Managing pathogen spillover at the wildlife–livestock interface is a key step towards improving global animal health, food security and wildlife conservation. However, predicting the effectiveness of management actions across host–pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework offers one means of classifying and prioritizing responses to novel pathogen spillover threats, and evaluating current management actions for pathogens emerging at the wildlife–livestock interface.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2018.0343","usgsCitation":"Manlove, K., Sam, L., Borremans, B., Cassirer, E.F., Miller, R.S., Pepin, K., Besser, T.E., and Cross, P., 2019, Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 374, no. 1782, 20180343, https://doi.org/10.1098/rstb.2018.0343.","productDescription":"20180343","ipdsId":"IP-103606","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467372,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6711312","text":"Publisher Index Page"},{"id":367417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"374","issue":"1782","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Manlove, K.R. 0000-0002-7200-5236","orcid":"https://orcid.org/0000-0002-7200-5236","contributorId":218981,"corporation":false,"usgs":false,"family":"Manlove","given":"K.R.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":770820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sam, L.","contributorId":218982,"corporation":false,"usgs":false,"family":"Sam","given":"L.","email":"","affiliations":[],"preferred":false,"id":770821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borremans, B. 0000-0002-7779-4107","orcid":"https://orcid.org/0000-0002-7779-4107","contributorId":218983,"corporation":false,"usgs":false,"family":"Borremans","given":"B.","email":"","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":770822,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cassirer, E. Frances","contributorId":198303,"corporation":false,"usgs":false,"family":"Cassirer","given":"E.","email":"","middleInitial":"Frances","affiliations":[],"preferred":false,"id":770826,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, R. S.","contributorId":172739,"corporation":false,"usgs":false,"family":"Miller","given":"R.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":770823,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pepin, K. 0000-0002-9931-8312","orcid":"https://orcid.org/0000-0002-9931-8312","contributorId":218984,"corporation":false,"usgs":false,"family":"Pepin","given":"K.","email":"","affiliations":[{"id":39647,"text":"USDA-APHIS","active":true,"usgs":false}],"preferred":false,"id":770824,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Besser, T. E. 0000-0003-0449-1989","orcid":"https://orcid.org/0000-0003-0449-1989","contributorId":215110,"corporation":false,"usgs":false,"family":"Besser","given":"T.","email":"","middleInitial":"E.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":770825,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cross, Paul","contributorId":218980,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":770819,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70204649,"text":"pp1814E - 2019 - Soil mineralogy and geochemistry along a north-south transect in Alaska and the relation to source-rock terrane","interactions":[{"subject":{"id":70204649,"text":"pp1814E - 2019 - Soil mineralogy and geochemistry along a north-south transect in Alaska and the relation to source-rock terrane","indexId":"pp1814E","publicationYear":"2019","noYear":false,"chapter":"E","displayTitle":"Soil Mineralogy and Geochemistry Along a North-South Transect in Alaska and the Relation to Source-Rock Terrane","title":"Soil mineralogy and geochemistry along a north-south transect in Alaska and the relation to source-rock terrane"},"predicate":"IS_PART_OF","object":{"id":70158938,"text":"pp1814 - 2015 - Studies by the U.S. Geological Survey in Alaska, Volume 15","indexId":"pp1814","publicationYear":"2015","noYear":false,"title":"Studies by the U.S. Geological Survey in Alaska, Volume 15"},"id":1}],"isPartOf":{"id":70158938,"text":"pp1814 - 2015 - Studies by the U.S. Geological Survey in Alaska, Volume 15","indexId":"pp1814","publicationYear":"2015","noYear":false,"title":"Studies by the U.S. Geological Survey in Alaska, Volume 15"},"lastModifiedDate":"2019-08-13T14:05:15","indexId":"pp1814E","displayToPublicDate":"2019-08-12T13:50:50","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1814","chapter":"E","displayTitle":"Soil Mineralogy and Geochemistry Along a North-South Transect in Alaska and the Relation to Source-Rock Terrane","title":"Soil mineralogy and geochemistry along a north-south transect in Alaska and the relation to source-rock terrane","docAbstract":"Soils collected along a predominately north-south transect in Alaska were used to evaluate regional differences in the soil mineralogy and geochemistry in the context of a geotectonic framework for Alaska. The approximately 1,395-kilometer-long transect followed the Dalton, Elliott, and Richardson Highways from near Prudhoe Bay to Valdez. Sites were selected with a site spacing of approximately 10 road-kilometers; soil was sampled by soil horizon at 175 sites. Terrane boundaries were estimated from digitized versions of the lithotectonic terrane map of Alaska (Silberling and others, 1994). Terrane assignments for each site were based on the site’s distance along the transect. We also present data for 15 minerals or mineral groups and 58 elements, as well as total, inorganic, and organic carbon. Quantitative mineralogy of the mineral-soil horizons was characterized by X-ray diffraction. Elemental contents were determined by a combination of inductively coupled plasma-atomic emission spectrometry (ICP-AES) and inductively coupled plasma-mass spectrometry (ICP-MS) analysis following a multi-acid or sodium-sinter decomposition of the samples. Total carbon and carbonate carbon contents were determined using an automated carbon analyzer and coulometric titration, respectively; organic carbon content was obtained by calculating the difference between total and carbonate carbon. Mercury and selenium were analyzed using cold-vapor atomic absorption (CV-AA), and hydride-generation atomic absorption spectrometry (HG-AAS), respectively. The mineralogical and geochemical patterns from these soils are used to assess the relation between soil characteristics and the geology of surrounding terranes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1814E","usgsCitation":"Wang, B., Hults, C., Eberl, D., Woodruff, L., Cannon, W., and Gough, L., 2019, Soil mineralogy and geochemistry along a north-south transect in Alaska and the relation to source-rock terrane in Dumoulin, J.A., ed., Studies by the U.S. Geological Survey in Alaska, vol. 15: U.S. Geological Survey Professional Paper 1814–E, 27 p., https://doi.org/10.3133/pp1814E.","productDescription":"Report: v, 27 p.; 4 Appendixes","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-092422","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":366450,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1814/e/pp1814e_appendix1.pdf","text":"Appendix 1","size":"964 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1814 Chapter E Appendix 1","linkHelpText":" — Summary Statisitics for Chemical Analyses of Soil Samples from the North-South Transect of Alaska"},{"id":366449,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1814/e/pp1814e.pdf","text":"Report","size":"7.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1814 Chapter E"},{"id":366448,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1814/e/coverthb.jpg"},{"id":366451,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1814/e/pp1814e_appendix_2.pdf","text":"Appendix 2","size":"777 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1814 Chapter E Appendix 2","linkHelpText":" — Plots of mineral contents in soil samples from the upper and lower mineral soil horizons at sites along the north-south transect of Alaska"},{"id":366452,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1814/e/pp1814e_appendix_3.pdf","text":"Appendix 3","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1814 Chapter E Appendix 3","linkHelpText":" — Box plots of elemental contents in soil samples at sites along the north-south transect of Alaska"},{"id":366453,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1814/e/pp1814e_appendix_4.xlsx","text":"Appendix 4","size":"531 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1814 Chapter E Appendix 4","linkHelpText":" — Mineralogical and chemical data for all transect soil samples, standard reference materials, and laboratory splits"}],"country":"United 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Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
The interpretation of geophysical survey results to answer hydrologic, engineering, and geologic questions is critical to diverse problems for management of water, energy, and mineral resources. Although geophysical images provide valuable qualitative insight into subsurface architecture and conditions, translating geophysical images into quantitative information (e.g., saturation, concentration, and hydraulic properties) often involves substantial nonuniqueness and uncertainty owing to the limited resolution of geophysical imaging and uncertainty in petrophysical relations. We have developed a machine-learning approach to address these challenges in the context of a field-based investigation to map zones where a hydrocarbon plume was discharging to surface water at the National Crude Oil Spill Fate and Natural Attenuation Research Site in Bemidji, Minnesota, USA. The two-step approach combines multiple types of geophysical and direct information and effectively bypasses inversion and its associated assumptions. Integrating multifrequency electromagnetic induction, ground-penetrating radar, and fluid-sampling data, we first identify discharge zones and second estimate specific conductance versus depth. Compared with conventional inversion results, the machine-learning results (1) directly address the study objectives (delineating the discharge zones); (2) better extract depth-dependent information from the data, for which sensitivity diminishes rapidly with depth; and (3) quantify the uncertainty of the predictions (i.e., discharge versus nondischarge zones), rather than the uncertainty of the geophysical estimates (i.e., the standard error of estimation for the logarithm of electrical conductivity).
","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/geo2018-0690.1","usgsCitation":"Terry, N., Day-Lewis, F.D., Lane, J., Trost, J.J., and Bekins, B.A., 2019, Geophysical mapping of plume discharge to surface water at a crude oil spill site: Inversion versus machine learning: Geophysics, v. 84, no. 5, p. EN67-EN80, https://doi.org/10.1190/geo2018-0690.1.","productDescription":"14 p.","startPage":"EN67","endPage":"EN80","ipdsId":"IP-105187","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":370676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","city":"Bemidji","otherGeospatial":"National Crude Oil Spill Fate and Natural Attenuation Research Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.0820,\n 47.5775\n ],\n [\n -95.0920,\n 47.5775\n ],\n [\n -95.0920,\n 47.5715\n ],\n [\n -95.0820,\n 47.5715\n ],\n [\n -95.0820,\n 47.5775\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Terry, Neil C. 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","middleInitial":"C.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":778454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":778455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lane, John W. Jr. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":210076,"corporation":false,"usgs":true,"family":"Lane","given":"John W.","suffix":"Jr.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":778456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Trost, Jared J. 0000-0003-0431-2151 jtrost@usgs.gov","orcid":"https://orcid.org/0000-0003-0431-2151","contributorId":3749,"corporation":false,"usgs":true,"family":"Trost","given":"Jared","email":"jtrost@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":778457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bekins, Barbara A. 0000-0002-1411-6018 babekins@usgs.gov","orcid":"https://orcid.org/0000-0002-1411-6018","contributorId":1348,"corporation":false,"usgs":true,"family":"Bekins","given":"Barbara","email":"babekins@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":778458,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205208,"text":"70205208 - 2019 - Confronting models with data: The challenges of estimating disease spillover","interactions":[],"lastModifiedDate":"2019-09-06T10:33:08","indexId":"70205208","displayToPublicDate":"2019-08-12T10:29:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3048,"text":"Philosophical Transactions of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Confronting models with data: The challenges of estimating disease spillover","docAbstract":"For pathogens known to transmit across host species, strategic investment in disease control requires knowledge about where and when spillover transmission is likely. One approach to estimating spillover is to directly correlate observed spillover events with covariates. An alternative is to mechanistically combine information on host density, distribution, and pathogen prevalence to predict where and when spillover events are expected to occur. We use several case studies at the wildlife-livestock disease interface to highlight the challenges, and potential solutions, to estimating spatio-temporal variation in spillover risk. Datasets on multiple host species often do not align in space, time or resolution, and may have no estimates of observation error. Linking these datasets requires they be related to a common spatial and temporal resolution and appropriately propagating errors in predictions can be difficult. Hierarchical models are one potential solution, but for fine-resolution predictions at broad spatial scales many models become computationally challenging. Despite these limitations, the confrontation of mechanistic predictions with observed events is an important avenue for developing a better understanding of pathogen spillover. Systems where data have been collected at all levels in the spillover process are rare, or non-existent, and require investment and sustained effort across disciplines.","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2018.0435","usgsCitation":"Cross, P.C., Prosser, D., Ramey, A.M., Hanks, E.M., and Pepin, K., 2019, Confronting models with data: The challenges of estimating disease spillover: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 374, no. 1782, 20180435, https://doi.org/10.1098/rstb.2018.0435.","productDescription":"20180435","ipdsId":"IP-103613","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467373,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6711303","text":"Publisher Index Page"},{"id":367254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"374","issue":"1782","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":770369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":770370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":770371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanks, Ephraim M. 0000-0003-0345-7164","orcid":"https://orcid.org/0000-0003-0345-7164","contributorId":210840,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim","email":"","middleInitial":"M.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":770372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pepin, Kim M. 0000-0002-9931-8312","orcid":"https://orcid.org/0000-0002-9931-8312","contributorId":187441,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim M.","affiliations":[],"preferred":false,"id":770373,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204739,"text":"70204739 - 2019 - Interactions between resident risk perceptions and wildfire risk mitigation: Evidence from simultaneous equations modeling","interactions":[],"lastModifiedDate":"2019-08-15T09:49:41","indexId":"70204739","displayToPublicDate":"2019-08-12T09:46:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Interactions between resident risk perceptions and wildfire risk mitigation: Evidence from simultaneous equations modeling","docAbstract":"Fire science emphasizes that mitigation actions on residential property, including structural hardening and maintaining defensible space, can reduce the risk of wildfire at a home. Accordingly, a rich body of social science literature investigates the determinants of wildfire risk mitigation behaviors of residents living in fire-prone areas. Here, we investigate relationships among wildfire hazards, residents’ risk perceptions, and conditions associated with mitigation actions using a combination of simulated wildfire conditions, household survey responses, and professionally assessed parcel characteristic data. We estimate a simultaneous model of these data that accounts for potential direct feedbacks between risk perceptions and parcel-level conditions. We also compare the use of self-reported versus assessed parcel-level data for estimating these relationships. Our analysis relies on paired survey and assessment data for approximately 2000 homes in western Colorado. Our simultaneous model demonstrates dual-directional interactions between risk perceptions and conditions associated with mitigation actions, with important implications for inference from simpler approaches. In addition to improving general understanding of decision-making about risk and natural hazards, our findings can support the effectiveness of publicly supported programs intended to encourage mitigation to reduce society’s overall wildfire risk.","language":"English","publisher":"MDPI","doi":"10.3390/fire2030046","usgsCitation":"Meldrum, J., Brenkert-Smith, H., Champ, P.A., Gomez, J., Falk, L.C., and Barth, C.M., 2019, Interactions between resident risk perceptions and wildfire risk mitigation: Evidence from simultaneous equations modeling: Fire, v. 2, no. 3, 46, 18 p., https://doi.org/10.3390/fire2030046.","productDescription":"46, 18 p.","ipdsId":"IP-109622","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467376,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fire2030046","text":"Publisher Index Page"},{"id":366559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":768256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brenkert-Smith, Hannah 0000-0001-6117-8863","orcid":"https://orcid.org/0000-0001-6117-8863","contributorId":195485,"corporation":false,"usgs":false,"family":"Brenkert-Smith","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":768257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Champ, Patricia A.","contributorId":195486,"corporation":false,"usgs":false,"family":"Champ","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":768258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gomez, Jamie","contributorId":218078,"corporation":false,"usgs":false,"family":"Gomez","given":"Jamie","email":"","affiliations":[{"id":38125,"text":"West Region Wildfire Council","active":true,"usgs":false}],"preferred":false,"id":768259,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Falk, Lilia C.","contributorId":210655,"corporation":false,"usgs":false,"family":"Falk","given":"Lilia","email":"","middleInitial":"C.","affiliations":[{"id":38125,"text":"West Region Wildfire Council","active":true,"usgs":false}],"preferred":false,"id":768260,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barth, Christopher M.","contributorId":195487,"corporation":false,"usgs":false,"family":"Barth","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":768261,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203978,"text":"sir20195063 - 2019 - Estimating potential wetland extent along selected river reaches in Indiana using streamflow statistics and flood-inundation mapping techniques","interactions":[],"lastModifiedDate":"2024-01-22T22:05:30.263439","indexId":"sir20195063","displayToPublicDate":"2019-08-12T06:05:02","publicationYear":"2019","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":"2019-5063","displayTitle":"Estimating Potential Wetland Extent along Selected River Reaches in Indiana using Streamflow Statistics and Flood-Inundation Mapping Techniques","title":"Estimating potential wetland extent along selected river reaches in Indiana using streamflow statistics and flood-inundation mapping techniques","docAbstract":"In this study potential wetland extents were estimated for 12 river reaches covering about 750 river miles in Indiana and parts of Illinois and Ohio. The study was completed by the U.S. Geological Survey in cooperation with the U.S. Department of Agriculture, Natural Resources Conservation Service. This study follows and adds to the work completed in a pilot study and determines that potential wetland extents can be estimated using streamflow statistics, streamgage data, and flood-inundation mapping techniques.
The study was designed to assist in the Agricultural Conservation Easement Program. The Agricultural Conservation Easement Program is a voluntary program administered by the Natural Resources Conservation Service that provides technical and financial assistance to private landowners and Tribes to restore, protect, and enhance wetlands in exchange for retiring eligible land from agriculture. For a site to be eligible for wetland restoration, it should be in a zone with sustained or frequent flooding. This study calculated the flows that lasted for a period of 7 consecutive days on average at least once every 2 years (a value termed the “7MQ2”) for all the U.S. Geological Survey streamgages within the selected river reaches. These 7MQ2 flows were related to the stage-discharge tables for each streamgage, and a corresponding water-surface elevation was determined. Maps of estimated wetland extent were prepared using the 7MQ2 inundation elevation data in conjunction with bare-earth land-surface elevation data made publicly available through the online geospatial data clearinghouses of Indiana, Illinois, and Ohio. Flood-inundation mapping techniques were applied with the aid of geographic information system software to generate water-surface planes that represent inundation elevations associated with the 7MQ2 streamflow. Land-surface elevation data from high-resolution digital elevation models were subtracted from the water-surface planes to produce maps of wetland extent. The 12 map products, including datasets and geoprocessing tools, produced from this study will aid the National Resources Conservation Service and its partners with the onsite inundation-zone verification in agricultural land for potential restoration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195063","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture, Natural Resources Conservation Service","usgsCitation":"Fowler, K.K., Sperl, B.J., and Kim, M.H., 2019, Estimating potential wetland extent along selected river reaches in Indiana using streamflow statistics and flood-inundation mapping techniques: U.S. Geological Survey Scientific Investigations Report 2019–5063, 12 p., https://doi.org/10.3133/sir20195063.","productDescription":"Report: iv, 12 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097069","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":366436,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LGXDJ8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets related to wetland extent maps for 12 stream reaches covering approximately 750 river miles in Indiana"},{"id":424708,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108893.htm","linkFileType":{"id":5,"text":"html"},"description":"108893"},{"id":424707,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108892.htm","linkFileType":{"id":5,"text":"html"},"description":"108892"},{"id":366472,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://wim.usgs.gov/geonarrative/indianawetlands/","text":"USGS story map","linkHelpText":"– Geo-narrative"},{"id":366438,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5063/coverthb2.jpg"},{"id":366435,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5063/sir20195063.pdf","text":"Report","size":"2.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5063"}],"country":"United States","state":"Illinois, Indiana, Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.582763671875,\n 37.21283151445594\n ],\n [\n -83.924560546875,\n 37.21283151445594\n ],\n [\n -83.924560546875,\n 41.934976500546604\n ],\n [\n -88.582763671875,\n 41.934976500546604\n ],\n [\n -88.582763671875,\n 37.21283151445594\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
The Upper Devonian Ignacio Formation (as stratigraphically revised) comprises a transgressive, tide-dominated estuarine depositional system in the San Juan Mountains (Colorado, USA). The unit backfills at least three bedrock paleovalleys (10–30 km wide and ≥42 m deep) with a consistent stratigraphy of tidally influenced fluvial, bayhead-delta, central estuarine-basin, mixed tidal-flat, and estuarine-mouth tidal sandbar deposits. Paleovalleys were oriented northwest while longshore transport was to the north. The deposits represent Upper Devonian lowstand and transgressive systems tracts. The overlying Upper Devonian Elbert Formation (upper member) consists of geographically extensive tidal-flat deposits and is interpreted as mixed siliciclastic-carbonate bay-fill facies that represents an early highstand systems tract. Stratigraphic revision of the Ignacio Formation includes reassigning the basal conglomerate to the East Lime Creek Conglomerate, recognizing an unconformity separating these two units, and incorporating strata previously mapped as the McCracken Sandstone Member (Elbert Formation) into the Ignacio Formation. The Ignacio Formation was previously interpreted as Cambrian, but evidence that it is Devonian includes reexamined fossil data and detrital zircon U-Pb geochronology. The Ignacio Formation has a stratigraphic trend of detrital zircon ages shifting from a single ca. 1.7 Ga age peak to bimodal ca. 1.4 Ga and ca. 1.7 Ga age peaks, which represents local source-area unroofing history. Specifically, the upper plate of a Proterozoic thrust system (ca. 1.7 Ga Twilight Gneiss) was eroded prior to exposure of the lower plate (ca. 1.4 Ga Uncompahgre Formation). These results are a significant alternative interpretation of the geologic history of the southern Rocky Mountains.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02085.1","usgsCitation":"Evans, J.E., Maurer, J.T., and Holm-Denoma, C.S., 2019, Recognition and significance of Late Devonian fluvial, estuarine, and mixed siliciclastic-carbonate nearshore marine environments in the San Juan Mountains (southwestern Colorado, U.S.A.): Multiple incised valleys backfilled by lowstand and transgressive system tracts: Geosphere, v. 15, no. 5, p. 1497-1507, https://doi.org/10.1130/GES02085.1.","productDescription":"11 p.","startPage":"1497","endPage":"1507","ipdsId":"IP-103463","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":467377,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02085.1","text":"Publisher Index Page"},{"id":437368,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SYHGUV","text":"USGS data release","linkHelpText":"U-Pb detrital zircon data for: lower Paleozoic sedimentary rocks near Silverton, CO USA"},{"id":367539,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"San Juan Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.04092407226562,\n 37.137329767248794\n ],\n [\n -107.63168334960936,\n 37.137329767248794\n ],\n [\n -107.63168334960936,\n 37.847748103485365\n ],\n [\n -108.04092407226562,\n 37.847748103485365\n ],\n [\n -108.04092407226562,\n 37.137329767248794\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, James E.","contributorId":194435,"corporation":false,"usgs":false,"family":"Evans","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":771316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maurer, Joshua T","contributorId":219120,"corporation":false,"usgs":false,"family":"Maurer","given":"Joshua","email":"","middleInitial":"T","affiliations":[{"id":13587,"text":"Bowling Green State University","active":true,"usgs":false}],"preferred":false,"id":771317,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440 cholm-denoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":2442,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher","email":"cholm-denoma@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":771315,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204890,"text":"70204890 - 2019 - Mid-piacenzian of the north Atlantic Ocean","interactions":[],"lastModifiedDate":"2020-04-04T17:14:58.403893","indexId":"70204890","displayToPublicDate":"2019-08-09T15:09:42","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3481,"text":"Stratigraphy","active":true,"publicationSubtype":{"id":10}},"title":"Mid-piacenzian of the north Atlantic Ocean","docAbstract":"The Piacenzian Age (Pliocene) represents a past climate interval within which frequency and magnitude of environmental changes during a period of past global warmth can be analyzed, climate models can be tested, and results can be placed in a context to better prepare for future change. Here we focus on the North Atlantic region, incorporating new and existing faunal assemblage and alkenone data from Ocean Drilling Program Sites 642, 662, 982, and 999, and International Ocean Discovery Program Sites 1308 and 1313 into our paleoenvironmental reconstruction. Cores and outcrop material containing Piacenzian sediments from the Atlantic Coastal Plain of Virginia, USA, are also included. These data allow us to characterize regional changes in temperature, salinity, upwelling, surface productivity, and diversity, associated with climate transitions, and make nuanced reconstructions of mid-Piacenzian conditions within a high-resolution temporal framework between ~3.40 and ~3.15 Ma, inclusive of Marine Isotope Stages M2 through KM5. We include an initial comparison of estimated sea-surface temperature to coupled climate model simulations, which shows improvement in model adherence to paleoclimate parameters over previous data-model comparisons for the Pliocene.","language":"English","publisher":"Micropress","doi":"10.29041/strat.16.3.119-144","usgsCitation":"Dowsett, H.J., Robinson, M.M., Foley, K.M., Herbert, T.D., Otto-Bliesner, B.L., and Spivey, W., 2019, Mid-piacenzian of the north Atlantic Ocean: Stratigraphy, v. 16, no. 3, p. 119-144, https://doi.org/10.29041/strat.16.3.119-144.","productDescription":"26 p.","startPage":"119","endPage":"144","ipdsId":"IP-099499","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":366807,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524 hdowsett@usgs.gov","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":949,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"hdowsett@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":768899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robinson, Marci M. 0000-0002-9200-4097 mmrobinson@usgs.gov","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":2082,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci","email":"mmrobinson@usgs.gov","middleInitial":"M.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":768900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foley, Kevin M. 0000-0003-1013-462X kfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-1013-462X","contributorId":2543,"corporation":false,"usgs":true,"family":"Foley","given":"Kevin","email":"kfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":768901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herbert, Timothy D.","contributorId":192841,"corporation":false,"usgs":false,"family":"Herbert","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":768902,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Otto-Bliesner, Bette L.","contributorId":209685,"corporation":false,"usgs":false,"family":"Otto-Bliesner","given":"Bette","email":"","middleInitial":"L.","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":768904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spivey, Whittney 0000-0003-1111-3361 wspivey@usgs.gov","orcid":"https://orcid.org/0000-0003-1111-3361","contributorId":214849,"corporation":false,"usgs":true,"family":"Spivey","given":"Whittney","email":"wspivey@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":768903,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203223,"text":"ofr20191046 - 2019 - Using scenarios to evaluate vulnerability of grassland communities to climate change in the Southern Great Plains of the United States","interactions":[],"lastModifiedDate":"2020-11-03T17:40:04.061442","indexId":"ofr20191046","displayToPublicDate":"2019-08-09T15:00:00","publicationYear":"2019","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":"2019-1046","displayTitle":"Using Scenarios to Evaluate Vulnerability of Grassland Communities to Climate Change in the Southern Great Plains of the United States","title":"Using scenarios to evaluate vulnerability of grassland communities to climate change in the Southern Great Plains of the United States","docAbstract":"Scenario planning is a useful tool for identifying key vulnerabilities of ecological systems to changing climates, informed by the potential outcomes for a set of divergent, plausible, and relevant climate scenarios. We evaluated potential vulnerabilities of grassland communities to changing climate in the Southern Great Plains (SGP) and the Landscape Conservation Design pilot area (LCD) for the U.S. Fish and Wildlife Service, Science Applications Program, Great Plains Landscape Conservation Cooperative. Four climate scenarios (warm-dry, warm-wet, hot-dry, and hot-wet) from atmospheric-ocean general circulation models were selected to represent a suite of plausible future climatic conditions. For each scenario, and for contemporary climatic conditions, we predicted the spatial patterns of relative productivity for indicator grass species using statistical models of relative above-ground net primary productivity (hereafter, productivity) based on temperature, precipitation, and soil texture (percent sand, silt, or clay).
Two indicator grass species were selected to represent each of four focal grassland communities: semi-desert grasslands, shortgrass prairie, mixed-grass prairie, and tallgrass prairie. Changes in spatial patterning of bioclimatic conditions conducive for each indicator species as predicted for each climate scenario relative to current land use were used to evaluate potential vulnerability and conservation opportunities for grassland communities. Specifically, the following questions were addressed for each focal grassland community: (1) Where is the productivity of each species predicted to increase, decrease, or remain stable relative to estimated contemporary productivity for the SGP and LCD pilot area, (2) where is the productivity of the two indicator species for each community predicted to increase, decrease, or remain stable, (3) which grassland communities are most vulnerable to changes in composition and vertical structure, (4) how do current land-use patterns contribute to potential vulnerabilities of grassland communities for the climate scenarios evaluated, and (5) how can managers use the vulnerabilities identified to evaluate conservation opportunities in the SGP and LCD?
Current land-use patterns, in combination with the potential effects of a changing climate, pose greater risks to mixed-grass and tallgrass prairies of the SGP compared to semi-desert grasslands and shortgrass prairie. For most climate scenarios evaluated, bioclimatic conditions conducive to the taller species were predicted to contract within some or all the current distribution of mixed-grass and tallgrass prairies within the SGP. An increase in precipitation, however, could potentially ameliorate the negative effects of increasing temperatures as evidenced by higher productivity for the hot-wet scenario compared to the other scenarios for the most vulnerable species. Compounding their greater vulnerability to increasing temperatures coupled with decreasing precipitation, the mixed-grass and tallgrass prairies have been greatly fragmented and converted, primarily by agriculture. In contrast, the climate scenarios evaluated are generally conducive to stable or increasing productivity of indicator species for semi-desert grasslands and shortgrass prairie. In addition, conversion and fragmentation of semi-desert grasslands and shortgrass prairie were relatively low. These results suggest that the synergistic effects of land use and changing climatic conditions could have the greatest effects on the composition and structure of mixed-grass and tallgrass prairies in the SGP. ScienceBase data release files that support this report are available at https://doi.org/10.5066/P9DGJHEP
(Manier and others, 2019).
Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Background.—Pesticide results from the U.S. Geological Survey (USGS) National Water Quality Laboratory (NWQL) are used for water-quality assessments by many agencies and organizations. The USGS is committed to providing data of the highest possible quality to the consumers of its data. A cooperator’s inquiries about specific pesticide detections in water revealed potential laboratory contamination issues for some results. Consequently, the USGS conducted an extensive evaluation of potential low-level contamination related to processing or analysis of water-quality samples at NWQL for 21 pesticide compounds of interest to the cooperator. This is the most comprehensive study of NWQL pesticide quality-control (QC) results to date.
Purpose and scope.—The purpose of this study was to document protocols used by the NWQL to censor pesticide results and to determine the effects of laboratory contamination—as determined from detections in laboratory set blanks—on pesticide detections in groundwater and surface-water samples. More than 30,000 pesticide results from 113 selected batches of samples (2 percent or less of total batches) analyzed by the NWQL during the 15 years from 2001 to 2015 were reviewed. All laboratory results from the selected batches, including results from environmental (surface water and groundwater) and QC (set-blank, blind-blank, and blind-spike) samples, were evaluated. The study includes results for 21 pesticide compounds analyzed in groundwater and surface-water samples collected across the United States. Eleven pesticide compounds were analyzed by a gas chromatography/mass spectrometry method and 10 compounds by a liquid chromatography/mass spectrometry method.
Objectives and methods.—The objectives of this study were to (1) determine the characteristics of laboratory contamination over time, (2) compare distributions of pesticide results in set blanks with distributions in environmental samples, (3) evaluate the potential for false-positive and false-negative reporting of results, and (4) evaluate the effects of reevaluating historical pesticide results using 2017 compound identification protocols on detections of pesticides in groundwater and surface-water samples. The 113 instrument batches selected for this study contained detections of one or more of the 21 pesticide compounds in set blanks or were among those batches with the highest pesticide detection frequencies in set blanks. As a result, the dataset for this study was targeted toward pesticides and batches with laboratory contamination. The objectives were addressed by statistically comparing environmental and set-blank results; computing moving averages of set-blank detection frequencies to identify periods of episodic contamination; and using summary statistics, tabular summaries, and graphical approaches, such as time-series plots and cumulative distribution functions.
Results.—Objective 1: Laboratory contamination, as determined by pesticide detections in set blanks, was found in 13 percent of set-blank results from the 113 targeted batches included in this study (as compared to 6 percent of set-blank results from all 7,620 batches analyzed during the study period). It is estimated that 92 percent of the laboratory contamination during the study period was episodic, meaning that it occurred during discrete periods of time. All 21 of the targeted pesticide compounds had periods of episodic contamination, with most episodes ranging in duration from about 1 to 8 months. The remaining 8 percent of laboratory contamination was random or from a known source (deterministic).
Objective 2: For some compounds, graphs of cumulative distribution functions of the entire distributions of set-blank and environmental samples overlap, suggesting that there is no difference in the distributions of the two types of samples. However, time-series graphs show that detections in set blanks often occur at different times (sometimes separated by years) than detections in environmental samples, indicating clear differences in those distributions, and indicating the importance of evaluating the timing of detections in all sample types.
For most compounds detected in set-blank and environmental samples, detection frequencies were significantly greater in set blanks than in groundwater or surface-water samples (p<0.05). There are several explanations for this finding, including that the 113 batches of samples chosen for this study targeted batches with detections in set blanks or that detections in set-blank samples were historically determined with less stringent identification criteria than for environmental samples (groundwater and surface-water samples).
Objective 3: The false-positive and false-negative rates from blind samples submitted during the study period by the USGS Quality Systems Branch generally were less than 1 and 5 percent, respectively, for the 21 pesticides. The only compound with a false-positive rate greater than 1 percent was flumetsulam (2.6 percent), indicating that there is a higher likelihood of flumetsulam being reported as a detection when it is not present in an environmental sample compared with the reporting of other compounds.
Objective 4: Altogether, for data in targeted batches, NWQL would have reported 0.1 percent of results from groundwater samples and 1.4 percent of results from surface-water samples differently if 2017 identification protocols were applied to historical pesticide results. In most of these cases, detections observed in historical results would change to nondetections. The small percentages of changes that would occur if historical data were reevaluated indicate that historical protocols used by the NWQL to identify detections in environmental samples were robust and produced results that are predominantly consistent with current [2017] practices.
Conclusions.—The NWQL produces high-quality pesticide results at environmentally relevant concentrations. NWQL identification protocols and censoring practices are largely effective at minimizing the reporting of false-positive and false-negative results. Laboratory contamination, when it occurred, tended to occur in episodes; thus, evaluating the timing and magnitude of detections in set blanks relative to detections in environmental samples was determined to be an important consideration for analysis of environmental results. Because NWQL censoring practices do not address all types and occurrences of laboratory contamination, options for additional censoring practices are provided for data users with more specific or stringent data-quality objectives. The methods used to analyze the 21 compounds for this report can similarly be applied to all 173 pesticide compounds that were analyzed by the NWQL during the same time period. This study also has helped to identify potential improvements in reporting USGS data, such as conducting more frequent review of set-blank datasets.
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The U.S. Geological Survey (USGS), the United States' official national topographic mapping organization, is building and maintaining geographic databases for fundamental base geographic layers of land cover, structures, boundaries, hydrography, geographic names, transportation, elevation, and orthoimagery as The National Map. Data from the 3D Elevation Program, the National Hydrography Dataset and other national programs provide public domain, authoritative, accurate, and reliable data for The National Map, and data are served to United States government organizations and the public. Products of The National Map include viewable and downloadable data for all data layers, derivative products including US Topo, and web services of the data. The US Topo product is automatically generated from national map databases and produces topographic maps every three years for all 48 of the contiguous United States, Hawaii, and the United States territories.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2019 US national report","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"ICC 2019","conferenceDate":"July 15-20, 2019","conferenceLocation":"Tokyo, Japan","language":"English","publisher":"Cartography and Geographic Information Society","usgsCitation":"Usery, E., 2019, U.S. Geological Survey accomplishments in cartography 2015-2019, in 2019 US national report, Tokyo, Japan, July 15-20, 2019, 3 p.","productDescription":"3 p.","ipdsId":"IP-108334","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":369871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":369870,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cartogis.org/2019/08/08/2019-us-national-report/"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Usery, E. Lynn 0000-0002-2766-2173","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":204684,"corporation":false,"usgs":true,"family":"Usery","given":"E. Lynn","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":763443,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70205609,"text":"70205609 - 2019 - Streamflow reconstruction in the Upper Missouri River Basin using a novel Bayesian network model","interactions":[],"lastModifiedDate":"2019-11-13T13:41:56","indexId":"70205609","displayToPublicDate":"2019-08-08T09:53:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Streamflow reconstruction in the Upper Missouri River Basin using a novel Bayesian network model","docAbstract":"A Bayesian model that uses the spatial dependence induced by the river network topology, and the leading principal components of regional tree-ring chronologies for paleo-streamflow reconstruction is presented. In any river basin, a convergent, dendritic network of tributaries comes together to form the main stem of a river. Consequently, it is natural to think of a spatial Markov process that recognizes this topological structure to develop a spatially consistent basin-scale streamflow reconstruction model that uses the information in streamflow and tree-ring chronology data to inform the reconstructed flows, while maintaining the space-time correlation structure of flows that is critical for water resource assessments and management. Given historical data from multiple streamflow gauges along a river, their tributaries in a watershed, and regional tree-ring chronologies, the model is fit and used to simultaneously reconstruct the full network of paleo-streamflow at all gauges in the basin progressing upstream to downstream along the river. The spatial network structure allows a substantial reduction in the uncertainty associated with paleo-streamflow as one proceeds downstream in the network and the spatial dependence structure increases the information content. Our application to eighteen streamflow gauges in the Upper Missouri River Basin shows that the mean adjusted-R2 for the basin is approximately 0.5 with good overall cross-validated skill as measured by five different skill metrics. A comparison with the traditional principal components regression shows that the spatial Bayesian model offers improvements, as downstream gauges are informed by the reconstruction of the upstream gauges, as well as the tree-ring chronologies.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR024901","usgsCitation":"Ravindranath, A., Devineni, N., Lall, U., Cook, E., Pederson, G.T., Martin, J.T., and Woodhouse, C.A., 2019, Streamflow reconstruction in the Upper Missouri River Basin using a novel Bayesian network model: Water Resources Research, v. 55, no. 9, p. 7694-7716, https://doi.org/10.1029/2019WR024901.","productDescription":"23 p.","startPage":"7694","endPage":"7716","ipdsId":"IP-104913","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467383,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019wr024901","text":"Publisher Index Page"},{"id":367776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Missouri River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.0,\n 48.5\n ],\n [\n -104.5,\n 48.5\n ],\n [\n -104.5,\n 42.0\n ],\n [\n -115.0,\n 42.0\n ],\n [\n -115.0,\n 48.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Ravindranath, Arun","contributorId":219272,"corporation":false,"usgs":false,"family":"Ravindranath","given":"Arun","email":"","affiliations":[{"id":39562,"text":"City University of New York","active":true,"usgs":false}],"preferred":false,"id":771848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Devineni, Naresh","contributorId":219273,"corporation":false,"usgs":false,"family":"Devineni","given":"Naresh","email":"","affiliations":[{"id":39562,"text":"City University of New York","active":true,"usgs":false}],"preferred":false,"id":771849,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lall, Upmanu 0000-0003-0529-8128","orcid":"https://orcid.org/0000-0003-0529-8128","contributorId":212142,"corporation":false,"usgs":false,"family":"Lall","given":"Upmanu","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":771850,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, Edward","contributorId":197078,"corporation":false,"usgs":false,"family":"Cook","given":"Edward","affiliations":[],"preferred":false,"id":771851,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":771847,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martin, Justin T. 0000-0002-3523-6596","orcid":"https://orcid.org/0000-0002-3523-6596","contributorId":215418,"corporation":false,"usgs":true,"family":"Martin","given":"Justin","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":771852,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Woodhouse, Connie A.","contributorId":187601,"corporation":false,"usgs":false,"family":"Woodhouse","given":"Connie","email":"","middleInitial":"A.","affiliations":[{"id":32413,"text":"University of Arizona, Tucson, AZ, USA, 85721","active":true,"usgs":false}],"preferred":false,"id":771853,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70234294,"text":"70234294 - 2019 - Size selectivity of sampling gears used to sample Kokanee","interactions":[],"lastModifiedDate":"2022-08-08T11:47:10.873554","indexId":"70234294","displayToPublicDate":"2019-08-08T06:44:24","publicationYear":"2019","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":"Size selectivity of sampling gears used to sample Kokanee","docAbstract":"Kokanee Oncorhynchus nerka provide valued recreational fisheries and also serve as a prey resource for economically, socially, and ecologically important fishes. As such, management of kokanee is a major focus of natural resource agencies. Kokanee are typically monitored using midwater trawls, but the interpretation of data collected using midwater trawls is difficult due to the unknown size selectivity of the gear. We sought to assess the length selectivity of midwater trawls by comparing estimates obtained from midwater trawls with estimates obtained from gill nets adjusted for size selectivity. Experimental curtain gill nets and midwater trawls were used in conjunction to sample kokanee in seven lentic systems in Idaho. The size selectivity of gill nets was estimated by accounting for the probability of encounter and the probability of retention. Estimates of size selectivity were then used to adjust the length distribution of fish sampled in gill nets. The adjusted length distribution of fish sampled in gill nets was compared with estimates obtained from midwater trawls to identify potential size selectivity of midwater trawls. A pattern of size selectivity was apparent for both sampling techniques. The average length of kokanee sampled with midwater trawls was 111 mm; whereas, kokanee sampled with gill nets had a mean length of 235 mm. Our results suggest experimental gill nets are useful for common sampling of kokanee (e.g., trend monitoring) because the gear is less size selective than midwater trawls and is adjustable for size selectivity. However, midwater trawls are likely the best gear for addressing questions associated with early life history. Overall, our results provide a better understanding of gill-net and midwater trawl selectivity and ultimately improve the ability to sample and manage the species.
Optimal hydrograph separation (OHS) uses a two-parameter recursive digital filter that applies specific conductance mass-balance constraints to estimate the base flow contribution to total streamflow at stream gages where discharge and specific conductance are measured. OHS was applied to U.S. Geological Survey (USGS) stream gages across the conterminous United States to examine the range/distribution of base flow inputs and the utility of this method to build a hydrologic model calibration dataset. OHS models with acceptable goodness-of-fit criteria were insensitive to drainage area, stream density, watershed slope, elevation, agricultural or perennial snow/ice land cover, average annual precipitation, runoff, or evapotranspiration, implying that OHS results are a viable calibration dataset applicable in diverse watersheds. OHS-estimated base flow contribution was compared to base flow-like model components from the USGS National Hydrologic Model Infrastructure run with the Precipitation-Runoff Modeling System (NHM-PRMS). The NHM-PRMS variable gwres_flow is most conceptually like a base flow component of streamflow but the gwres_flow contribution to total streamflow is generally smaller than the OHS-estimated base flow contribution. The NHM-PRMS variable slow_flow, added to gwres_flow, produced similar or greater estimates of base flow contributions to total streamflow than the OHS-estimated base flow contribution but was dependent on the total flow magnitude.
","language":"English","publisher":"MDPI","doi":"10.3390/w11081629","usgsCitation":"Foks, S., Raffensperger, J.P., Penn, C.A., and Driscoll, J.M., 2019, Estimation of base flow by optimal hydrograph separation for the conterminous United States and implications for national-extent hydrologic models: Water, v. 11, no. 8, 1629, 25 p., https://doi.org/10.3390/w11081629.","productDescription":"1629, 25 p.","ipdsId":"IP-104087","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":467387,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w11081629","text":"Publisher Index Page"},{"id":437370,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XF3C11","text":"USGS data 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Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":768089,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203068,"text":"ofr20191039 - 2019 - Streamflow, water quality, and constituent loads and yields, Scituate Reservoir Drainage Area, Rhode Island, Water Year 2017","interactions":[],"lastModifiedDate":"2019-08-07T10:23:41","indexId":"ofr20191039","displayToPublicDate":"2019-08-07T10:30:00","publicationYear":"2019","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":"2019-1039","displayTitle":"Streamflow, Water Quality, and Constituent Loads and Yields, Scituate Reservoir Drainage Area, Rhode Island, Water Year 2017","title":"Streamflow, water quality, and constituent loads and yields, Scituate Reservoir Drainage Area, Rhode Island, Water Year 2017","docAbstract":"As part of a long-term cooperative program to monitor water quality within the Scituate Reservoir drainage area, the U.S. Geological Survey, in cooperation with the Providence Water Supply Board, collected streamflow and water-quality data at the Scituate Reservoir and tributaries. Streamflow and concentrations of chloride and sodium estimated from records of specific conductance were used to calculate loads of chloride and sodium during water year 2017 (October 1, 2016, through September 30, 2017) for tributaries to the Scituate Reservoir, Rhode Island. Streamflow was measured or estimated by the U.S. Geological Survey following standard methods at 23 streamgages; 14 of these streamgages are equipped with instrumentation capable of continuously monitoring water level, specific conductance, and water temperature. Water-quality samples were collected by the Providence Water Supply Board at 36 sampling stations, which also include the 14 continuous-record streamgages maintained by the U.S. Geological Survey, during water year 2017 as part of a long-term sampling program; all stations are in the Scituate Reservoir drainage area. Water-quality data collected by the Providence Water Supply Board are summarized by using values of central tendency and are used, in combination with measured (or estimated) streamflows, to calculate loads and yields (loads per unit area) of selected water-quality constituents for water year 2017.
The Ponaganset River, which is the largest tributary to the reservoir and was monitored by the U.S. Geological Survey, contributed a mean streamflow of 29 cubic feet per second to the reservoir during water year 2017. For the same period, annual mean streamflows measured (or estimated) for the other monitoring stations in this study ranged from about 0.44 to about 20 cubic feet per second. Together, tributaries equipped with instrumentation capable of continuously monitoring specific conductance transported about 3,100 metric tons of chloride and 1,900 metric tons of sodium to the Scituate Reservoir during water year 2017; chloride yields for the tributaries ranged from 16 to 140 metric tons per square mile, and sodium yields, from 10 to 80 metric tons per square mile.
At the stations where water-quality samples were collected by the Providence Water Supply Board, the medians of the median concentrations were 25.3 milligrams per liter for chloride, 0.002 milligram per liter as nitrogen for nitrite, 0.10 milligram per liter as nitrogen for nitrate, 0.05 milligram per liter as phosphate for orthophosphate, 1,200 colony forming units per 100 milliliters for total coliform bacteria, and 14 colony forming units per 100 milliliters for Escherichia coli (E. coli). The medians of the median daily loads of chloride, nitrite, nitrate, orthophosphate, total coliform, and E. coli bacteria were 230 kilograms per day, 17 grams per day, 860 grams per day, 690 grams per day, 84,000 million colony forming units per day, and 1,200 million colony forming units per day, respectively. The medians of the median yields of chloride, nitrite, nitrate, orthophosphate, total coliform, and E. coli bacteria were were 87 kilograms per day per square mile, 6.1 grams per day per square mile, 280 grams per day per square mile, 260 grams per day per square mile, 44,000 million colony forming units per day per square mile, and 655 million colony forming units per day per square mile, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191039","collaboration":"Prepared in cooperation with the Providence Water Supply Board, Rhode Island","usgsCitation":"Smith, K.P., 2019, Streamflow, water quality, and constituent loads and yields, Scituate Reservoir drainage area, Rhode Island, water year 2017: U.S. Geological Survey Open-File Report 2019–1039, 33 p., https://doi.org/10.3133/ofr20191039.","productDescription":"Report: v, 33 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102155","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":365646,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PPAKP6","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-quality data from the Providence Water Supply Board for tributary streams to the Scituate Reservoir, water year 2017"},{"id":365582,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1039/coverthb.jpg"},{"id":365583,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1039/ofr20191039.pdf","text":"Report","size":"1.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1039"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Scituate Reservoir Drainage Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.78878784179688,\n 41.72110557838152\n ],\n [\n -71.53610229492188,\n 41.72110557838152\n ],\n [\n -71.53610229492188,\n 41.97174336327968\n ],\n [\n -71.78878784179688,\n 41.97174336327968\n ],\n [\n -71.78878784179688,\n 41.72110557838152\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
The U.S. Geological Survey assessed the effective solubilities of organic analytes at the BKK Class Ⅰ Landfill site, West Covina, California, in cooperation with the California Department of Toxic Substances Control, using available data for liquid samples collected within (in-waste) and below (sub-waste) the landfill in 2014–16. The primary purpose of the effective solubility calculations was to determine the likely presence or absence of dense non-aqueous phase liquids (DNAPLs), which is important for understanding the sources, persistence, and movement of the leachate contaminants. Percent effective solubility (a measure of the degree of deviation of a measured liquid concentration of a compound from the aqueous effective solubility) greater than 1 percent is the threshold that commonly has been used to infer the presence of DNAPLs or mixed DNAPLs in aqueous monitoring results. In the present study, however, thresholds higher than 1 percent were used because of elevated temperatures and concentrations of cosolvents in the liquid samples—thresholds of 10 percent or 100 percent, respectively, were used for liquid and solid (at 25 degrees Celsius) organic compounds for potential non-aqueous phase liquid presence.
Overall, the effective solubility calculations indicate the likely presence of DNAPLs or mixed DNAPLs in some samples for a range of compounds, including tetrachloroethene, trichloroethene, 1,1-dichloroethene, vinyl chloride, 1,2,4-trichlorobenzene, 1,4-dichlorobenzene, 1,2-dichlorobenzene, naphthalene, toluene, ethylbenzene, and xylenes. Samples with the highest calculated percent effective solubilities for chlorinated ethenes, ethanes, and benzenes were from a location where liquid in the waste prism is known to be in contact with the groundwater beneath the landfill. Trends in the effective solubilities for the chlorinated ethenes and ethanes were generally consistent between the in-waste and sub-waste samples, supporting a similar source composition for these liquids. Percent effective solubilities were less than 10 for the chlorinated ethanes in all the in-waste and sub-waste samples, indicating that DNAPL of these compounds is not present. Percent effective solubilities of chlorinated benzenes, ethylbenzene, and xylenes exceeded the 10-percent effective solubility threshold in more of the sub-waste samples than the in-waste liquid samples. Volatilization also may influence the patterns in the calculated effective solubilities but were not included in this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191080","collaboration":"Prepared in cooperation with the California Department of Toxic Substances Control","usgsCitation":"Lorah, M.M., Majcher, E.H., and Morel, C.J., 2019, Effective solubility assessment for organic analytes in liquid samples, BKK Class Ⅰ Landfill, West Covina, California, 2014–16: U.S. Geological Survey Open-File Report 2019–1080, 18 p., https://doi.org/10.3133/ofr20191080.","productDescription":"Report: v, 18p.; Tables","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105175","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":366110,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1080/ofr20191080.pdf","text":"Report","size":"8.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1080"},{"id":366109,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1080/coverthb.jpg"},{"id":366188,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1080/ofr20191080_table1.xlsx","text":"Tables SI-1 through SI-11","size":"1.25 MB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Supplemental Information Worksheet - Mole Fraction and Effective Solubility Calculations"}],"country":"United States","state":"California","city":"West Covinia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.99917221069335,\n 34.064463311552615\n ],\n [\n -118.01462173461914,\n 34.04000041165585\n ],\n [\n -117.95145034790039,\n 34.03729768165777\n ],\n [\n -117.91471481323242,\n 34.03800893474363\n ],\n [\n -117.90956497192383,\n 34.04782361826847\n ],\n [\n -117.90939331054688,\n 34.0715732952909\n ],\n [\n -117.94235229492188,\n 34.07143110146331\n ],\n [\n -117.99917221069335,\n 34.064463311552615\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
Accurate forecasts of the streamflow expected during late spring and summer in the Upper Klamath River Basin in southern-central Oregon and northern California are used by water management agencies to balance water allocations for agriculture, aquatic habitat, and hydropower-production needs. Streamflow forecasts are also used by irrigation farmers for planning. The forecasts are typically made twice a month starting as early in the water year as December. Multiple regression equations relating real-time snowpack and precipitation conditions to seasonal streamflow volumes have been used for many years in forecasting. However, with warming temperature trends and lower snowpack, such forecasts based on historical data could become less reliable in the future. If the timing and relation of snowpack and precipitation are outside of the range of the historical data used to create the equations, the forecasts become extrapolations. Statistical forecast equations are also limited in their ability to forecast streamflow in groundwater-dominated basins having inter-annual lag. As an additional method for seasonal streamflow forecasting, a physical-process-based hydrologic model employing the Precipitation-Runoff Modeling System (PRMS) was developed in cooperation with the U.S. Bureau of Reclamation for the Upper Klamath Basin in this study. The model was calibrated for the portion of the basin draining into Upper Klamath Lake. PRMS is a deterministic, distributed-parameter, physical-process-based modeling system developed by the U.S. Geological Survey. It simulates daily streamflow, snow, solar radiation, evapotranspiration, surface-water, and groundwater processes within the basin. A model calibration and validation period for water years 2000–15 and water years 1984–99, respectively, was used. The model was calibrated and validated using measured streamflow, snowpack, evapotranspiration, and solar radiation data sets. Interpolated daily precipitation and air temperature data from 32 meteorological stations within and surrounding the Upper Klamath Basin were used as model input. Performance statistics, used to evaluate how well simulated daily streamflow matched with measured streamflow included percent bias, percent relative error, and root-mean-square error. The statistics were computed annually, monthly, for October–March, and for April–September. With the exception of the October–March period, percent bias statistics were all within plus or minus 5-percent for both the calibration and validation periods. Limitations to using the model are error in the precipitation and air temperature input time series data, which include measurement error and error in the spatial interpolation method. Other errors include measured daily streamflow data, which were adjusted for consumptive use losses to make them more closely resemble natural streamflow for calibration.
The model developed for the Upper Klamath Basin can be used to forecast streamflow from the Sprague and Williamson River Basins and inflow to Upper Klamath Lake. Reliable forecasts at these locations are needed for managing water for irrigation, ecosystem health, and power production. Using the models in a forecast application requires assembling model input data sets of anticipated daily precipitation and minimum and maximum air temperature for the period after the date the forecast is made and the end of the forecasted period. These climate data sets can be based on historical or synthetic records, at the discretion of the forecaster. With the Ensemble Streamflow Prediction method, a suite of streamflow scenarios is simulated using multiple years of climate data as model input. The forecasted streamflow is determined from knowing the exceedance probabilities of the simulated streamflows. In this study, the model and the Ensemble Streamflow Prediction method were used to forecast the volume of inflow to Upper Klamath Lake for a 6-month period from April 1, 2015, to September 30, 2015, using a range of climate data sets based on El Niño Southern Oscillation (ENSO) criteria. Because 2015 was a warm phase ENSO period, climate data for 10 warm phase ENSO years from 1980 to 2010 were used as input to the model. The simulated April–September 2015 UKL inflow volume based on measured 2015 climate data was 482,000 acre-feet, which was very close to the 50th percent exceedance probability computed from 10 simulated scenarios that used warm phase ENSO climate input data from 1980–2010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195044","collaboration":"Prepared in cooperation with the U.S. Bureau of Reclamation","usgsCitation":"Risley, J.C., 2019, Using the precipitation-runoff modeling system to predict seasonal water availability in the upper Klamath River basin, Oregon and California: U.S. Geological Survey Scientific Investigations Report 2019–5044, 37 p., https://doi.org/10.3133/sir20195044.","productDescription":"vi, 37 p.","onlineOnly":"Y","ipdsId":"IP-098864","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":366315,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5044/coverthb.jpg"},{"id":366316,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5044/sir20195044.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5044"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Upper Klamath River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.42041015624999,\n 40.76806170936614\n ],\n [\n -119.94323730468749,\n 40.76806170936614\n ],\n [\n -119.94323730468749,\n 43.205175817237304\n ],\n [\n -123.42041015624999,\n 43.205175817237304\n ],\n [\n -123.42041015624999,\n 40.76806170936614\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Observations of seismic anisotropy can provide direct constraints on the character of mantle flow in subduction zones, critical for our broader understanding of subduction dynamics. Here we present over 750 new SKS splitting measurements in the vicinity of Mount St. Helens in the Cascadia subduction zone using a combination of stations from the iMUSH broadband array and Cascades Volcano Observatory network. This provides the highest density of splitting measurements yet available in Cascadia, acting as a focused “telescope” for seismic anisotropy in the subduction zone. We retrieve spatially consistent splitting parameters (mean fast direction Φ: 74°, mean delay time ∂t: 1.0 s) with the azimuthal occurrence of nulls in agreement with the fast direction of splitting. When averaged across the array, a 90° periodicity in splitting parameters as a function of back azimuth is revealed, which has not been recovered previously with single‐station observations. The periodicity is characterized by a sawtooth pattern in Φ with a clearly defined 45° trend. We present new equations that reproduce this behavior based upon known systematic errors when calculating shear wave splitting from data with realistic seismic noise. The corrected results suggest a single layer of anisotropy with an ENE‐WSW fast axis parallel to the motion of the subducting Juan de Fuca plate; in agreement with predictions for entrained subslab mantle flow. The splitting pattern is consistent with that seen throughout Cascadia, suggesting that entrainment of the underlying asthenosphere with the subducting slab is coherent and widespread.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GC008433","usgsCitation":"Eakin, C.M., Wirth, E.A., Wallace, A., Ulberg, C.W., Creager, K.C., and Abers, G.A., 2019, SKS splitting beneath Mount St. Helens: Constraints on subslab mantle entrainment: Geochemistry, Geophysics, Geosystems, v. 20, no. 8, p. 4202-4217, https://doi.org/10.1029/2019GC008433.","productDescription":"16 p.","startPage":"4202","endPage":"4217","ipdsId":"IP-106294","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":467394,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gc008433","text":"Publisher Index Page"},{"id":371738,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.06335449218749,\n 45.916765867649005\n ],\n [\n -121.05285644531249,\n 45.916765867649005\n ],\n [\n -121.05285644531249,\n 47.28295557691231\n ],\n [\n -123.06335449218749,\n 47.28295557691231\n ],\n [\n -123.06335449218749,\n 45.916765867649005\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Eakin, Caroline M","contributorId":221907,"corporation":false,"usgs":false,"family":"Eakin","given":"Caroline","email":"","middleInitial":"M","affiliations":[{"id":27305,"text":"Australia National University","active":true,"usgs":false}],"preferred":false,"id":780685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wirth, Erin A. 0000-0002-8592-4442","orcid":"https://orcid.org/0000-0002-8592-4442","contributorId":197865,"corporation":false,"usgs":true,"family":"Wirth","given":"Erin","email":"","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":780684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wallace, Abraham","contributorId":221908,"corporation":false,"usgs":false,"family":"Wallace","given":"Abraham","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":780686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ulberg, Carl W 0000-0001-6198-809X","orcid":"https://orcid.org/0000-0001-6198-809X","contributorId":221909,"corporation":false,"usgs":false,"family":"Ulberg","given":"Carl","email":"","middleInitial":"W","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":780687,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Creager, Kenneth C 0000-0003-4501-7415","orcid":"https://orcid.org/0000-0003-4501-7415","contributorId":221910,"corporation":false,"usgs":false,"family":"Creager","given":"Kenneth","email":"","middleInitial":"C","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":780688,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Abers, Geoffrey A","contributorId":221911,"corporation":false,"usgs":false,"family":"Abers","given":"Geoffrey","email":"","middleInitial":"A","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":780689,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228359,"text":"70228359 - 2019 - Migratory connectivity of American woodcock derived using satellite telemetry","interactions":[],"lastModifiedDate":"2022-02-09T17:56:43.922336","indexId":"70228359","displayToPublicDate":"2019-08-05T11:50:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Migratory connectivity of American woodcock derived using satellite telemetry","docAbstract":"American woodcock (Scolopax minor; woodcock) migratory connectivity (i.e., association between breeding and wintering areas) is largely unknown, even though current woodcock management is predicated on such associations. Woodcock are currently managed in the Eastern and Central management regions in the United States with the boundary between management regions analogous to the boundary between the Atlantic and Mississippi flyways, based largely on analysis of band returns from hunters. Factors during migration influence survival and fitness, and existing data derived from banding and very high frequency telemetry provide only coarse-scale information to assess factors influencing woodcock migratory movement patterns and behavior. To assess whether current management-region boundaries correspond with woodcock migratory connectivity in the Central Management Region and to describe migration patterns with higher resolution than has been previously possible, we deployed satellite transmitters on 73 woodcock (25 adult and 28 juvenile females, and 8 adult and 12 juvenile males) and recorded 87 autumn or spring migration paths from 2014 to 2016. Marked woodcock used 2 primary migrations routes: a Western Route and a Central Route. The Western Route ran north-south, connecting the breeding and wintering grounds within the Central Management Region. The hourglass-shaped Central Route connected an area on the wintering grounds reaching from Texas to Florida, to sites throughout northeastern North America in both the Eastern Management Region and Central Management Region and woodcock following this route migrated through the area between the Appalachian Mountains and the Mississippi Alluvial Valley in western Tennessee during autumn and spring. Two of 17 woodcock captured associated with breeding areas in Michigan, Wisconsin, or Minnesota migrated to wintering sites in the Eastern Management Region and 12 marked woodcock captured on wintering areas in Texas and Louisiana migrated to breeding sites in the Eastern Management Region. Woodcock that used the Western Route exhibited high concentrations of stopovers during spring in the Arkansas Ozark Mountains and northern Missouri, and along the Mississippi River on the border between Wisconsin and Minnesota, and autumn concentrations of stopovers in southwestern Iowa, central Missouri, the Arkansas portion of the Ozark Mountains, and around the junction of Texas, Louisiana, Oklahoma, and Arkansas. Woodcock that used the Central Route exhibited high concentrations of stopovers during spring in northern Mississippi through western Tennessee, western Kentucky, and the Missouri Bootheel, and autumn concentrations of stopovers in northern Illinois, southwestern Ohio, and the portions of Kentucky and Tennessee west of the Appalachian Mountains. We suggest that current management of woodcock based on 2 management regions may not be consistent with the apparent lack of strong migratory connectivity we observed. Our results also suggest where management of migration habitat might be most beneficial to woodcock.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21741","usgsCitation":"Moore, J.D., Andersen, D.E., Cooper, T.R., Duguay, J.P., Oldenburger, S., Stewart, C.A., and Krementz, D.G., 2019, Migratory connectivity of American woodcock derived using satellite telemetry: Journal of Wildlife Management, v. 83, no. 7, p. 1617-1627, https://doi.org/10.1002/jwmg.21741.","productDescription":"11 p.","startPage":"1617","endPage":"1627","ipdsId":"IP-098884","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395699,"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 -96.85546875,\n 25.958044673317843\n ],\n [\n -94.74609375,\n 29.075375179558346\n ],\n [\n -90.17578124999999,\n 28.998531814051795\n ],\n [\n -84.638671875,\n 30.44867367928756\n ],\n [\n -85.69335937499999,\n 35.24561909420681\n ],\n [\n -83.75976562499999,\n 34.95799531086792\n ],\n [\n -81.474609375,\n 37.020098201368114\n ],\n [\n -83.3203125,\n 36.80928470205937\n ],\n [\n -79.62890625,\n 37.78808138412046\n ],\n [\n -74.1796875,\n 40.713955826286046\n ],\n [\n -69.08203125,\n 40.84706035607122\n ],\n [\n -63.6328125,\n 46.800059446787316\n ],\n [\n -63.6328125,\n 49.03786794532644\n ],\n [\n -68.64257812499999,\n 49.781264058178344\n ],\n [\n -83.232421875,\n 50.45750402042058\n ],\n [\n -99.49218749999999,\n 52.53627304145948\n ],\n [\n -97.55859375,\n 44.02442151965934\n ],\n [\n -98.0859375,\n 36.73888412439431\n ],\n [\n -98.701171875,\n 30.751277776257812\n ],\n [\n -97.998046875,\n 26.27371402440643\n ],\n [\n -96.85546875,\n 25.958044673317843\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","issue":"7","noUsgsAuthors":false,"publicationDate":"2019-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, J. D.","contributorId":275291,"corporation":false,"usgs":false,"family":"Moore","given":"J.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":833938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooper, Thomas R.","contributorId":191468,"corporation":false,"usgs":false,"family":"Cooper","given":"Thomas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":834075,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duguay, J. P.","contributorId":275292,"corporation":false,"usgs":false,"family":"Duguay","given":"J.","email":"","middleInitial":"P.","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":833940,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oldenburger, Shaun L.","contributorId":275294,"corporation":false,"usgs":false,"family":"Oldenburger","given":"Shaun L.","affiliations":[{"id":56759,"text":"Texas Parks & Wildlife","active":true,"usgs":false}],"preferred":false,"id":833942,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stewart, C. A.","contributorId":275295,"corporation":false,"usgs":false,"family":"Stewart","given":"C.","email":"","middleInitial":"A.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":833943,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833944,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70205291,"text":"70205291 - 2019 - De facto reuse and disinfection by-products in drinking water systems in the Shenandoah River watershed","interactions":[],"lastModifiedDate":"2019-10-09T10:05:19","indexId":"70205291","displayToPublicDate":"2019-08-05T10:17:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5112,"text":"Environmental Science: Water Research & Technology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"De facto reuse and disinfection by-products in drinking water systems in the Shenandoah River watershed","title":"De facto reuse and disinfection by-products in drinking water systems in the Shenandoah River watershed","docAbstract":"De facto reuse is increasingly being studied among the variety of stressors that are relevant to drinking water systems that obtain their source water from surface waters. De facto reuse may influence the levels and types of precursors relevant to formation of disinfection by-products (DBPs) in surface water systems. DBPs such as trihalomethanes (THMs) and haloacetic acids (HAAs) have been associated with bladder cancer and other health concerns in people who use drinking water provided by public water systems (PWSs). In this study, we used compliance monitoring data from conventional surface water PWSs in the Shenandoah River watershed to evaluate the relationship between de facto reuse in the watershed with DBP formation in those systems. The Shenandoah River watershed was selected for this study because it has a relatively small group of PWSs that draw their source water from surface waters in the watershed, the majority of whom treat their water using chlorine, and are less likely to have confounding factors (such as complex distribution systems or lengthy residence times) than other watersheds. We found that concentrations of THM4 and HAA5 increase in drinking water systems as de facto reuse increases in their source waters and that the relation is observed at both annual average and low streamflow conditions (annual average and low streamflow increases were statistically significant for THM4 with p values of 0.027 and 0,021, respectively). In addition, using a t-test, we found that a 1% level of de facto reuse was associated with significantly higher levels of THM4 and HAA5 (p < 0.05) under annual average streamflow conditions. While the concentrations of HAA5 were also higher under annual average and low streamflow conditions, we did not find that they achieved a level of a significant difference. Results from this research will be helpful to operators of PWSs and other researchers and stakeholders with an interest in water reuse and DBPs.
","language":"English","publisher":"The Royal Society of Chemistry","doi":"10.1039/C9EW00326F","usgsCitation":"Weisman, R.J., Barber, L., Rapp, J., and Ferreira, C.M., 2019, De facto reuse and disinfection by-products in drinking water systems in the Shenandoah River watershed: Environmental Science: Water Research & Technology, v. 5, no. 10, p. 1699-1708, https://doi.org/10.1039/C9EW00326F.","productDescription":"10 p.","startPage":"1699","endPage":"1708","ipdsId":"IP-106374","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":367385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Shenandoah River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.189697265625,\n 39.67337039176558\n ],\n [\n -80.474853515625,\n 37.47485808497102\n ],\n [\n -79.9365234375,\n 37.23032838760387\n ],\n [\n -78.673095703125,\n 37.47485808497102\n ],\n [\n -77.47558593749999,\n 38.91668153637508\n ],\n [\n -77.266845703125,\n 39.639537564366684\n ],\n [\n -78.189697265625,\n 39.67337039176558\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","issue":"10","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weisman, Richard J","contributorId":218952,"corporation":false,"usgs":false,"family":"Weisman","given":"Richard","email":"","middleInitial":"J","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":770757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barber, Larry B. 0000-0002-0561-0831","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":218953,"corporation":false,"usgs":true,"family":"Barber","given":"Larry B.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":770758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rapp, Jennifer 0000-0003-2253-9886","orcid":"https://orcid.org/0000-0003-2253-9886","contributorId":218954,"corporation":false,"usgs":true,"family":"Rapp","given":"Jennifer","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770759,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ferreira, Celso M","contributorId":218955,"corporation":false,"usgs":false,"family":"Ferreira","given":"Celso","email":"","middleInitial":"M","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":770760,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206160,"text":"70206160 - 2019 - Quantifying trends and uncertainty in prehistoric forest composition","interactions":[],"lastModifiedDate":"2019-12-04T06:27:40","indexId":"70206160","displayToPublicDate":"2019-08-05T06:57:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying trends and uncertainty in prehistoric forest composition","docAbstract":"Forest ecosystems in eastern North America were in flux over the last\nseveral thousand years, well before Euro-American land clearance and the\n20th-century onset of anthropogenic climate change. However, the\nmagnitude and uncertainty of prehistoric vegetation change have been\ndifficult to quantify because of the multiple ecological, dispersal, and\nsedimentary processes that govern the relationship between forest\ncomposition and fossil pollen assemblages. Here we extend STEPPS, a\nBayesian hierarchical spatio-temporal pollen-vegetation model, to estimate\nchanges in forest composition in the upper Midwestern United States from\nabout 2000 to 200 years ago. Using this approach, we identify areas of\nstatistically and ecologically significant change. Between 2000 and 200\nyears ago, forest composition significantly changed across broad regions of\nnorth-central Wisconsin and Minnesota. Rates of compositional change\nvaried spatially, and can be linked to previously reported events. The single\nlargest change is the infilling of Tsuga canadensis in northern Wisconsin\nover the past 2000 years. Despite this range in-filling, the range limit of T.\ncanadensis was largely stable, with modest expansion westward. The\nregional ecotone between temperate hardwood forests and northern mixed\nhardwood/conifer forests shifted southwestward by 15-20 km in Minnesota\nand Northwestern Wisconsin. Fraxinus, Ulmus, and other mesic hardwoods\nexpanded in the Big Woods region of southern Minnesota. However, some\nareas showed no significant change, suggesting high complexity in the\nspatiotemporal patterns of past forest dynamics. The increasing density of\npaleoecological data networks and advances in statistical modeling\napproaches now enables the confident detection of subtle but significant\nchanges in forest composition over the last 2000 years.","language":"English","publisher":"Wiley","doi":"10.1002/ecy.2856","usgsCitation":"Andria Dawson, Christopher J. Paciorek, Goring, S., Jackson, S., Jason S. McLachlan, and John W. Williams, 2019, Quantifying trends and uncertainty in prehistoric forest composition: Ecology, v. 100, no. 12, e02856, https://doi.org/10.1002/ecy.2856.","productDescription":"e02856","ipdsId":"IP-096645","costCenters":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"links":[{"id":467395,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.2856","text":"Publisher Index Page"},{"id":368548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.328125,\n 42.85985981506279\n ],\n [\n -87.56103515625,\n 42.85985981506279\n ],\n [\n -87.56103515625,\n 44.653024159812\n ],\n [\n -96.328125,\n 44.653024159812\n ],\n [\n -96.328125,\n 42.85985981506279\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"100","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Andria Dawson","contributorId":219996,"corporation":false,"usgs":false,"family":"Andria Dawson","affiliations":[{"id":40107,"text":"Mount Royal University","active":true,"usgs":false}],"preferred":false,"id":773745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christopher J. Paciorek","contributorId":219997,"corporation":false,"usgs":false,"family":"Christopher J. Paciorek","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":773746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goring, Simon","contributorId":219998,"corporation":false,"usgs":false,"family":"Goring","given":"Simon","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":773747,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Stephen 0000-0002-1487-4652","orcid":"https://orcid.org/0000-0002-1487-4652","contributorId":219995,"corporation":false,"usgs":true,"family":"Jackson","given":"Stephen","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":773744,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jason S. McLachlan","contributorId":219999,"corporation":false,"usgs":false,"family":"Jason S. McLachlan","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":773748,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"John W. Williams","contributorId":197556,"corporation":false,"usgs":false,"family":"John W. Williams","affiliations":[],"preferred":false,"id":773749,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}