This study compares and evaluates one-dimensional (1D) and three-dimensional (3D) numerical models of volcanic eruption columns in a set of different inter-comparison exercises. The exercises were designed as a blind test in which a set of common input parameters was given for two reference eruptions, representing a strong and a weak eruption column under different meteorological conditions. Comparing the results of the different models allows us to evaluate their capabilities and target areas for future improvement. Despite their different formulations, the 1D and 3D models provide reasonably consistent predictions of some of the key global descriptors of the volcanic plumes. Variability in plume height, estimated from the standard deviation of model predictions, is within ~ 20% for the weak plume and ~ 10% for the strong plume. Predictions of neutral buoyancy level are also in reasonably good agreement among the different models, with a standard deviation ranging from 9 to 19% (the latter for the weak plume in a windy atmosphere). Overall, these discrepancies are in the range of observational uncertainty of column height. However, there are important differences amongst models in terms of local properties along the plume axis, particularly for the strong plume. Our analysis suggests that the simplified treatment of entrainment in 1D models is adequate to resolve the general behaviour of the weak plume. However, it is inadequate to capture complex features of the strong plume, such as large vortices, partial column collapse, or gravitational fountaining that strongly enhance entrainment in the lower atmosphere. We conclude that there is a need to more accurately quantify entrainment rates, improve the representation of plume radius, and incorporate the effects of column instability in future versions of 1D volcanic plume models.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2016.01.017","usgsCitation":"Costa, A., Suzuki, Y., Cerminara, M., Devenish, B.J., Esposti Ongaro, T., Herzog, M., Van Eaton, A.R., Denby, L., Bursik, M., de’ Michieli Vitturi, M., Engwell, S., Neri, A., Barsotti, S., Folch, A., Macedonio, G., Girault, F., Carazzo, G., Tait, S., Kaminski, E., Mastin, L.G., Woodhouse, M.J., Phillips, J.C., Hogg, A.J., Degruyter, W., and Bonadonna, C., 2016, Results of the eruptive column model inter-comparison study: Journal of Volcanology and Geothermal Research, v. 326, p. 2-25, https://doi.org/10.1016/j.jvolgeores.2016.01.017.","productDescription":"24 p.","startPage":"2","endPage":"25","ipdsId":"IP-069305","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470520,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://hdl.handle.net/1983/31590258-99a4-4b9c-bdff-db520c226a79","text":"Publisher Index Page"},{"id":348124,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"326","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fc2ea6e4b0531197b27f89","contributors":{"authors":[{"text":"Costa, Antonio","contributorId":194290,"corporation":false,"usgs":false,"family":"Costa","given":"Antonio","email":"","affiliations":[{"id":27088,"text":"Istituto Nazionale di Geofisica e Vulcanologia (INGV)","active":true,"usgs":false}],"preferred":false,"id":719805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Suzuki, Yujiro","contributorId":194289,"corporation":false,"usgs":false,"family":"Suzuki","given":"Yujiro","email":"","affiliations":[],"preferred":false,"id":719806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cerminara, M.","contributorId":199703,"corporation":false,"usgs":false,"family":"Cerminara","given":"M.","affiliations":[],"preferred":false,"id":719807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Devenish, Ben J.","contributorId":199704,"corporation":false,"usgs":false,"family":"Devenish","given":"Ben","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":719808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Esposti Ongaro, T.","contributorId":199705,"corporation":false,"usgs":false,"family":"Esposti Ongaro","given":"T.","affiliations":[],"preferred":false,"id":719809,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herzog, Michael","contributorId":194293,"corporation":false,"usgs":false,"family":"Herzog","given":"Michael","email":"","affiliations":[],"preferred":false,"id":719810,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":719811,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Denby, L.C.","contributorId":199706,"corporation":false,"usgs":false,"family":"Denby","given":"L.C.","affiliations":[],"preferred":false,"id":719812,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bursik, Marcus","contributorId":199707,"corporation":false,"usgs":false,"family":"Bursik","given":"Marcus","affiliations":[],"preferred":false,"id":719813,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"de’ Michieli Vitturi, Mattia","contributorId":199708,"corporation":false,"usgs":false,"family":"de’ Michieli Vitturi","given":"Mattia","email":"","affiliations":[],"preferred":false,"id":719814,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Engwell, S.","contributorId":199709,"corporation":false,"usgs":false,"family":"Engwell","given":"S.","affiliations":[],"preferred":false,"id":719815,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Neri, Augusto","contributorId":199710,"corporation":false,"usgs":false,"family":"Neri","given":"Augusto","email":"","affiliations":[],"preferred":false,"id":719816,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Barsotti, Sara","contributorId":199711,"corporation":false,"usgs":false,"family":"Barsotti","given":"Sara","email":"","affiliations":[],"preferred":false,"id":719817,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Folch, Arnau","contributorId":199712,"corporation":false,"usgs":false,"family":"Folch","given":"Arnau","email":"","affiliations":[],"preferred":false,"id":719818,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Macedonio, Giovanni","contributorId":199713,"corporation":false,"usgs":false,"family":"Macedonio","given":"Giovanni","email":"","affiliations":[],"preferred":false,"id":719819,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Girault, F.","contributorId":199714,"corporation":false,"usgs":false,"family":"Girault","given":"F.","email":"","affiliations":[],"preferred":false,"id":719820,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Carazzo, G.","contributorId":199715,"corporation":false,"usgs":false,"family":"Carazzo","given":"G.","affiliations":[],"preferred":false,"id":719821,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Tait, S.","contributorId":199716,"corporation":false,"usgs":false,"family":"Tait","given":"S.","email":"","affiliations":[],"preferred":false,"id":719822,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Kaminski, E.","contributorId":199717,"corporation":false,"usgs":false,"family":"Kaminski","given":"E.","email":"","affiliations":[],"preferred":false,"id":719823,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Mastin, Larry G. 0000-0002-4795-1992 lgmastin@usgs.gov","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":555,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"lgmastin@usgs.gov","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":719804,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Woodhouse, Mark J.","contributorId":199718,"corporation":false,"usgs":false,"family":"Woodhouse","given":"Mark","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":719824,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Phillips, Jeremy C.","contributorId":199719,"corporation":false,"usgs":false,"family":"Phillips","given":"Jeremy","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":719825,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Hogg, Andrew J.","contributorId":199720,"corporation":false,"usgs":false,"family":"Hogg","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":719826,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Degruyter, Wim","contributorId":145532,"corporation":false,"usgs":false,"family":"Degruyter","given":"Wim","email":"","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":719827,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Bonadonna, Costanza","contributorId":199721,"corporation":false,"usgs":false,"family":"Bonadonna","given":"Costanza","email":"","affiliations":[],"preferred":false,"id":719828,"contributorType":{"id":1,"text":"Authors"},"rank":25}]}} ,{"id":70192733,"text":"70192733 - 2016 - Consequences of changes in vegetation and snow cover for climate feedbacks in Alaska and northwest Canada","interactions":[],"lastModifiedDate":"2017-11-08T13:14:53","indexId":"70192733","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Consequences of changes in vegetation and snow cover for climate feedbacks in Alaska and northwest Canada","docAbstract":"Changes in vegetation and snow cover may lead to feedbacks to climate through changes in surface albedo and energy fluxes between the land and atmosphere. In addition to these biogeophysical feedbacks, biogeochemical feedbacks associated with changes in carbon (C) storage in the vegetation and soils may also influence climate. Here, using a transient biogeographic model (ALFRESCO) and an ecosystem model (DOS-TEM), we quantified the biogeophysical feedbacks due to changes in vegetation and snow cover across continuous permafrost to non-permafrost ecosystems in Alaska and northwest Canada. We also computed the changes in carbon storage in this region to provide a general assessment of the direction of the biogeochemical feedback. We considered four ecoregions, or Landscape Conservations Cooperatives (LCCs; including the Arctic, North Pacific, Western Alaska, and Northwest Boreal). We examined the 90 year period from 2010 to 2099 using one future emission scenario (A1B), under outputs from two general circulation models (MPI-ECHAM5 and CCCMA-CGCM3.1). We found that changes in snow cover duration, including both the timing of snowmelt in the spring and snow return in the fall, provided the dominant positive biogeophysical feedback to climate across all LCCs, and was greater for the ECHAM (+3.1 W m−2 decade−1regionally) compared to the CCCMA (+1.3 W m−2 decade−1 regionally) scenario due to an increase in loss of snow cover in the ECHAM scenario. The greatest overall negative feedback to climate from changes in vegetation cover was due to fire in spruce forests in the Northwest Boreal LCC and fire in shrub tundra in the Western LCC (−0.2 to −0.3 W m−2 decade−1). With the larger positive feedbacks associated with reductions in snow cover compared to the smaller negative feedbacks associated with shifts in vegetation, the feedback to climate warming was positive (total feedback of +2.7 W m−2decade regionally in the ECHAM scenario compared to +0.76 W m−2 decade regionally in the CCCMA scenario). Overall, increases in C storage in the vegetation and soils across the study region would act as a negative feedback to climate. By exploring these feedbacks to climate, we can reach a more integrated understanding of the manner in which climate change may impact interactions between high-latitude ecosystems and the global climate system.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/11/10/105003","usgsCitation":"Euskirchen, E., Bennett, A.P., Breen, A.L., Genet, H., Lindgren, M.A., Kurkowski, T., McGuire, A.D., and Rupp, T., 2016, Consequences of changes in vegetation and snow cover for climate feedbacks in Alaska and northwest Canada: Environmental Research Letters, v. 11, p. 1-19, https://doi.org/10.1088/1748-9326/11/10/105003.","productDescription":"Article 105003; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-075009","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470523,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/11/10/105003","text":"Publisher Index Page"},{"id":348455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -179.560546875,\n 50.958426723359935\n ],\n [\n -125.20019531249999,\n 50.958426723359935\n ],\n [\n -125.20019531249999,\n 71.38514208411495\n ],\n [\n -179.560546875,\n 71.38514208411495\n ],\n [\n -179.560546875,\n 50.958426723359935\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-03","publicationStatus":"PW","scienceBaseUri":"5a0425bee4b0dc0b45b453e7","contributors":{"authors":[{"text":"Euskirchen, Eugénie S.","contributorId":83378,"corporation":false,"usgs":false,"family":"Euskirchen","given":"Eugénie S.","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":721167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bennett, A. P.","contributorId":200154,"corporation":false,"usgs":false,"family":"Bennett","given":"A.","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":721168,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breen, Amy L.","contributorId":81396,"corporation":false,"usgs":true,"family":"Breen","given":"Amy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":721169,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Genet, Helene","contributorId":95370,"corporation":false,"usgs":true,"family":"Genet","given":"Helene","affiliations":[],"preferred":false,"id":721170,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lindgren, Michael A.","contributorId":33237,"corporation":false,"usgs":true,"family":"Lindgren","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":721171,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kurkowski, Tom","contributorId":198681,"corporation":false,"usgs":false,"family":"Kurkowski","given":"Tom","affiliations":[],"preferred":false,"id":721172,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGuire, A. David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":716792,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rupp, T. Scott","contributorId":21395,"corporation":false,"usgs":true,"family":"Rupp","given":"T. Scott","affiliations":[],"preferred":false,"id":721173,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70176901,"text":"70176901 - 2016 - Considerations for building climate-based species distribution models","interactions":[],"lastModifiedDate":"2016-10-20T14:11:27","indexId":"70176901","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Considerations for building climate-based species distribution models","docAbstract":"Climate plays an important role in the distribution of species. A given species may adjust to new conditions in-place, move to new areas with suitable climates, or go extinct. Scientists and conservation practitioners use mathematical models to predict the effects of future climate change on wildlife and plan for a biodiverse future. This 8-page fact sheet written by David N. Bucklin, Mathieu Basille, Stephanie S. Romañach, Laura A. Brandt, Frank J. Mazzotti, and James I. Watling and published by the Department of Wildlife Ecology and Conservation explains how, with a better understanding of species distribution models, we can predict how species may respond to climate change. The models alone cannot tell us how a certain species will actually respond to changes in climate, but they can inform conservation planning that aims to allow species to both adapt in place and (for those that are able to) move to newly suitable areas. Such planning will likely minimize loss of biodiversity due to climate change.","language":"English","publisher":"University of Florida IFAS Extension","usgsCitation":"Bucklin, D.N., Basille, M., Romanach, S.S., Brandt, L.A., Mazzotti, F., and Watling, J.I., 2016, Considerations for building climate-based species distribution models, 8 p.","productDescription":"8 p","ipdsId":"IP-075201","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":330262,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":329494,"type":{"id":15,"text":"Index Page"},"url":"https://edis.ifas.ufl.edu/UW420"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5809d7c3e4b0f497e78fca5d","contributors":{"authors":[{"text":"Bucklin, David N.","contributorId":175273,"corporation":false,"usgs":false,"family":"Bucklin","given":"David","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":650661,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Basille, Mathieu","contributorId":175274,"corporation":false,"usgs":false,"family":"Basille","given":"Mathieu","email":"","affiliations":[],"preferred":false,"id":650662,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanach, Stephanie S. 0000-0003-0271-7825 sromanach@usgs.gov","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":140419,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","email":"sromanach@usgs.gov","middleInitial":"S.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":650660,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brandt, Laura A.","contributorId":146646,"corporation":false,"usgs":false,"family":"Brandt","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":650663,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mazzotti, Frank J.","contributorId":12358,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Frank J.","affiliations":[{"id":12604,"text":"Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, 3205 College Avenue, University of Florida, Davie, FL 33314, USA","active":true,"usgs":false}],"preferred":false,"id":650664,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Watling, James I.","contributorId":175275,"corporation":false,"usgs":false,"family":"Watling","given":"James","email":"","middleInitial":"I.","affiliations":[{"id":27555,"text":"John Carroll University","active":true,"usgs":false}],"preferred":false,"id":650665,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178043,"text":"70178043 - 2016 - Simulation modeling to explore the effects of length-based harvest regulations for Ictalurus fisheries","interactions":[],"lastModifiedDate":"2016-11-01T12:54:35","indexId":"70178043","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","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":"Simulation modeling to explore the effects of length-based harvest regulations for Ictalurus fisheries","docAbstract":"Management of Blue Catfish Ictalurus furcatus and Channel Catfish I. punctatus for trophy production has recently become more common. Typically, trophy management is attempted with length-based regulations that allow for the moderate harvest of small fish but restrict the harvest of larger fish. However, the specific regulations used vary considerably across populations, and no modeling efforts have evaluated their effectiveness. We used simulation modeling to compare total yield, trophy biomass (Btrophy), and sustainability (spawning potential ratio [SPR] > 0.30) of Blue Catfish and Channel Catfish populations under three scenarios: (1) current regulation (typically a length-based trophy regulation), (2) the best-performing minimum length regulation (MLRbest), and (3) the best-performing length-based trophy catfish regulation (LTRbest; “best performing” was defined as the regulation that maximized yield, Btrophy, and sustainability). The Btrophy produced did not differ among the three scenarios. For each fishery, the MLRbest and LTRbest produced greater yield (>22% more) than the current regulation and maintained sustainability at higher finite exploitation rates (>0.30) than the current regulation. The MLRbest and LTRbest produced similar yields and SPRs for Channel Catfish and similar yields for Blue Catfish; however, the MLRbest for Blue Catfish produced more resilient fisheries (higher SPR) than the LTRbest. Overall, the variation in yield, Btrophy, and SPR among populations was greater than the variation among regulations applied to any given population, suggesting that population-specific regulations may be preferable to regulations applied to geographic regions. We conclude that LTRs are useful for improving catfish yield and maintaining sustainability without overly restricting harvest but are not effective at increasing the Btrophy of catfish.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2016.1204391","usgsCitation":"Stewart, D., Long, J.M., and Shoup, D.E., 2016, Simulation modeling to explore the effects of length-based harvest regulations for Ictalurus fisheries: North American Journal of Fisheries Management, v. 36, no. 5, p. 1190-1204, https://doi.org/10.1080/02755947.2016.1204391.","productDescription":"15 p.","startPage":"1190","endPage":"1204","ipdsId":"IP-068502","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":330607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"5","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-13","publicationStatus":"PW","scienceBaseUri":"5819a9c2e4b0bb36a4c91015","contributors":{"authors":[{"text":"Stewart, David R.","contributorId":141323,"corporation":false,"usgs":false,"family":"Stewart","given":"David R.","affiliations":[],"preferred":false,"id":652624,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":652588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoup, Daniel E.","contributorId":141325,"corporation":false,"usgs":false,"family":"Shoup","given":"Daniel","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":652625,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176610,"text":"ofr20161170 - 2016 - Preliminary geologic mapping of Cretaceous and Tertiary formations in the eastern part of the Little Snake River coal field, Carbon County, Wyoming","interactions":[],"lastModifiedDate":"2016-09-30T14:14:25","indexId":"ofr20161170","displayToPublicDate":"2016-09-30T13:00:00","publicationYear":"2016","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":"2016-1170","title":"Preliminary geologic mapping of Cretaceous and Tertiary formations in the eastern part of the Little Snake River coal field, Carbon County, Wyoming","docAbstract":"In the 1970s and 1980s, C.S. Venable Barclay conducted geologic mapping of areas primarily underlain by Cretaceous coals in the eastern part of the Little Snake River coal field (LSR) in Carbon County, southwest Wyoming. With some exceptions, most of the mapping data were never published. Subsequently, after his retirement from the U.S. Geological Survey (USGS), his field maps and field notebooks were archived in the USGS Field Records. Due to a pending USGS coal assessment of the Little Snake River coal field area and planned geological mapping to be conducted by the Wyoming State Geological Survey, Barclay’s mapping data needed to be published to support these efforts. Subsequently, geologic maps were scanned and georeferenced into a geographic information system, and project and field notes were scanned into Portable Document Format (PDF) files. Data for seventeen 7½-minute quadrangles are presented in this report. This publication is solely intended to compile the mapping data as it was last worked on by Barclay and provides no interpretation or modification of his work.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161170","usgsCitation":"Haacke, J.E., Barclay, C.S.V., and Hettinger, R.D., 2016, Preliminary geologic mapping of Cretaceous and Tertiary formations in the eastern part of the Little Snake River coal field, Carbon County, Wyoming: U.S. Geological Survey Open-File Report 2016–1170, 9 p., https://dx.doi.org/10.3133/ofr20161170.","productDescription":"Report: iii, 9 p.; Field Notes; Metadata; Read Me; Spatial Data","numberOfPages":"12","onlineOnly":"Y","ipdsId":"IP-070800","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":329139,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2016/1170/ofr20161170_Field Notes.zip","text":"Field Notes","size":"444 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2016-1170 Field 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Director, USGS Central Energy Resources Science Center
Box 25046, Mail Stop 939
Denver, CO 80225
Bridge-scour equations presented in the Federal Highway Administration Hydraulic Engineering Circular No. 18 reflect the current state-of-the practice for predicting scour at bridges. Although these laboratory-derived equations provide an important resource for assessing scour potential, there is a measure of uncertainty when applying these equations to field conditions. The uncertainty and limitations have been acknowledged by laboratory researchers and confirmed in field investigations.
Because of the uncertainty associated with bridge-scour equations, HEC-18 recommends that engineers evaluate the computed scour depths obtained from the equations and modify the resulting data if they appear unreasonable. Perhaps the best way to evaluate the reasonableness of predicted scour is to compare it to field measurements of historic scour. Historic field data show scour depths resulting from high flows and provide a reference for evaluating predicted scour. It is rare, however, that such data are available at or near a site of interest, making the evaluation of predicted scour as compared to field data difficult if not impossible. Realizing the value of historic scour measurements, the U.S. Geological Survey (USGS), in cooperation with the South Carolina Department of Transportation (SCDOT), conducted a series of three field investigations to collect historic scour data with the goal of understanding regional trends of scour at riverine bridges in South Carolina.
Historic scour measurements, including measurements of clear-water abutment, contraction, and pier scour, as well as live-bed contraction and pier scour, were made at more than 200 bridges. These field investigations provided valuable insights into regional scour trends and yielded regional bridge-scour envelope curves that can be used as supplementary tools for assessing all components of scour at riverine bridges in South Carolina.
The application and limitations of these envelope curves were documented in four reports. Because each report addresses different components of bridge scour, it was recognized that there was a need to develop an integrated procedure for applying the envelope curves to help assess scour potential at riverine bridges in South Carolina. The result of that effort is detailed in Benedict and others (2016) and summarized in this fact sheet.
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Carolina\",\"nation\":\"USA \"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road, Suite 129
Columbia, SC 29210
http://sc.water.usgs.gov/
Multiple sampling trips during calendar years 2013 through 2015 were coordinated to provide measurements of interdependent benthic processes that potentially affect contaminant transport in Upper Klamath Lake (UKL), Oregon. The measurements were motivated by recognition that such internal processes (for example, solute benthic flux, bioturbation and solute efflux by benthic invertebrates, and physical groundwater-surface water interactions) were not integrated into existing management models for UKL. Up until 2013, all of the benthic-flux studies generally had been limited spatially to a number of sites in the northern part of UKL and limited temporally to 2–3 samplings per year. All of the benthic invertebrate studies also had been limited to the northern part of the lake; however, intensive temporal (weekly) studies had previously been completed independent of benthic-flux studies. Therefore, knowledge of both the spatial and temporal variability in benthic flux and benthic invertebrate distributions for the entire lake was lacking. To address these limitations, we completed a lakewide spatial study during 2013 and a coordinated temporal study with weekly sampling of benthic flux and benthic invertebrates during 2014. Field design of the spatially focused study in 2013 involved 21 sites sampled three times as the summer cyanobacterial bloom developed (that is, May 23, June 13, and July 3, 2013). Results of the 27-week, temporally focused study of one site in 2014 were summarized and partitioned into three periods (referred to herein as pre-bloom, bloom and post-bloom periods), each period involving 9 weeks of profiler deployments, water column and benthic sampling. Partitioning of the pre-bloom, bloom, and post-bloom periods were based on water-column chlorophyll concentrations and involved the following date intervals, respectively: April 15 through June 10, June 17 through August 13, and August 20 through October 16, 2014.
To examine dissolved-solute (0.2-micrometer [μm] filtered) benthic flux, sets of nonmetallic pore-water profilers (U.S. Patent 8,051,727 B1) were deployed. In 2013, the deployment of profilers at 21 UKL sites occurred at the beginning of the annual cyanobacterial bloom of Aphanizomenon flos–aquae (AFA), in the middle of the bloom period, and at the peak of the bloom. Coordinated benthic invertebrate collections also were made. Based on results from 2013, weekly deployments of profilers and collection of benthic invertebrate samples from late spring to early autumn were used to estimate temporal trends in solute flux and benthic invertebrate densities. Estimates of nutrient efflux by benthic invertebrates were determined in the spring and autumn from 2011 through 2013 and three times (spring, summer, and autumn) in 2015. This work extends UKL studies that began in 2006 to quantify the importance of benthic solute sources in the lake. In 2015, piezometers and thermistor sets were deployed to quantify potential groundwater exchange with the lake water column.
Analysis of the 2013 soluble reactive phosphorus (SRP) benthic flux indicated no effect of location (lake region), habitat, or sampling period, and the average lakewide flux values were consistent with earlier studies that had been confined to the northern region of UKL and adjacent wetlands. The 2014 study therefore focused on estimating temporal trends at a site within Ball Bay. During both 2013 and 2014 field studies, fluxes of macronutrients (soluble reactive phosphorus (SRP) and ammonia) and micronutrients (iron [Fe] and manganese [Mn]) were consistently positive and increased prior to the initial AFA bloom, varied or lagged with water-column chlorophyll during the summer bloom period, then decreased after the cyanobacterial blooms, only to rebound toward pre-bloom conditions in the final weeks of sampling. These four solutes exhibited benthic loads greater than maximum riverine loads estimated during the spring and early summers of 2013 and 2014. However, consistently detectable concentrations for all four solutes provide no evidence that they consistently serve as the limiting nutrient for primary production in the lake. In contrast to the four solutes (SRP, ammonia, Fe, and Mn), benthic fluxes of dissolved arsenic (As) were both negative and positive (that is, the lakebed currently serves as both a source and a sink for dissolved As, depending on season). In a further contrast with SRP, ammonia, dissolved Fe, and Mn, dissolved-As riverine loads to UKL were of similar magnitude to benthic loads. A negative relationship between dissolved-As flux and water-column As over the 2014 temporal study provides a potential advantage for the management of water-quality in contrast to solutes, like SRP or ammonia, with consistently positive flux.
The mean total benthic invertebrate density during 2013 was 12,610 individuals per square meter (n=63). Although benthic invertebrate density did not change over the study period, it was higher in littoral habitats than open-lake or trench habitats and higher in the northern region compared to the central or southern regions of UKL. Mean total benthic invertebrate density during 2014 was 19,726 individuals m−2 (n=27). Density during the pre-bloom and bloom periods of April 15 to August 13, 2014 (the first two thirds of the 2014 sampling period), were similar to 2013. However, benthic invertebrate density more than doubled during the latter one-third of the study, that is, the post-bloom period between August 20 to October 16, 2014. Oligochaeta, Chironomidae and Hirudinea represented well over 90 percent of the benthic fauna; Oligochaeta were twice as abundant as Chironomidae or Hirudinea, the latter two of which were similar in density.
Benthic invertebrates may enhance dissolved-nutrient (or toxicant) transport across the sediment-water interface by (1) modifying diffusion-layer thicknesses and permeability through bioturbation, (2) enhancing advective flow across the interface through bioirrigation, and (3) excreting or expelling dissolved or particulate solutes directly into the overlying water column (Boudreau and Jorgensen, 2001). We evaluated SRP efflux via excretion for approximately 15 different major taxa in UKL. Once these measures were scaled, it was evident that benthic invertebrates potentially contribute approximately 1.5 times the amount of SRP to the water column of Upper Klamath Lake as diffusive SRP flux alone, measured in profiler deployments.
Sets of piezometers and temperature loggers were deployed in UKL to obtain estimates of vertical advective solute flux. The pressure transducer installations, within the piezometers, did not perform as designed, rendering the head gradient data unreliable. However, in terms of future research, this field work did demonstrate the feasibility of collecting vertical gradient data with piezometer deployments. Advective flux estimates herein are based solely on heat-flow modeling based on temperature data from four lake sites, without use of transducer data. Given the magnitudes (both positive or negative) of the heat-transfer fluxes for SRP, relative to diffusive-flux and macroinvertebrate efflux measurements (all positive but spanning the same orders of magnitude), further examination of solute advective flux is recommended as a potential transport process to integrate into existing water-quality (for example, Total Maximum Daily Load [TMDL]) models.
As a complement to the biogeochemical focus of this study, initial analyses of suspended-particle (floc) characteristics and settling velocities from the water column were derived near the surface and lakebed at two UKL sites. To better understand changing particle characteristics during the AFA-bloom period, suspended particles were examined in 2015 using a LabSFLOC (LF), which is a Laboratory Spectral Flocculation Characteristics version of an In-Situ Settling Velocity instrument (INSSEV-LF). Particle characteristics and settling velocities were analyzed from the water column near the surface (sample dp_10) and lakebed (sample dp_90) at two lake sites (open-lake site ML and littoral site LS01). The term “floc” refers herein to suspended particles that may aggregate or disaggregate to change in size, composition, and settling velocity.
During pre-bloom (May) conditions, where maximum suspended particulate matter concentration (SPMC) was 140 milligrams per liter (mg L−1) was now observed at site LS01 in close proximity to the bed, where Dmean peaked at 305 μm, and the corresponding Wsmean was 3.9 millimeters per second (mm s−1). The high near-bed SPMC (828 mg L−1) experienced during post-bloom October 2015 at LS01 formed a benthic nepheloid layer (BNL) above the lake’s bed. Numerous low density, fast settling macrofloc-sized organic aggregates (D >160 μm) were observed (some up to 1 mm in size) near bed at LS01 both during the bloom and post-bloom conditions; many of these flocs displayed fibrous organic structures. In terms of mass settling fluxes, the post-bloom BNL produced a total MSF of 4,139 milligrams per square meter per second (mg m−2 s−1) (92.1 percent of MSF credited to the macrofloc-sized organic aggregates/cyanobacterial colonies); that was nearly three times the corresponding near-bed settling flux observed during the July 2015 bloom and 360 times greater than the pre-bloom conditions from May 2015 (98.8 percent and 14 percent of MSF credited to the macrofloc-sized fractions for those respective months). Such changes in the near-bed settling flux demonstrate the highly significant seasonal effects that the AFA bloom has on the floc depositional fluxes in UKL and highlights the importance of seasonal monitoring of these conditions in order to correctly parameterize the wide range in depositional characteristics and floc properties measured throughout UKL.
Collectively, floc populations observed within UKL demonstrated a wide range in settling velocity (Ws) for a given particle size, D. Similarly, a given settling velocity was not associated with a specific particle size. This variability in particle characteristics and properties indicates the influence of varying floc effective density and its effect on mass and mass settling fluxes (MSF). The use of instruments, such as the INSSEV-LF, enables measuring the variability of settling velocity and its relation to particle density and size.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161175","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Kuwabara, J.S., Topping, B.R., Carter, J.L., Carlson, R.A., Parchaso, F., Fend, S.V., Stauffer-Olsen, N., Manning, A.J., Land, J.M., 2016, Benthic processes affecting contaminant transport in Upper Klamath Lake, Oregon (ver. 1.1, October 2016): U.S. Geological Survey Open-File Report 2016–1175, 103 p., https://dx.doi.org/10.3133/ofr20161175. ","productDescription":"Report: viii, 103 p.; 2 Tables","numberOfPages":"115","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":329222,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1175/coverthb.jpg"},{"id":329223,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1175/ofr20161175.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1175"},{"id":329224,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1175/ofr20161175_table4.xlsx","text":"Table 4","size":"96 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1175 Table 4"},{"id":329425,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2016/1175/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016-1175 Version History"},{"id":329424,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1175/ofr20161175_table19.xlsx","text":"Table 19","size":"18 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1175 Table 19"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.09793090820311,\n 42.231567925608616\n ],\n [\n -122.09793090820311,\n 42.70464124398721\n ],\n [\n -121.79992675781249,\n 42.70464124398721\n ],\n [\n -121.79992675781249,\n 42.231567925608616\n ],\n [\n -122.09793090820311,\n 42.231567925608616\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted September 30, 2016; Version 1.1: October 11, 2016","contact":"NRP staff
Water Resources National Research Program
U.S. Geological Survey
345 Middlefield Road, MS-435
Menlo Park, CA 94025
National Research Program
In the spring of 2009, the U.S. Geological Survey, in cooperation with the San Bernardino Valley Municipal Water District, began working on a gravity survey in the Yucaipa area to explore the three-dimensional shape of the sedimentary fill (alluvial deposits) and the surface of the underlying crystalline basement rocks. As water use has increased in pace with rapid urbanization, water managers have need for better information about the subsurface geometry and the boundaries of groundwater subbasins in the Yucaipa area. The large density contrast between alluvial deposits and the crystalline basement complex permits using modeling of gravity data to estimate the thickness of alluvial deposits. The bottom of the alluvial deposits is considered to be the top of crystalline basement rocks. The gravity data, integrated with geologic information from surface outcrops and 51 subsurface borings (15 of which penetrated basement rock), indicated a complex basin configuration where steep slopes coincide with mapped faults―such as the Crafton Hills Fault and the eastern section of the Banning Fault―and concealed ridges separate hydrologically defined subbasins.
Gravity measurements and well logs were the primary data sets used to define the thickness and structure of the groundwater basin. Gravity measurements were collected at 256 new locations along profiles that totaled approximately 104.6 km (65 mi) in length; these data supplemented previously collected gravity measurements. Gravity data were reduced to isostatic anomalies and separated into an anomaly field representing the valley fill. The ‘valley-fill-deposits gravity anomaly’ was converted to thickness by using an assumed, depth-varying density contrast between the alluvial deposits and the underlying bedrock.
To help visualize the basin geometry, an animation of the elevation of the top of the basement-rocks was prepared. The animation “flies over” the Yucaipa groundwater basin, viewing the land surface, geology, faults, and ridges and valleys of the shaded-relief elevation of the top of the basement complex.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161127","collaboration":"Prepared in cooperation with the San Bernardino Valley Municipal Water District","usgsCitation":"Mendez, G.O., Langenheim, V.E., Morita, Andrew, and Danskin, W.R., 2016, Geologic structure of the Yucaipa area inferred from gravity data, San Bernardino and Riverside Counties, California: U.S. Geological Survey Open-File Report 2016–1127, 22 p., https://dx.doi.org/10.3133/ofr20161127.","productDescription":"Report: vii, 23 p.; Video Animation","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077241","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":329070,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1127/ofr20161127.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1127"},{"id":329071,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2016/1127/ofr20161127_gravity.mp4","text":"Video animation","size":"47.3 MB mp4","description":"OFR 2016-1127 Video Animation","linkHelpText":"Land surface, geology, faults, wells, and elevation of the basement rocks in the Yucaipa area, California."},{"id":329069,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1127/coverthb.jpg"}],"country":"United States","state":"California","county":"San Bernardino County, Riverside County","otherGeospatial":"Yucaipa Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.15888977050781,\n 33.96842016198477\n ],\n [\n -117.15888977050781,\n 34.08962997133382\n ],\n [\n -116.97212219238281,\n 34.08962997133382\n ],\n [\n -116.97212219238281,\n 33.96842016198477\n ],\n [\n -117.15888977050781,\n 33.96842016198477\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
http://ca.water.usgs.gov
MT3D-USGS, a U.S. Geological Survey updated release of the groundwater solute transport code MT3DMS, includes new transport modeling capabilities to accommodate flow terms calculated by MODFLOW packages that were previously unsupported by MT3DMS and to provide greater flexibility in the simulation of solute transport and reactive solute transport. Unsaturated-zone transport and transport within streams and lakes, including solute exchange with connected groundwater, are among the new capabilities included in the MT3D-USGS code. MT3D-USGS also includes the capability to route a solute through dry cells that may occur in the Newton-Raphson formulation of MODFLOW (that is, MODFLOW-NWT). New chemical reaction Package options include the ability to simulate inter-species reactions and parent-daughter chain reactions. A new pump-and-treat recirculation package enables the simulation of dynamic recirculation with or without treatment for combinations of wells that are represented in the flow model, mimicking the above-ground treatment of extracted water. A reformulation of the treatment of transient mass storage improves conservation of mass and yields solutions for better agreement with analytical benchmarks. Several additional features of MT3D-USGS are (1) the separate specification of the partitioning coefficient (Kd) within mobile and immobile domains; (2) the capability to assign prescribed concentrations to the top-most active layer; (3) the change in mass storage owing to the change in water volume now appears as its own budget item in the global mass balance summary; (4) the ability to ignore cross-dispersion terms; (5) the definition of Hydrocarbon Spill-Source Package (HSS) mass loading zones using regular and irregular polygons, in addition to the currently supported circular zones; and (6) the ability to specify an absolute minimum thickness rather than the default percent minimum thickness in dry-cell circumstances.
Benchmark problems that implement the new features and packages test the accuracy of new code through comparison to analytical benchmarks, as well as to solutions from other published codes. The input file structure for MT3D-USGS adheres to MT3DMS conventions for backward compatibility: the new capabilities and packages described herein are readily invoked by adding three-letter package name acronyms to the name file or by setting input flags as needed. Memory is managed in MT3D-USGS using FORTRAN modules in order to simplify code development and expansion.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Ground water in Book 6: Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A53","collaboration":"Prepared in collaboration with S.S. Papadopulos & Associates, Inc.","usgsCitation":"Bedekar, Vivek, Morway, E.D., Langevin, C.D., and Tonkin, Matt, 2016, MT3D-USGS version 1: A U.S. Geological Survey release of MT3DMS updated with new and expanded transport capabilities for use with MODFLOW:\nU.S. Geological Survey Techniques and Methods 6-A53, 69 p., https://dx.doi.org/10.3133/tm6A53.","productDescription":"Report: x, 69 p.; Application Site","numberOfPages":"84","onlineOnly":"Y","ipdsId":"IP-053896","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":329190,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a53/coverthb.jpg"},{"id":329191,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a53/tm06a53.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-A53"},{"id":329192,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://dx.doi.org/10.5066/F75T3HKD","text":"MT3D-USGS Version 1","description":"TM 6-A53 MT3D-USGS Version 1"}],"publicComments":"Ground Water Resources Program\nThis report is Chapter 53 of Section A: Ground water in Book 6: Modeling techniques.","contact":"Office of Groundwater
U.S. Geological Survey
Mail Stop 411
12201 Sunrise Valley Drive
Reston, VA 20192
http://water.usgs.gov/ogw/
A daily watershed model of the Sacramento River Basin of northern California was developed to simulate streamflow and suspended sediment transport to the San Francisco Bay-Delta. To compensate for sparse data, a unique combination of model inputs was developed, including meteorological variables, potential evapotranspiration, and parameters defining hydraulic geometry. A slight decreasing trend of sediment loads and concentrations was statistically significant in the lowest 50% of flows, supporting the observed historical sediment decline. Historical changes in climate, including seasonality and decline of snowpack, contribute to changes in streamflow, and are a significant component describing the mechanisms responsible for the decline in sediment. Several wet and dry hypothetical climate change scenarios with temperature changes of 1.5 °C and 4.5 °C were applied to the base historical conditions to assess the model sensitivity of streamflow and sediment to changes in climate. Of the scenarios evaluated, sediment discharge for the Sacramento River Basin increased the most with increased storm magnitude and frequency and decreased the most with increases in air temperature, regardless of changes in precipitation. The model will be used to develop projections of potential hydrologic and sediment trends to the Bay-Delta in response to potential future climate scenarios, which will help assess the hydrological and ecological health of the Bay-Delta into the next century.
","language":"English","publisher":"Molecular Diversity Preservation International","publisherLocation":"Basel, Switzerland","doi":"10.3390/w8100432","usgsCitation":"Stern, M.A., Flint, L.E., Minear, J.T., Flint, A.L., and Wright, S., 2016, Characterizing changes in streamflow and sediment supply in the Sacramento River Basin, California, using hydrological simulation program—FORTRAN (HSPF): Water, v. 8, no. 10, https://doi.org/10.3390/w8100432.","startPage":"432","numberOfPages":"21","ipdsId":"IP-073991","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":462073,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w8100432","text":"Publisher Index Page"},{"id":329512,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.5,\n 38.25\n ],\n [\n -123.5,\n 41\n ],\n [\n -121,\n 41\n ],\n [\n -121,\n 38.25\n ],\n [\n -123.5,\n 38.25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"10","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-30","publicationStatus":"PW","scienceBaseUri":"57ffdefee4b0824b2d179cf4","contributors":{"authors":[{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650712,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650713,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minear, Justin Toby jminear@usgs.gov","contributorId":3736,"corporation":false,"usgs":true,"family":"Minear","given":"Justin","email":"jminear@usgs.gov","middleInitial":"Toby","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":650714,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Scott 0000-0002-0387-5713 sawright@usgs.gov","orcid":"https://orcid.org/0000-0002-0387-5713","contributorId":1536,"corporation":false,"usgs":true,"family":"Wright","given":"Scott","email":"sawright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650716,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70174219,"text":"sir20165094 - 2016 - Using inferential sensors for quality control of Everglades Depth Estimation Network water-level data","interactions":[],"lastModifiedDate":"2016-09-29T10:11:07","indexId":"sir20165094","displayToPublicDate":"2016-09-29T10:00:00","publicationYear":"2016","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":"2016-5094","title":"Using inferential sensors for quality control of Everglades Depth Estimation Network water-level data","docAbstract":"The Everglades Depth Estimation Network (EDEN), with over 240 real-time gaging stations, provides hydrologic data for freshwater and tidal areas of the Everglades. These data are used to generate daily water-level and water-depth maps of the Everglades that are used to assess biotic responses to hydrologic change resulting from the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. The generation of EDEN daily water-level and water-depth maps is dependent on high quality real-time data from water-level stations. Real-time data are automatically checked for outliers by assigning minimum and maximum thresholds for each station. Small errors in the real-time data, such as gradual drift of malfunctioning pressure transducers, are more difficult to immediately identify with visual inspection of time-series plots and may only be identified during on-site inspections of the stations. Correcting these small errors in the data often is time consuming and water-level data may not be finalized for several months. To provide daily water-level and water-depth maps on a near real-time basis, EDEN needed an automated process to identify errors in water-level data and to provide estimates for missing or erroneous water-level data.
The Automated Data Assurance and Management (ADAM) software uses inferential sensor technology often used in industrial applications. Rather than installing a redundant sensor to measure a process, such as an additional water-level station, inferential sensors, or virtual sensors, were developed for each station that make accurate estimates of the process measured by the hard sensor (water-level gaging station). The inferential sensors in the ADAM software are empirical models that use inputs from one or more proximal stations. The advantage of ADAM is that it provides a redundant signal to the sensor in the field without the environmental threats associated with field conditions at stations (flood or hurricane, for example). In the event that a station does malfunction, ADAM provides an accurate estimate for the period of missing data. The ADAM software also is used in the quality assurance and quality control of the data. The virtual signals are compared to the real-time data, and if the difference between the two signals exceeds a certain tolerance, corrective action to the data and (or) the gaging station can be taken. The ADAM software is automated so that, each morning, the real-time EDEN data are compared to the inferential sensor signals and digital reports highlighting potential erroneous real-time data are generated for appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165094","collaboration":"Greater Everglades Priority Ecosystems Science","usgsCitation":"Petkewich, M.D., Daamen, R.C., Roehl, E.A., and Conrads, P.A., 2016, Using inferential sensors for quality control of Everglades Depth Estimation Network water-level data: U.S. Geological Survey Scientific Investigations Report 2016–5094, 25 p., https://dx.doi.org/10.3133/sir20165094.","productDescription":"v, 25 p.","onlineOnly":"Y","ipdsId":"IP-066447","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":329015,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20161116","text":"Open-File Report 2016–1116","description":"Open-File Report 2016–1116","linkHelpText":"- User’s Manual for the Automated Data Assurance and Management Application Developed for Quality Control of Everglades Depth Estimation Network Water-Level Data"},{"id":329013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5094/coverthb.jpg"},{"id":329014,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5094/sir20165094.pdf","text":"Report","size":"14.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5094"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.36749267578125,\n 25.916055815010868\n ],\n [\n -81.3482666015625,\n 26.25893609446839\n ],\n [\n -80.88958740234375,\n 26.261399213411483\n ],\n [\n -80.88134765625,\n 26.152972606566966\n ],\n [\n -80.84564208984375,\n 26.09625490696853\n ],\n [\n -80.82916259765625,\n 26.335268473041793\n ],\n [\n -80.53802490234375,\n 26.337729970839195\n ],\n [\n -80.4583740234375,\n 26.477948705162902\n ],\n [\n -80.45562744140625,\n 26.681821407205977\n ],\n [\n -80.35400390625,\n 26.681821407205977\n ],\n [\n -80.34576416015625,\n 26.64745870265938\n ],\n [\n -80.2716064453125,\n 26.5958952526538\n ],\n [\n -80.22491455078125,\n 26.519735305660795\n ],\n [\n -80.2166748046875,\n 26.423849388805877\n ],\n [\n -80.233154296875,\n 26.369724677109726\n ],\n [\n -80.2880859375,\n 26.35988109485539\n ],\n [\n -80.299072265625,\n 26.194876675795218\n ],\n [\n -80.3594970703125,\n 26.123384198664976\n ],\n [\n -80.4364013671875,\n 26.14804172600284\n ],\n [\n -80.43090820312499,\n 25.94816628853973\n ],\n [\n -80.48309326171875,\n 25.8962911755466\n ],\n [\n -80.4913330078125,\n 25.668760323272966\n ],\n [\n -80.5572509765625,\n 25.57465306409282\n ],\n [\n -80.4803466796875,\n 25.29437116258816\n ],\n [\n -80.75225830078125,\n 25.227304826281653\n ],\n [\n -81.36749267578125,\n 25.916055815010868\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
Stephenson Center, Suite 129
Gracern Road
Columbia, SC 29210
https://www2.usgs.gov/water/southatlantic/
A hydrogeologic framework was constructed to represent the altitudes and thicknesses of hydrogeologic units within the Ozark Plateaus aquifer system as part of a regional groundwater-flow model supported by the U.S. Geological Survey Water Availability and Use Science Program. The Ozark Plateaus aquifer system study area is nearly 70,000 square miles and includes parts of Arkansas, Kansas, Missouri, and Oklahoma. Nine hydrogeologic units were selected for delineation within the aquifer system and include the Western Interior Plains confining system, the Springfield Plateau aquifer, the Ozark confining unit, the Ozark aquifer, which was divided into the upper, middle, and lower Ozark aquifers to better capture the spatial variation in the hydrologic properties, the St. Francois confining unit, the St. Francois aquifer, and the basement confining unit. Geophysical and well-cutting logs, along with lithologic descriptions by well drillers, were compiled and interpreted to create hydrologic altitudes for each unit. The final compiled dataset included more than 23,000 individual altitude points (excluding synthetic points) representing the nine hydrogeologic units within the Ozark Plateaus aquifer system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165130","collaboration":"Water Availability and Use Science Program","usgsCitation":"Westerman, D.A., Gillip, J.A., Richards, J.M., Hays, P.D., Clark, B.R., 2016, Altitudes and thicknesses of hydrogeologic units of the Ozark Plateaus aquifer system in Arkansas, Kansas, Missouri, and Oklahoma: U.S. Geological Survey Scientific Investigations Report 2016–5130, 32 p., https://dx.doi.org/10.3133/sir20165130. 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U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
The Bi-State distinct population segment (DPS) of greater sage-grouse (Centrocercus urophasianus) that occurs along the Nevada–California border was proposed for listing as threatened under the Endangered Species Act (ESA) by the U.S. Fish and Wildlife Service (FWS) in October 2013. However, in April 2015, the FWS determined that the Bi-State DPS no longer required protection under the ESA and withdrew the proposed rule to list the Bi-State DPS (U.S. Fish and Wildlife Service, 2015). The Bi-State DPS occupies portions of Alpine, Mono, and Inyo Counties in California, and Douglas, Esmeralda, Lyon, Carson City, and Mineral Counties in Nevada. Unique threats facing this population include geographic isolation, expansion of single-leaf pinyon (Pinus monophylla) and Utah juniper (Juniperus osteosperma), anthropogenic activities, and recent changes in predator communities. Estimating population vital rates, identifying seasonal habitat, quantifying threats, and identifying movement patterns are important first steps in developing effective sage-grouse management and conservation plans. During 2011–15, we radio- and Global Positioning System (GPS)-marked (2012–14 only) 44, 47, 17, 9, and 3 sage-grouse, respectively, for a total of 120, in the Pine Nut Mountains Population Management Unit (PMU). No change in lek attendance was detected at Mill Canyon (maximum=18 males) between 2011 and 2012; however, 1 male was observed in 2014 and no males were observed in 2013 and 2015. Males were observed near Bald Mountain in 2013, making it the first year this lek was observed to be active during the study period. Males were observed at a new site in the Buckskin Range in 2014 during trapping efforts and again observed during surveys in 2015. Findings indicate that pinyon-juniper is avoided by sage-grouse during every life stage. Nesting females selected increased sagebrush cover, sagebrush height, and understory horizontal cover, and brood-rearing females selected similar areas, but also preferred increased perennial forb abundance. Using maximum likelihood estimation, nest survival for the Pine Nut Mountains PMU during 2011–14 was 23.8 percent (95-percent confidence interval [CI]=0.3–40.6 percent) and appeared lower in comparison to the average 42 percent nest success for sage-grouse range-wide.
Brood survival for 50-day brood-rearing phase in the Pine Nut Mountains PMU during 2011–14 was 53.8 percent (95-percent CI=30.0–73.4 percent). Adult survival during 2011–15 was 67.4 percent (95-percent CI=56.1–76.5 percent). During 2011–14, 696 raptor/raven surveys were completed and results indicate a greater number of raven detections (n=464) in the Pine Nut Mountains PMU than at other study areas in Nevada. These data will be used to develop a predator index. We conducted a more minimal monitoring effort of sage-grouse populations during the 2015 field season, which included trapping efforts, general telemetry, brood monitoring, and GPS monitoring. Nest monitoring, microhabitat sampling, and raptor/raven surveys were not conducted in the 2015 season. Deployment of GPS transmitters has expanded our knowledge of movement corridors and fine-scale movement patterns by sage-grouse in the Pine Nut Mountains PMU. Movement corridors between seasonal habitats were identified with one sage-grouse traveling greater than 100 kilometers south to the Bodie Mountains in California for the winter season. The use of GPS technology to monitor movements in conjunction with intensive field efforts will be important in developing habitat models and maps for the Pine Nut Mountains PMU.
Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://www.werc.usgs.gov/
The Souris River Basin is a 61,000 square kilometer basin in the provinces of Saskatchewan and Manitoba and the state of North Dakota. Record setting rains in May and June of 2011 led to record flooding with peak annual streamflow values (762 cubic meters per second [m3/s]) more than twice that of any previously recorded peak streamflow and more than five times the estimated 100 year postregulation streamflow (142 m3/s) at the U.S. Geological Survey (USGS) streamflow-gaging station above Minot, North Dakota. Upstream from Minot, N. Dak., the Souris River is regulated by three reservoirs in Saskatchewan (Rafferty, Boundary, and Alameda) and Lake Darling in North Dakota. During the 2011 flood, the city of Minot, N. Dak., experienced devastating damages with more than 4,000 homes flooded and 11,000 evacuated. As a result, the Souris River Basin Task Force recommended the U.S. Geological Survey (in cooperation with the North Dakota State Water Commission) develop a model for estimating the probabilities of future flooding and drought. The model that was developed took on four parts: (1) looking at past climate, (2) predicting future climate, (3) developing a streamflow model in response to certain climatic variables, and (4) combining future climate estimates with the streamflow model to predict future streamflow events. By taking into consideration historical climate record and trends in basin response to various climatic conditions, it was determined flood risk will remain high in the Souris River Basin until the wet climate state ends.
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U.S. Geological Survey
821 East Interstate Ave.
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This is the eighth annual report highlighting U.S. Geological Survey (USGS) science and decision-support activities conducted for the Wyoming Landscape Conservation Initiative (WLCI). The activities address specific management needs identified by WLCI partner agencies. In 2015, USGS scientists continued 24 WLCI projects in 5 categories: (1) acquiring and analyzing resource-condition data to form a foundation for understanding and monitoring landscape conditions and projecting changes; (2) using new technologies to improve the scope and accuracy of landscape-scale monitoring and assessments, and applying them to monitor indicators of ecosystem conditions and the effectiveness of on-the-ground habitat projects; (3) conducting research to elucidate the mechanisms that drive wildlife and habitat responses to changing land uses; (4) managing and making accessible the large number of databases, maps, and other products being developed; and (5) coordinating efforts among WLCI partners, helping them to use USGS-developed decision-support tools, and integrating WLCI outcomes with future habitat enhancement and research projects. Of the 24 projects, 21 were ongoing, including those that entered new phases or more in-depth lines of inquiry, 2 were new, and 1 was completed.
A highlight of 2015 was the WLCI science conference sponsored by the USGS, Bureau of Land Management, and National Park Service in coordination with the Wyoming chapter of The Wildlife Society. Of 260 participants, 41 were USGS professionals representing 13 USGS science centers, field offices, and Cooperative Wildlife Research Units. Major themes of USGS presentations included using new technologies for developing more efficient research protocols for modeling and monitoring natural resources, researching effects of energy development and other land uses on wildlife species and habitats of concern, and modeling species distributions, population trends, habitat use, and effects of land-use changes. There was also a special session on the effectiveness of Wyoming’s Sage-Grouse Executive Order. Combined, USGS presentations provided WLCI partners with a wealth of information and conservation tools.
The project completed in 2015 yielded an index of important agricultural lands in the WLCI region. The index improves upon existing measures of agricultural productivity and provides planners and managers with additional values to consider when making decisions about land use and conservation actions. The two new projects include an analysis of satellite imagery to quantify sagebrush productivity and mortality, and an evaluation of how groundwater and small streams interact in the upper Green River Basin. Initiated in response to concern among WLCI partners that large areas of sagebrush appear to have died recently, the sagebrush study objectives are to assess effects of these mortality events on overall sagebrush ecosystem productivity, evaluate the feasibility of using satellite imagery to detect patterns in sagebrush mortality over time, and identify factors driving these mortality events. The groundwater-streamflow interaction study is being conducted by hydrologists and fish ecologists to better understand how groundwater-streamflow interactions are affected by energy-resource development and how native fish communities are affected by these factors. Expected outcomes of both new projects will provide WLCI partners with additional information and decision-support tools.
Highlights of ongoing science foundation activities included simulations of nine alternative build-out scenarios for oil and gas development and an associated online fact sheet that explains how the simulations were conducted, with an applied example for the Atlantic Rim. Also completed in 2015 was an update of the USGS online inventory of mineral resources data, and publication of a USGS uranium resource survey for the WLCI region. Combined, the outcomes of this work provide decisionmakers and managers with important baseline information for existing and (or) future planning and monitoring efforts.
Terrestrial monitoring activities in 2015 emphasized the use of satellite data in combination with other technologies and field data to monitor, assess, and (or) forecast distribution patterns and (or) trends in sagebrush ecosystems, seasonal and migration stopover habitats used by mule deer and elk, and semi-arid aspen woodlands. Several professional papers detailing new monitoring models and results have been published. Combined, this and related work will help managers understand distribution patterns and trends among priority habitats, identify areas in need of restoration or conservation, and monitor the effectiveness of habitat-management actions.
Aquatic monitoring activities entailed not only the new groundwater-streamflow interaction study already mentioned, but also continued monitoring with streamgages paired with nearby wells in the Green River Basin to assess groundwater effects on streamflow and surface water temperatures. A map that portrays groundwater levels and general direction of flow in the Green River Basin was published as well. Overall, outcomes of USGS hydrological research and monitoring will inform WLCI partners about water resources in the WLCI region and help to explain fish-community responses to energy-resource development.
In 2015, USGS terrestrial wildlife ecologists continued to make crucial strides towards better understanding wildlife species responses to energy-resource development and other land-use changes. This body of research includes six taxa that require or heavily depend on sagebrush habitats: sage-grouse, pygmy rabbits, 3 songbird species, and mule deer. Native fish communities are also being evaluated. Approaches include modeling and mapping wildlife species distributions, abundances, and trends; using satellite and other technologies to track wildlife seasonal movements; conducting successive phases of research that build on the knowledge gained through prior phases to reveal the specific factors or thresholds that drive population- or individual-level responses to changes; and conducting population viability analyses. Additionally, wildlife habitat association models for pygmy rabbit and sage-grouse were combined with the oil and gas build-out scenarios to project species responses to alternative energy development scenarios. Outcomes of the wildlife response research are helping decisionmakers and managers identify specific factors that contribute to species population trends, the potential for spatial overlap between important wildlife habitats and proposed energy-resource development, locations of priority habitats for restoration and conservation, and more.
Data and WLCI Web site management highlights of 2015 included not only ongoing software upgrades, but also an update of the datasets displayed in two of the online products developed for the WLCI effort: (1) a map of 15,532 oil and natural gas well pad scars and other features associated with oil and gas extraction, and (2) a map of oil and gas, oil shale, uranium, and solar energy production, both for southwestern Wyoming. In addition, a map viewer was developed for a previously published map of coal and wind production in relation to sage-grouse distribution and core management areas in southwestern Wyoming. Combined, these maps place valuable decision-support tools in the hands of WLCI partners.
The USGS coordination efforts on behalf of the WLCI in 2015 included significant work on planning and executing the WLCI science conference. They also included ongoing efforts to support Local Project Development Teams and the WLCI Coordination Team (CT) with developing conservation priorities and strategies, identifying priority areas for future conservation actions, supporting the evaluation and ranking of conservation projects, and evaluating the ways in which proposed habitat projects relate to WLCI priorities. In 2015, the USGS also assisted the WLCI CT with updating the WLCI Conservation Action Plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161141","usgsCitation":"Bowen, Z.H., Aldridge, C.L., Anderson, P.J., Assal, T.J., Bartos, T.T., Chalfoun, A.D., Chong, G.W., Dematatis, M.K., Eddy-Miller, C.A., Garman, S.L., Germaine, S.S., Homer, C.G., Huber, C.C., Kauffman, M.J., Manier, D.J., Melcher, C.P., Miller, K.A., Norkin, Tamar, Sanders, L.E., Walters, A.W., Wilson, A.B., and Wyckoff, T.B., 2016, U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2015 annual report: U.S. Geological Survey Open-File Report 2016–1141, 59 p., https://dx.doi.org/10.3133/ofr20161141.","productDescription":"viii, 59 p.","onlineOnly":"Y","ipdsId":"IP-075182","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":329020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1141/coverthb.jpg"},{"id":329021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1141/ofr20161141.pdf","text":"Report","size":"36.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1141"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.07177734375,\n 43.40504748787035\n ],\n [\n -111.0498046875,\n 41\n ],\n [\n -105.99609375,\n 41\n ],\n [\n -105.99609375,\n 41.80407814427234\n ],\n [\n -105.35888671875,\n 41.82045509614034\n ],\n [\n -105.380859375,\n 42.47209690919285\n ],\n [\n -106.5673828125,\n 42.52069952914966\n ],\n [\n -107.20458984375,\n 42.48830197960227\n ],\n [\n -107.70996093749999,\n 42.53689200787315\n ],\n [\n -108.5009765625,\n 42.779275360241904\n ],\n [\n -108.80859375,\n 42.98857645832184\n ],\n [\n -109.22607421875,\n 43.229195113965005\n ],\n [\n -109.3798828125,\n 43.42100882994726\n ],\n [\n -109.79736328125,\n 43.5326204268101\n ],\n [\n -110.41259765625,\n 43.56447158721811\n ],\n [\n -111.07177734375,\n 43.40504748787035\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"
Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
The U.S. Geological Survey (USGS) conducts Asian carp research focused on early detection, risk assessment, and development of control tools and strategies. The goals are to prevent the establishment of invasive Asian carp in the Great Lakes and to reduce their impacts in the Ohio River and Mississippi River Basins and elsewhere. Managers can use the information, tools, and strategies for early detection of Asian carp and to control them when their presence is first evident. New detection and control tools are designed to accommodate expansion to other invasive species and application in geographically diverse areas.
This USGS focus complements goals of the Great Lakes Restoration Initiative (GLRI), a multi-agency collaboration started in 2010 to protect and restore the Great Lakes. As a member of the Asian Carp Regional Coordinating Committee, which guides Asian carp efforts, the USGS works closely with Federal and State agencies, Canada, and others to address high-priority Asian carp issues and provide science to inform management decisions.
The USGS has gained extensive knowledge of Asian carp biology and life history over the past 30 years. That knowledge guides the design, development, and application of control strategies, and is essential for developing approaches in line with modern principles and practices of integrated pest management (IPM). IPM is a process used to solve pest problems while minimizing risks to people and the environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163063","usgsCitation":"Kolar, C.S., and Morrison, S.S., 2016, USGS science and technology help managers battle invading Asian carp: U.S. Geological Survey Fact Sheet 2016-3063, 4 p., https:/dx.doi.org/10.3133/fs20163063.","productDescription":"4 p.","ipdsId":"IP-077431","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":329024,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3063/fs20163063.pdf","text":"Report","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3063"},{"id":329023,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3063/coverthb.jpg"}],"contact":"Ecosystems Mission Area
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The city of Sioux Falls is the fastest growing community in South Dakota. In response to this continued growth and planning for future development, Sioux Falls requires a sustainable supply of municipal water. Planning and managing sustainable groundwater supplies requires a thorough understanding of local groundwater resources. The Big Sioux aquifer consists of glacial outwash sands and gravels and is hydraulically connected to the Big Sioux River, which provided about 90 percent of the city’s source-water production in 2015. Managing sustainable groundwater supplies also requires an understanding of groundwater availability. An effective mechanism to inform water management decisions is the development and utilization of a groundwater-flow model. A groundwater-flow model provides a quantitative framework for synthesizing field information and conceptualizing hydrogeologic processes. These groundwater-flow models can support decision making processes by mapping and characterizing the aquifer. Accordingly, the city of Sioux Falls partnered with the U.S. Geological Survey to construct a groundwater-flow model. Model inputs will include data from advanced geophysical techniques, specifically airborne electromagnetic methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163075","collaboration":"Prepared in cooperation with the City of Sioux Falls","usgsCitation":"Valder, J.F., Delzer, G.C., Carter, J.M., Smith, B.D., and Smith, D.V., 2016, Construction of a groundwater-flow model for the Big Sioux aquifer using airborne electromagnetic methods, Sioux Falls, South Dakota: U.S. Geological Survey Fact Sheet 2016–3075, 4 p., https://dx.doi.org/10.3133/fs20163075.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-079092","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":328947,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3075/fs20163075.pdf","text":"Fact Sheet","size":"4.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3075"},{"id":328946,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3075/coverthb.jpg"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.79023742675781,\n 43.56098868633536\n ],\n [\n -96.79023742675781,\n 43.823133635349556\n ],\n [\n -96.69136047363281,\n 43.823133635349556\n ],\n [\n -96.69136047363281,\n 43.56098868633536\n ],\n [\n -96.79023742675781,\n 43.56098868633536\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Dakota Water Science Center
U.S. Geological Survey
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U.S. Geological Survey (USGS) Professional Paper 1794–D is the fourth in a four-volume series on the status and trends of the Nation’s land use and land cover, providing an assessment of the rates and causes of land-use and land-cover change in the Eastern United States between 1973 and 2000. Volumes A, B, and C provide similar analyses for the Western United States, the Great Plains of the United States, and the Midwest–South Central United States, respectively. The assessments of land-use and land-cover trends are conducted on an ecoregion-by-ecoregion basis, and each ecoregion assessment is guided by a nationally consistent study design that includes mapping, statistical methods, field studies, and analysis. Individual assessments provide a picture of the characteristics of land change occurring in a given ecoregion; in combination, they provide a framework for understanding the complex national mosaic of change and also the causes and consequences of change. Thus, each volume in this series provides a regional assessment of how (and how fast) land use and land cover are changing, and why. The four volumes together form the first comprehensive picture of land change across the Nation.
Geographic understanding of land-use and land-cover change is directly relevant to a wide variety of stakeholders, including land and resource managers, policymakers, and scientists. The chapters in this volume present brief summaries of the patterns and rates of land change observed in each ecoregion in the Eastern United States, together with field photographs, statistics, and comparisons with other assessments. In addition, a synthesis chapter summarizes the scope of land change observed across the entire Eastern United States. The studies provide a way of integrating information across the landscape, and they form a critical component in the efforts to understand how land use and land cover affect important issues such as the provision of ecological goods and services and also the determination of risks to, and vulnerabilities of, human communities. Results from this project also are published in peer-reviewed journals, and they are further used to produce maps of change and other tools for land management, as well as to provide inputs for carbon-cycle modeling and other climate change research.
This report is only one of the products produced by USGS on land-use and land-cover change in the United States. Other reports and land-cover statistics are available online at http://landcovertrends.usgs.gov.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Status and trends of land change in the United States--1973 to 2000 (Professional Paper 1794)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1794D","usgsCitation":"Sayler, K.L., Acevedo, W., and Taylor, J.L., eds., 2016, Status and trends of land change in the Eastern United States—1973 to 2000: U.S. Geological Survey Professional Paper 1794–D, 195 p., https://dx.doi.org/10.3133/pp1794D.","productDescription":"vi, 195 p.","numberOfPages":"206","onlineOnly":"Y","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":329045,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/pp1794A","text":"Professional Paper 1794-A","linkHelpText":"Status and trends of land change in the Western United 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\"}}]}","contact":"Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
http://eros.usgs.gov/
Warm Mineral Springs, located in southern Sarasota County, Florida, is a warm, highly mineralized, inland spring. Since 1946, a bathing spa has been in operation at the spring, attracting vacationers and health enthusiasts. During the winter months, the warm water attracts manatees to the adjoining spring run and provides vital habitat for these mammals. Well-preserved late Pleistocene to early Holocene-age human and animal bones, artifacts, and plant remains have been found in and around the spring, and indicate the surrounding sinkhole formed more than 12,000 years ago. The spring is a multiuse resource of hydrologic importance, ecological and archeological significance, and economic value to the community.
The pool of Warm Mineral Springs has a circular shape that reflects its origin as a sinkhole. The pool measures about 240 feet in diameter at the surface and has a maximum depth of about 205 feet. The sinkhole developed in the sand, clay, and dolostone of the Arcadia Formation of the Miocene-age to Oligocene-age Hawthorn Group. Underlying the Hawthorn Group are Oligocene-age to Eocene-age limestones and dolostones, including the Suwannee Limestone, Ocala Limestone, and Avon Park Formation. Mineralized groundwater, under artesian pressure in the underlying aquifers, fills the remnant sink, and the overflow discharges into Warm Mineral Springs Creek, to Salt Creek, and subsequently into the Myakka River. Aquifers described in the vicinity of Warm Mineral Springs include the surficial aquifer system, the intermediate aquifer system within the Hawthorn Group, and the Upper Floridan aquifer in the Suwannee Limestone, Ocala Limestone, and Avon Park Formation. The Hawthorn Group acts as an upper confining unit of the Upper Floridan aquifer.
Groundwater flow paths are inferred from the configuration of the potentiometric surface of the Upper Floridan aquifer for September 2010. Groundwater flow models indicate the downward flow of water into the Upper Floridan aquifer in inland areas, and upward flow toward the surface in coastal areas, such as at Warm Mineral Springs. Warm Mineral Springs is located in a discharge area. Changes in water use in the region have affected the potentiometric surface of the Upper Floridan aquifer. Historical increase in groundwater withdrawals resulted in a 10- to 20-foot regional decline in the potentiometric surface of the Upper Floridan aquifer by May 1975 relative to predevelopment levels and remained at approximately that level in May 2007 in the area of Warm Mineral Springs. Discharge measurements at Warm Mineral Springs (1942–2014) decreased from about 11–12 cubic feet per second in the 1940s to about 6–9 cubic feet per second in the 1970s and remained at about that level for the remainder of the period of record. Similarity of changes in regional water use and discharge at Warm Mineral Springs indicates that basin-scale changes to the groundwater system have affected discharge at Warm Mineral Springs. Water temperature had no significant trend in temperature over the period of record, 1943–2015, and outliers were identified in the data that might indicate inconsistencies in measurement methods or locations.
Within the regional groundwater basin, Warm Mineral Springs is influenced by deep Upper Floridan aquifer flow paths that discharge toward the coast. Associated with these flow paths, the groundwater temperatures increase with depth and toward the coast. Multiple lines of evidence indicate that a source of warm groundwater to Warm Mineral Springs is likely the permeable zone of the Avon Park Formation within the Upper Floridan aquifer at a depth of about 1,400 to 1,600 feet, or deeper sources. The permeable zone contains saline groundwater with water temperatures of at least 95 degrees Fahrenheit.
The water quality of Warm Mineral Springs, when compared with other springs in Florida had the highest temperature and the greatest mineralized content. Warm Mineral Springs water is characterized by a slight-green color, with varying water clarity, low dissolved oxygen (indicative of deep groundwater), and a hydrogen sulfide odor. Water-quality samples detected ammonium-nitrogen and nitrates, but at low concentrations. The drinking water standard for nitrate adopted by the U.S. Environmental Protection Agency is 10 milligrams per liter, measured as nitrogen. Water samples collected at spring vents by divers on April 29, 2015, had concentrations of 0.9 milligram per liter nitrate-nitrogen at vent A and 0.04–0.05 milligram per liter at vents B, C, and D. Typically, the water clarity is highest in the morning (about 30 feet Secchi depth) and often decreases throughout the day.
Analysis of existing data provided some insight into the hydrologic processes affecting Warm Mineral Springs; however, data have been sparsely and discontinuously collected since the 1940s. Continuous monitoring of hydrologic characteristics such as discharge, water temperature, specific conductance, and water-quality indicators, such as nitrate and turbidity (water clarity), would be valuable for monitoring and development of models of spring discharge and water quality. In addition, water samples could be analyzed for isotopic tracers, such as strontium, and the results used to identify and quantify the sources of groundwater that discharge at Warm Mineral Springs. Groundwater flow/transport models could be used to evaluate the sensitivity of the quality and quantity of water flowing from Warm Mineral Springs to changes in climate, aquifer levels, and water use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161166","collaboration":"Prepared in cooperation with the City of North Port and Sarasota County ","usgsCitation":"Metz, P.A., 2016, Discharge, water temperature, and water quality of Warm Mineral Springs, Sarasota County, Florida: A retrospective analysis: U.S. Geological Survey Open-File Report 2016–1166, 31 p., https://dx.doi.org/10.3133/ofr20161166.","productDescription":"vi, 31 p.","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-066216","costCenters":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"links":[{"id":328877,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1166/coverthb2.jpg"},{"id":328878,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1166/ofr20161166.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1166"}],"country":"United States","state":"Florida","county":"Sarasota County","otherGeospatial":"Warm Mineral Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.26177334785461,\n 27.058867814427533\n ],\n [\n -82.26177334785461,\n 27.060654482992454\n ],\n [\n -82.25942373275757,\n 27.060654482992454\n ],\n [\n -82.25942373275757,\n 27.058867814427533\n ],\n [\n -82.26177334785461,\n 27.058867814427533\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
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http://fl.water.usgs.gov
Effective conservation of rare species requires an understanding of how potential threats affect population dynamics. Unfortunately, information about population demographics prior to threats (i.e., baseline data) is lacking for many species. Perturbations, caused by climate change, disease, or other stressors can lead to population declines and heightened conservation concerns. Boreal toads (Anaxyrus boreas boreas) have undergone rangewide declines due mostly to the amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd), with only a few sizable populations remaining in the southern Rocky Mountains, USA, that are disease-free. Despite the apparent region-wide occurrence of Bd, our focal populations in central Colorado were disease free over a 14-year capture-mark-recapture study until the recent discovery of Bd at one of the sites. We used recapture data and the Pradel reverse-time model to assess the influence of environmental and site-specific conditions on survival and recruitment. We then forecast changes in the toad populations with 2 growth models; one using an average lambda value to initiate the projection, and one using the most recent value to capture potential effects of the incursion of disease into the system. Adult survival was consistently high at the 3 sites, whereas recruitment was more variable and markedly low at 1 site. We found that active season moisture, active season length, and breeding shallows were important factors in estimating recruitment. Population growth models indicated a slight increase at 1 site but decreasing trends at the 2 other sites, possibly influenced by low recruitment. Insight into declining species management can be gained from information on survival and recruitment and how site-specific environmental factors influence these demographic parameters. Our data are particularly useful because they provide baseline data on demographics in populations before a disease outbreak and enhance our ability to detect changes in population parameters potentially caused by the disease.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21118","usgsCitation":"Lambert, B.A., Schorr, R.A., Schneider, S.C., and Muths, E.L., 2016, Influence of demography and environment on persistence in toad populations: Journal of Wildlife Management, v. 80, no. 7, p. 1256-1266, https://doi.org/10.1002/jwmg.21118.","productDescription":"11 p.","startPage":"1256","endPage":"1266","ipdsId":"IP-068989","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":329017,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-12","publicationStatus":"PW","scienceBaseUri":"57f7c63ce4b0bc0bec09c84e","contributors":{"authors":[{"text":"Lambert, Brad A.","contributorId":173020,"corporation":false,"usgs":false,"family":"Lambert","given":"Brad","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":649431,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schorr, Robert A.","contributorId":105239,"corporation":false,"usgs":true,"family":"Schorr","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":649432,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schneider, Scott C.","contributorId":174943,"corporation":false,"usgs":false,"family":"Schneider","given":"Scott","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":649727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":649430,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259111,"text":"70259111 - 2016 - Geologic setting of the West Flank, a FORGE site adjacent to the Coso geothermal field","interactions":[],"lastModifiedDate":"2024-09-27T15:03:46.501771","indexId":"70259111","displayToPublicDate":"2016-09-27T07:19:03","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Geologic setting of the West Flank, a FORGE site adjacent to the Coso geothermal field","docAbstract":"The West Flank FORGE (Frontier Observatory for Research in Geothermal Energy) site is located immediately west and outside of the Coso geothermal field, eastern California. Coso is a fluid-dominated, high temperature geothermal system that has been producing power continuously since 1987. The reservoir is composed of highly faulted, fractured and hydrothermally altered Cretaceous and Jurassic plutonic basement rocks. The heat source is a shallow, silicic magma chamber associated with overlying, Quaternary rhyolites and basalts of the Coso volcanic field. Over 30 years of development drilling and associated investigations demonstrates that several well-defined boundaries exist at Coso beyond which there is no commercial, hydrothermal geothermal potential. The West Flank is just west of one of these boundaries and it meets all required FORGE temperature (175-225˚C), host rock (crystalline rock), and depth (1.5-4.0 km) criteria.
It has been hypothesized that the Coso volcanic field exists within a right-releasing step-over between two NW-striking, dextral fault systems. Two distinct fault populations yield high permeability drilling targets in the geothermal field. The first population contains WNW-trending with antithetical, NE-trending strike-slip faults and the second includes N- to NNE-trending normal faults. The West Flank appears to be separated from the geothermal field by one such northerly trending fault bounded by a felsic dike swarm. The West Flank’s 83-11 well suggests that the West Flank is comprised of weakly altered, leucogranite and diorites typical of the Jurassic to Cretaceous basement. The stress field in the West Flank has been rotated to 81˚ in contrast to the minimum principal stress orientations within the geothermal field which range from 103˚ to 108˚. Limited drilling, geophysics and other data sets are being assessed and synthesized to develop a working, 3d conceptual geologic model of the West Flank for FORGE.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 41st Workshop on Geothermal Reservoir Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"41st Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 22-24, 2016","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Sabin, A., Blake, K., Lazaro, M., Meade, D., Blankenship, D., Kennedy, M., McCulloch, J., DeOreo, S., Hickman, S.H., Glen, J.M., Kaven, J., Schoenball, M., Williams, C.F., Phelps, G., Faulds, J., Hinz, N., Calvin, W., Siler, D., and Robertson-Tait, A., 2016, Geologic setting of the West Flank, a FORGE site adjacent to the Coso geothermal field, in Proceedings of the 41st Workshop on Geothermal Reservoir Engineering, Stanford, CA, February 22-24, 2016, 11 p.","productDescription":"11 p.","ipdsId":"IP-073183","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics 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The Hanford Natural Resource Trustee Council requested that the U.S. Geological Survey perform this interpolation to assess the accuracy of delineations previously conducted by the U.S. Department of Energy and its contractors, in order to assure that the Natural Resource Damage Assessment could rely on these analyses. This study is based on previously existing chemical (or radionuclide) sampling and analysis data downloaded from publicly available Hanford Site Internet sources, geostatistically selected and interpreted as representative of current (from 2009 through part of 2012) but average conditions for groundwater contamination in the study area. The study is limited in scope to five contaminants—hexavalent chromium, tritium, nitrate, strontium-90, and carbon-14, all detected at concentrations greater than regulatory limits in the past.
All recent analytical concentrations (or activities) for each contaminant, adjusted for radioactive decay, non-detections, and co-located wells, were converted to log-normal distributions and these transformed values were averaged for each well location. The log-normally linearized well averages were spatially interpolated on a 50 × 50-meter (m) grid extending across the combined 100-N and 100-K Areas study area but limited to avoid unrepresentative extrapolation, using the minimum curvature geostatistical interpolation method provided by SURFER®data analysis software. Plume extents were interpreted by interpolating the log-normally transformed data, again using SURFER®, along lines of equal contaminant concentration at an appropriate established regulatory concentration . Total areas for each plume were calculated as an indicator of relative environmental damage. These plume extents are shown graphically and in tabular form for comparison to previous estimates. Plume data also were interpolated to a finer grid (10 × 10 m) for some processing, particularly to estimate volumes of contaminated groundwater. However, hydrogeologic transport modeling was not considered for the interpolation. The compilation of plume extents for each contaminant also allowed estimates of overlap of the plumes or areas with more than one contaminant above regulatory standards.
A mapping of saturated aquifer thickness also was derived across the 100-K and 100–N study area, based on the vertical difference between the groundwater level (water table) at the top and the altitude of the top of the Ringold Upper Mud geologic unit, considered the bottom of the uppermost unconfined aquifer. Saturated thickness was calculated for each cell in the finer (10 × 10 m) grid. The summation of the cells’ saturated thickness values within each polygon of plume regulatory exceedance provided an estimate of the total volume of contaminated aquifer, and the results also were checked using a SURFER® volumetric integration procedure. The total volume of contaminated groundwater in each plume was derived by multiplying the aquifer saturated thickness volume by a locally representative value of porosity (0.3).
Estimates of the uncertainty of the plume delineation also are presented. “Upper limit” plume delineations were calculated for each contaminant using the same procedure as the “average” plume extent except with values at each well that are set at a 95-percent upper confidence limit around the log-normally transformed mean concentrations, based on the standard error for the distribution of the mean value in that well; “lower limit” plumes are calculated at a 5-percent confidence limit around the geometric mean. These upper- and lower-limit estimates are considered unrealistic because the statistics were increased or decreased at each well simultaneously and were not adjusted for correlation among the well distributions (i.e., it is not realistic that all wells would be high simultaneously). Sources of the variability in the distributions used in the upper- and lower-extent maps include time varying concentrations and analytical errors.
The plume delineations developed in this study are similar to the previous plume descriptions developed by U.S. Department of Energy and its contractors. The differences are primarily due to data selection and interpolation methodology. The differences in delineated plumes are not sufficient to result in the Hanford Natural Resource Trustee Council adjusting its understandings of contaminant impact or remediation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161161","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Hanford Natural Resource Trustee Council","usgsCitation":"Johnson, K.H., 2016, Groundwater contaminant plume maps and volumes, 100-K and 100–N Areas, Hanford Site, Washington: U.S. Geological Survey Open-File Report 2016–1161, 64 p., https://dx.doi.org/10.3133/ofr20161161.","productDescription":"Report: vi, 64 p.; Appendix A","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-071546","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":329028,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1161/coverthb.jpg"},{"id":329029,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1161/ofr20161161.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1161"},{"id":329030,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1161/ofr20161161_appendixa.xlsx","text":"Appendix A","size":"2.1 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1161 Appendix A"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.83886718750001,\n 46.25204849722291\n ],\n [\n -119.83886718750001,\n 46.82637528602131\n ],\n [\n -119.19616699218749,\n 46.82637528602131\n ],\n [\n -119.19616699218749,\n 46.25204849722291\n ],\n [\n -119.83886718750001,\n 46.25204849722291\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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Tacoma, Washington 98402
http://wa.water.usgs.gov
Three different geophysical sensor types were used to characterize the underwater pressure waves and ground velocities generated by the underwater firing of seismic water guns. These studies evaluated the use of water guns as a tool to alter the movement of Asian carp. Asian carp are aquatic invasive species that threaten to move into the Great Lakes Basin from the Mississippi River Basin. Previous studies have identified a threshold of approximately 5 pounds per square inch (lb/in2) for behavioral modification and for structural limitation of a water gun barrier.
Two studies were completed during August 2014 and May 2015 in a backwater pond connected to the Illinois River at a sand and gravel quarry near Morris, Illinois. The August 2014 study evaluated the performance of two 80-cubic-inch (in3) water guns. Data from the 80-in3 water guns showed that the pressure field had the highest pressures and greatest extent of the 5-lb/in2 target value at a depth of 5 feet (ft). The maximum recorded pressure was 13.7 lb/in2, approximately 25 ft from the guns. The produced pressure field took the shape of a north-south-oriented elongated sphere with the 5-lb/in2 target value extending across the entire study area at a depth of 5 ft. Ground velocities were consistent over time, at 0.0067 inches per second (in/s) in the transverse direction, 0.031 in/s in the longitudinal direction, and 0.013 in/s in the vertical direction.
The May 2015 study evaluated the performance of one and two 100-in3 water guns. Data from the 100-in3 water guns, fired both individually and simultaneously, showed that the pressure field had the highest pressures and greatest extent of the 5-lb/in2 target value at a depth of 5 ft. The maximum pressure was 57.4 lb/in2, recorded at the underwater blast sensor closest to the water guns (at a horizontal distance of approximately 3 ft), as two guns fired simultaneously. Pressures and extent of the 5-lb/in2 target value decrease above and below this 5-ft depth, producing a relatively north-south-oriented pressure field shaped like an elongated sphere.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165098","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency as part of the Great Lakes Restoration Initiative","usgsCitation":"Koebel, C.M., and Egly, R.M., 2016, Water pressure and ground vibrations induced by water guns at a backwater pond on the Illinois River near Morris, Illinois: U.S. Geological Survey Scientific Investigations Report 2016–5098, 29 p., https://dx.doi.org/10.3133/sir20165098.","productDescription":"Report: v, 29 p.; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-072620","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":328926,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2016/5098/sir20165098_morris_seismic_data.zip","text":"Seismic Data","size":"890.7 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5098 Seismic Data"},{"id":328934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5098/coverthb.jpg"},{"id":328935,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5098/sir20165098.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5098"}],"country":"United States","state":"Illinois","city":"Morris","otherGeospatial":"Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.50388526916504,\n 41.32726186584144\n ],\n [\n -88.50388526916504,\n 41.334738165428035\n ],\n [\n -88.46534729003906,\n 41.334738165428035\n ],\n [\n -88.46534729003906,\n 41.32726186584144\n ],\n [\n -88.50388526916504,\n 41.32726186584144\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director , Illinois-Iowa Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801
http://il.water.usgs.gov
MODPATH is a particle-tracking post-processing program designed to work with MODFLOW, the U.S. Geological Survey (USGS) finite-difference groundwater flow model. MODPATH version 7 is the fourth major release since its original publication. Previous versions were documented in USGS Open-File Reports 89–381 and 94–464 and in USGS Techniques and Methods 6–A41.
MODPATH version 7 works with MODFLOW-2005 and MODFLOW–USG. Support for unstructured grids in MODFLOW–USG is limited to smoothed, rectangular-based quadtree and quadpatch grids.
A software distribution package containing the computer program and supporting documentation, such as input instructions, output file descriptions, and example problems, is available from the USGS over the Internet (http://water.usgs.gov/ogw/modpath/).
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U.S. Geological Survey
Mail Stop 411
12201 Sunrise Valley Drive
Reston, VA 20192
http://water.usgs.gov/ogw/