{"pageNumber":"305","pageRowStart":"7600","pageSize":"25","recordCount":40783,"records":[{"id":70208420,"text":"70208420 - 2019 - Increases in life-safety risks to building occupants from induced earthquakes in the central United States","interactions":[],"lastModifiedDate":"2020-02-09T13:19:55","indexId":"70208420","displayToPublicDate":"2019-11-28T13:16:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Increases in life-safety risks to building occupants from induced earthquakes in the central United States","docAbstract":"Earthquake occurrence rates in some parts of the central United States have been elevated for a number of years; this increase has been widely attributed to deep wastewater injection associated with oil and gas activities. This induced seismicity has caused damage to buildings and infrastructure and substantial public concern. In March 2016, the U.S. Geological Survey (USGS) published its first earthquake ground motion hazard model that accounts for the elevated seismicity, producing a one-year forecast encompassing both induced and natural earthquakes. To assess the potential impacts of the elevated seismicity on buildings and the public, this paper quantifies forecasted risks of a) building collapse and b) falling of nonstructural building components, by combining the 2016 USGS hazard model with fragility curves for generic modern code-compliant buildings. The assessment shows significant increases in both types of risk compared to that due to non-induced earthquakes alone; the magnitudes of the increases vary from a few times to more than a 100 times, depending on location, building period (which is correlated to building height), alternatives for the hazard model, and the type of risk of interest. For exploratory purposes only, we also estimate revised values of the risk-targeted ground motion that are currently used for designing buildings.","language":"English","publisher":"SAGE","doi":"10.1193/041618EQS095M","usgsCitation":"Liu, T., Luco, N., and Liel, A.B., 2019, Increases in life-safety risks to building occupants from induced earthquakes in the central United States: Earthquake Spectra, v. 35, no. 2, p. 471-488, https://doi.org/10.1193/041618EQS095M.","productDescription":"18 p.","startPage":"471","endPage":"488","ipdsId":"IP-103586","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":372173,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas, Oklahoma","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -100.26123046875,\n              32.08257455954592\n            ],\n            [\n              -94.46044921875,\n              32.08257455954592\n            ],\n            [\n              -94.46044921875,\n              36.94989178681327\n            ],\n            [\n              -100.26123046875,\n              36.94989178681327\n            ],\n            [\n              -100.26123046875,\n              32.08257455954592\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"35","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Taojun","contributorId":201798,"corporation":false,"usgs":false,"family":"Liu","given":"Taojun","email":"","affiliations":[{"id":6713,"text":"University of Colorado, Boulder CO","active":true,"usgs":false}],"preferred":false,"id":781813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":781812,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liel, Abbie B.","contributorId":184158,"corporation":false,"usgs":false,"family":"Liel","given":"Abbie","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":781814,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70223764,"text":"70223764 - 2019 - Salinity and water clarity dictate seasonal variability in coastal submerged aquatic vegetation in subtropical estuarine environments","interactions":[],"lastModifiedDate":"2021-09-07T15:09:15.564186","indexId":"70223764","displayToPublicDate":"2019-11-28T10:04:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":860,"text":"Aquatic Biology","active":true,"publicationSubtype":{"id":10}},"title":"Salinity and water clarity dictate seasonal variability in coastal submerged aquatic vegetation in subtropical estuarine environments","docAbstract":"<p><span>Spatial and temporal variability characterize submerged aquatic vegetation (SAV) assemblages, but understanding the complex interactions of environmental drivers of SAV assemblages remains elusive. We documented SAV composition and biomass across a salinity gradient in a coastal estuary over 12 mo. Ten macrophyte species were identified. The dominant species,&nbsp;</span><i>Ceratophyllum demersum</i><span>&nbsp;and&nbsp;</span><i>Myriophyllum spicatum,</i><span>&nbsp;accounted for over 40% of total biomass. Only&nbsp;</span><i>Ruppia maritima</i><span>&nbsp;occurred across the salinity gradient. Salinity, water depth and clarity delineated 3 assemblages: a saline assemblage, and 2 groups of fresher-water species, one associated with deeper water and lower water clarity and the other associated with shallow water and higher water clarity. These assemblages exhibited intra-annual variation, with at least 5 times more biomass in late spring/mid-summer compared to early winter. This pattern was consistent across the estuary, although the difference between peak and low biomass varied by habitat type; brackish exhibited the greatest magnitude. This variation is likely due to higher variation in salinity and the species composition of this habitat. As climate change and coastal restoration impact timing and range of salinity, water depth and clarity in this region, these data can be used to help inform predictive models and management decisions.</span></p>","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/ab00719","usgsCitation":"Hillmann, E.R., DeMarco, K., and La Peyre, M., 2019, Salinity and water clarity dictate seasonal variability in coastal submerged aquatic vegetation in subtropical estuarine environments: Aquatic Biology, v. 28, p. 175-186, https://doi.org/10.3354/ab00719.","productDescription":"12 p.","startPage":"175","endPage":"186","ipdsId":"IP-105293","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":459093,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/ab00719","text":"Publisher Index Page"},{"id":388877,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -91.483154296875,\n              28.748396571187406\n            ],\n            [\n              -88.956298828125,\n              28.748396571187406\n            ],\n            [\n              -88.956298828125,\n              30.330212685432734\n            ],\n            [\n              -91.483154296875,\n              30.330212685432734\n            ],\n            [\n              -91.483154296875,\n              28.748396571187406\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"28","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hillmann, Eva R.","contributorId":200686,"corporation":false,"usgs":false,"family":"Hillmann","given":"Eva","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":822573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeMarco, Kristin","contributorId":200003,"corporation":false,"usgs":false,"family":"DeMarco","given":"Kristin","email":"","affiliations":[],"preferred":false,"id":822574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":822576,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70216102,"text":"70216102 - 2019 - The behavior of the Salesforce Tower, the tallest building in San Francisco, California inferred from earthquake and ambient shaking","interactions":[],"lastModifiedDate":"2020-11-05T13:39:35.933412","indexId":"70216102","displayToPublicDate":"2019-11-28T07:31:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"The behavior of the Salesforce Tower, the tallest building in San Francisco, California inferred from earthquake and ambient shaking","docAbstract":"<div class=\"hlFld-Abstract\"><div class=\"abstractSection abstractInFull\"><p>The newly constructed tallest building designed in conformance with performance-based design procedure in San Francisco, California is a 61-story building equipped with an accelerometric array that recorded the January 4, 2018 M4.4 Berkeley earthquake. The building is designed with concrete core shear walls and perimeter gravity steel columns. The earthquake records as well as on-demand recorded ambient responses of the building are studied to determine its dynamic characteristics and building-specific behavior. At the level of shaking of either the earthquake or ambient excitation, the frequencies and low modal damping ratios (&lt;2%) are similar. The building exhibits torsional behavior most likely due to abrupt asymmetrical changes in the size of the core shear wall. The translational and torsional modes during the earthquake are closely coupled, which leads to a beating effect, the period of which is calculable. Due to the relatively low-amplitude shaking during the earthquake, the drift ratios were small and did not cause any damage. It is expected that during stronger shaking levels, these characteristics may change.</p></div></div>","language":"English","publisher":"Sage Journals","doi":"10.1193/112918EQS273M","usgsCitation":"Celebi, M., Haddadi, H., Huang, M., Valley, M., Hooper, J., and Klemencic, R., 2019, The behavior of the Salesforce Tower, the tallest building in San Francisco, California inferred from earthquake and ambient shaking: Earthquake Spectra, v. 35, no. 4, p. 1711-1737, https://doi.org/10.1193/112918EQS273M.","productDescription":"27 p.","startPage":"1711","endPage":"1737","ipdsId":"IP-114320","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":380183,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.52639770507812,\n              37.667516276171426\n            ],\n            [\n              -122.32864379882811,\n              37.667516276171426\n            ],\n            [\n              -122.32864379882811,\n              37.820632846207864\n            ],\n            [\n              -122.52639770507812,\n              37.820632846207864\n            ],\n            [\n              -122.52639770507812,\n              37.667516276171426\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"35","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-11-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":804095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haddadi, H.","contributorId":12673,"corporation":false,"usgs":false,"family":"Haddadi","given":"H.","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":804096,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huang, Moh","contributorId":146970,"corporation":false,"usgs":false,"family":"Huang","given":"Moh","email":"","affiliations":[],"preferred":false,"id":804097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Valley, Michael","contributorId":48464,"corporation":false,"usgs":true,"family":"Valley","given":"Michael","affiliations":[],"preferred":false,"id":804129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hooper, John","contributorId":146972,"corporation":false,"usgs":false,"family":"Hooper","given":"John","affiliations":[],"preferred":false,"id":804130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Klemencic, Ron","contributorId":146973,"corporation":false,"usgs":false,"family":"Klemencic","given":"Ron","email":"","affiliations":[],"preferred":false,"id":804131,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70206787,"text":"ofr20191081 - 2019 - 3D geologic framework for use with the U.S. Geological Survey National Crustal Model, Phase 1—Western United States","interactions":[],"lastModifiedDate":"2022-04-21T18:33:47.275626","indexId":"ofr20191081","displayToPublicDate":"2019-11-27T11:10:00","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":"2019-1081","displayTitle":"3D Geologic Framework for Use with the U.S. Geological Survey National Crustal Model, Phase 1—Western United States","title":"3D geologic framework for use with the U.S. Geological Survey National Crustal Model, Phase 1—Western United States","docAbstract":"<p>A 3D geologic framework is presented here as part of the U.S. Geological Survey National Crustal Model for the western United States, which will be used to improve seismic hazard assessment. The framework is based on 1:250,000 to 1:1,000,000-scale state geologic maps and depths of multiple subsurface unit boundaries. The geology at or near the Earth’s surface is based on published maps with modifications to remove discontinuities across state borders. Extrapolation of rock type and age in the subsurface is achieved by iterative stripping of units of a given age, nearest neighbor interpolation of the remaining units, and constraints on basement geology. The subsurface depth of the interfaces between units is determined by a range of models with varying quantity and quality of constraints. Bedrock depth is derived primarily from a proxy model with added geophysical constraint in some areas. The depths to the base of Cenozoic and Phanerozoic sedimentary and extrusive volcanic rocks are constrained by geophysical methods in many areas. Elsewhere, a simple method is used to estimate their subsurface depth based on the distance to the edge of the geologic units. The remaining continental units are evenly distributed above, below, and between depending on age. The oceanic crust is treated as a simple four-layer model with the added complexity of subduction beneath the North American plate along the Cascadia subduction zone.</p><p>Refinements to this technique may be accomplished in future versions of the model with more specific information including the location of faults to produce discontinuities in geologic structure and additional information obtained from boreholes and geophysical studies. Further improvements to the geologic framework may be made by incorporating information from more local studies, for example, hydrogeologic studies.</p><p><br data-mce-bogus=\"1\"></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191081","usgsCitation":"Boyd, O.S., 2019, 3D Geologic framework for use with the U.S. Geological Survey National Crustal Model, Phase 1—Western United States: U.S. Geological Survey Open-File Report 2019–1081, 36 p., https://doi.org/10.3133/ofr20191081.","productDescription":"Report: vii, 36 p.; Data Release","onlineOnly":"Y","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":437274,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94MGWUR","text":"USGS data release","linkHelpText":"GeoFram"},{"id":399412,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109580.htm"},{"id":369408,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SBQENM","text":"USGS data release","linkHelpText":"3D Geologic Framework for use with the U.S. Geological Survey National Crustal Model, Phase 1: Western United States"},{"id":369407,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1081/ofr20191081.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1081"},{"id":369405,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1081/coverthb2.jpg"},{"id":370582,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1081/versionHist.txt","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2019-1081 version history"}],"otherGeospatial":"Western United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -125,\n              31.3289\n            ],\n            [\n              -100,\n              31.3289\n            ],\n            [\n              -100,\n              49\n            ],\n            [\n              -125,\n              49\n            ],\n            [\n              -125,\n              31.3289\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/geohazards\" data-mce-href=\"https://www.usgs.gov/centers/geohazards\">Geologic Hazards Science Center</a><br>U.S. Geological Survey<br>Box 25046, MS-966<br>Denver, CO 80225-0046</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Geology and Age of Rocks at or Near the Earth’s Surface</li><li>Lithology and Age of Subsurface Layers</li><li>Subsurface Layer Depth and Elevation</li><li>Model Cross Sections</li><li>Discussion</li><li>Conclusions</li><li>References Cited</li><li>Appendix 1. Age Dictionary and Mapping</li><li>Appendix 2. Lithology Dictionary and Mapping</li><li>Appendix 3. Exceptions to Geologic Map Modification Rules</li></ul>","publishedDate":"2019-11-27","revisedDate":"2019-12-20","noUsgsAuthors":false,"publicationDate":"2019-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":775750,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70206037,"text":"sir20195117 - 2019 - Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota","interactions":[],"lastModifiedDate":"2019-11-27T09:54:48","indexId":"sir20195117","displayToPublicDate":"2019-11-27T06:42:07","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":"2019-5117","displayTitle":"Groundwater-Flow Model and Analysis of Groundwater and Surface-Water Interactions for the Big Sioux Aquifer, Sioux Falls, South Dakota","title":"Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota","docAbstract":"<p>The city of Sioux Falls, in southeastern South Dakota, is the largest city in South Dakota. The U.S. Geological Survey (USGS), in cooperation with the city of Sioux Falls, completed a groundwater-flow model to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer in the model area.</p><p>The model area includes the Big Sioux aquifer and the underlying hydrogeologic units from Dell Rapids, South Dakota, to the confluence of the Big Sioux River and the outlet of the Sioux Falls Diversion Channel in eastern Sioux Falls, S. Dak. The Big Sioux aquifer is the primary aquifer in the model area and the focus of the groundwater-flow model. The Big Sioux River is the largest stream in the model area and is in hydraulic connection with the Big Sioux aquifer.</p><p>A conceptual model for the area was constructed and includes a characterization of the hydrogeologic framework, analysis and construction of potentiometric surfaces, and summary of estimated water budget components in the model area. The primary hydrogeologic units in the model area consist of (1) the Big Sioux aquifer, (2) a glacial till confining unit, and (3) bedrock aquifers (Split Rock Creek and Sioux Quartzite aquifers). Sources of groundwater recharge included infiltration of precipitation, stream seepage, and groundwater exchanges among the hydraulically connected Big Sioux aquifer, glacial till confining unit, and bedrock aquifers. Groundwater losses included evapotranspiration, groundwater discharge to streams, and groundwater withdrawal to supply water-use needs.</p><p>A numerical groundwater-flow model (numerical model) was constructed and was used to simulate all aspects of the conceptual model for predevelopment (steady-state) and time-varying (transient) monthly conditions for 1950–2017. The numerical model was constructed using the USGS modular hydrologic simulation program, MODFLOW–6, and was calibrated using the Parameter ESTimation software, PEST++.</p><p>The transient numerical model was calibrated for steady-state and transient monthly conditions for 1950–2017. Calibration targets were observations of hydraulic head, changes in hydraulic head, monthly mean streamflow (as a rate), and cumulative monthly stream discharge (as a volume). Parameters adjusted during model calibration were horizontal and vertical hydraulic conductivity, specific storage, specific yield, recharge and evapotranspiration multipliers, and streambed hydraulic conductivity. Horizontal and vertical hydraulic conductivity were estimated at pilot points distributed within the model area; specific storage and specific yield were assigned to uniform values in each layer in the model area; recharge and evapotranspiration multipliers were assigned uniformly for every stress period in the numerical model; and streambed hydraulic conductivity values were assigned uniformly between stream confluences.</p><p>The final calibrated parameter values of horizontal and vertical hydraulic conductivity, specific yield, specific storage, streambed hydraulic conductivity, recharge, and evapotranspiration were considered reasonable for the hydrogeologic materials and conditions in the model area for 1950–2017.</p><p>Overall, simulated hydraulic head altitudes had a linear regression coefficient of determination (R<sup>2</sup>) of 0.48. Hydraulic head altitude residuals for the glacial till confining unit and bedrock aquifers were typically greater in magnitude when compared to residuals in the Big Sioux aquifer, but simulated hydraulic head altitudes in the Big Sioux aquifer compared favorably with mean observed hydraulic head altitudes and had a linear regression R<sup>2</sup> of 0.93.</p><p>Simulated streamflow hydrographs matched the general trends of observed increases and decreases in streamflow for USGS streamgages 06482000 (Big Sioux River at Sioux Falls, S. Dak.) and 06482020 (Big Sioux River at North Cliff Avenue at Sioux Falls, S. Dak.), but larger streamflows were overestimated at the first streamgage and underestimated at the second streamgage. The numerical model reasonably estimated cumulative monthly stream discharge for the first 10–15 years of available streamflow records at both USGS streamgages. After the first 10–15 years of available streamflow record,&nbsp;cumulative monthly stream discharge was closely estimated for USGS streamgage 06482000 and underestimated at USGS streamgage 06482020.</p><p>Composite sensitivities without regularization were calculated by PEST++ for the calibrated numerical model parameters and were averaged by parameter group. The parameter group with the highest mean composite sensitivity was the recharge multiplier parameter group.</p><p>Model simplifications, assumptions, and limitations were necessary for construction of the conceptual and numerical models and for calibration efficiency. Spatial simplification of hydraulic properties could cause the numerical model to misrepresent reactions to changes in localized stresses, such as additional demands for groundwater withdrawal. The numerical model was temporally discretized into monthly periods and required scaling daily rates into representative monthly rates for model input and calibration targets. Based on the comparison between the observed and simulated groundwater levels, monthly mean streamflow and cumulative monthly stream discharge, and general groundwater distribution and flow, the numerical model favorably simulated the flow in the Big Sioux aquifer.</p><p>Eventual capture was calculated in the model area using a steady-state numerical groundwater-flow model. The eventual capture map shows areas of higher streamflow capture adjacent to the Big Sioux River north of the city of Sioux Falls and along the lower part of the Sioux Falls Diversion Channel, and areas of lower streamflow capture along aquifer boundaries and near the southern Sioux Quartzite barrier.</p><p>The timing of capture was determined using a transient numerical groundwater-flow model to determine the likely captured water sources for 30 years of groundwater withdrawal at three hypothetical wells using three continuous withdrawal rates (112.5, 450.0, and 900.0 gallons per minute). Supply for all three hypothetical wells became capture-dominated after only a short period of continuous withdrawal. Capture stabilized after about 10–15 years for well A, and after 20–25 years for well B, and after about 10–15 years for well C.</p><p>The groundwater-flow model is a suitable tool to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer near Sioux Falls, S. Dak. The numerical model can be used to simulate hydrologic scenarios, advance understanding of groundwater budgets, compute system response to stress, and determine likely sources of water supplied to wells.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195117","collaboration":"Prepared in cooperation with the city of Sioux Falls","usgsCitation":"Davis, K.W., Eldridge, W.G., Valder, J.F., and Valseth, K.J., 2019, Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5117, 86 p., https://doi.org/10.3133/sir20195117.","productDescription":"Report: xi, 86 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","ipdsId":"IP-105956","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":369602,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20195013","text":"SIR 2019–5013","linkHelpText":"– Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota"},{"id":369600,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3393","text":"SIM 3393","linkHelpText":"– Delineation of the hydrogeologic framework of the Big Sioux aquifer near Sioux Falls, South Dakota, using airborne electromagnetic data"},{"id":369601,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/F79885XC","text":"USGS data release for SIM 3393","linkHelpText":"– Airborne electromagnetic and magnetic survey data, Big Sioux aquifer, October 2015, Sioux Falls, South Dakota"},{"id":369603,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9LUB44J","text":"USGS data release for SIR 2019–5013","linkHelpText":"– Water-level data and AQTESOLV Pro analysis results for slug tests in the Big Sioux Aquifer, Sioux Falls, South Dakota, 2017"},{"id":369535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5117/coverthb.jpg"},{"id":369536,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5117/sir20195117.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5117"},{"id":369537,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O59RO0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-6 model of the Big Sioux aquifer, Sioux Falls, South Dakota"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.06146240234375,\n              43.29919735147067\n            ],\n            [\n              -96.42425537109375,\n              43.29919735147067\n            ],\n            [\n              -96.42425537109375,\n              43.757208878849376\n            ],\n            [\n              -97.06146240234375,\n              43.757208878849376\n            ],\n            [\n              -97.06146240234375,\n              43.29919735147067\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/dakota-water\" href=\"https://www.usgs.gov/centers/dakota-water\">Dakota Water Science Center</a><br>U.S. Geological Survey<br>821 East Interstate Avenue<br>Bismarck, ND 58503<br>1608 Mountain View Road<br>Rapid City, SD 57702</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Groundwater-Flow Model</li><li>Analysis of Groundwater and Surface-Water Interactions</li><li>Summary and Conclusions</li><li>References Cited</li><li>Appendix 1. Hydraulic Conductivity Estimates with Small-Diameter Nuclear Magnetic Resonance Logging Tool</li><li>Appendix 2. Analysis of Recharge and Evapotranspiration using a Soil-Water-Balance Model</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-11-27","noUsgsAuthors":false,"publicationDate":"2019-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Kyle W. 0000-0002-8723-0110","orcid":"https://orcid.org/0000-0002-8723-0110","contributorId":201549,"corporation":false,"usgs":true,"family":"Davis","given":"Kyle W.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eldridge, William G. 0000-0002-3562-728X","orcid":"https://orcid.org/0000-0002-3562-728X","contributorId":208529,"corporation":false,"usgs":true,"family":"Eldridge","given":"William","email":"","middleInitial":"G.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valder, Joshua F. 0000-0003-3733-8868 jvalder@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-8868","contributorId":139256,"corporation":false,"usgs":true,"family":"Valder","given":"Joshua","email":"jvalder@usgs.gov","middleInitial":"F.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":false,"id":773380,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Valseth, Kristen J. 0000-0003-4257-6094","orcid":"https://orcid.org/0000-0003-4257-6094","contributorId":203447,"corporation":false,"usgs":true,"family":"Valseth","given":"Kristen","email":"","middleInitial":"J.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773381,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70212716,"text":"70212716 - 2019 - Combining sediment fingerprinting with age-dating sediment using fallout radionuclides for an agricultural stream, Walnut Creek, Iowa, USA","interactions":[],"lastModifiedDate":"2020-08-27T15:33:34.188151","indexId":"70212716","displayToPublicDate":"2019-11-26T10:08:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2457,"text":"Journal of Soils and Sediments","active":true,"publicationSubtype":{"id":10}},"title":"Combining sediment fingerprinting with age-dating sediment using fallout radionuclides for an agricultural stream, Walnut Creek, Iowa, USA","docAbstract":"<h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Purpose</h3><p>The main purpose of this study was to demonstrate the utility of the sediment fingerprinting approach to apportion surface-derived sediment, and then age date that portion using short-lived fallout radionuclides. In systems where a large mass of mobile sediment is in channel storage, age dating provides an understanding of the transfer of sediment through the watershed and the time scales over which management actions to reduce sediment loadings may be effective.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Materials and methods</h3><p>In the agricultural Walnut Creek watershed, Iowa, the sediment-fingerprinting approach with elemental analysis was used to apportion the sources of fine-grained sediment (croplands, prairie, unpaved roads, and channel banks). Fallout radionuclides (<sup>7</sup>Be,<span>&nbsp;</span><sup>210</sup>Pb<sub>ex</sub>) were used to age the portion of suspended sediment that was derived from agricultural topsoil. Age dating was performed at two different scales:<span>&nbsp;</span><sup>210</sup>Pb<sub>ex</sub><span>&nbsp;</span>which can date sediment to ~ 85&nbsp;years and<span>&nbsp;</span><sup>7</sup>Be to ~ 1&nbsp;year.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Results and discussion</h3><p>Sediment fingerprinting results indicated that the majority of suspended sediment is derived from cropland (62%) with streambanks contributing 36%, and prairie, pasture, and unpaved roads each contributing ≤ 1%. The topsoil–derived portion of sediment (primarily agriculture) dated using<span>&nbsp;</span><sup>210</sup>Pb<sub>ex</sub><span>&nbsp;</span>has ages ranging from 1 to 58&nbsp;years, and using<span>&nbsp;</span><sup>7</sup>Be, a component of much younger sediment that yields ages ranging from 44 to 205&nbsp;days. The occurrence of<span>&nbsp;</span><sup>7</sup>Be indicates that some portion of the sediment is young, on the order of months, whereas the dating based on<span>&nbsp;</span><sup>210</sup>Pb<sub>ex</sub><span>&nbsp;</span>indicates that some of the surface-derived sediment has been in channel storage for decades. Published studies in Walnut Creek indicate that a large component of sediment is stored in the channel bed.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Conclusions</h3><p>We conclude that the<span>&nbsp;</span><sup>210</sup>Pb<sub>ex</sub>-based ages are a reasonable estimate for the mean age of the surface-derived fraction and that<span>&nbsp;</span><sup>7</sup>Be activities are evidence that there is a smaller fraction of very young sediment in the stream. We propose a geomorphic model where agricultural soil is delivered to the channel and conveyed to the watershed outlet at three time scales: a geologic-millennial time scale, decades, and a young time scale (&lt; 1&nbsp;year).</p>","language":"English","publisher":"Springer","doi":"10.1007/s11368-018-2168-z","usgsCitation":"Gellis, A.C., Fuller, C.C., Van Metre, P.C., Filstrup, C.T., Cole, K., and Sabitov, T., 2019, Combining sediment fingerprinting with age-dating sediment using fallout radionuclides for an agricultural stream, Walnut Creek, Iowa, USA: Journal of Soils and Sediments, v. 19, p. 3374-3396, https://doi.org/10.1007/s11368-018-2168-z.","productDescription":"23 p.","startPage":"3374","endPage":"3396","ipdsId":"IP-090014","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":377887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","county":"Jasper County","otherGeospatial":"Walnut Creek","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-93.234,41.8622],[-93.1187,41.8624],[-93.0035,41.8624],[-92.8845,41.8619],[-92.7674,41.8618],[-92.7683,41.776],[-92.768,41.6879],[-92.7683,41.6007],[-92.7567,41.6011],[-92.7564,41.509],[-92.8729,41.5082],[-92.9894,41.5083],[-93.1047,41.5078],[-93.2181,41.5076],[-93.3304,41.5074],[-93.3314,41.6004],[-93.3504,41.6004],[-93.3496,41.688],[-93.3494,41.7757],[-93.3492,41.8624],[-93.234,41.8622]]]},\"properties\":{\"name\":\"Jasper\",\"state\":\"IA\"}}]}","volume":"19","noUsgsAuthors":false,"publicationDate":"2018-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Metre, Peter C. 0000-0001-7564-9814","orcid":"https://orcid.org/0000-0001-7564-9814","contributorId":211144,"corporation":false,"usgs":true,"family":"Van Metre","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":797342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Filstrup, Christopher T.","contributorId":169032,"corporation":false,"usgs":false,"family":"Filstrup","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":797343,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cole, Kevin","contributorId":208183,"corporation":false,"usgs":false,"family":"Cole","given":"Kevin","email":"","affiliations":[{"id":37761,"text":"USDA-ARS, National Laboratory for Agriculture and the Environment, 1015 N. University Blvd, Ames. IA 50011","active":true,"usgs":false}],"preferred":false,"id":797344,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sabitov, Timur","contributorId":236885,"corporation":false,"usgs":false,"family":"Sabitov","given":"Timur","email":"","affiliations":[{"id":47559,"text":"Geology and Geophysics, Academy of Science of Uzbekistan","active":true,"usgs":false}],"preferred":false,"id":797345,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70220223,"text":"70220223 - 2019 - Investigating the accuracy of one‐dimensional volcanic plume models using laboratory experiments and field data","interactions":[],"lastModifiedDate":"2021-04-28T13:13:55.608928","indexId":"70220223","displayToPublicDate":"2019-11-26T08:11:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Investigating the accuracy of one‐dimensional volcanic plume models using laboratory experiments and field data","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>During volcanic eruptions, model predictions of plume height are limited by the accuracy of entrainment coefficients used in many plume models. Typically, two parameters are used,<span>&nbsp;</span><i>α</i><span>&nbsp;</span>and<span>&nbsp;</span><i>β</i>, which relate the entrained air speed to the jet speed in the axial and cross‐flow directions, respectively. To improve estimates of these parameters, wind tunnel experiments have been conducted for a range of cross‐wind velocities and turbulence conditions. Measurements are compared directly to computations from the 1‐D plume model, Plumeria, in the near‐field, bending region of the jet. Entrainment coefficients are determined through regression analysis, demonstrating optimal combinations of effective<span>&nbsp;</span><i>α</i><span>&nbsp;</span>and<span>&nbsp;</span><i>β</i><span>&nbsp;</span>values. For turbulent conditions, all wind speeds overlapped at a single combination,<span>&nbsp;</span><i>α</i><span>&nbsp;</span>= 0.06 and<span>&nbsp;</span><i>β</i>=0.46, each of&nbsp;which are slightly reduced from standard values. Refined coefficients were used to model plume heights for 20 historical eruptions. Model accuracy improves modestly in most cases, agreeing to within 3&nbsp;km with observed plume heights. For weak eruptions, uncertainty in field measurements can outweigh the effects of these refinements, illustrating the challenge of applying plume models in practice.</p></div></div></div></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018jb017224","usgsCitation":"McNeal, J., Mastin, L.G., Cal, R., and Solovitz, S.A., 2019, Investigating the accuracy of one‐dimensional volcanic plume models using laboratory experiments and field data: Journal of Volcanology and Geothermal Research, v. 124, no. 11, p. 11290-11304, https://doi.org/10.1029/2018jb017224.","productDescription":"15 p.","startPage":"11290","endPage":"11304","ipdsId":"IP-101393","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":459113,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018jb017224","text":"Publisher Index Page"},{"id":385350,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"124","issue":"11","noUsgsAuthors":false,"publicationDate":"2019-11-30","publicationStatus":"PW","contributors":{"authors":[{"text":"McNeal, James S.","contributorId":257656,"corporation":false,"usgs":false,"family":"McNeal","given":"James S.","affiliations":[{"id":52077,"text":"Washington State University, Vancouver","active":true,"usgs":false}],"preferred":false,"id":814847,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastin, Larry G. 0000-0002-4795-1992 lgmastin@usgs.gov","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":555,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"lgmastin@usgs.gov","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":814850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cal, Raul B.","contributorId":257658,"corporation":false,"usgs":false,"family":"Cal","given":"Raul B.","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":814849,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Solovitz, Stephen A. 0000-0001-7019-2958","orcid":"https://orcid.org/0000-0001-7019-2958","contributorId":257659,"corporation":false,"usgs":false,"family":"Solovitz","given":"Stephen","email":"","middleInitial":"A.","affiliations":[{"id":52077,"text":"Washington State University, Vancouver","active":true,"usgs":false}],"preferred":false,"id":814852,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70209462,"text":"70209462 - 2019 - Watershed geomorphology modifies the sensitivity of aquatic ecosystem metabolism to temperature","interactions":[],"lastModifiedDate":"2020-05-04T17:57:42.379102","indexId":"70209462","displayToPublicDate":"2019-11-26T07:58:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Watershed geomorphology modifies the sensitivity of aquatic ecosystem metabolism to temperature","docAbstract":"How carbon cycles are regulated by environmental temperature remains a substantial uncertainty in our understanding of how watersheds will respond to ongoing climate change. Aquatic ecosystems are important components of carbon flux to the atmosphere and ocean, yet we have limited understanding of how temperature modifies ecosystem metabolic processes and, therefore, aquatic contributions to carbon cycles at watershed to global scales.  We propose that geomorphology controls the landscape-scale distribution and quality of organic material that forms the metabolic base of aquatic ecosystems and, therefore, how aquatic ecosystem metabolism responds to changes in temperature. Across 23 streams and four years in a boreal river basin, we estimated how temperature sensitivity of ecosystem respiration (ER) varied among streams draining watersheds with different geomorphic characteristics. We found that geomorphic conditions imposed strong ultimate controls on temperature sensitivity; ER in streams draining flat watersheds was up to six times more sensitive to temperature than streams draining steeper watersheds.  Further, we show that the link between watershed geomorphology and temperature sensitivity of ER was related to the quality of carbon substrates that changes systematically across the gradient in geomorphic conditions. These results suggest that geomorphology will ultimately control how carbon is transported, stored, and incorporated into river food webs as climate warms.","language":"English","publisher":"Nature","doi":"10.1038/s41598-019-53703-3","collaboration":"","usgsCitation":"Jankowski, K.J., and Schindler, D., 2019, Watershed geomorphology modifies the sensitivity of aquatic ecosystem metabolism to temperature: Scientific Reports, v. 9, 17619, 10 p., https://doi.org/10.1038/s41598-019-53703-3.","productDescription":"17619, 10 p.","ipdsId":"IP-102157","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":459115,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-019-53703-3","text":"Publisher Index Page"},{"id":373857,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2019-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":786569,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schindler, Daniel E.","contributorId":223885,"corporation":false,"usgs":false,"family":"Schindler","given":"Daniel E.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":786570,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70206863,"text":"70206863 - 2019 - Employing an ecosystem services framework to deliver decision ready science","interactions":[],"lastModifiedDate":"2019-11-26T07:08:17","indexId":"70206863","displayToPublicDate":"2019-11-26T07:06:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5217,"text":"Advances in Ecological Research","active":true,"publicationSubtype":{"id":10}},"title":"Employing an ecosystem services framework to deliver decision ready science","docAbstract":"Public land managers have limited information to allow for the integration and balancing of multiple objectives in land management decisions including the social (cultural and health), economic (monetary and nonmonetary), and environmental aspects.  In this article, we document an approach to consider the many facets of decision making by incorporating them into a decision context using an ecosystem services framework.  This analysis is based on a multi-partner project led by the US Geological Survey and the US Fish and Wildlife Service to provide land management decision support for the Great Dismal Swamp National Wildlife Refuge. It is an integrated ecologic-economic analysis of baseline (current) and potential future quantities, qualities, and values of selected ecosystem services from the Refuge. Alternative management scenarios are modeled to consider the impact of specific management actions or natural disturbances on priority ecosystem services. We examine the benefits and challenges of using this framework. Key lessons learned from this effort include the mismatch in timing between physical and social science; the challenge of integrating methods from multiple disciplines; the importance of frequent communication to overcome siloed research; and the utility of an integrating framework for  ecosystem services and supporting tools such as the dynamic ecosystem model.","language":"English","publisher":"Science Signpost Publishing","usgsCitation":"Pindilli, E., Hogan, D.M., and Zhu, Z., 2019, Employing an ecosystem services framework to deliver decision ready science: Advances in Ecological Research, v. 4, no. 11, p. 302-323.","productDescription":"22 p.","startPage":"302","endPage":"323","ipdsId":"IP-103007","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":369609,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":369560,"type":{"id":15,"text":"Index Page"},"url":"https://www.ss-pub.org/aeer/employing-an-ecosystem-services-framework-to-deliver-decision-ready-science/"}],"volume":"4","issue":"11","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pindilli, Emily 0000-0002-5101-1266 epindilli@usgs.gov","orcid":"https://orcid.org/0000-0002-5101-1266","contributorId":140262,"corporation":false,"usgs":true,"family":"Pindilli","given":"Emily","email":"epindilli@usgs.gov","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":776095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogan, Dianna M. 0000-0003-1492-4514 dhogan@usgs.gov","orcid":"https://orcid.org/0000-0003-1492-4514","contributorId":131137,"corporation":false,"usgs":true,"family":"Hogan","given":"Dianna","email":"dhogan@usgs.gov","middleInitial":"M.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":776096,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":776097,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70205808,"text":"sir20195111 - 2019 - Evaluating associations between environmental variables and Escherichia coli levels for predictive modeling at Pawtuckaway Beach in Nottingham, New Hampshire, from 2015 to 2017","interactions":[],"lastModifiedDate":"2019-11-25T09:58:08","indexId":"sir20195111","displayToPublicDate":"2019-11-25T09:35:00","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":"2019-5111","displayTitle":"Evaluating Associations Between Environmental Variables and <i>Escherichia Coli</i> Levels for Predictive Modeling at Pawtuckaway Beach in Nottingham, New Hampshire, From 2015 to 2017","title":"Evaluating associations between environmental variables and Escherichia coli levels for predictive modeling at Pawtuckaway Beach in Nottingham, New Hampshire, from 2015 to 2017","docAbstract":"<p>From 2015 through 2017, the U.S. Geological Survey in cooperation with the New Hampshire Department of Health and Human Services and the New Hampshire Department of Environmental Services studied occurrences of high levels of <i>Escherichia coli</i> (<i>E. coli</i>) bacteria at the Pawtuckaway State Park Beach in Nottingham, New Hampshire. Historic data collected by the New Hampshire Department of Environmental Services indicated that <i>E. coli</i> concentrations in the water typically increased through the beach season to levels considered potentially harmful to beachgoers. During the three beach seasons that were studied, <i>E. coli</i> samples were collected three to four times per week, and water-quality and meteorological data were collected continuously. The Virtual Beach software was used to generate a predictive model for each year of the study (2015–2017), and the model for each of these years was tested with data from the other two. Additionally, data from all study years were combined to generate a comprehensive model to help identify independent variables that might characterize environmental conditions relative to <i>E. coli</i> levels during multiple seasons. The accuracy of the models in predicting the occurrence of high <i>E. coli</i> levels was marginal, but the models did provide insights into the likely mechanisms for increased <i>E. coli</i> levels during the seasons. Variables most important in explaining high <i>E. coli</i> levels were the presence of geese at the beach, the progression of the season, the number of visitors at the beach, and wind vectors relative to beach orientation.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195111","collaboration":"Prepared in cooperation with the New Hampshire Department of Health and Human Services and the New Hampshire Department of Environmental Services","usgsCitation":"Coles, J.F., and Bush, K.F., 2019, Evaluating associations between environmental variables and <i>Escherichia coli</i> levels for predictive modeling at Pawtuckaway Beach in Nottingham, New Hampshire, from 2015 to 2017: U.S. Geological Survey Scientific Investigations Report 2019–5111, 28 p., https://doi.org/10.3133/sir20195111.","productDescription":"Report: vii, 28 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101776","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":369290,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5111/coverthb.jpg"},{"id":369288,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5cc70bf4e4b09b8c0b77e5b7","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Data collected at Pawtuckaway Beach in Nottingham, New Hampshire, 2015–2017"},{"id":369409,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5111/sir20195111.pdf","text":"Report","size":"4.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5111"}],"country":"United States","state":"New Hampshire","city":"Nottingham","otherGeospatial":"Pawtuckaway Beach","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -71.1595630645752,\n              43.08080002811761\n            ],\n            [\n              -71.14797592163086,\n              43.08080002811761\n            ],\n            [\n              -71.14797592163086,\n              43.08650455068649\n            ],\n            [\n              -71.1595630645752,\n              43.08650455068649\n            ],\n            [\n              -71.1595630645752,\n              43.08080002811761\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto: dc_nweng@usgs.gov\" data-mce-href=\"mailto: dc_nweng@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-england-water\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>331 Commerce Way, Suite 2<br>Pembroke, NH 03275</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Objectives and Approach</li><li>Methods</li><li>Results</li><li>Discussion</li><li>Summary</li><li>Selected References</li><li>Appendix 1. The Virtual Beach Modeling Tool</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2019-11-25","noUsgsAuthors":false,"publicationDate":"2019-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Coles, James F. 0000-0002-1953-012X jcoles@usgs.gov","orcid":"https://orcid.org/0000-0002-1953-012X","contributorId":2239,"corporation":false,"usgs":true,"family":"Coles","given":"James","email":"jcoles@usgs.gov","middleInitial":"F.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":772439,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bush, Kathleen F.","contributorId":219516,"corporation":false,"usgs":false,"family":"Bush","given":"Kathleen","email":"","middleInitial":"F.","affiliations":[{"id":40019,"text":"NH-Dept. Health and Human Services","active":true,"usgs":false}],"preferred":false,"id":772440,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70208559,"text":"70208559 - 2019 - Assessing the ecological impacts of biomass harvesting along a disturbance severity gradient","interactions":[],"lastModifiedDate":"2020-02-17T07:00:24","indexId":"70208559","displayToPublicDate":"2019-11-23T06:59:06","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the ecological impacts of biomass harvesting along a disturbance severity gradient","docAbstract":"Disturbance is a central driver of forest development and ecosystem processes with variable effects within and across ecosystems.  Despite the high levels of variation in disturbance severity often observed in forests following natural and anthropogenic disturbance, studies quantifying disturbance impacts often rely on categorical classifications, thus limiting opportunities to examine potential gradients in ecosystem response to a given disturbance or management regime.  Given the potential increases in disturbance severity associated with global change, as well as shifts in management regimes related to procurement of biofuel feedstocks, there is an increasing need to quantitatively describe disturbance severity and associated responses of forest development, soil processes, and structural conditions.  This study took advantage of two, replicated large-scale studies of forest biomass harvesting in Populus tremuloides and Pinus bansksiana forests, respectively, to develop and test the utility of a continuous, quantitative index of disturbance severity (DSI) for describing post-harvest response of plant communities and nutrient pools to different levels of biomass removal and legacy retention (i.e., live trees and coarse woody material). There was a high-degree of variability in DSI within categorical treatments associated with different levels of legacy retention and regression models using DSI as a predictor explained a portion of the variation (>50%) for many of the ecosystem- and community-level responses to biomass harvesting examined. Nutrient losses associated with biomass harvesting were positively related to disturbance severity, particularly in P. tremuloides forests, with post-harvest nutrient availability generally declining along the gradient of impacts. Consistent with expectations from ecological theory, species richness and diversity of woody plant communities were greatest at intermediate disturbance severities and regeneration densities of dominant trees species most abundant at highest levels of disturbance. Although categorical benchmarks will continue to be the primary way through which management guidelines are conveyed to practitioners, evaluation of their effectiveness at sustaining ecosystem functioning should be through continuous analyses, such as the DSI approach used in this study, to allow for the identification of minimum benchmarks that ensure a range of desirable outcomes exist across managed landscapes.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2042","usgsCitation":"Kurth, V.J., Amato, A.W., Bradford, J., Palik, B.J., and Looney, C.E., 2019, Assessing the ecological impacts of biomass harvesting along a disturbance severity gradient: Ecological Applications, e02042, 11 p., https://doi.org/10.1002/eap.2042.","productDescription":"e02042, 11 p.","ipdsId":"IP-101814","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":372378,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Kurth, Valerie J.","contributorId":222542,"corporation":false,"usgs":false,"family":"Kurth","given":"Valerie","email":"","middleInitial":"J.","affiliations":[{"id":40556,"text":"University of Minnesota, Department of Forest Resources, Green Hall, 1530 Cleveland Avenue N, St. Paul, MN 55108","active":true,"usgs":false}],"preferred":false,"id":782483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Amato, Anthony W.D.","contributorId":222543,"corporation":false,"usgs":false,"family":"Amato","given":"Anthony","email":"","middleInitial":"W.D.","affiliations":[{"id":40557,"text":"University of Vermont, Rubenstein School of Environment and Natural Resources, 81 Carrigan Drive, Burlington, VT 05095, USA","active":true,"usgs":false}],"preferred":false,"id":782484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":782485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palik, Brian J.","contributorId":190301,"corporation":false,"usgs":false,"family":"Palik","given":"Brian","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":782486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Looney, Christopher E.","contributorId":222544,"corporation":false,"usgs":false,"family":"Looney","given":"Christopher","email":"","middleInitial":"E.","affiliations":[{"id":40558,"text":"University of Minnesota, Department of Forest Resources, Green Hall, 1530 Cleveland Ave. N, St. Paul, MN 55108, USA","active":true,"usgs":false}],"preferred":false,"id":782487,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70207039,"text":"70207039 - 2019 - Using δ13C and δ18O to analyze loblolly pine (Pinus taeda L.) response to experimental drought and fertilization","interactions":[],"lastModifiedDate":"2019-12-05T06:36:50","indexId":"70207039","displayToPublicDate":"2019-11-21T15:26:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3649,"text":"Tree Physiology","active":true,"publicationSubtype":{"id":10}},"title":"Using δ13C and δ18O to analyze loblolly pine (Pinus taeda L.) response to experimental drought and fertilization","docAbstract":"Drought frequency and intensity are projected to increase throughout the southeastern USA, the natural range of loblolly pine (Pinus taeda L.), and are expected to have major ecological and economic implications. We analyzed the carbon and oxygen isotopic compositions in tree ring cellulose of loblolly pine in a factorial drought (~30% throughfall reduction) and fertilization experiment, supplemented with trunk sap flow, allometry and microclimate data. We then simulated leaf temperature and applied a multi-dimensional sensitivity analysis to interpret the changes in the oxygen isotope data. This analysis found that the observed changes in tree ring cellulose could only be accounted for by inferring a change in the isotopic composition of the source water, indicating that the drought treatment increased the uptake of stored moisture from earlier precipitation events. The drought treatment also increased intrinsic water-use efficiency, but had no effect on growth, indicating that photosynthesis remained relatively unaffected despite 19% decrease in canopy conductance. In contrast, fertilization increased growth, but had no effect on the isotopic composition of tree ring cellulose, indicating that the fertilizer gains in biomass were attributable to greater leaf area and not to changes in leaf-level gas exchange. The multi-dimensional sensitivity analysis explored model behavior under different scenarios, highlighting the importance of explicit consideration of leaf temperature in the oxygen isotope discrimination (Δ18Oc) simulation and is expected to expand the inference space of the Δ18Oc models for plant ecophysiological studies.","language":"English","publisher":"Oxford Academic","doi":"10.1093/treephys/tpz096","usgsCitation":"Lin, W., Domec, J., Ward, E., Marshall, J.D., King, J.S., Laviner, M.A., Fox, T.R., West, J.B., Sun, G., McNulty, S., and Noormets, A., 2019, Using δ13C and δ18O to analyze loblolly pine (Pinus taeda L.) response to experimental drought and fertilization: Tree Physiology, tpz096, https://doi.org/10.1093/treephys/tpz096.","productDescription":"tpz096","ipdsId":"IP-109073","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":459124,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1093/treephys/tpz096","text":"External Repository"},{"id":369919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Lin, Wen","contributorId":221015,"corporation":false,"usgs":false,"family":"Lin","given":"Wen","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":776600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Domec, Jean-Christophe","contributorId":146460,"corporation":false,"usgs":false,"family":"Domec","given":"Jean-Christophe","email":"","affiliations":[],"preferred":false,"id":776601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ward, Eric 0000-0002-5047-5464","orcid":"https://orcid.org/0000-0002-5047-5464","contributorId":221014,"corporation":false,"usgs":true,"family":"Ward","given":"Eric","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":776599,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marshall, John D.","contributorId":176597,"corporation":false,"usgs":false,"family":"Marshall","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":776602,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, John S","contributorId":221017,"corporation":false,"usgs":false,"family":"King","given":"John","email":"","middleInitial":"S","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":776604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Laviner, Marshall A.","contributorId":221018,"corporation":false,"usgs":false,"family":"Laviner","given":"Marshall","email":"","middleInitial":"A.","affiliations":[{"id":40311,"text":"Virginia Polytechnic Institute and University","active":true,"usgs":false}],"preferred":false,"id":776605,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fox, Thomas R","contributorId":221016,"corporation":false,"usgs":false,"family":"Fox","given":"Thomas","email":"","middleInitial":"R","affiliations":[{"id":40311,"text":"Virginia Polytechnic Institute and University","active":true,"usgs":false}],"preferred":false,"id":776603,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"West, Jason B.","contributorId":221019,"corporation":false,"usgs":false,"family":"West","given":"Jason","email":"","middleInitial":"B.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":776606,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sun, Ge","contributorId":145893,"corporation":false,"usgs":false,"family":"Sun","given":"Ge","email":"","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":776607,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McNulty, Steve G","contributorId":145897,"corporation":false,"usgs":false,"family":"McNulty","given":"Steve G","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":776608,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Noormets, Asko","contributorId":217423,"corporation":false,"usgs":false,"family":"Noormets","given":"Asko","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":776609,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70208996,"text":"70208996 - 2019 - General external uncertainty models of three-plane intersection point for 3D absolute accuracy assessment of lidar point cloud","interactions":[],"lastModifiedDate":"2020-03-10T13:53:55","indexId":"70208996","displayToPublicDate":"2019-11-21T13:47:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"General external uncertainty models of three-plane intersection point for 3D absolute accuracy assessment of lidar point cloud","docAbstract":"The traditional practice to assess accuracy in lidar data involves calculating RMSEz (root mean square error of the vertical component). Accuracy assessment of lidar point clouds in full 3D (dimension) is not routinely performed. The main challenge in assessing accuracy in full 3D is how to identify a conjugate point of a ground-surveyed checkpoint in the lidar point cloud with the smallest possible uncertainty value.  Relatively coarse point-spacing in airborne lidar data makes it challenging to determine a conjugate point accurately. As a result, a substantial unwanted error is added to the inherent positional uncertainty of the lidar data. Unless we keep this additional error small enough, the 3D accuracy assessment result will not properly represent the inherent uncertainty. We call this added error “external uncertainty,” which is associated with conjugate point identification. This research developed a general external uncertainty model using three-plane intersections and accounts for several factors (sensor precision, feature dimension, and point density). This method can be used for lidar point cloud data from a wide range of sensor qualities, point densities, and sizes of the features of interest. The external uncertainty model was derived as a semi-analytical function that takes the number of points on a plane as an input. It is a normalized general function that can be scaled by smooth surface precision (SSP) of a lidar system. This general uncertainty model provides a quantitative guideline on the required conditions for the conjugate point based on the geometric features. Applications of external uncertainty model was demonstrated using various lidar point cloud data from US Geological Survey (USGS) 3D Elevation Program (3DEP) library to determine the valid conditions for a conjugate point from three-plane.","language":"English","publisher":"MDPI","doi":"10.3390/rs11232737","usgsCitation":"Kim, M., Park, S., Danielson, J.J., Irwin, J., Stensaas, G.L., Stoker, J.M., and Nimetz, J., 2019, General external uncertainty models of three-plane intersection point for 3D absolute accuracy assessment of lidar point cloud: Remote Sensing, v. 11, no. 23, 2737, 18 p., https://doi.org/10.3390/rs11232737.","productDescription":"2737, 18 p.","ipdsId":"IP-113404","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":459128,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11232737","text":"Publisher Index Page"},{"id":373048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"23","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Kim, Minsu 0000-0003-4472-0926 minsukim@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":216429,"corporation":false,"usgs":true,"family":"Kim","given":"Minsu","email":"minsukim@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":784451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Park, Seonkyung 0000-0003-3203-1998","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":223182,"corporation":false,"usgs":true,"family":"Park","given":"Seonkyung","email":"","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":784452,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":784453,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irwin, Jeffrey 0000-0001-5828-0787 jrirwin@usgs.gov","orcid":"https://orcid.org/0000-0001-5828-0787","contributorId":222485,"corporation":false,"usgs":true,"family":"Irwin","given":"Jeffrey","email":"jrirwin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":784454,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stensaas, Gregory L. 0000-0001-6679-2416 stensaas@usgs.gov","orcid":"https://orcid.org/0000-0001-6679-2416","contributorId":2551,"corporation":false,"usgs":true,"family":"Stensaas","given":"Gregory","email":"stensaas@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":784455,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":784456,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nimetz, Joshua 0000-0002-7132-9992","orcid":"https://orcid.org/0000-0002-7132-9992","contributorId":223183,"corporation":false,"usgs":true,"family":"Nimetz","given":"Joshua","email":"","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":784457,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70207442,"text":"70207442 - 2019 - Holocene earthquake history and slip rate of the southern Teton fault, Wyoming, USA","interactions":[],"lastModifiedDate":"2020-07-09T14:28:34.224535","indexId":"70207442","displayToPublicDate":"2019-11-21T13:12:47","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1786,"text":"Geological Society of America Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Holocene earthquake history and slip rate of the southern Teton fault, Wyoming, USA","docAbstract":"The 72-km-long Teton normal fault bounds the eastern base of the Teton Range in northwestern Wyoming, USA. Although geomorphic surfaces along the fault record latest Pleistocene to Holocene fault movement, the postglacial earthquake history of the fault has remained enigmatic. We excavated a paleoseismic trench at the Buffalo Bowl site along the southernmost part of the fault to determine its Holocene rupture history and slip rate. At the site, ∼6.3 m of displacement postdates an early Holocene (ca. 10.5 ka) alluvial-fan surface. We document evidence of three surface-faulting earthquakes based on packages of scarp-derived colluvium that postdate the alluvial-fan units. Bayesian modeling of radiocarbon and luminescence ages yields earthquake times of ca. 9.9 ka, ca. 7.1 ka, and ca. 4.6 ka, forming the longest, most complete paleoseismic record of the Teton fault. We integrate these data with a displaced deglacial surface 4 km NE at Granite Canyon to calculate a postglacial to mid-Holocene (14.4−4.6 ka) slip rate of ∼1.1 mm/yr. Our analysis also suggests that the postglacial to early Holocene (14.4−9.9 ka) slip rate exceeds the Holocene (9.9−4.6 ka) rate by a factor of ∼2 (maximum of 3); however, a uniform rate for the fault is possible considering the 95% slip-rate errors. The ∼5 k.y. elapsed time since the last rupture of the southernmost Teton fault implies a current slip deficit of ∼4−5 m, which is possibly explained by spatially/temporally incomplete paleoseismic data, irregular earthquake recurrence, and/or variable per-event displacement. Our study emphasizes the importance of minimizing slip-rate uncertainties by integrating paleoseismic and geomorphic data sets and capturing multiple earthquake cycles.","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35363.1","usgsCitation":"DuRoss, C., Gold, R.D., Briggs, R.W., Delano, J.E., Ostenaa, D.A., Zellman, M., Cholewinski, N., Wittke, S., and Mahan, S.A., 2019, Holocene earthquake history and slip rate of the southern Teton fault, Wyoming, USA: Geological Society of America Bulletin, v. 132, no. 7-8, p. 1566-1586, https://doi.org/10.1130/B35363.1.","productDescription":"21 p.","startPage":"1566","endPage":"1586","ipdsId":"IP-111318","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":370499,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"132","issue":"7-8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":778051,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":778052,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":139002,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":778053,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Delano, Jaime E. 0000-0003-2601-2600","orcid":"https://orcid.org/0000-0003-2601-2600","contributorId":210604,"corporation":false,"usgs":true,"family":"Delano","given":"Jaime","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":778054,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ostenaa, Dean A.","contributorId":39467,"corporation":false,"usgs":false,"family":"Ostenaa","given":"Dean","email":"","middleInitial":"A.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":778055,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zellman, Mark","contributorId":167020,"corporation":false,"usgs":false,"family":"Zellman","given":"Mark","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":778056,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cholewinski, Nicole","contributorId":221401,"corporation":false,"usgs":false,"family":"Cholewinski","given":"Nicole","email":"","affiliations":[{"id":40365,"text":"GEI Consultants","active":true,"usgs":false}],"preferred":false,"id":778057,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wittke, Seth","contributorId":221402,"corporation":false,"usgs":false,"family":"Wittke","given":"Seth","email":"","affiliations":[{"id":40366,"text":"Wyoming State Geological  Survey","active":true,"usgs":false}],"preferred":false,"id":778058,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":778059,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70209051,"text":"70209051 - 2019 - Using age tracers and decadal sampling to discern trends in nitrate, arsenic and uranium in groundwater beneath irrigated cropland","interactions":[],"lastModifiedDate":"2020-03-12T13:16:44","indexId":"70209051","displayToPublicDate":"2019-11-21T13:06:34","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Using age tracers and decadal sampling to discern trends in nitrate, arsenic and uranium in groundwater beneath irrigated cropland","docAbstract":"Repeat sampling and age tracers were used to examine trends in nitrate, arsenic and uranium concentrations in groundwater beneath irrigated cropland. Much higher nitrate concentrations in shallow modern groundwater were observed at both the Columbia Plateau and High Plains sites (median values of 10.2 and 15.4 mg/L as N, respectively) than in groundwater that recharged prior to the onset of intensive irrigation (median values of <1 and <4 mg/L as N, respectively). Repeat sampling of these well networks indicates that high nitrate concentrations in modern, shallow groundwater have been sustained for decades, posing a future risk to older, deeper groundwater used for drinking water. In fact, nitrate concentrations in older modern water (30-60 years since recharge) at the High Plains site have increased in the last decade. Groundwater irrigated areas in the Columbia Plateau tend to have higher nitrate concentrations than surface-water irrigated areas suggesting that repeated dissolution of land applied fertilizer during recirculation may be an important factor causing high nitrate concentrations in groundwater. Mobilization of uranium and arsenic by land surface activities is suggested by the higher concentrations of these constituents in modern, shallow groundwater than in older, deeper groundwater at the Columbia Plateau site. Bicarbonate concentrations in modern groundwater are positively correlated with uranium (r=0.72, p<0.01), suggesting bicarbonate may mobilize uranium in this system. A positive correlation between arsenic and phosphorus concentrations in modern groundwater (r=0.55, p<0.01) suggests that phosphate from fertilizer outcompetes arsenate for sorption sites, mobilizing sorbed arsenic derived from past pesticide use or other sources.","language":"English","publisher":"ACS","doi":"10.1021/acs.est.9b03459","usgsCitation":"Tesoriero, A.J., Burow, K.R., Frans, L., Haynes, J.V., Hobza, C.M., Lindsey, B.D., and Solder, J.E., 2019, Using age tracers and decadal sampling to discern trends in nitrate, arsenic and uranium in groundwater beneath irrigated cropland: Environmental Science and Technology, v. 53, no. 24, p. 14152-14164, https://doi.org/10.1021/acs.est.9b03459.","productDescription":"13 p.","startPage":"14152","endPage":"14164","ipdsId":"IP-107801","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":437278,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UY8L30","text":"USGS data release","linkHelpText":"Dissolved gas and tracer concentrations from the Columbia Plateau Aquifer, Vertical Flowpath Study Network"},{"id":437277,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VLFXTM","text":"USGS data release","linkHelpText":"Dissolved Gas and Tracer Concentrations for the High Plains Aquifer, Vertical Flowpath Study Network"},{"id":373200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"24","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burow, Karen R. 0000-0001-6006-6667 krburow@usgs.gov","orcid":"https://orcid.org/0000-0001-6006-6667","contributorId":1504,"corporation":false,"usgs":true,"family":"Burow","given":"Karen","email":"krburow@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frans, Lonna 0000-0002-3217-1862","orcid":"https://orcid.org/0000-0002-3217-1862","contributorId":210896,"corporation":false,"usgs":true,"family":"Frans","given":"Lonna","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haynes, Jonathan V. 0000-0001-6530-6252 jhaynes@usgs.gov","orcid":"https://orcid.org/0000-0001-6530-6252","contributorId":3113,"corporation":false,"usgs":true,"family":"Haynes","given":"Jonathan","email":"jhaynes@usgs.gov","middleInitial":"V.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hobza, Christopher M. 0000-0002-6239-934X cmhobza@usgs.gov","orcid":"https://orcid.org/0000-0002-6239-934X","contributorId":2393,"corporation":false,"usgs":true,"family":"Hobza","given":"Christopher","email":"cmhobza@usgs.gov","middleInitial":"M.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784635,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":175346,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce","email":"blindsey@usgs.gov","middleInitial":"D.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":784636,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Solder, John E. 0000-0002-0660-3326","orcid":"https://orcid.org/0000-0002-0660-3326","contributorId":201953,"corporation":false,"usgs":true,"family":"Solder","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784637,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70206758,"text":"70206758 - 2019 - Synergistic interaction of climate and land-use drivers alter the function of North American, Prairie-pothole Wetlands","interactions":[],"lastModifiedDate":"2019-11-22T11:05:51","indexId":"70206758","displayToPublicDate":"2019-11-21T11:03:30","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3504,"text":"Sustainability","active":true,"publicationSubtype":{"id":10}},"title":"Synergistic interaction of climate and land-use drivers alter the function of North American, Prairie-pothole Wetlands","docAbstract":"Prairie-pothole wetlands provide the critical habitat necessary for supporting North American migratory waterfowl populations. However, climate and land-use change threaten the sustainability of these wetland ecosystems. Very few experiments and analyses have been designed to investigate the relative impacts of climate and land-use change drivers, as well as the antagonistic or synergistic interactions among these drivers on ecosystem processes. Prairie-pothole wetland water budgets are highly dependent on atmospheric inputs and especially surface runoff, which makes them especially susceptible to changes in climate and land use. Here, we present the history of prairie-pothole climate and land-use change research and address the following research questions: 1) What are the relative effects of climate and land-use change on the sustainability of prairie-pothole wetlands? and 2) Do the effects of climate and land-use change interact differently under different climatic conditions? To address these research questions, we modeled 25 wetland basins (1949–2018) and measured the response of the lowest wetland in the watershed to wetland drainage and climate variability. We found that during an extremely wet period (1993–2000) wetland drainage decreased the time at which the lowest wetland reached its spill point by four years, resulting in 10 times the amount of water spilling out of the watershed towards local stream networks. By quantifying the relative effects of both climate and land-use drivers on wetland ecosystems our findings can help managers cope with uncertainties about flooding risks and provide insight into how to manage wetlands to restore functionality","language":"English","publisher":"MDPI","doi":"10.3390/su11236581","usgsCitation":"McKenna, O.P., Kucia, S.R., Mushet, D.M., Anteau, M.J., and Wiltermuth, M.T., 2019, Synergistic interaction of climate and land-use drivers alter the function of North American, Prairie-pothole Wetlands: Sustainability, v. 11, no. 23, 6581, https://doi.org/10.3390/su11236581.","productDescription":"6581","ipdsId":"IP-112410","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":459133,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/su11236581","text":"Publisher Index Page"},{"id":369465,"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              -103.798828125,\n              52.32191088594773\n            ],\n            [\n              -103.88671875,\n              53.225768435790194\n            ],\n            [\n              -115.224609375,\n              54.521081495443596\n            ],\n            [\n              -113.99414062499999,\n              47.635783590864854\n            ],\n            [\n              -103.798828125,\n              41.83682786072714\n            ],\n            [\n              -94.39453125,\n              38.54816542304656\n            ],\n            [\n              -88.857421875,\n              38.95940879245423\n            ],\n            [\n              -89.736328125,\n              44.5278427984555\n            ],\n            [\n              -103.798828125,\n              52.32191088594773\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","issue":"23","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":775691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kucia, Samuel Richard","contributorId":220767,"corporation":false,"usgs":false,"family":"Kucia","given":"Samuel","email":"","middleInitial":"Richard","affiliations":[],"preferred":false,"id":775690,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":775694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anteau, Michael J. 0000-0002-5173-5870 manteau@usgs.gov","orcid":"https://orcid.org/0000-0002-5173-5870","contributorId":3427,"corporation":false,"usgs":true,"family":"Anteau","given":"Michael","email":"manteau@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":775693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiltermuth, Mark T. 0000-0002-8871-2816 mwiltermuth@usgs.gov","orcid":"https://orcid.org/0000-0002-8871-2816","contributorId":708,"corporation":false,"usgs":true,"family":"Wiltermuth","given":"Mark","email":"mwiltermuth@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":775692,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70207991,"text":"70207991 - 2019 - Accumulating evidence in ecology: Once is not enough","interactions":[],"lastModifiedDate":"2020-01-23T06:32:34","indexId":"70207991","displayToPublicDate":"2019-11-21T06:31:34","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Accumulating evidence in ecology: Once is not enough","docAbstract":"Many published studies in ecological science are viewed as stand-alone investigations that purport to provide new insights into how ecological systems behave based on single analyses. But it is rare for results of single studies to provide definitive results, as evidenced in current discussions of the “reproducibility crisis” in science. The key step in science is the comparison of hypothesis-based predictions with observations, where the predictions are typically generated by hypothesis-specific models. Repeating this step allows us to gain confidence in the predictive ability of a model, and its corresponding hypothesis, and thus to accumulate evidence and eventually knowledge. This accumulation may occur via an ad hoc approach, via meta-analyses, or via a more systematic approach based on the anticipated evolution of an information state. We argue the merits of this latter approach, provide an example, and discuss implications for designing sequences of studies focused on a particular question. We conclude by discussing current data collection programs that are pre-adapted to use this approach and argue that expanded use would increase the rate of learning in ecology, as well as our confidence in what is learned.","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5836","usgsCitation":"Nichols, J.D., Kendall, W., and Boomer, G., 2019, Accumulating evidence in ecology: Once is not enough: Ecology and Evolution, v. 9, no. 24, p. 13991-14004, https://doi.org/10.1002/ece3.5836.","productDescription":"14 p.","startPage":"13991","endPage":"14004","ipdsId":"IP-108580","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":459138,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5836","text":"Publisher Index Page"},{"id":371489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"24","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":200533,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":780056,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William 0000-0002-7632-3000","orcid":"https://orcid.org/0000-0002-7632-3000","contributorId":221720,"corporation":false,"usgs":true,"family":"Kendall","given":"William","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":780057,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boomer, G.Scott","contributorId":221721,"corporation":false,"usgs":false,"family":"Boomer","given":"G.Scott","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":780058,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70207583,"text":"70207583 - 2019 - Remote sensing of tracer dye concentrations to support dispersion studies in river channels","interactions":[],"lastModifiedDate":"2019-12-30T11:23:49","indexId":"70207583","displayToPublicDate":"2019-11-20T11:17:18","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5513,"text":"Journal of Ecohydraulics","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing of tracer dye concentrations to support dispersion studies in river channels","docAbstract":"In river channels the flow field influences the dispersion of biota, contaminants, and other suspended or dissolved materials. Insight on patterns and rates of dispersion can be gained by injecting a pulse of visible dye and observing spatial and temporal variations in dye concentration as the pulse moves downstream. We evaluated the potential of passive optical remote sensing to enhance such tracer experiments by providing spatially distributed concentration information. During tests performed in both an experimental flume facility and a large natural channel, we made field measurements of Rhodamine WT dye concentration and above-water spectral reflectance.  At Korea's River Experiment Center, a small unmanned aircraft system (sUAS) was used to acquire hyperspectral images of a sinuous outdoor flume.  On the Kootenai River in northern Idaho, USA, field spectra were collected from a boat and  hyperspectral image data and high resolution aerial photographs were obtained from manned aircraft. We modified an Optimal Band Ratio Analysis (OBRA) algorithm to identify wavelength combinations that yielded strong correlations between a spectrally based quantity X and dye concentration C. For both the flume and field tests, we obtained very strong (R^2 from 0.94 to 0.99) relationships between X and C across a broad range of visible wavelengths. On the Kootenai, we found that X vs. C relations derived from field spectra could be applied to airborne hyperspectral images and that dye concentrations could be estimated nearly as reliably from  relatively simple three-band images as from hyperspectral data.  These results imply that remote sensing could become a powerful tool for mapping dye patterns.  Such a capability would advance our understanding of dispersion processes by enabling more rigorous testing of numerical flow models.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/24705357.2019.1662339","usgsCitation":"Legleiter, C.J., McDonald, R.R., Nelson, J.M., Kinzel, P.J., Perroy, R.L., Baek, D., and Seo, I.W., 2019, Remote sensing of tracer dye concentrations to support dispersion studies in river channels: Journal of Ecohydraulics, v. 4, no. 2, p. 131-146, https://doi.org/10.1080/24705357.2019.1662339.","productDescription":"15 p.","startPage":"131","endPage":"146","ipdsId":"IP-106338","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":437280,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CV4XEO","text":"USGS data release","linkHelpText":"Remotely sensed data and field measurements from a tracer dye experiment on the Kootenai River, ID, September 25-27, 2017"},{"id":437279,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V3Y334","text":"USGS data release","linkHelpText":"Hyperspectral image data and Rhodamine WT dye concentrations from a tracer study at the River Experiment Center, Korea, in May 2017"},{"id":370852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"South Korea","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[128.34972,38.61224],[129.21292,37.43239],[129.46045,36.78419],[129.4683,35.63214],[129.09138,35.08248],[128.18585,34.89038],[127.38652,34.47567],[126.48575,34.39005],[126.37392,34.93456],[126.55923,35.68454],[126.1174,36.72548],[126.86014,36.89392],[126.17476,37.74969],[126.23734,37.84038],[126.68372,37.80477],[127.07331,38.25611],[127.78004,38.30454],[128.20575,38.3704],[128.34972,38.61224]]]},\"properties\":{\"name\":\"South Korea\"}}]}","volume":"4","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":778610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDonald, Richard R. 0000-0002-0703-0638 rmcd@usgs.gov","orcid":"https://orcid.org/0000-0002-0703-0638","contributorId":2428,"corporation":false,"usgs":true,"family":"McDonald","given":"Richard","email":"rmcd@usgs.gov","middleInitial":"R.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":778611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":778612,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":778613,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Perroy, Ryan L. 0000-0002-4210-3281","orcid":"https://orcid.org/0000-0002-4210-3281","contributorId":205505,"corporation":false,"usgs":false,"family":"Perroy","given":"Ryan","email":"","middleInitial":"L.","affiliations":[{"id":37113,"text":"University of Hawaii - Hilo","active":true,"usgs":false}],"preferred":false,"id":778614,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baek, Donghae","contributorId":214366,"corporation":false,"usgs":false,"family":"Baek","given":"Donghae","email":"","affiliations":[{"id":37780,"text":"Seoul National University","active":true,"usgs":false}],"preferred":false,"id":778615,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Seo, Il Won","contributorId":214367,"corporation":false,"usgs":false,"family":"Seo","given":"Il","email":"","middleInitial":"Won","affiliations":[{"id":37780,"text":"Seoul National University","active":true,"usgs":false}],"preferred":false,"id":778616,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70208193,"text":"70208193 - 2019 - Optical wave gauging using deep neural networks","interactions":[],"lastModifiedDate":"2020-01-29T19:33:09","indexId":"70208193","displayToPublicDate":"2019-11-19T19:27:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Optical wave gauging using deep neural networks","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"d1e1085\" class=\"abstract author\"><div id=\"d1e1088\"><p id=\"d1e1089\">We develop a remote wave gauging technique to estimate wave height and period from imagery of waves in the surf zone. In this proof-of-concept study, we apply the same framework to three datasets: the first, a set of close-range monochrome infrared (IR) images of individual nearshore waves at Duck, NC, USA; the second, a set of visible (i.e. RGB) band orthomosaics of a larger nearshore area near Santa Cruz, CA, USA; and the third, a set of oblique (unrectified) images from the same site. The network is trained using coincident images and<span>&nbsp;</span><i>in situ</i><span>&nbsp;</span>wave measurements. The optical wave gauge (OWG) consists of a deep convolutional neural network (CNN) to extract features from imagery — called a ‘base model’, with additional layers to distill the feature information into lower dimensional spaces, and a final layer of dense neurons to predict continuously varying quantities. Four base models are compared. The OWG is trained for both individual wave height and period, and statistical quantities like significant wave height and peak wave period. The best performing OWG on the IR dataset achieved RMS errors of 0.14 m and 0.41 s for height and period, respectively, capturing up to 98% of the variance in these quantities. The best performing OWG on the visible band rectified dataset achieved RMS errors of 0.08 m and 0.79 s, respectively, for height and period. The same values for the oblique RGB imagery were 0.11 m and 0.81 s for height and period, respectively. Overall, wave height and period accuracy is sensitive to choice of base model; OWGs built upon MobilenetV2 tend to perform worst and those built on Inception-ResnetV2 have the smallest RMS error. The presence or otherwise of residual layers in the model makes little systematic difference to the final OWG accuracy. Smaller batch sizes used in model training tend to result in more accurate OWGs. An out-of-calibration validation, using images associated with wave heights or periods outside the range of values represented in the training data, showed that the ability for OWGs to predict the bottom 5% of low wave heights and the top 5% of high wave heights was reasonably good, but the same was not generally true of wave period. The same framework, not optimized for either dataset, predicts both quantities with high accuracy when trained on imagery, despite the differences in electromagnetic band, perspective, and scale. The OWG estimates wave properties from an image in less than 100&nbsp;ms on a modestly sized CPU, allowing for the possibility of continuous real-time wave estimates.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2019.103593","usgsCitation":"Buscombe, D.D., Carini, R.J., Harrison, S., Chickadel, C.C., and Warrick, J.A., 2019, Optical wave gauging using deep neural networks: Coastal Engineering, v. 155, 103593, 18 p., https://doi.org/10.1016/j.coastaleng.2019.103593.","productDescription":"103593, 18 p.","ipdsId":"IP-106980","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":459152,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2019.103593","text":"Publisher Index Page"},{"id":371746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, North Carolina","county":"Santa Cruz, Duck","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.80978393554688,\n              36.27527883184338\n            ],\n            [\n              -75.7562255859375,\n              36.07851703597173\n            ],\n            [\n              -75.69374084472656,\n              36.08295654486136\n            ],\n            [\n              -75.77545166015625,\n              36.274725267505474\n            ],\n            [\n              -75.80978393554688,\n              36.27527883184338\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n       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]\n}","volume":"155","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":198817,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","middleInitial":"D.","affiliations":[],"preferred":false,"id":780897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carini, Roxanne J 0000-0001-9682-890X","orcid":"https://orcid.org/0000-0001-9682-890X","contributorId":221996,"corporation":false,"usgs":false,"family":"Carini","given":"Roxanne","email":"","middleInitial":"J","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":780898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrison, Shawn 0000-0002-8711-4427","orcid":"https://orcid.org/0000-0002-8711-4427","contributorId":221997,"corporation":false,"usgs":true,"family":"Harrison","given":"Shawn","email":"","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":780899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chickadel, C Chris 0000-0002-0770-7725","orcid":"https://orcid.org/0000-0002-0770-7725","contributorId":221998,"corporation":false,"usgs":false,"family":"Chickadel","given":"C","email":"","middleInitial":"Chris","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":780900,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":780896,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70206744,"text":"70206744 - 2019 - A parametric numerical analysis of factors controlling ground ruptures caused by groundwater pumping","interactions":[],"lastModifiedDate":"2020-01-03T10:41:12","indexId":"70206744","displayToPublicDate":"2019-11-19T15:52:39","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":"A parametric numerical analysis of factors controlling ground ruptures caused by groundwater pumping","docAbstract":"A modeling analysis is used to investigate the relative susceptibility of various hydrogeologic configurations to aseismic rupture generation due to deformation of aquifer systems  accompanying groundwater pumping. An advanced numerical model (GEPS3D) is used to simulate rupture generation and propagation for three typical processes: (i) reactivation of a preexisting fault, (ii) differential compaction due to variations in thickness of aquifer/aquitard layers constituting the aquifer system, and (iii) tensile fracturing above a bedrock ridge that forms the base of the aquifer system. A sensitivity analysis is developed to address the relative importance of various factors, including aquifer depletion, aquifer thickness, the possible uneven distribution and depth below land surface of the aquifer/aquitard layers susceptible to aquifer-system compaction, and the height of bedrock ridges beneath the aquifer system which contributes to thinning of the aquifer system. The rupture evolution is classified in two occurrences. In one, the rupture develops at the top of the aquifer or at land surface and does not propagate. In the other, the developed rupture propagates from the aquifer top toward the land surface and/or from the land surface downward. The aquifer depth is the most important factor controlling rupture evolution. Specifically, the probability of a significant rupture propagation is higher when the aquifer top is near land surface. The numerical results are processed by a statistical regression analysis to provide a general methodology for a preliminary evaluation of possible ruptures development in exploited aquifer systems susceptible to aquifer-system compaction and accompanying land subsidence. A comparison with a few representative case studies in Arizona, USA, China, and Mexico supports the study outcomes.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR025034","usgsCitation":"Frigo, M., Ferronato, M., Yu, J., Ye, S., Galloway, D., Carreon-Freyre, D., and Teatini, P., 2019, A parametric numerical analysis of factors controlling ground ruptures caused by groundwater pumping: Water Resources Research, v. 55, no. 11, p. 9500-9518, https://doi.org/10.1029/2019WR025034.","productDescription":"19 p.","startPage":"9500","endPage":"9518","ipdsId":"IP-113487","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":369361,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China, Mexico, United States","state":"Arizona","volume":"55","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Frigo, Matteo","contributorId":220754,"corporation":false,"usgs":false,"family":"Frigo","given":"Matteo","email":"","affiliations":[{"id":40265,"text":"Department of Civil, Architectural and Environmental Engineering, University of Padova, Padova, Italy","active":true,"usgs":false}],"preferred":false,"id":775626,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferronato, Massimiliano","contributorId":220755,"corporation":false,"usgs":false,"family":"Ferronato","given":"Massimiliano","email":"","affiliations":[{"id":40265,"text":"Department of Civil, Architectural and Environmental Engineering, University of Padova, Padova, Italy","active":true,"usgs":false}],"preferred":false,"id":775627,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yu, Jun","contributorId":220756,"corporation":false,"usgs":false,"family":"Yu","given":"Jun","email":"","affiliations":[{"id":40266,"text":"Key Laboratory of Earth Fissures Geological Disaster, Ministry of Land and Resources, Geological Survey of Jiangsu Province, Nanjing, China","active":true,"usgs":false}],"preferred":false,"id":775628,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ye, Shujun","contributorId":203532,"corporation":false,"usgs":false,"family":"Ye","given":"Shujun","email":"","affiliations":[{"id":36646,"text":"Dept. of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing P. R. China","active":true,"usgs":false}],"preferred":false,"id":775629,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galloway, Devin 0000-0003-0904-5355","orcid":"https://orcid.org/0000-0003-0904-5355","contributorId":215888,"corporation":false,"usgs":true,"family":"Galloway","given":"Devin","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":775625,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carreon-Freyre, Dora","contributorId":203530,"corporation":false,"usgs":false,"family":"Carreon-Freyre","given":"Dora","email":"","affiliations":[{"id":36644,"text":"Centro de Geociencias, Universidad Nacional Autónoma de México, Campus Juriquilla, Queretaro, Mexico","active":true,"usgs":false}],"preferred":false,"id":775630,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Teatini, Pietro","contributorId":203529,"corporation":false,"usgs":false,"family":"Teatini","given":"Pietro","email":"","affiliations":[{"id":36643,"text":"Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, Italy","active":true,"usgs":false}],"preferred":false,"id":775631,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70206737,"text":"70206737 - 2019 - Geospatial scaling of runoff and erosion modeling in the Chihuahuan Desert","interactions":[],"lastModifiedDate":"2019-11-19T15:39:57","indexId":"70206737","displayToPublicDate":"2019-11-19T15:39:47","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":833,"text":"Applied Engineering in Agriculture","active":true,"publicationSubtype":{"id":10}},"title":"Geospatial scaling of runoff and erosion modeling in the Chihuahuan Desert","docAbstract":"<p><span>Large-scale assessments of rangeland runoff and erosion require methods to extend plot-scale parameterizations to large areas. In this study, Rangeland Hydrology and Erosion Model (RHEM) parameters were developed from plot-scale foliar and ground-cover transect data for an arid, grass-shrub rangeland in southern New Mexico, and a method was assessed to upscale transect-plot parameters to a large landscape. The transect-plot data compared favorably to corresponding cell data generated from publicly available geospatial data for total foliar cover but less favorably for litter cover and poorly for rock cover. The RHEM effective hydraulic conductivity (K</span><sub>e</sub><span>) parameter was comparable between transect-plot and geospatial-cell methods, but the splash and sheet erosion factor (K</span><sub>ss</sub><span>) had poor agreement between the two methods. Simulated runoff and erosion reflected differences in transect-plot and geospatial-cell-based RHEM parameterizations, with low error and very good agreement for runoff but high error and poor agreement for soil loss. These results demonstrate that K</span><sub>e</sub><span>&nbsp;parameters developed using geospatial data calibrated to plot data can be extrapolated to large spatial areas and provide reasonable simulation of runoff using RHEM. However, these same geospatial methods do not provide reasonable estimation of K</span><sub>ss</sub><span>&nbsp;or simulation of soil loss. Poor representation of litter and rock cover variables, which are highly spatially heterogeneous at the plot scale, was inadequate to accurately represent K</span><sub>ss</sub><span>&nbsp;or soil loss using RHEM. High resolution ground cover data, such as from unmanned aerial systems, may improve parameterization of K</span><sub>ss</sub><span>, and, ultimately, arid rangeland soil erosion simulation.</span></p>","language":"English","publisher":"American Society of Agricultural and Biological Engineers","doi":"10.13031/aea.13275","usgsCitation":"Ball, G., and Douglas-Mankin, K., 2019, Geospatial scaling of runoff and erosion modeling in the Chihuahuan Desert: Applied Engineering in Agriculture, v. 5, no. 35, p. 733-743, https://doi.org/10.13031/aea.13275.","productDescription":"11 p.","startPage":"733","endPage":"743","ipdsId":"IP-104120","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":369346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Chihuahuan Desert","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -105.732421875,\n              34.88593094075317\n            ],\n            [\n              -105.13916015625,\n              33.60546961227188\n            ],\n            [\n              -105.2490234375,\n              32.861132322810946\n            ],\n            [\n              -105.75439453125,\n              32.491230287947594\n            ],\n            [\n              -106.9189453125,\n              34.34343606848294\n            ],\n            [\n              -107.1826171875,\n              33.970697997361626\n            ],\n            [\n              -107.698974609375,\n              32.80574473290688\n            ],\n            [\n              -109.09423828125,\n              33.19273094190692\n            ],\n            [\n              -109.10522460937499,\n              31.325486676506983\n            ],\n            [\n              -108.226318359375,\n              31.325486676506983\n            ],\n            [\n              -108.204345703125,\n              31.774877618507386\n            ],\n            [\n              -106.578369140625,\n              31.765537409484374\n            ],\n            [\n              -106.644287109375,\n              31.970803930433096\n            ],\n            [\n              -103.11767578124999,\n              32.01739159980399\n            ],\n            [\n              -103.084716796875,\n              34.994003757575776\n            ],\n            [\n              -105.732421875,\n              34.88593094075317\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"5","issue":"35","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ball, Grady 0000-0003-3030-055X","orcid":"https://orcid.org/0000-0003-3030-055X","contributorId":220746,"corporation":false,"usgs":true,"family":"Ball","given":"Grady","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":775597,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":200849,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle R.","affiliations":[],"preferred":false,"id":775598,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70215182,"text":"70215182 - 2019 - Using morphological measurements to predict subspecies of Midcontinent sandhill cranes","interactions":[],"lastModifiedDate":"2020-10-09T13:08:18.959986","indexId":"70215182","displayToPublicDate":"2019-11-19T08:06:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Using morphological measurements to predict subspecies of Midcontinent sandhill cranes","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>The Midcontinent population of sandhill cranes (<i>Antigone canadensis</i>) has historically been classified into 3 putative subspecies, but genetic analyses have identified only 2 genetically distinct subspecies. Previous studies have successfully used morphometrics in combination with an individual's sex to differentiate subspecies of sandhill cranes that had been inferred based on breeding area, but no study has used a sample of genetically determined subspecies to discriminate and develop predictive models. Using measurements from 843 adult sandhill cranes captured throughout their range and annual cycle (in 4 States and 1 Canadian province during 1998–2007), we used linear discriminant analysis to classify genetically identified<span>&nbsp;</span><i>A.&nbsp;c. canadensis</i><span>&nbsp;</span>(lesser) and<span>&nbsp;</span><i>A.&nbsp;c. tabida</i><span>&nbsp;</span>(greater) sandhill crane subspecies, and developed a field‐ready tool to predict subspecies using common morphometric measurements without determination of an individual's sex. Our top‐ranked model was 89.5% accurate overall, and used flattened wing chord, total culmen, and tarsometatarsus lengths to correctly identify 93.1% of<span>&nbsp;</span><i>A.&nbsp;c. canadensis</i><span>&nbsp;</span>and 82.8% of<span>&nbsp;</span><i>A.&nbsp;c. tabida</i><span>&nbsp;</span>subspecies. Additionally, we identified measurement thresholds based on posterior probabilities of correct classification to aid in subspecies determination when the linear discriminant procedure provided equivocal results. We also investigated whether sex determination could increase accuracy of our top‐ranked model, and found that accuracy increased &lt;1% when including this information. We suggest collection of the morphometric measurements used in our top‐ranked model to determine subspecies of adult Midcontinent sandhill cranes. Our method does not require determining sex of the individual to correctly classify subspecies, allows for accurate and rapid subspecies determination, and can largely avoid additional costs and time associated with genetic analyses to determine subspecies. © 2019 The Wildlife Society.</p></div></div>","language":"English","publisher":"Wildlife Society","doi":"10.1002/wsb.1020","usgsCitation":"VonBank, J., Brandt, D.A., Pearse, A.T., Wester, D.B., and Ballard, B.M., 2019, Using morphological measurements to predict subspecies of Midcontinent sandhill cranes: Wildlife Society Bulletin, v. 4, no. 43, p. 737-744, https://doi.org/10.1002/wsb.1020.","productDescription":"8 p.","startPage":"737","endPage":"744","ipdsId":"IP-102356","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":499861,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/d19b606702144f31add6ec00ffc7b42d","text":"External Repository"},{"id":379271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"43","noUsgsAuthors":false,"publicationDate":"2019-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"VonBank, Jay A","contributorId":242902,"corporation":false,"usgs":false,"family":"VonBank","given":"Jay A","affiliations":[{"id":13724,"text":"Texas A&M University-Kingsville","active":true,"usgs":false}],"preferred":false,"id":801074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brandt, David A. 0000-0001-9786-307X dbrandt@usgs.gov","orcid":"https://orcid.org/0000-0001-9786-307X","contributorId":149929,"corporation":false,"usgs":true,"family":"Brandt","given":"David","email":"dbrandt@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":801075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":801076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wester, David B.","contributorId":200945,"corporation":false,"usgs":false,"family":"Wester","given":"David","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":801077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ballard, Bart M","contributorId":242903,"corporation":false,"usgs":false,"family":"Ballard","given":"Bart","email":"","middleInitial":"M","affiliations":[{"id":13724,"text":"Texas A&M University-Kingsville","active":true,"usgs":false}],"preferred":false,"id":801078,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70206691,"text":"70206691 - 2019 - Santa Barbara area coastal ecosystem vulnerability assessment","interactions":[],"lastModifiedDate":"2019-11-19T08:06:37","indexId":"70206691","displayToPublicDate":"2019-11-19T08:05:16","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Santa Barbara area coastal ecosystem vulnerability assessment","docAbstract":"The Santa Barbara Area Coastal Ecosystem Vulnerability Assessment (SBA CEVA)\nis a multidisciplinary research project that investigates future changes to southern\nSanta Barbara County climate, beaches, watersheds, wetland habitats and beach\necosystems. The target audience is local land use planners and decision makers.\nThe main objective is to provide information that assists the Cities of Santa Barbara,\nCarpinteria, and Goleta, the County of Santa Barbara, and UC Santa Barbara in\nclimate adaptation planning with a clear focus on coastal ecosystems.\nLed by California Sea Grant, SBA CEVA was developed from the work of three\nof the state’s leading ecological and climatological research programs: UCSB’s\nSanta Barbara Coastal Long-Term Ecological Research (LTER) Program, the UCSD\nScripps Institution of Oceanography (SIO) and their activities within the California\nand Nevada Applications Program Regional Integrated Science and Assessment\n(CNAP RISA), the California 4th Climate Assessment and the Southwest Climate\nScience Center Program, and USGS Coastal Storm Modeling System (CoSMoS)","language":"English","publisher":"California Sea Grant","collaboration":"CA Sea Grant, NOAA, County of Santa Barbara, Cities of Goleta, Carpinteria and Santa Barbara","usgsCitation":"Myers, M., Cayan, D., Iacobellis, S., Melack, J., Beighley, R., Barnard, P., Dugan, J., and Page, H., 2019, Santa Barbara area coastal ecosystem vulnerability assessment (CASG-17-009), 207 p.","productDescription":"207 p.","ipdsId":"IP-111905","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":369323,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":369279,"type":{"id":15,"text":"Index Page"},"url":"https://caseagrant.ucsd.edu/sites/default/files/SBA-CEVA-final-0917_0.pdf"}],"country":"United States","state":"California","city":"Santa Barbara","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              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D.R.","contributorId":25961,"corporation":false,"usgs":false,"family":"Cayan","given":"D.R.","email":"","affiliations":[{"id":16196,"text":"Scripps Institution of Oceanography, La Jolla, CA","active":true,"usgs":false}],"preferred":false,"id":775470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iacobellis, S.F.","contributorId":220702,"corporation":false,"usgs":false,"family":"Iacobellis","given":"S.F.","email":"","affiliations":[{"id":37799,"text":"SCRIPPS","active":true,"usgs":false}],"preferred":false,"id":775471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Melack, J.M.","contributorId":220703,"corporation":false,"usgs":false,"family":"Melack","given":"J.M.","email":"","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":775472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beighley, 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,{"id":70206110,"text":"ofr20191120 - 2019 - Differentiating sediment sources using sediment fingerprinting techniques, in the Sprague River Basin, South-Central Oregon","interactions":[],"lastModifiedDate":"2019-11-19T06:33:51","indexId":"ofr20191120","displayToPublicDate":"2019-11-18T13:59:03","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":"2019-1120","displayTitle":"Differentiating Sediment Sources Using Sediment Fingerprinting Techniques, in the Sprague River Basin, South-Central Oregon","title":"Differentiating sediment sources using sediment fingerprinting techniques, in the Sprague River Basin, South-Central Oregon","docAbstract":"<p class=\"p1\">Identifying sources of sediment to streams in the Sprague River Basin, in south-central Oregon, is important for restoration efforts that are focused on reducing sediment erosion and transport. Reducing sediment loads in these streams also contributes to compliance with the total maximum daily load reduction requirements for total phosphorus in this basin. In the Sprague River Basin, phosphorus occurs in surface waters in both dissolved phase and particulate phase, and particulate phosphorus is readily transported in streams on fine-grained suspended sediments, which eventually deposit in Upper Klamath Lake. The lake has seasonal blooms of cyanobacteria that require phosphorus for growth and degrade water-quality conditions, violating State water-quality standards and creating conditions that are stressful to two endangered suckers that reside in the lake. Identifying sources of sediment to the Sprague River could help inform restoration actions by determining the principal locations in the basin contributing fine sediment to the river. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, conducted a proof-of-concept study to determine if sediment fingerprinting can differentiate sources of bank erosion by source material, basin, river reach, and soil horizon. The sediment fingerprinting approach uses properties of streambank and streambed sediment to differentiate between multiple sediment sources by determining a composite signature, or fingerprint. The composite fingerprint is established by combining fingerprint properties from laboratory results of elemental analysis, stable isotopes, and total carbon and nitrogen. The methods for differentiating sediment samples for this study include grouping bank and bed samples by basin, river reach, and soil horizon, and using non-parametric statistics to determine which fingerprint properties could be used to differentiate the sample groups. Results indicate that fingerprint properties differentiated source material, river reach, and basin, and were more successful at differentiating samples grouped by geographic location (basin and reach) compared to source material. Source material (banks, bed, levees) were differentiated with three fingerprint properties—Antimony (Sb), copper (Cu), and manganese (Mn). The basin category (South Fork and main-stem Sprague River) differentiated the South Fork and main stem with stable nitrogen isotopes (δ<span class=\"s1\">15</span>N), aluminum (Al), silicon (Si), and vanadium (V). Specific river reaches within the study area were differentiated with 11 different fingerprint properties. These results can be used&nbsp;for apportionment studies using suspended sediment samples and mixing models to determine sediment source contributions within the basin.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191120","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Schenk, L.N., Harden, T.M., and Kelson, J.K., 2019, Differentiating sediment sources using sediment fingerprinting techniques, in the Sprague River Basin, south-central Oregon: U.S. Geological Survey Open-File Report 2019-1120, 25 p., https://doi.org/10.3133/ofr20191120.","productDescription":"Report: vi, 25 p.; 2 Tables; Appendix","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106755","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":369299,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1120/ofr20191120.pdf","text":"Report","size":"7.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1120"},{"id":369302,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1120/ofr20191120_appendix1.xlsx","text":"Appendix 1 –","size":"41 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1120 Appendix 1","linkHelpText":" Analytical Results and Site Characteristics"},{"id":369298,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1120/coverthb.jpg"},{"id":369300,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1120/ofr20191120_table03.xlsx","text":"Table 3","size":"21 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1120 Table 3"},{"id":369301,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1120/ofr20191120_table05.xlsx","text":"Table 5","size":"28 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1120 Table 5"}],"country":"United States","state":"Oregon","otherGeospatial":"Sprague River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.947998046875,\n              41.95949009892467\n            ],\n            [\n              -119.21264648437499,\n              41.95949009892467\n            ],\n            [\n              -119.21264648437499,\n              44.04811573082351\n            ],\n            [\n              -122.947998046875,\n              44.04811573082351\n            ],\n            [\n              -122.947998046875,\n              41.95949009892467\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_or@usgs.gov\" data-mce-href=\"mailto:dc_or@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/or-water\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/or-water\">Oregon Water Science Center</a><br>U.S. Geological Survey<br>2130 SW 5th Avenue<br>Portland, Oregon 97201</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Study Area</li><li>Methods</li><li>Results</li><li>Discussion</li><li>Future Sprague River Sediment Fingerprinting Studies</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. Analytical Results and Site Characteristics</li></ul>","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2019-11-18","noUsgsAuthors":false,"publicationDate":"2019-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Schenk, Liam N. 0000-0002-2491-0813 lschenk@usgs.gov","orcid":"https://orcid.org/0000-0002-2491-0813","contributorId":4273,"corporation":false,"usgs":true,"family":"Schenk","given":"Liam","email":"lschenk@usgs.gov","middleInitial":"N.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harden, Tessa M. 0000-0001-9854-1347 tharden@usgs.gov","orcid":"https://orcid.org/0000-0001-9854-1347","contributorId":192153,"corporation":false,"usgs":true,"family":"Harden","given":"Tessa","email":"tharden@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelson, Julia K. 0000-0002-0588-5018","orcid":"https://orcid.org/0000-0002-0588-5018","contributorId":220716,"corporation":false,"usgs":false,"family":"Kelson","given":"Julia K.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":773616,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70205547,"text":"cir1460 - 2019 - Woods Hole Coastal and Marine Science Center—2018 annual report","interactions":[],"lastModifiedDate":"2019-11-19T06:43:46","indexId":"cir1460","displayToPublicDate":"2019-11-18T13:55:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1460","displayTitle":"Woods Hole Coastal and Marine Science Center—2018 Annual Report","title":"Woods Hole Coastal and Marine Science Center—2018 annual report","docAbstract":"<p>The 2018 annual report of the U.S. Geological Survey Woods Hole Coastal and Marine Science Center summarizes the work of the center, as well as the work of each of its science groups, highlights accomplishments of 2018, and includes a list of publications published in 2018. This product allows readers to gain a general understanding of the focus areas of the center’s scientific research and learn more about specific projects and progress made throughout 2018, all while enjoying applicable photos taken in the field and of various models, maps, and web pages.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1460","usgsCitation":"Ernst, S., 2019, Woods Hole Coastal and Marine Science Center—2018 annual report: U.S. Geological Survey Circular 1460, 36 p., https://doi.org/10.3133/cir1460.","productDescription":"iv, 36 p.","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-108282","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":369283,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1460/cir1460.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1460"},{"id":368016,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1460/coverthb.jpg"}],"country":"United States","state":"Massachusetts","city":"Falmouth","otherGeospatial":"Woods Hole Coastal and Marine Science Center","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -70.68672180175781,\n              41.513719082873486\n            ],\n            [\n              -70.61119079589844,\n              41.513719082873486\n            ],\n            [\n              -70.61119079589844,\n              41.55278330492603\n            ],\n            [\n              -70.68672180175781,\n              41.55278330492603\n            ],\n            [\n              -70.68672180175781,\n              41.513719082873486\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:WHSC_science_director@usgs.gov\" data-mce-href=\"mailto:WHSC_science_director@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/whcmsc\" data-mce-href=\"https://www.usgs.gov/centers/whcmsc\">Woods Hole Coastal and Marine Science Center</a><br>U.S. Geological Survey<br>384 Woods Hole Road<br>Quissett Campus<br>Woods Hole, MA 02543-1598<br>(508) 548–8700 or (508) 457–2200</p>","tableOfContents":"<ul><li>Coastal and Marine Science Based in Woods Hole, Massachusetts</li><li>Coastal and Shelf Geology</li><li>Sediment Transport</li><li>Energy and Geohazards</li><li>Environmental Geoscience</li><li>Sea-Floor Mapping</li><li>Information Science</li><li>2018 Publications</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2019-10-07","noUsgsAuthors":false,"publicationDate":"2019-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Ernst, Sara 0000-0001-7825-3209","orcid":"https://orcid.org/0000-0001-7825-3209","contributorId":219205,"corporation":false,"usgs":true,"family":"Ernst","given":"Sara","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":771592,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
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