":true,"family":"Pivarunas","given":"Anthony","email":"","middleInitial":"Francis","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":884313,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gordon, Gabriel Wells 0000-0003-0937-6250 ggordon@usgs.gov","orcid":"https://orcid.org/0000-0003-0937-6250","contributorId":330200,"corporation":false,"usgs":true,"family":"Gordon","given":"Gabriel","email":"ggordon@usgs.gov","middleInitial":"Wells","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":884314,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blakely, Richard J. 0000-0003-1701-5236 blakely@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-5236","contributorId":1540,"corporation":false,"usgs":true,"family":"Blakely","given":"Richard","email":"blakely@usgs.gov","middleInitial":"J.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":884315,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stelten, Mark E. 0000-0002-5294-3161 mstelten@usgs.gov","orcid":"https://orcid.org/0000-0002-5294-3161","contributorId":145923,"corporation":false,"usgs":true,"family":"Stelten","given":"Mark","email":"mstelten@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":884316,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Evarts, Russell C. 0000-0001-5103-9085 revarts@usgs.gov","orcid":"https://orcid.org/0000-0001-5103-9085","contributorId":295784,"corporation":false,"usgs":true,"family":"Evarts","given":"Russell","email":"revarts@usgs.gov","middleInitial":"C.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":884317,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ]}