{"pageNumber":"402","pageRowStart":"10025","pageSize":"25","recordCount":46619,"records":[{"id":70190038,"text":"70190038 - 2016 - A rare moderate‐sized (Mw 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection","interactions":[],"lastModifiedDate":"2017-08-06T16:19:26","indexId":"70190038","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A rare moderate‐sized (<i>M</i><sub>w</sub> 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection","title":"A rare moderate‐sized (Mw 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection","docAbstract":"<p><span>The largest recorded earthquake in Kansas occurred northeast of Milan on 12 November 2014 (</span><i>M</i><sub>w</sub><span>&nbsp;4.9) in a region previously devoid of significant seismic activity. Applying multistation processing to data from local stations, we are able to detail the rupture process and rupture geometry of the mainshock, identify the causative fault plane, and delineate the expansion and extent of the subsequent seismic activity. The earthquake followed rapid increases of fluid injection by multiple wastewater injection wells in the vicinity of the fault. The source parameters and behavior of the Milan earthquake and foreshock–aftershock sequence are similar to characteristics of other earthquakes induced by wastewater injection into permeable formations overlying crystalline basement. This earthquake also provides an opportunity to test the empirical relation that uses felt area to estimate moment magnitude for historical earthquakes for Kansas.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220160100","usgsCitation":"Choy, G., Rubinstein, J.L., Yeck, W.L., McNamara, D.E., Mueller, C., and Boyd, O.S., 2016, A rare moderate‐sized (Mw 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection: Seismological Research Letters, v. 87, no. 6, p. 1433-1441, https://doi.org/10.1785/0220160100.","productDescription":"9 p.","startPage":"1433","endPage":"1441","ipdsId":"IP-076956","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":344606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"87","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-14","publicationStatus":"PW","scienceBaseUri":"59882a94e4b05ba66e9ffdd8","contributors":{"authors":[{"text":"Choy, George choy@usgs.gov","contributorId":2161,"corporation":false,"usgs":true,"family":"Choy","given":"George","email":"choy@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":707276,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rubinstein, Justin L. 0000-0003-1274-6785 jrubinstein@usgs.gov","orcid":"https://orcid.org/0000-0003-1274-6785","contributorId":2404,"corporation":false,"usgs":true,"family":"Rubinstein","given":"Justin","email":"jrubinstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":707278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":707277,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":707279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mueller, Charles 0000-0002-1868-9710 cmueller@usgs.gov","orcid":"https://orcid.org/0000-0002-1868-9710","contributorId":140380,"corporation":false,"usgs":true,"family":"Mueller","given":"Charles","email":"cmueller@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":707280,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":707281,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70193968,"text":"70193968 - 2016 - River rating complexity","interactions":[],"lastModifiedDate":"2025-01-29T15:54:06.16656","indexId":"70193968","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"River rating complexity","docAbstract":"<p>Accuracy of streamflow data depends on the veracity of the rating model used to derive a continuous time series of discharge from the surrogate variables that can readily be collected autonomously at a streamgage. Ratings are typically represented as a simple monotonic increasing function (simple rating), meaning the discharge is a function of stage alone, however this is never truly the case unless the flow is completely uniform at all stages and in transitions from one stage to the next. For example, at some streamflow-monitoring sites the discharge on the rising limb of the hydrograph is discernably larger than the discharge at the same stage on the falling limb of the hydrograph. This is the so-called “loop rating curve” (loop rating). In many cases, these loops are quite small and variation between rising- and falling-limb discharge measurements made at the same stage are well within the accuracy of the measurements. However, certain hydraulic conditions can produce a loop that is large enough to preclude use of a monotonic rating. A detailed data campaign for the Mississippi River at St. Louis, Missouri during a multi-peaked flood over a 56-day period in 2015 demonstrates the rating complexity at this location. The shifting-control method used to deal with complexity at this site matched all measurements within 8%.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"River flow 2016","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Proceedings of the International Conference on Fluvial Hydraulics (River flow 2016)","conferenceDate":"July 11-14, 2016","conferenceLocation":"St. Louis, MO","language":"English","publisher":"CRC Press","usgsCitation":"Holmes, R.R., 2016, River rating complexity, <i>in</i> River flow 2016, St. Louis, MO, July 11-14, 2016, p. 679-686.","productDescription":"8 p.","startPage":"679","endPage":"686","ipdsId":"IP-071265","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":348967,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":348966,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.crcpress.com/River-Flow-2016-Iowa-City-USA-July-11-14-2016/Constantinescu-Garcia-Hanes/p/book/9781138029132","linkFileType":{"id":5,"text":"html"}},{"id":350997,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ja/70193968/70193968.pdf","text":"USGS open-access version of article","size":"507 kB","linkFileType":{"id":1,"text":"pdf"},"description":"USGS open-access version of article"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc9be4b06e28e9c24040","contributors":{"authors":[{"text":"Holmes, Robert R. Jr. 0000-0002-5060-3999 bholmes@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-3999","contributorId":156293,"corporation":false,"usgs":true,"family":"Holmes","given":"Robert","suffix":"Jr.","email":"bholmes@usgs.gov","middleInitial":"R.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":false,"id":721769,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70178373,"text":"70178373 - 2016 - Using structural equation modeling to link human activities to wetland ecological integrity","interactions":[],"lastModifiedDate":"2016-12-01T13:28:42","indexId":"70178373","displayToPublicDate":"2016-11-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Using structural equation modeling to link human activities to wetland ecological integrity","docAbstract":"<p><span>The integrity of wetlands is of global concern. A common approach to evaluating ecological integrity involves bioassessment procedures that quantify the degree to which communities deviate from historical norms. While helpful, bioassessment provides little information about how altered conditions connect to community response. More detailed information is needed for conservation and restoration. We have illustrated an approach to addressing this challenge using structural equation modeling (SEM) and long-term monitoring data from Rocky Mountain National Park (RMNP). Wetlands in RMNP are threatened by a complex history of anthropogenic disturbance including direct alteration of hydrologic regimes; elimination of elk, wolves, and grizzly bears; reintroduction of elk (absent their primary predators); and the extirpation of beaver. More recently, nonnative moose were introduced to the region and have expanded into the park. Bioassessment suggests that up to half of the park's wetlands are not in reference condition. We developed and evaluated a general hypothesis about how human alterations influence wetland integrity and then develop a specific model using RMNP wetlands. Bioassessment revealed three bioindicators that appear to be highly sensitive to human disturbance (HD): (1) conservatism, (2) degree of invasion, and (3) cover of native forbs. SEM analyses suggest several ways human activities have impacted wetland integrity and the landscape of RMNP. First, degradation is highest where the combined effects of all types of direct HD have been the greatest (i.e., there is a general, overall effect). Second, specific HDs appear to create a “mixed-bag” of complex indirect effects, including reduced invasion and increased conservatism, but also reduced native forb cover. Some of these effects are associated with alterations to hydrologic regimes, while others are associated with altered shrub production. Third, landscape features created by historical beaver activity continue to influence wetland integrity years after beavers have abandoned sites via persistent landforms and reduced biomass of tall shrubs. Our model provides a system-level perspective on wetland integrity and provides a context for future evaluations and investigations. It also suggests scientifically supported natural resource management strategies that can assist in the National Park Service mission of maintaining or, when indicated, restoring ecological integrity “unimpaired for future generations.”</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1548","usgsCitation":"Schweiger, E.W., Grace, J.B., Cooper, D., Bobowski, B., and Britten, M., 2016, Using structural equation modeling to link human activities to wetland ecological integrity: Ecosphere, v. 7, no. 11, p. 1-30, https://doi.org/10.1002/ecs2.1548.","productDescription":"e01548; 30 p.","startPage":"1","endPage":"30","ipdsId":"IP-074267","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":470418,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1548","text":"Publisher Index Page"},{"id":331006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountain National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -105.93292236328124,\n              40.15998434802335\n            ],\n            [\n              -105.93292236328124,\n              40.69625781921317\n            ],\n            [\n              -105.4302978515625,\n              40.69625781921317\n            ],\n            [\n              -105.4302978515625,\n              40.15998434802335\n            ],\n            [\n              -105.93292236328124,\n              40.15998434802335\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-10","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bf8","chorus":{"doi":"10.1002/ecs2.1548","url":"http://dx.doi.org/10.1002/ecs2.1548","publisher":"Wiley-Blackwell","authors":"Schweiger E. William, Grace James B., Cooper David, Bobowski Ben, Britten Mike","journalName":"Ecosphere","publicationDate":"11/2016"},"contributors":{"authors":[{"text":"Schweiger, E. William","contributorId":53635,"corporation":false,"usgs":true,"family":"Schweiger","given":"E.","email":"","middleInitial":"William","affiliations":[],"preferred":false,"id":653814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooper, David","contributorId":176856,"corporation":false,"usgs":false,"family":"Cooper","given":"David","affiliations":[],"preferred":false,"id":653816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bobowski, Ben","contributorId":176857,"corporation":false,"usgs":false,"family":"Bobowski","given":"Ben","email":"","affiliations":[],"preferred":false,"id":653817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Britten, Mike","contributorId":176858,"corporation":false,"usgs":false,"family":"Britten","given":"Mike","email":"","affiliations":[],"preferred":false,"id":653818,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70178356,"text":"70178356 - 2016 - An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data","interactions":[],"lastModifiedDate":"2017-01-17T19:03:37","indexId":"70178356","displayToPublicDate":"2016-11-15T00:00:00","publicationYear":"2016","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":"An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data","docAbstract":"<p><span>Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MAD</span><sub>training</sub><span> = 2.5 and MAD</span><sub>testing</sub><span> = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs8110943","usgsCitation":"Gu, Y., Wylie, B.K., Boyte, S.P., Picotte, J.J., Howard, D., Smith, K., and Nelson, K., 2016, An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data: Remote Sensing, v. 8, p. 1-13, https://doi.org/10.3390/rs8110943.","productDescription":"Article 943; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-079805","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470423,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110943","text":"Publisher Index Page"},{"id":331008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bfa","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","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":653754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","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":653755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":653756,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":653757,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howard, Danny 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":176610,"corporation":false,"usgs":true,"family":"Howard","given":"Danny","email":"danny.howard.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":653758,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Kelcy 0000-0001-6811-1485 kelcy.smith.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":176844,"corporation":false,"usgs":true,"family":"Smith","given":"Kelcy","email":"kelcy.smith.ctr@usgs.gov","affiliations":[],"preferred":false,"id":653760,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nelson, Kurtis 0000-0003-4911-4511 knelson@usgs.gov","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":3602,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis","email":"knelson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":653759,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70178375,"text":"70178375 - 2016 - Critical considerations for the application of environmental DNA methods to detect aquatic species","interactions":[],"lastModifiedDate":"2017-11-27T10:25:57","indexId":"70178375","displayToPublicDate":"2016-11-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Critical considerations for the application of environmental DNA methods to detect aquatic species","docAbstract":"<ol id=\"mee312595-list-0001\" class=\"o-list--numbered o-list--paragraph\"><li>Species detection using environmental DNA (eDNA) has tremendous potential for contributing to the understanding of the ecology and conservation of aquatic species. Detecting species using eDNA methods, rather than directly sampling the organisms, can reduce impacts on sensitive species and increase the power of field surveys for rare and elusive species. The sensitivity of eDNA methods, however, requires a heightened awareness and attention to quality assurance and quality control protocols. Additionally, the interpretation of eDNA data demands careful consideration of multiple factors. As eDNA methods have grown in application, diverse approaches have been implemented to address these issues. With interest in eDNA continuing to expand, supportive guidelines for undertaking eDNA studies are greatly needed.</li><li>Environmental DNA researchers from around the world have collaborated to produce this set of guidelines and considerations for implementing eDNA methods to detect aquatic macroorganisms.</li><li>Critical considerations for study design include preventing contamination in the field and the laboratory, choosing appropriate sample analysis methods, validating assays, testing for sample inhibition and following minimum reporting guidelines. Critical considerations for inference include temporal and spatial processes, limits of correlation of eDNA with abundance, uncertainty of positive and negative results, and potential sources of allochthonous DNA.</li><li>We present a synthesis of knowledge at this stage for application of this new and powerful detection method.</li></ol>","language":"English","publisher":"Wiley","doi":"10.1111/2041-210X.12595","usgsCitation":"Goldberg, C.S., Turner, C.R., Deiner, K., Klymus, K.E., Thomsen, P.F., Murphy, M.A., Spear, S.F., McKee, A., Oyler-McCance, S.J., Cornman, R.S., Laramie, M.B., Mahon, A., Lance, R.F., Pilliod, D., Strickler, K.M., Waits, L.P., Fremier, A., Takahara, T., Herder, J.E., and Taberlet, P., 2016, Critical considerations for the application of environmental DNA methods to detect aquatic species: Methods in Ecology and Evolution, v. 7, no. 11, p. 1299-1307, https://doi.org/10.1111/2041-210X.12595.","productDescription":"9 p.","startPage":"1299","endPage":"1307","ipdsId":"IP-070947","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470419,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.12595","text":"Publisher Index Page"},{"id":331021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-22","publicationStatus":"PW","scienceBaseUri":"582c2ce2e4b0c253be072bf4","contributors":{"authors":[{"text":"Goldberg, Caren S.","contributorId":76879,"corporation":false,"usgs":false,"family":"Goldberg","given":"Caren","email":"","middleInitial":"S.","affiliations":[{"id":5132,"text":"Washington State University, 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,{"id":70176823,"text":"ds1023 - 2016 - Quality of surface water in Missouri, water year 2015","interactions":[],"lastModifiedDate":"2016-11-14T12:56:11","indexId":"ds1023","displayToPublicDate":"2016-11-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1023","title":"Quality of surface water in Missouri, water year 2015","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a series of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2015 (October 1, 2014, through September 30, 2015), data were collected at 74 stations—72 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and select pesticide compound summaries are presented for 71 of these stations. The stations primarily have been classified into groups corresponding to the physiography of the State, primary land use, or unique station types. In addition, a summary of hydrologic conditions in the State including peak streamflows, monthly mean streamflows, and 7-day low flows is presented.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1023","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Barr, M.N., and Heimann, D.C., 2016, Quality of surface water in Missouri, water year 2015: U.S. Geological Survey Data Series 1023, 22 p., https://dx.doi.org/10.3133/ds1023.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-077875","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":330865,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1023/ds1023.pdf","text":"Report","size":"5.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 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 \"}}]}","contact":"<p>Director, Missouri Water Science Center <br>U.S. Geological Survey <br>1400 Independence Road <br>Rolla, MO 65401</p><p><a href=\"http://mo.water.usgs.gov/\" data-mce-href=\"http://mo.water.usgs.gov/\">http://mo.water.usgs.gov/</a></p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>The Ambient Water-Quality Monitoring Network<br></li><li>Laboratory Reporting Conventions<br></li><li>Data Analysis Methods<br></li><li>Station Classification for Data Analysis<br></li><li>Hydrologic Conditions<br></li><li>Distribution, Concentration, and Detection Frequency of Select Constituents<br></li><li>Summary<br></li><li>References Cited<br></li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-11-14","noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"582adb45e4b0c253bdfff0a9","contributors":{"authors":[{"text":"Barr, Miya N. 0000-0002-9961-9190 mnbarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9961-9190","contributorId":3686,"corporation":false,"usgs":true,"family":"Barr","given":"Miya","email":"mnbarr@usgs.gov","middleInitial":"N.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heimann, David C. 0000-0003-0450-2545 dheimann@usgs.gov","orcid":"https://orcid.org/0000-0003-0450-2545","contributorId":3822,"corporation":false,"usgs":true,"family":"Heimann","given":"David","email":"dheimann@usgs.gov","middleInitial":"C.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650466,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70178338,"text":"70178338 - 2016 - Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability","interactions":[],"lastModifiedDate":"2016-11-14T12:27:13","indexId":"70178338","displayToPublicDate":"2016-11-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability","docAbstract":"<p><span>Understanding and managing dynamic coastal landscapes for beach-dependent species requires biological and geological data across the range of relevant environments and habitats. It is difficult to acquire such information; data often have limited focus due to resource constraints, are collected by non-specialists, or lack observational uniformity. We developed an open-source smartphone application called iPlover that addresses these difficulties in collecting biogeomorphic information at piping plover (</span><i>Charadrius melodus</i><span>) nest sites on coastal beaches. This paper describes iPlover development and evaluates data quality and utility following two years of collection (</span><i>n</i><span> = 1799 data points over 1500 km of coast between Maine and North Carolina, USA). We found strong agreement between field user and expert assessments and high model skill when data were used for habitat suitability prediction. Methods used here to develop and deploy a distributed data collection system have broad applicability to interdisciplinary environmental monitoring and modeling.</span></p>","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0164979","usgsCitation":"Thieler, E.R., Zeigler, S.L., Winslow, L., Hines, M., Read, J.S., and Walker, J.I., 2016, Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability: PLoS ONE, v. 11, no. 11, e0164979; 22 p., https://doi.org/10.1371/journal.pone.0164979.","productDescription":"e0164979; 22 p.","ipdsId":"IP-077649","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470425,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0164979","text":"Publisher Index Page"},{"id":330973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"11","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"582adb45e4b0c253bdfff0a5","contributors":{"authors":[{"text":"Thieler, E. Robert 0000-0003-4311-9717 rthieler@usgs.gov","orcid":"https://orcid.org/0000-0003-4311-9717","contributorId":2488,"corporation":false,"usgs":true,"family":"Thieler","given":"E.","email":"rthieler@usgs.gov","middleInitial":"Robert","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zeigler, Sara L. 0000-0002-5472-769X szeigler@usgs.gov","orcid":"https://orcid.org/0000-0002-5472-769X","contributorId":169601,"corporation":false,"usgs":true,"family":"Zeigler","given":"Sara","email":"szeigler@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winslow, Luke 0000-0002-8602-5510 lwinslow@usgs.gov","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":168947,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke","email":"lwinslow@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hines, Megan 0000-0002-9845-4849 mhines@usgs.gov","orcid":"https://orcid.org/0000-0002-9845-4849","contributorId":4783,"corporation":false,"usgs":true,"family":"Hines","given":"Megan","email":"mhines@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":653638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653639,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walker, Jordan I. 0000-0003-2226-3373 jiwalker@usgs.gov","orcid":"https://orcid.org/0000-0003-2226-3373","contributorId":4608,"corporation":false,"usgs":true,"family":"Walker","given":"Jordan","email":"jiwalker@usgs.gov","middleInitial":"I.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":653640,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70178093,"text":"ofr20161187 - 2016 - Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts","interactions":[],"lastModifiedDate":"2018-03-08T16:08:01","indexId":"ofr20161187","displayToPublicDate":"2016-11-09T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1187","title":"Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts","docAbstract":"<h1>Introduction</h1><p>Understanding if and how community exposure to coastal hazards may change over time is crucial information for coastal managers tasked with developing climate adaptation plans. This report summarizes estimates of population and asset exposure to coastal-inundation hazards associated with sea-level-rise and storm scenarios in six coastal communities of the Great Marsh region of Essex County, Massachusetts. This U.S. Geological Survey (USGS) analysis was conducted in collaboration with National Wildlife Federation (NWF) representatives, who are working with local stakeholders to develop local climate adaptation plans for the Towns of Salisbury, Newbury, Rowley, Ipswich, and Essex and the City of Newburyport (hereafter referred to as communities). Community exposure was characterized by integrating various community indicators (land cover and land use, population, economic assets, critical facilities, and infrastructure) with coastal-hazard zones that estimate inundation extents and water depth for three time periods.</p><p>Estimates of community exposure are based on the presence of people, businesses, and assets in hazard zones that are calculated from geospatial datasets using geographic-information-system (GIS) tools. Results are based on current distributions of people and assets in hazard zones and do not take into account projections of human population, asset, or land-use changes over time. Results are not loss estimates based on engineering analysis or field surveys for any particular facility and do not take into account aspects of individual and household preparedness before an extreme event, adaptive capacity of a community during an event, or long-term resilience of individuals and communities after an event. Potential losses would match reported inventories only if all residents, business owners, public managers, and elected officials were unaware of what to do if warned of an imminent threat, failed to take protective measures during an extreme event, or failed to implement any long-term strategies to mitigate potential impacts. This analysis is intended to serve as a foundation for additional risk-related studies, plans, and mitigation efforts that are tailored to local needs. After a summary of the geospatial methods used in the analysis, results are organized by community so that local officials can easily use them in their local adaptation planning efforts.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161187","usgsCitation":"Abdollahian, Nina, Jones, J.L., and Wood, N.J., 2016, Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts: U.S. Geological Survey Open-File Report 2016–1187, 87 p., https://dx.doi.org/10.3133/ofr20161187.","productDescription":"ix, 97 p.","onlineOnly":"Y","ipdsId":"IP-076664","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":330895,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1187/ofr20161187.pdf","text":"Report","size":"37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1187"},{"id":330894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1187/coverthb.jpg"}],"country":"United States","state":"Massachusetts","county":"Essex County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -70.71006774902344,\n              42.68193062780271\n            ],\n            [\n              -70.68603515625,\n              42.661736441708726\n            ],\n            [\n              -70.67985534667969,\n              42.645576368740564\n            ],\n            [\n              -70.68946838378906,\n              42.61374895431491\n            ],\n            [\n              -70.67779541015624,\n              42.58594981115061\n            ],\n            [\n              -70.70938110351562,\n              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data-mce-href=\"http://geography.wr.usgs.gov/\">http://geography.wr.usgs.gov/</a></p>","tableOfContents":"<ul><li>Acknowledgments<br></li><li>Introduction<br></li><li>Methods<br></li><li>Salisbury<br></li><li>Newburyport<br></li><li>Newbury<br></li><li>Rowley<br></li><li>Ipswich<br></li><li>Essex<br></li><li>References Cited<br></li><li>Appendix 1. Inundation Probability Maps<br></li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2016-11-09","noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"582443f1e4b09065cdf30509","contributors":{"authors":[{"text":"Abdollahian, Nina 0000-0002-8607-2202 nabdollahian@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-2202","contributorId":92149,"corporation":false,"usgs":true,"family":"Abdollahian","given":"Nina","email":"nabdollahian@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":652723,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ratliff, Jamie L. 0000-0002-9967-3314 jratliff@usgs.gov","orcid":"https://orcid.org/0000-0002-9967-3314","contributorId":665,"corporation":false,"usgs":true,"family":"Ratliff","given":"Jamie","email":"jratliff@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":652724,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":652725,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70176947,"text":"sim3368 - 2016 - Sedimentation survey of Lago Caonillas, Utuado, Puerto Rico, September–November 2012","interactions":[],"lastModifiedDate":"2016-11-09T10:18:09","indexId":"sim3368","displayToPublicDate":"2016-11-09T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3368","title":"Sedimentation survey of Lago Caonillas, Utuado, Puerto Rico, September–November 2012","docAbstract":"<p>During September–November 2012, the U.S. Geological Survey, in cooperation with the Puerto Rico Aqueduct and Sewer Authority, conducted a sedimentation survey of Lago Caonillas to estimate current (2012) reservoir storage capacity and the recent (2000–2012) reservoir sedimentation rate by comparing the 2012 bathymetric survey data with the February 2000 data. The Lago Caonillas storage capacity, which was 42.27 million cubic meters in February 2000, decreased to 39.55 million cubic meters by September–November 2012. The intersurvey (2000–2012) storage capacity loss was about 6 percent, corresponding to a decrease of about 0.5 percent per year; this loss represents a reservoir sedimentation rate of about 226,670 cubic meters per year between 2000 and 2012. On a long-term basis, however, the sedimentation rate has remained nearly constant, decreasing from about 257,500 to 251,720 cubic meters per year during 1948–2000 and 1948–2012, respectively. Most of the sediment accumulation and associated storage capacity loss of Lago Caonillas has occurred within the eastern and Río Caonillas branches of the reservoir. In the vicinity of the Caonillas Dam, minor sediment deposition and scour have occurred. The Lago Caonillas drainage area sediment yield has decreased by about 2 percent since the previous survey, from 1,266 cubic meters per square kilometer per year in 2000 to 1,237 cubic meters per square kilometer per year in 2012. If the long-term sedimentation rate of 251,720 cubic meters per year remains constant, the useful life of Lago Caonillas may end in about 2169.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3368","collaboration":"Prepared in cooperation with the Puerto Rico Aqueduct and Sewer Authority","usgsCitation":"Soler-López, L.R., 2016, Sedimentation survey of Lago Caonillas, Utuado, Puerto Rico, September–November 2012: U.S. Geological Survey Scientific Investigations Map 3368, 1 sheet, https://dx.doi.org/10.3133/sim3368.","productDescription":"29.00 x 30.83 inches","numberOfPages":"1","onlineOnly":"Y","ipdsId":"IP-055426","costCenters":[{"id":156,"text":"Caribbean Water Science Center","active":true,"usgs":true}],"links":[{"id":438510,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74M92NT","text":"USGS data release","linkHelpText":"Spatial Data for Sedimentation Survey of Lago Caonillas, Utuado, Puerto Rico, SeptemberNovember 2012"},{"id":330594,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3368/sim3368.pdf","text":"Sheet 1","size":"1.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3368"},{"id":330593,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3368/coverthb.jpg"},{"id":330666,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F74M92NT","text":"USGS data release - Spatial Data for Sedimentation Survey of Lago Caonillas, Utuado, Puerto Rico, September–November 2012"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -66.8,\n              18.2\n            ],\n            [\n              -66.8,\n              18.5\n            ],\n            [\n              -66.5,\n              18.5\n            ],\n            [\n              -66.5,\n              18.2\n            ],\n            [\n              -66.8,\n              18.2\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director,&nbsp;Caribbean-Florida Water Science Center<br>U.S. Geological Survey<br>4446 Pet Lane, Suite 108 <br>Lutz, FL 33559<br></p><p><a href=\"http://fl.water.usgs.gov/\" data-mce-href=\"http://fl.water.usgs.gov/\">http://fl.water.usgs.gov/</a></p>","tableOfContents":"<ul><li>Introduction<br></li><li>Methods of Survey and Analysis<br></li><li>Storage Capacity, Sedimentation Rate, and Useful Life<br></li><li>Summary and Conclusions<br></li><li>Selected References<br></li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2016-11-09","noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"582443f3e4b09065cdf3050e","contributors":{"authors":[{"text":"Soler-Lopez, Luis R. lssoler@usgs.gov","contributorId":1212,"corporation":false,"usgs":true,"family":"Soler-Lopez","given":"Luis","email":"lssoler@usgs.gov","middleInitial":"R.","affiliations":[{"id":156,"text":"Caribbean Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650834,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70189628,"text":"70189628 - 2016 - St. Louis area earthquake hazards mapping project; seismic and liquefaction hazard maps","interactions":[],"lastModifiedDate":"2017-07-19T10:47:31","indexId":"70189628","displayToPublicDate":"2016-11-09T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"St. Louis area earthquake hazards mapping project; seismic and liquefaction hazard maps","docAbstract":"We present probabilistic and deterministic seismic and liquefaction hazard maps for the densely populated St. Louis metropolitan area that account for the expected effects of surficial geology on earthquake ground shaking. Hazard calculations were based on a map grid of 0.005°, or about every 500 m, and are thus higher in resolution than any earlier studies. To estimate ground motions at the surface of the model (e.g., site amplification), we used a new detailed near‐surface shear‐wave velocity model in a 1D equivalent‐linear response analysis. When compared with the 2014 U.S. Geological Survey (USGS) National Seismic Hazard Model, which uses a uniform firm‐rock‐site condition, the new probabilistic seismic‐hazard estimates document much more variability. Hazard levels for upland sites (consisting of bedrock and weathered bedrock overlain by loess‐covered till and drift deposits), show up to twice the ground‐motion values for peak ground acceleration (PGA), and similar ground‐motion values for 1.0 s spectral acceleration (SA). Probabilistic ground‐motion levels for lowland alluvial floodplain sites (generally the 20–40‐m‐thick modern Mississippi and Missouri River floodplain deposits overlying bedrock) exhibit up to twice the ground‐motion levels for PGA, and up to three times the ground‐motion levels for 1.0 s SA. Liquefaction probability curves were developed from available standard penetration test data assuming typical lowland and upland water table levels. A simplified liquefaction hazard map was created from the 5%‐in‐50‐year probabilistic ground‐shaking model. The liquefaction hazard ranges from low (<40% of area expected to liquefy) in the uplands to severe (>60% of area expected to liquefy) in the lowlands. Because many transportation routes, power and gas transmission lines, and population centers exist in or on the highly susceptible lowland alluvium, these areas in the St. Louis region are at significant potential risk from seismically induced liquefaction and associated ground deformation","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220160028","usgsCitation":"Cramer, C.H., Bauer, R.A., Chung, J., Rogers, D., Pierce, L., Voigt, V., Mitchell, B., Gaunt, D., Williams, R., Hoffman, D., Hempen, G.L., Steckel, P., Boyd, O.S., Watkins, C.M., Tucker, K., and McCallister, N., 2016, St. Louis area earthquake hazards mapping project; seismic and liquefaction hazard maps: Seismological Research Letters, v. 88, no. 1, p. 206-223, https://doi.org/10.1785/0220160028.","productDescription":"18 p.","startPage":"206","endPage":"223","ipdsId":"IP-079759","costCenters":[{"id":300,"text":"Geologic Hazards Science 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H.","contributorId":194851,"corporation":false,"usgs":false,"family":"Cramer","given":"Chris","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":705490,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bauer, Robert A.","contributorId":194852,"corporation":false,"usgs":false,"family":"Bauer","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":705491,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chung, Jae-won","contributorId":194853,"corporation":false,"usgs":false,"family":"Chung","given":"Jae-won","email":"","affiliations":[],"preferred":false,"id":705492,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogers, David","contributorId":194854,"corporation":false,"usgs":false,"family":"Rogers","given":"David","email":"","affiliations":[],"preferred":false,"id":705493,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pierce, Larry","contributorId":194855,"corporation":false,"usgs":false,"family":"Pierce","given":"Larry","email":"","affiliations":[],"preferred":false,"id":705494,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Voigt, Vicki","contributorId":194856,"corporation":false,"usgs":false,"family":"Voigt","given":"Vicki","email":"","affiliations":[],"preferred":false,"id":705495,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mitchell, Brad","contributorId":194857,"corporation":false,"usgs":false,"family":"Mitchell","given":"Brad","email":"","affiliations":[],"preferred":false,"id":705496,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gaunt, David","contributorId":194858,"corporation":false,"usgs":false,"family":"Gaunt","given":"David","email":"","affiliations":[],"preferred":false,"id":705497,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Williams, Robert 0000-0002-2973-8493 rawilliams@usgs.gov","orcid":"https://orcid.org/0000-0002-2973-8493","contributorId":140741,"corporation":false,"usgs":true,"family":"Williams","given":"Robert","email":"rawilliams@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705498,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hoffman, David","contributorId":194859,"corporation":false,"usgs":false,"family":"Hoffman","given":"David","affiliations":[],"preferred":false,"id":705499,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hempen, Gregory L.","contributorId":194860,"corporation":false,"usgs":false,"family":"Hempen","given":"Gregory","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":705500,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Steckel, Phyllis","contributorId":194861,"corporation":false,"usgs":false,"family":"Steckel","given":"Phyllis","email":"","affiliations":[],"preferred":false,"id":705501,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"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":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705502,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Watkins, Connor M.","contributorId":194862,"corporation":false,"usgs":false,"family":"Watkins","given":"Connor","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":705503,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Tucker, Kathleen","contributorId":194863,"corporation":false,"usgs":false,"family":"Tucker","given":"Kathleen","email":"","affiliations":[],"preferred":false,"id":705504,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"McCallister, Natasha","contributorId":194864,"corporation":false,"usgs":false,"family":"McCallister","given":"Natasha","email":"","affiliations":[],"preferred":false,"id":705505,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70178185,"text":"70178185 - 2016 - Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)","interactions":[],"lastModifiedDate":"2016-11-07T10:33:48","indexId":"70178185","displayToPublicDate":"2016-11-07T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2498,"text":"Journal of Visualized Experiments","active":true,"publicationSubtype":{"id":10}},"title":"Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)","docAbstract":"<p><span>Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (</span><i>Tamarix</i><span> spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.</span></p>","language":"English","publisher":"JoVE","doi":"10.3791/54578","usgsCitation":"West, A.M., Evangelista, P.H., Jarnevich, C.S., Young, N.E., Stohlgren, T.J., Talbert, C., Talbert, M., Morisette, J., and Anderson, R., 2016, Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM): Journal of Visualized Experiments, v. 116, e54578, https://doi.org/10.3791/54578.","productDescription":"e54578","ipdsId":"IP-070978","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470431,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5092193","text":"Publisher Index Page"},{"id":330808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"116","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-11","publicationStatus":"PW","scienceBaseUri":"5821a0dbe4b02f1a881de95a","contributors":{"authors":[{"text":"West, Amanda M.","contributorId":176705,"corporation":false,"usgs":false,"family":"West","given":"Amanda","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":653200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evangelista, Paul H.","contributorId":14747,"corporation":false,"usgs":true,"family":"Evangelista","given":"Paul","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":653201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":653202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Nicholas E.","contributorId":58572,"corporation":false,"usgs":true,"family":"Young","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":653203,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stohlgren, Thomas J. 0000-0001-9696-4450 stohlgrent@usgs.gov","orcid":"https://orcid.org/0000-0001-9696-4450","contributorId":2902,"corporation":false,"usgs":true,"family":"Stohlgren","given":"Thomas","email":"stohlgrent@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":653204,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talbert, Colin talbertc@usgs.gov","contributorId":4668,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":653205,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Talbert, Marian mtalbert@usgs.gov","contributorId":5180,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":false,"id":653206,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morisette, Jeffrey","contributorId":100739,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","affiliations":[],"preferred":false,"id":653207,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Anderson, Ryan","contributorId":106029,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","affiliations":[],"preferred":false,"id":653208,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70168511,"text":"sir20155142 - 2016 - Estimated use of water in the Delaware River Basin in Delaware, New Jersey, New York, and Pennsylvania, 2010","interactions":[],"lastModifiedDate":"2021-09-27T18:32:45.902579","indexId":"sir20155142","displayToPublicDate":"2016-11-07T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5142","title":"Estimated use of water in the Delaware River Basin in Delaware, New Jersey, New York, and Pennsylvania, 2010","docAbstract":"<p>The Delaware River Basin (DRB) was selected as a Focus Area Study in 2011 by the U.S. Geological Survey (USGS) as part of the USGS National Water Census. The National Water Census is a USGS research program that focuses on national water availability and use and then develops new water accounting tools and assesses water availability at both the regional and national scales. One of the water management needs that the DRB study addressed, and that was identified by stakeholder groups from the DRB, was to improve the integration of state water use and water-supply data and to provide the compiled water use information to basin users. This water use information was also used in the hydrologic modeling and ecological components of the study.</p><p>Instream and offstream water use was calculated for 2010 for the DRB based on information received from Delaware, New Jersey, New York, and Pennsylvania. Water withdrawal, interbasin transfers, return flow, and hydroelectric power generation release data were compiled for 11 categories by hydrologic subregion, basin, subbasin, and subwatershed. Data availability varied by state. Site-specific data were used whenever possible to calculate public supply, irrigation (golf courses, nurseries, sod farms, and crops), aquaculture, self-supplied industrial, commercial, mining, thermoelectric, and hydroelectric power withdrawals. Where site-specific data were not available, primarily for crop irrigation, livestock, and domestic use, various techniques were used to estimate water withdrawals.</p><p>Total water withdrawals in the Delaware River Basin were calculated to be about 7,130 million gallons per day (Mgal/d) in 2010. Calculations of withdrawals by source indicate that freshwater withdrawals were about 4,130 Mgal/d (58 percent of the total) and the remaining 3,000 Mgal/d (42 percent) were from saline water. Total surface-water withdrawals were calculated to be 6,590 Mgal/d, or 92 percent of the total; about 54 percent (3,590 Mgal/d) of surface water withdrawn was freshwater. Total groundwater withdrawals were calculated to be 545 Mgal/d (8 percent of the total), all of which was freshwater. During 2010, calculated withdrawals by category, in decreasing order, were: thermoelectric power, 4,910 Mgal/d; public supply, 1,490 Mgal/d; self-supplied industrial, 350 Mgal/d; irrigation, 175 Mgal/d; self-supplied domestic, 117 Mgal/d; mining, 41.3 Mgal/d; aquaculture, 19.3 Mgal/d; livestock, 6.72 Mgal/d, and commercial, 5.89 Mgal/d. The amount of instream use for hydroelectric power generation purposes in 2010 was reported to be 273 Mgal/d for the Wallenpaupack Plant and 127 Mgal/d for the Mongaup River system.</p><p>Total return flows in the DRB were 2,960 Mgal/d in 2010. Although municipal wastewater-treatment plants accounted for 539 (97 percent) of the return-flow sites, they accounted for about 70 percent of the total return flows in the DRB. There was limited information on return flows from thermoelectric power.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155142","usgsCitation":"Hutson, S.S., Linsey, K.S., Ludlow, R.A., Reyes, Betzaida, and Shourds, J.L., 2016, Estimated use of water in the Delaware River Basin in Delaware, New Jersey, New York, and Pennsylvania, 2010: U.S. Geological Survey Scientific Investigations Report 2015–5142, 76 p., https://dx.doi.org/10.3133/sir20155142.","productDescription":"Report: vii, 76 p.; 2 Appendixes; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-058986","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":438513,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TM787C","text":"USGS data release","linkHelpText":"Estimated Use of Water by Subbasin (HUC8 and HUC12) in the Delaware River Basin, 2010"},{"id":330727,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142_appendix3.xlsx","text":"Appendix 3","size":"269 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5142 Appendix 3","linkHelpText":"- Delaware River Basin Water Use by Subbasin, 2010"},{"id":330724,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5142/coverthb.jpg"},{"id":330725,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142.pdf","text":"Report","size":"57.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5142"},{"id":330726,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142_appendix2.docx","text":"Appendix 2","size":"2.49 MB docx","description":"SIR 2015-5142 Appendix 2","linkHelpText":"- Hydrologic Subbasins, Watersheds, and Subwatersheds in the Delaware River Basin"},{"id":330728,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7TM787C","text":"USGS data release","description":"USGS data release","linkHelpText":"Estimated Use of Water by Subbasin (HUC8) and Subwatershed (HUC12) in the Delaware River Basin, 2010"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.652099609375,\n              39.18117526158749\n            ],\n            [\n              -74.50927734375,\n              40.195659093364654\n            ],\n            [\n              -74.586181640625,\n              41.36856413680967\n            ],\n            [\n              -74.300537109375,\n              42.09822241118974\n            ],\n            [\n              -74.619140625,\n              42.53689200787315\n            ],\n            [\n              -75.498046875,\n              42.10637370579324\n            ],\n            [\n              -75.9375,\n              41.12074559016745\n            ],\n            [\n              -76.607666015625,\n              40.36328834091583\n            ],\n            [\n              -75.860595703125,\n              39.715638134796336\n            ],\n            [\n              -75.43212890625,\n              38.685509760012\n            ],\n            [\n              -75.069580078125,\n              38.77121637244273\n            ],\n            [\n              -74.652099609375,\n              39.18117526158749\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Coordinator—National Water Census<br> U.S. Geological Survey<br> 1770 Corporate Drive, Suite 500<br> Norcross, GA 30093</p><p>Or visit the National Water Census Web site at: <a href=\"http://water.usgs.gov/watercensus\" data-mce-href=\"http://water.usgs.gov/watercensus\">http://water.usgs.gov/watercensus</a></p>","tableOfContents":"<ul><li>Abstract&nbsp;</li><li>Introduction</li><li>Data Compilation, Sources of Information, and Methodology</li><li>Water Use</li><li>Summary</li><li>Selected References</li><li>Glossary&nbsp;</li><li>Appendix 1.&nbsp;Description of the Watershed Boundary Dataset&nbsp;</li><li>Appendix 2. Hydrologic Subbasins, Watersheds, and Subwatersheds in the Delaware River Basin</li><li>Appendix 3.&nbsp;Delaware River Basin Water Use by Subbasin</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2016-11-07","noUsgsAuthors":false,"publicationDate":"2016-11-07","publicationStatus":"PW","scienceBaseUri":"5821a0dbe4b02f1a881de95e","contributors":{"authors":[{"text":"Hutson, Susan S. sshutson@usgs.gov","contributorId":2040,"corporation":false,"usgs":true,"family":"Hutson","given":"Susan","email":"sshutson@usgs.gov","middleInitial":"S.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":620741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Linsey, Kristin S. 0000-0001-6492-7639 kslinsey@usgs.gov","orcid":"https://orcid.org/0000-0001-6492-7639","contributorId":3678,"corporation":false,"usgs":true,"family":"Linsey","given":"Kristin","email":"kslinsey@usgs.gov","middleInitial":"S.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":620740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ludlow, Russell A. 0000-0001-6483-6817 raludlow@usgs.gov","orcid":"https://orcid.org/0000-0001-6483-6817","contributorId":5820,"corporation":false,"usgs":true,"family":"Ludlow","given":"Russell","email":"raludlow@usgs.gov","middleInitial":"A.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":620742,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reyes, Betzaida 0000-0002-1398-0824 breyes@usgs.gov","orcid":"https://orcid.org/0000-0002-1398-0824","contributorId":2250,"corporation":false,"usgs":true,"family":"Reyes","given":"Betzaida","email":"breyes@usgs.gov","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":620743,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shourds, Jennifer L. 0000-0002-7631-9734 jshourds@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-9734","contributorId":5821,"corporation":false,"usgs":true,"family":"Shourds","given":"Jennifer","email":"jshourds@usgs.gov","middleInitial":"L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":620744,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70177942,"text":"fs20163093 - 2016 - The 3D Elevation Program and America's infrastructure","interactions":[],"lastModifiedDate":"2017-01-30T11:57:27","indexId":"fs20163093","displayToPublicDate":"2016-11-07T08:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3093","title":"The 3D Elevation Program and America's infrastructure","docAbstract":"<p>Infrastructure—the physical framework of transportation, energy, communications, water supply, and other systems—and construction management—the overall planning, coordination, and control of a project from beginning to end—are critical to the Nation’s prosperity. The American Society of Civil Engineers has warned that, despite the importance of the Nation’s infrastructure, it is in fair to poor condition and needs sizable and urgent investments to maintain and modernize it, and to ensure that it is sustainable and resilient. </p><p>Three-dimensional (3D) light detection and ranging (lidar) elevation data provide valuable productivity, safety, and cost-saving benefits to infrastructure improvement projects and associated construction management. By providing data to users, the 3D Elevation Program (3DEP) of the U.S. Geological Survey reduces users’ costs and risks and allows them to concentrate on their mission objectives. 3DEP includes (1) data acquisition partnerships that leverage funding, (2) contracts with experienced private mapping firms, (3) technical expertise, lidar data standards, and specifications, and (4) most important, public access to high-quality 3D elevation data. </p><p>The size and breadth of improvements for the Nation’s infrastructure and construction management needs call for an efficient, systematic approach to acquiring foundational 3D elevation data. The 3DEP approach to national data coverage will yield large cost savings over individual project-by-project acquisitions and will ensure that data are accessible for other critical applications.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163093","usgsCitation":"Lukas, Vicki, and Carswell, W.J., Jr., 2016, The 3D Elevation Program and America's infrastructure: U.S. Geological Survey Fact Sheet 2016–3093, 2 p., https://dx.doi.org/10.3133/fs20163093.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-077294","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":330670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3093/fs20163093.pdf","text":"Report","size":"425 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3093"},{"id":330669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3093/coverthb.jpg"}],"contact":"<p>Director, National Geospatial Program<br> U.S. Geological Survey<br> 511 National Center<br> 12201 Sunrise Valley Drive<br> Reston, VA 20192</p><p>Email: <a href=\"mailto:3DEP@usgs.gov\" data-mce-href=\"mailto:3DEP@usgs.gov\">3DEP@usgs.gov</a><br> <a href=\"http://www.usgs.gov/ngpo/\" data-mce-href=\"http://www.usgs.gov/ngpo/\">http://www.usgs.gov/ngpo/</a> <br> <a href=\"http://nationalmap.gov/3DEP/\" data-mce-href=\"http://nationalmap.gov/3DEP/\">http://nationalmap.gov/3DEP/</a></p>","tableOfContents":"<ul><li>Infrastructure Connects Us All</li><li>Uses of 3D Elevation Data</li><li>Benefits of 3D Elevation Data</li><li>Maximized Benefits and Minimized Risks</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2016-11-07","noUsgsAuthors":false,"publicationDate":"2016-11-07","publicationStatus":"PW","scienceBaseUri":"5821a0dce4b02f1a881de963","contributors":{"authors":[{"text":"Lukas, Vicki 0000-0002-3151-6689 vlukas@usgs.gov","orcid":"https://orcid.org/0000-0002-3151-6689","contributorId":2890,"corporation":false,"usgs":true,"family":"Lukas","given":"Vicki","email":"vlukas@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":652775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carswell carswell@usgs.gov","contributorId":176472,"corporation":false,"usgs":true,"family":"Carswell","email":"carswell@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":false,"id":652438,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70179159,"text":"70179159 - 2016 - Dissolved methane in the Beaufort Sea and the Arctic Ocean, 1992-2009; sources and atmospheric flux","interactions":[],"lastModifiedDate":"2016-12-20T11:59:51","indexId":"70179159","displayToPublicDate":"2016-11-07T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Dissolved methane in the Beaufort Sea and the Arctic Ocean, 1992-2009; sources and atmospheric flux","docAbstract":"Methane concentration and isotopic composition was measured in ice-covered and ice-free waters of the Arctic Ocean during eleven surveys spanning the years of 1992-1995 and 2009. During ice-free periods, methane flux from the Beaufort shelf varies from 0.14 to 0.43 mg CH4 m-2 day-1. Maximum fluxes from localized areas of high methane concentration are up to 1.52 mg CH4 m-2 day-1.  Seasonal buildup of methane under ice can produce short-term fluxes of methane from the Beaufort shelf that varies from 0.28 to 1.01 to mg CH4 m-2 day-1.   Scaled-up estimates of minimum methane flux from the Beaufort Sea and pan-Arctic shelf for both ice-free and ice-covered periods range from 0.02 Tg CH4 yr-1 and 0.30 Tg CH4 yr-1 respectively to maximum fluxes of 0.18 Tg CH4 yr-1 and 2.2 Tg CH4 yr-1 respectively.  A methane flux of 0.36 Tg CH4 yr-1from the deep Arctic Ocean was estimated using data from 1993-94.  The flux can be as much as 2.35 Tg CH4 yr-1 estimated from maximum methane concentrations and wind speeds of 12 m/s, representing only 0.42% of the annual atmospheric methane budget of ~560 Tg CH4 yr-1.  There were no significant changes in methane fluxes during the time period of this study. Microbial methane sources predominate with minor influxes from thermogenic methane offshore Prudhoe Bay and the Mackenzie River delta and may include methane from gas hydrate. Methane oxidation is locally important on the shelf and is a methane sink in the deep Arctic Ocean.","language":"English","publisher":"Wiley","doi":"10.1002/lno.10457","usgsCitation":"Lorenson, T., Greinert, J., and Coffin, R.B., 2016, Dissolved methane in the Beaufort Sea and the Arctic Ocean, 1992-2009; sources and atmospheric flux: Limnology and Oceanography, v. 61, no. 51, p. S300-S323, https://doi.org/10.1002/lno.10457.","productDescription":"24 p.","startPage":"S300","endPage":"S323","ipdsId":"IP-079351","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470432,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.10457","text":"Publisher Index Page"},{"id":332340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic Ocean","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -163.125,\n              72.39570570653261\n            ],\n            [\n              -146.6015625,\n              70.1403642720717\n            ],\n            [\n              -136.7578125,\n              69.53451763078358\n            ],\n            [\n              -128.32031249999997,\n              70.37785394109224\n            ],\n            [\n              -117.7734375,\n              78.42019327591201\n            ],\n            [\n              -95.2734375,\n              81.20141954209073\n            ],\n            [\n              -76.2890625,\n              83.19489563661588\n            ],\n            [\n              -30.585937499999996,\n              83.82994542398042\n            ],\n            [\n              -23.90625,\n              84.89714695160268\n            ],\n            [\n              -51.67968749999999,\n              85.0511287798066\n            ],\n            [\n              -86.1328125,\n              85.11141578062661\n            ],\n            [\n              -133.2421875,\n              85.02070774312594\n            ],\n            [\n              -172.96875,\n              81.4139332828511\n            ],\n            [\n              -176.1328125,\n              71.52490903732816\n            ],\n            [\n              -161.015625,\n              71.41317683396566\n            ],\n            [\n              -163.125,\n              72.39570570653261\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"61","issue":"51","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-07","publicationStatus":"PW","scienceBaseUri":"585a51bde4b01224f329b5e5","contributors":{"authors":[{"text":"Lorenson, Thomas D.","contributorId":177573,"corporation":false,"usgs":false,"family":"Lorenson","given":"Thomas D.","affiliations":[],"preferred":false,"id":656252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greinert, Jens","contributorId":101809,"corporation":false,"usgs":true,"family":"Greinert","given":"Jens","email":"","affiliations":[],"preferred":false,"id":656253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coffin, Richard B.","contributorId":177575,"corporation":false,"usgs":false,"family":"Coffin","given":"Richard","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":656254,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70191521,"text":"70191521 - 2016 - Streamflow data","interactions":[],"lastModifiedDate":"2020-08-25T16:55:00.091561","indexId":"70191521","displayToPublicDate":"2016-11-06T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"13","title":"Streamflow data","docAbstract":"<p><span>Streamflow data are vital for a variety of water-resources issues, from flood warning to water supply planning. The collection of streamflow data is usually an involved and complicated process. This chapter serves as an overview of the streamflow data collection process. Readers with the need for the detailed information on the streamflow data collection process are referred to the many references noted in this chapter.</span><br></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Flood forecasting: A global perspective","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Academic Press","doi":"10.1016/B978-0-12-801884-2.00013-X","usgsCitation":"Wiche, G.J., and Holmes, R.R., 2016, Streamflow data, chap. 13 <i>of</i> Flood forecasting: A global perspective, p. 371-398, https://doi.org/10.1016/B978-0-12-801884-2.00013-X.","productDescription":"28 p.","startPage":"371","endPage":"398","ipdsId":"IP-066793","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":348996,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc9be4b06e28e9c24042","contributors":{"authors":[{"text":"Wiche, Gregg J. gjwiche@usgs.gov","contributorId":1675,"corporation":false,"usgs":true,"family":"Wiche","given":"Gregg","email":"gjwiche@usgs.gov","middleInitial":"J.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":712608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holmes, Robert R. Jr. 0000-0002-5060-3999 bholmes@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-3999","contributorId":1624,"corporation":false,"usgs":true,"family":"Holmes","given":"Robert","suffix":"Jr.","email":"bholmes@usgs.gov","middleInitial":"R.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":false,"id":722458,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70178180,"text":"70178180 - 2016 - Subsea ice-bearing permafrost on the U.S. Beaufort Margin: 2. Borehole constraints","interactions":[],"lastModifiedDate":"2016-12-29T09:20:29","indexId":"70178180","displayToPublicDate":"2016-11-04T15:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Subsea ice-bearing permafrost on the U.S. Beaufort Margin: 2. Borehole constraints","docAbstract":"<p><span>Borehole logging data from legacy wells directly constrain the contemporary distribution of subsea permafrost in the sedimentary section at discrete locations on the U.S. Beaufort Margin and complement recent regional analyses of exploration seismic data to delineate the permafrost's offshore extent. Most usable borehole data were acquired on a ∼500 km stretch of the margin and within 30 km of the contemporary coastline from north of Lake Teshekpuk to nearly the U.S.-Canada border. Relying primarily on deep resistivity logs that should be largely unaffected by drilling fluids and hole conditions, the analysis reveals the persistence of several hundred vertical meters of ice-bonded permafrost in nearshore wells near Prudhoe Bay and Foggy Island Bay, with less permafrost detected to the east and west. Permafrost is inferred beneath many barrier islands and in some nearshore and lagoonal (back-barrier) wells. The analysis of borehole logs confirms the offshore pattern of ice-bearing subsea permafrost distribution determined based on regional seismic analyses and reveals that ice content generally diminishes with distance from the coastline. Lacking better well distribution, it is not possible to determine the absolute seaward extent of ice-bearing permafrost, nor the distribution of permafrost beneath the present-day continental shelf at the end of the Pleistocene. However, the recovery of gas hydrate from an outer shelf well (Belcher) and previous delineation of a log signature possibly indicating gas hydrate in an inner shelf well (Hammerhead 2) imply that permafrost may once have extended across much of the shelf offshore Camden Bay.</span></p>","language":"English","publisher":"AGU Publications","doi":"10.1002/2016GC006582","usgsCitation":"Ruppel, C., Herman, B.M., Brothers, L., and Hart, P.E., 2016, Subsea ice-bearing permafrost on the U.S. Beaufort Margin: 2. Borehole constraints: Geochemistry, Geophysics, Geosystems, v. 17, no. 11, p. 4333-4353, https://doi.org/10.1002/2016GC006582.","productDescription":"21 p.","startPage":"4333","endPage":"4353","ipdsId":"IP-077887","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470435,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gc006582","text":"Publisher Index Page"},{"id":330774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"11","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-04","publicationStatus":"PW","scienceBaseUri":"581d9e2ae4b0dee4cc90cbb9","contributors":{"authors":[{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":145770,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn D.","email":"cruppel@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":653153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herman, Bruce M.","contributorId":176704,"corporation":false,"usgs":false,"family":"Herman","given":"Bruce","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":653154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brothers, Laura L. lbrothers@usgs.gov","contributorId":4502,"corporation":false,"usgs":true,"family":"Brothers","given":"Laura L.","email":"lbrothers@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":653155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Patrick E. 0000-0002-5080-1426 hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5080-1426","contributorId":2879,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick","email":"hart@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653157,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70178178,"text":"70178178 - 2016 - Subsea ice-bearing permafrost on the U.S. Beaufort Margin: 1. Minimum seaward extent defined from multichannel seismic reflection data","interactions":[],"lastModifiedDate":"2017-05-18T11:07:17","indexId":"70178178","displayToPublicDate":"2016-11-04T15:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Subsea ice-bearing permafrost on the U.S. Beaufort Margin: 1. Minimum seaward extent defined from multichannel seismic reflection data","docAbstract":"<p><span>Subsea ice-bearing permafrost (IBPF) and associated gas hydrate in the Arctic have been subject to a warming climate and saline intrusion since the last transgression at the end of the Pleistocene. The consequent degradation of IBPF is potentially associated with significant degassing of dissociating gas hydrate deposits. Previous studies interpreted the distribution of subsea permafrost on the U.S. Beaufort continental shelf based on geographically sparse data sets and modeling of expected thermal history. The most cited work projects subsea permafrost to the shelf edge (∼100 m isobath). This study uses a compilation of stacking velocity analyses from ∼100,000 line-km of industry-collected multichannel seismic reflection data acquired over 57,000 km</span><sup>2</sup><span> of the U.S. Beaufort shelf to delineate continuous subsea IBPF. Gridded average velocities of the uppermost 750 ms two-way travel time range from 1475 to 3110 m s</span><sup>−1</sup><span>. The monotonic, cross-shore pattern in velocity distribution suggests that the seaward extent of continuous IBPF is within 37 km of the modern shoreline at water depths &lt; 25 m. These interpretations corroborate recent Beaufort seismic refraction studies and provide the best, margin-scale evidence that continuous subsea IBPF does not currently extend to the northern limits of the continental shelf.</span></p>","language":"English","publisher":"AGU Publications","doi":"10.1002/2016GC006584","usgsCitation":"Brothers, L.L., Herman, B.M., Hart, P.E., and Ruppel, C., 2016, Subsea ice-bearing permafrost on the U.S. Beaufort Margin: 1. Minimum seaward extent defined from multichannel seismic reflection data: Geochemistry, Geophysics, Geosystems, v. 17, no. 11, p. 4354-4365, https://doi.org/10.1002/2016GC006584.","startPage":"4354","endPage":"4365","ipdsId":"IP-074615","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470436,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/2016gc006584","text":"External Repository"},{"id":330775,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"11","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-04","publicationStatus":"PW","scienceBaseUri":"581d9e2ae4b0dee4cc90cbbb","contributors":{"authors":[{"text":"Brothers, Laura L. 0000-0003-2986-5166 lbrothers@usgs.gov","orcid":"https://orcid.org/0000-0003-2986-5166","contributorId":176698,"corporation":false,"usgs":true,"family":"Brothers","given":"Laura","email":"lbrothers@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herman, Bruce M.","contributorId":176704,"corporation":false,"usgs":false,"family":"Herman","given":"Bruce","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":653142,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Patrick E. 0000-0002-5080-1426 hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5080-1426","contributorId":2879,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick","email":"hart@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653143,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":145770,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn D.","email":"cruppel@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":653144,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70178130,"text":"70178130 - 2016 - Fault segmentation: New concepts from the Wasatch Fault Zone, Utah, USA","interactions":[],"lastModifiedDate":"2016-11-03T13:17:00","indexId":"70178130","displayToPublicDate":"2016-11-03T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Fault segmentation: New concepts from the Wasatch Fault Zone, Utah, USA","docAbstract":"<p><span>The question of whether structural segment boundaries along multisegment normal faults such as the Wasatch fault zone (WFZ) act as persistent barriers to rupture is critical to seismic hazard analyses. We synthesized late Holocene paleoseismic data from 20 trench sites along the central WFZ to evaluate earthquake rupture length and fault segmentation. For the youngest (&lt;3 ka) and best-constrained earthquakes, differences in earthquake timing across prominent primary segment boundaries, especially for the most recent earthquakes on the north-central WFZ, are consistent with segment-controlled ruptures. However, broadly constrained earthquake times, dissimilar event times along the segments, the presence of smaller-scale (subsegment) boundaries, and areas of complex faulting permit partial-segment and multisegment (e.g., spillover) ruptures that are shorter (~20–40 km) or longer (~60–100 km) than the primary segment lengths (35–59 km). We report a segmented WFZ model that includes 24 earthquakes since ~7 ka and yields mean estimates of recurrence (1.1–1.3 kyr) and vertical slip rate (1.3–2.0 mm/yr) for the segments. However, additional rupture scenarios that include segment boundary spatial uncertainties, floating earthquakes, and multisegment ruptures are necessary to fully address epistemic uncertainties in rupture length. We compare the central WFZ to paleoseismic and historical surface ruptures in the Basin and Range Province and central Italian Apennines and conclude that displacement profiles have limited value for assessing the persistence of segment boundaries but can aid in interpreting prehistoric spillover ruptures. Our comparison also suggests that the probabilities of shorter and longer ruptures on the WFZ need to be investigated.</span></p>","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2015JB012519","usgsCitation":"DuRoss, C., Personius, S.F., Crone, A.J., Olig, S.S., Hylland, M., Lund, W., and Schwartz, D.P., 2016, Fault segmentation: New concepts from the Wasatch Fault Zone, Utah, USA: Journal of Geophysical Research B: Solid Earth, v. 121, no. 2, p. 1131-1157, https://doi.org/10.1002/2015JB012519.","productDescription":"27 p.","startPage":"1131","endPage":"1157","ipdsId":"IP-071316","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":470439,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012519","text":"Publisher Index Page"},{"id":330706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Wasatch Fault Zone","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.3,\n              39.333333\n            ],\n            [\n              -111.3,\n              41.9\n            ],\n            [\n              -112.35,\n              41.9\n            ],\n            [\n              -112.35,\n              39.333333\n            ],\n            [\n              -111.3,\n              39.333333\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"121","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-18","publicationStatus":"PW","scienceBaseUri":"581c4cbfe4b09688d6e90f95","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":652945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Personius, Stephen F. personius@usgs.gov","contributorId":1214,"corporation":false,"usgs":true,"family":"Personius","given":"Stephen","email":"personius@usgs.gov","middleInitial":"F.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":652946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crone, Anthony J. 0000-0002-3006-406X crone@usgs.gov","orcid":"https://orcid.org/0000-0002-3006-406X","contributorId":790,"corporation":false,"usgs":true,"family":"Crone","given":"Anthony","email":"crone@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":652947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olig, Susan S.","contributorId":87640,"corporation":false,"usgs":true,"family":"Olig","given":"Susan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":652948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hylland, Michael D.","contributorId":106031,"corporation":false,"usgs":true,"family":"Hylland","given":"Michael D.","affiliations":[],"preferred":false,"id":652949,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lund, William R.","contributorId":48320,"corporation":false,"usgs":true,"family":"Lund","given":"William R.","affiliations":[],"preferred":false,"id":652950,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schwartz, David P. 0000-0001-5193-9200 dschwartz@usgs.gov","orcid":"https://orcid.org/0000-0001-5193-9200","contributorId":1940,"corporation":false,"usgs":true,"family":"Schwartz","given":"David","email":"dschwartz@usgs.gov","middleInitial":"P.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":652951,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70178116,"text":"70178116 - 2016 - Deposition, accumulation, and alteration of Cl−, NO3−, ClO4− and ClO3− salts in a hyper-arid polar environment: Mass balance and isotopic constraints","interactions":[],"lastModifiedDate":"2018-08-06T13:08:45","indexId":"70178116","displayToPublicDate":"2016-11-03T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Deposition, accumulation, and alteration of Cl<sup>−</sup>, NO<sub>3</sub><sup>−</sup>, ClO<sub>4</sub><sup>−</sup> and ClO<sub>3</sub><sup>−</sup> salts in a hyper-arid polar environment: Mass balance and isotopic constraints","title":"Deposition, accumulation, and alteration of Cl−, NO3−, ClO4− and ClO3− salts in a hyper-arid polar environment: Mass balance and isotopic constraints","docAbstract":"<p><span>The salt fraction in permafrost soils/sediments of the McMurdo Dry Valleys (MDV) of Antarctica can be used as a proxy for cold desert geochemical processes and paleoclimate reconstruction. Previous analyses of the salt fraction in MDV permafrost soils have largely been conducted in coastal regions where permafrost soils are variably affected by aqueous processes and mixed inputs from marine and stratospheric sources. We expand upon this work by evaluating permafrost soil/sediments in University Valley, located in the ultraxerous zone where both liquid water transport and marine influences are minimal. We determined the abundances of Cl</span><sup>−</sup><span>, NO</span><sub>3</sub><sup>−</sup><span>, ClO</span><sub>4</sub><sup>−</sup><span> and ClO</span><sub>3</sub><sup>−</sup><span> in dry and ice-cemented soil/sediments, snow and glacier ice, and also characterized Cl</span><sup>−</sup><span> and NO</span><sub>3</sub><sup>−</sup><span>isotopically. The data are not consistent with salt deposition in a sublimation till, nor with nuclear weapon testing fall-out, and instead point to a dominantly stratospheric source and to varying degrees of post depositional transformation depending on the substrate, from minimal alteration in bare soils to significant alteration (photodegradation and/or volatilization) in snow and glacier ice. Ionic abundances in the dry permafrost layer indicate limited vertical transport under the current climate conditions, likely due to percolation of snowmelt. Subtle changes in ClO</span><sub>4</sub><sup>−</sup><span>/NO</span><sub>3</sub><sup>−</sup><span> ratios and NO</span><sub>3</sub><sup>−</sup><span> isotopic composition with depth and location may reflect both transport related fractionation and depositional history. Low molar ratios of ClO</span><sub>3</sub><sup>−</sup><span>/ClO</span><sub>4</sub><sup>−</sup><span> in surface soils compared to deposition and other arid systems suggest significant post depositional loss of ClO</span><sub>3</sub><sup>−</sup><span>, possibly due to reduction by iron minerals, which may have important implications for oxy-chlorine species on Mars. Salt accumulation varies with distance along the valley and apparent accumulation times based on multiple methods range from ∼10 to 30&nbsp;kyr near the glacier to 70–200&nbsp;kyr near the valley mouth. The relatively young age of the salts and relatively low and homogeneous anion concentrations in the ice-cemented sediments point to either a mechanism of recent salt removal, or to relatively modern permafrost soils (&lt;1&nbsp;million&nbsp;years). Together, our results show that near surface salts in University Valley serve as an end-member of stratospheric sources not subject to biological processes or extensive remobilization.</span></p>","language":"English","publisher":"Geochemical Society","publisherLocation":"New York, NY","doi":"10.1016/j.gca.2016.03.012","usgsCitation":"Jackson, A., Davila, A.F., Böhlke, J., Sturchio, N.C., Sevanthi, R., Estrada, N., Brundrett, M., Lacelle, D., McKay, C.P., Poghosyan, A., Pollard, W., and Zacny, K., 2016, Deposition, accumulation, and alteration of Cl−, NO3−, ClO4− and ClO3− salts in a hyper-arid polar environment: Mass balance and isotopic constraints: Geochimica et Cosmochimica Acta, v. 182, p. 197-215, https://doi.org/10.1016/j.gca.2016.03.012.","productDescription":"18 p.","startPage":"197","endPage":"215","ipdsId":"IP-073229","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":470437,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2016.03.012","text":"Publisher Index Page"},{"id":330687,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, University Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              160.666667,\n              -77.845833\n            ],\n            [\n              160.666667,\n              -77.911111\n            ],\n            [\n              160.779167,\n              -77.911111\n            ],\n            [\n              160.779167,\n              -77.845833\n            ],\n            [\n              160.666667,\n              -77.845833\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"182","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"581c4cc1e4b09688d6e90fa7","contributors":{"authors":[{"text":"Jackson, Andrew","contributorId":176588,"corporation":false,"usgs":false,"family":"Jackson","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":652873,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davila, Alfonso F.","contributorId":16282,"corporation":false,"usgs":true,"family":"Davila","given":"Alfonso","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":652874,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Böhlke, John Karl 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":1285,"corporation":false,"usgs":true,"family":"Böhlke","given":"John Karl","email":"jkbohlke@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":652875,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sturchio, Neil C.","contributorId":149375,"corporation":false,"usgs":false,"family":"Sturchio","given":"Neil","email":"","middleInitial":"C.","affiliations":[{"id":15289,"text":"University of Illinois, Ven Te Chow Hydrosystems Laboratory","active":true,"usgs":false}],"preferred":false,"id":652876,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sevanthi, Ritesh","contributorId":14301,"corporation":false,"usgs":true,"family":"Sevanthi","given":"Ritesh","affiliations":[],"preferred":false,"id":652877,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Estrada, Nubia","contributorId":176622,"corporation":false,"usgs":false,"family":"Estrada","given":"Nubia","affiliations":[],"preferred":false,"id":652879,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brundrett, Maeghan","contributorId":176623,"corporation":false,"usgs":false,"family":"Brundrett","given":"Maeghan","email":"","affiliations":[],"preferred":false,"id":652880,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lacelle, Denis","contributorId":176624,"corporation":false,"usgs":false,"family":"Lacelle","given":"Denis","email":"","affiliations":[],"preferred":false,"id":652881,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McKay, Christopher P.","contributorId":58156,"corporation":false,"usgs":true,"family":"McKay","given":"Christopher","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":652882,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Poghosyan, Armen","contributorId":176625,"corporation":false,"usgs":false,"family":"Poghosyan","given":"Armen","email":"","affiliations":[],"preferred":false,"id":652883,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pollard, Wayne","contributorId":176626,"corporation":false,"usgs":false,"family":"Pollard","given":"Wayne","email":"","affiliations":[],"preferred":false,"id":652884,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Zacny, Kris","contributorId":176627,"corporation":false,"usgs":false,"family":"Zacny","given":"Kris","email":"","affiliations":[],"preferred":false,"id":652885,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70178097,"text":"70178097 - 2016 - The automated reference toolset: A soil-geomorphic ecological potential matching algorithm","interactions":[],"lastModifiedDate":"2016-11-02T15:03:07","indexId":"70178097","displayToPublicDate":"2016-11-02T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3420,"text":"Soil Science Society of America Journal","active":true,"publicationSubtype":{"id":10}},"title":"The automated reference toolset: A soil-geomorphic ecological potential matching algorithm","docAbstract":"<p><span>Ecological inventory and monitoring data need referential context for interpretation. Identification of appropriate reference areas of similar ecological potential for site comparison is demonstrated using a newly developed automated reference toolset (ART). Foundational to identification of reference areas was a soil map of particle size in the control section (PSCS), a theme in US Soil Taxonomy. A 30-m resolution PSCS map of the Colorado Plateau (366,000 km</span><sup>2</sup><span>) was created by interpolating ∼5000 field soil observations using a random forest model and a suite of raster environmental spatial layers representing topography, climate, general ecological community, and satellite imagery ratios. The PSCS map had overall out of bag accuracy of 61.8% (Kappa of 0.54, </span><i>p</i><span> &lt; 0.0001), and an independent validation accuracy of 93.2% at a set of 356 field plots along the southern edge of Canyonlands National Park, Utah. The ART process was also tested at these plots, and matched plots with the same ecological sites (ESs) 67% of the time where sites fell within 2-km buffers of each other. These results show that the PSCS and ART have strong application for ecological monitoring and sampling design, as well as assessing impacts of disturbance and land management action using an ecological potential framework. Results also demonstrate that PSCS could be a key mapping layer for the USDA-NRCS provisional ES development initiative.</span></p>","language":"English","publisher":"Soil Science Society of America","doi":"10.2136/sssaj2016.05.0151","usgsCitation":"Nauman, T.W., and Duniway, M.C., 2016, The automated reference toolset: A soil-geomorphic ecological potential matching algorithm: Soil Science Society of America Journal, v. 80, no. 5, p. 1317-1328, https://doi.org/10.2136/sssaj2016.05.0151.","productDescription":"12 p.","startPage":"1317","endPage":"1328","ipdsId":"IP-076162","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":438514,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XS5SW0","text":"USGS data release","linkHelpText":"Automated Reference Toolset (ART)Data"},{"id":330664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-13","publicationStatus":"PW","scienceBaseUri":"581afb63e4b0bb36a4ca6649","contributors":{"authors":[{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":652733,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":652734,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70177172,"text":"ds1024 - 2016 - Characterization of sediment and measurement of groundwater levels and temperatures, Camas National Wildlife Refuge, eastern Idaho","interactions":[],"lastModifiedDate":"2016-11-03T07:32:31","indexId":"ds1024","displayToPublicDate":"2016-11-02T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1024","title":"Characterization of sediment and measurement of groundwater levels and temperatures, Camas National Wildlife Refuge, eastern Idaho","docAbstract":"<p class=\"p1\">The Camas National Wildlife Refuge (Refuge) in eastern Idaho, established in 1937, contains wetlands, ponds, and wet meadows that are essential resting and feeding habitat for migratory birds and nesting habitat for waterfowl. Initially, natural sources of water supported these habitats. However, during the past few decades, climate change and changes in surrounding land use have altered and reduced natural groundwater and surface water inflows such that the wetlands, ponds, and wet meadows are now maintained through water management and groundwater pumping. These water management activities have proven to be inefficient and costly, prompting the Refuge to develop alternative water management options that are more efficient and less expensive. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, is studying the hydrogeology at the Refuge to provide information for developing alternative water management options.</p><p class=\"p1\">The hydrogeologic studies at the Refuge included characterizing the type, distribution, and hydraulic conductivity of surficial sediments and measuring water levels and temperatures in monitoring wells. Four monitoring wells and seven soil probe coreholes were drilled at the Refuge. Seven water level and temperature data loggers were installed in the wells and water levels and temperatures were continuously recorded from November 2014 to June 2016. Sediment cores were collected from the coreholes and sediment type and distribution were characterized from drillers’ notes, geophysical logs, corehole samples, and particle grain-size analysis. The hydraulic conductivities of sediments were estimated using the measured average grain size and the assumed textural maturity of the sediment, and ranged from about 20 to 290 feet per day.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1024","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Twining, B.V., and Rattray, G.W., 2016, Characterization of sediment and measurement of groundwater levels and temperatures, Camas National Wildlife Refuge, eastern Idaho: U.S. Geological Survey Data Series 1024, 23 p.,\nhttps://dx.doi.org/10.3133/ds1024.","productDescription":"Report: v, 23 p.; Appendix","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078192","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":330654,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1024/ds1024.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1024"},{"id":330653,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1024/coverthb.jpg"},{"id":330655,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1024/ds1024_appendixa.pdf","text":"Appendix A","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1024 Appendix A"}],"country":"United States","state":"Idaho","otherGeospatial":"Camas National Wildlife Refuge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.2480010986328,\n              43.99318499277654\n      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           ],\n            [\n              -112.25761413574219,\n              43.97144553284128\n            ],\n            [\n              -112.25812911987305,\n              43.97836349721919\n            ],\n            [\n              -112.25263595581055,\n              43.979104659888236\n            ],\n            [\n              -112.25366592407227,\n              43.98169865637306\n            ],\n            [\n              -112.26327896118164,\n              43.98219273809204\n            ],\n            [\n              -112.26327896118164,\n              43.98540416903878\n            ],\n            [\n              -112.25263595581055,\n              43.98614524381678\n            ],\n            [\n              -112.25332260131836,\n              43.98923295580709\n            ],\n            [\n              -112.2480010986328,\n              43.98972697481996\n            ],\n            [\n              -112.2480010986328,\n              43.99318499277654\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_id@usgs.gov\" data-mce-href=\"mailto:dc_id@usgs.gov\">Director</a>, Idaho Water Science Center<br> U.S. Geological Survey<br> 230 Collins Road<br> Boise, Idaho 83702<br> <a href=\"http://id.water.usgs.gov\" target=\"blank\" data-mce-href=\"http://id.water.usgs.gov\">http://id.water.usgs.gov</a></p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>Methods<br></li><li>Characterization of Sediment<br></li><li>Groundwater Levels and Temperatures<br></li><li>Summary<br></li><li>Acknowledgments<br></li><li>References Cited<br></li><li>Appendix A. Results of Particle-Grain Size Analyses on 49 Sediment Samples That Were Separated from the Seven Soil Probe Sediment Cores<br></li></ul>","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2016-11-02","noUsgsAuthors":false,"publicationDate":"2016-11-02","publicationStatus":"PW","scienceBaseUri":"581afb67e4b0bb36a4ca665b","contributors":{"authors":[{"text":"Twining, Brian V. 0000-0003-1321-4721 btwining@usgs.gov","orcid":"https://orcid.org/0000-0003-1321-4721","contributorId":2387,"corporation":false,"usgs":true,"family":"Twining","given":"Brian","email":"btwining@usgs.gov","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651438,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rattray, Gordon W. 0000-0002-1690-3218 grattray@usgs.gov","orcid":"https://orcid.org/0000-0002-1690-3218","contributorId":2521,"corporation":false,"usgs":true,"family":"Rattray","given":"Gordon","email":"grattray@usgs.gov","middleInitial":"W.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651437,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70178081,"text":"70178081 - 2016 - Exploiting differential vegetation phenology for satellite-based mapping of semiarid grass vegetation in the southwestern United States and northern Mexico","interactions":[],"lastModifiedDate":"2016-11-02T10:55:52","indexId":"70178081","displayToPublicDate":"2016-11-02T00:00:00","publicationYear":"2016","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":"Exploiting differential vegetation phenology for satellite-based mapping of semiarid grass vegetation in the southwestern United States and northern Mexico","docAbstract":"<p><span>We developed and evaluated a methodology for subpixel discrimination and large-area mapping of the perennial warm-season (C</span><sub>4</sub><span>) grass component of vegetation cover in mixed-composition landscapes of the southwestern United States and northern Mexico. We describe the methodology within a general, conceptual framework that we identify as the differential vegetation phenology (DVP) paradigm. We introduce a DVP index, the Normalized Difference Phenometric Index (NDPI) that provides vegetation type-specific information at the subpixel scale by exploiting differential patterns of vegetation phenology detectable in time-series spectral vegetation index (VI) data from multispectral land imagers. We used modified soil-adjusted vegetation index (MSAVI</span><sub>2</sub><span>) data from Landsat to develop the NDPI, and MSAVI</span><sub>2</sub><span> data from MODIS to compare its performance relative to one alternate DVP metric (difference of spring average MSAVI</span><sub>2</sub><span> and summer maximum MSAVI</span><sub>2</sub><span>), and two simple, conventional VI metrics (summer average MSAVI</span><sub>2</sub><span>, summer maximum MSAVI</span><sub>2</sub><span>). The NDPI in a scaled form (NDPI</span><sub>s</sub><span>) performed best in predicting variation in perennial C</span><sub>4</sub><span> grass cover as estimated from landscape photographs at 92 sites (R</span><sup>2</sup><span> = 0.76, </span><i>p</i><span> &lt; 0.001), indicating improvement over the alternate DVP metric (R</span><sup>2</sup><span> = 0.73, </span><i>p</i><span> &lt; 0.001) and substantial improvement over the two conventional VI metrics (R</span><sup>2</sup><span> = 0.62 and 0.56, </span><i>p</i><span> &lt; 0.001). The results suggest DVP-based methods, and the NDPI in particular, can be effective for subpixel discrimination and mapping of exposed perennial C</span><sub>4</sub><span> grass cover within mixed-composition landscapes of the Southwest, and potentially for monitoring of its response to drought, climate change, grazing and other factors, including land management. With appropriate adjustments, the method could potentially be used for subpixel discrimination and mapping of grass or other vegetation types in other regions where the vegetation components of the landscape exhibit contrasting seasonal patterns of phenology.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs8110889","usgsCitation":"Dye, D.G., Middleton, B.R., Vogel, J.M., Wu, Z., and Velasco, M.G., 2016, Exploiting differential vegetation phenology for satellite-based mapping of semiarid grass vegetation in the southwestern United States and northern Mexico: Remote Sensing, v. 8, no. 11, p. 1-33, https://doi.org/10.3390/rs8110889.","productDescription":"Article 889; 33 p.","startPage":"1","endPage":"33","ipdsId":"IP-069667","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470445,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110889","text":"Publisher Index Page"},{"id":330648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112,\n              31\n            ],\n            [\n              -112,\n              33\n            ],\n            [\n              -110,\n              33\n            ],\n            [\n              -110,\n              31\n            ],\n            [\n              -112,\n              31\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"8","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-28","publicationStatus":"PW","scienceBaseUri":"581afb64e4b0bb36a4ca664b","contributors":{"authors":[{"text":"Dye, Dennis G. 0000-0002-7100-272X ddye@usgs.gov","orcid":"https://orcid.org/0000-0002-7100-272X","contributorId":4233,"corporation":false,"usgs":true,"family":"Dye","given":"Dennis","email":"ddye@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":652712,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Middleton, Barry R. 0000-0001-8924-4121 bmiddleton@usgs.gov","orcid":"https://orcid.org/0000-0001-8924-4121","contributorId":3947,"corporation":false,"usgs":true,"family":"Middleton","given":"Barry","email":"bmiddleton@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":652713,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vogel, John M. 0000-0002-8226-1188 jvogel@usgs.gov","orcid":"https://orcid.org/0000-0002-8226-1188","contributorId":3167,"corporation":false,"usgs":true,"family":"Vogel","given":"John","email":"jvogel@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":652714,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":652715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Velasco, Miguel G. 0000-0003-2559-7934 mvelasco@usgs.gov","orcid":"https://orcid.org/0000-0003-2559-7934","contributorId":2103,"corporation":false,"usgs":true,"family":"Velasco","given":"Miguel","email":"mvelasco@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":652716,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70192498,"text":"70192498 - 2016 -  Light Goose Conservation Order effects on nontarget waterfowl behavior and energy expenditure","interactions":[],"lastModifiedDate":"2017-10-30T11:13:13","indexId":"70192498","displayToPublicDate":"2016-11-02T00:00:00","publicationYear":"2016","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":" Light Goose Conservation Order effects on nontarget waterfowl behavior and energy expenditure","docAbstract":"<p>When the Light Goose Conservation Order (LGCO) was established during 1999 in the Rainwater Basin of Nebraska, USA, LGCO activities were limited to 4 days/week and 16 public wetlands were closed to the LGCO to limit disturbance to nontarget waterfowl during this energetically important time period. However, the effects of LGCO activities on waterfowl behavior and energy expenditure are relatively unknown in this critical waterfowl staging area. To evaluate LGCO effects on target and nontarget species, we paired wetlands open and closed to LGCO and recorded waterfowl behavior and hunter encounters during springs 2011 and 2012. We constructed hourly energy expenditure models based on behavior data collected for mallards (<i>Anas platyrhynchos</i>) and northern pintails (<i>A. acuta</i>). In 2011, dabbling ducks (<i>Anas</i> spp.) spent more time feeding and less time resting in wetlands closed to hunting during early season when the majority of hunting encounters occurred; behaviors did not differ between hunt categories during late season when hunting activities subsided. However, in 2012, dabbling ducks spent more time feeding and less time resting in wetlands open to hunting during early and late seasons. We detected no differences in behaviors of lesser snow geese (<i>Chen caerulescens</i>) or greater white-fronted geese (<i>Anser albifrons</i>) between hunting categories in early season. Mallards had slightly greater energy expenditure on wetlands closed to hunting (<span class=\"math-equation-construct\" data-equation-construct=\"true\"><span class=\"math-equation-image\" data-equation-image=\"true\"><img class=\"inlineGraphic\" src=\"http://onlinelibrary.wiley.com/store/10.1002/wsb.704/asset/equation/wsb704-math-0004.png?v=1&amp;s=7ad02ca916ca9968e7ede77f6a01513319795a9c\" alt=\"math formula\" data-mce-src=\"http://onlinelibrary.wiley.com/store/10.1002/wsb.704/asset/equation/wsb704-math-0004.png?v=1&amp;s=7ad02ca916ca9968e7ede77f6a01513319795a9c\"></span></span><span> </span> = 38.94 ± 0.31 kJ/bird/hr), compared with wetlands open to hunting (<span class=\"math-equation-construct\" data-equation-construct=\"true\"><span class=\"math-equation-image\" data-equation-image=\"true\"><img class=\"inlineGraphic\" src=\"http://onlinelibrary.wiley.com/store/10.1002/wsb.704/asset/equation/wsb704-math-0004.png?v=1&amp;s=7ad02ca916ca9968e7ede77f6a01513319795a9c\" alt=\"math formula\" data-mce-src=\"http://onlinelibrary.wiley.com/store/10.1002/wsb.704/asset/equation/wsb704-math-0004.png?v=1&amp;s=7ad02ca916ca9968e7ede77f6a01513319795a9c\"></span></span><span> </span> = 37.87 ± 0.32 kJ/bird/hr); therefore, greater energy spent by mallards cannot be attributed to hunting disturbance. We also detected no differences in dabbling duck behavior or energy expenditure between days open or closed to hunting in the region. A refuge system of wetlands closed to LGCO activities in the Rainwater Basin may be an important management strategy in providing reduced disturbance for nontarget waterfowl species in some years. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.</p>","language":"English","publisher":"Wiley","doi":"10.1002/wsb.704","usgsCitation":"Dinges, A.J., Webb, E.B., and Vrtiska, M.P., 2016,  Light Goose Conservation Order effects on nontarget waterfowl behavior and energy expenditure: Wildlife Society Bulletin, v. 40, no. 4, p. 694-704, https://doi.org/10.1002/wsb.704.","productDescription":"11 p.","startPage":"694","endPage":"704","ipdsId":"IP-065245","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499900,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/b799acf54ffe4e03877db7aa149ef52f","text":"External Repository"},{"id":347505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Rainwater Basin","volume":"40","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-02","publicationStatus":"PW","scienceBaseUri":"59f83a3ae4b063d5d30980f9","contributors":{"authors":[{"text":"Dinges, Andrew J.","contributorId":145935,"corporation":false,"usgs":false,"family":"Dinges","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":716467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":716079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vrtiska, Mark P.","contributorId":54008,"corporation":false,"usgs":true,"family":"Vrtiska","given":"Mark","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":716468,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70175168,"text":"70175168 - 2016 - Determination of eruption temperature of Io's lavas using lava tube skylights","interactions":[],"lastModifiedDate":"2018-11-08T16:27:43","indexId":"70175168","displayToPublicDate":"2016-11-01T13:56:34","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Determination of eruption temperature of Io's lavas using lava tube skylights","docAbstract":"<p><span>Determining the eruption temperature of Io's dominant silicate lavas would constrain Io's present interior state and composition. We have examined how eruption temperature can be estimated at lava tube skylights through synthesis of thermal emission from the incandescent lava flowing within the lava tube. Lava tube skylights should be present along Io's long-lived lava flow fields, and are attractive targets because of their temporal stability and the narrow range of near-eruption temperatures revealed through them. We conclude that these skylights are suitable and desirable targets (perhaps&nbsp;</span><i>the</i><span>&nbsp;very best targets) for the purposes of constraining eruption temperature, with a 0.9:0.7-µm radiant flux ratio ≤6.3 being diagnostic of ultramafic lava temperatures. Because the target skylights may be small – perhaps only a few m or 10</span><span>&nbsp;</span><span>s of m across – such observations will require a future Io-dedicated mission that will obtain high spatial resolution ( &lt; 100</span><span>&nbsp;</span><span>m/pixel), unsaturated observations of Io's surface at multiple wavelengths in the visible and near-infrared, ideally at night. In contrast to observations of lava fountains or roiling lava lakes, where accurate determination of surface temperature distribution requires simultaneous or near-simultaneous ( &lt; 0.1</span><span>&nbsp;</span><span>s) observations at different wavelengths, skylight thermal emission data are superior for the purposes of temperature derivation, as emission is stable on much longer time scales (minutes, or longer), so long as viewing geometry does not greatly change during that time.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2016.06.003","usgsCitation":"Davies, A., Keszthelyi, L.P., and McEwen, A.S., 2016, Determination of eruption temperature of Io's lavas using lava tube skylights: Icarus, v. 278, p. 266-278, https://doi.org/10.1016/j.icarus.2016.06.003.","productDescription":"13 p.","startPage":"266","endPage":"278","ipdsId":"IP-070452","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":470447,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/621256","text":"External Repository"},{"id":356290,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"278","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc83be4b0f5d57878ec18","contributors":{"authors":[{"text":"Davies, Ashley G.","contributorId":36827,"corporation":false,"usgs":true,"family":"Davies","given":"Ashley G.","affiliations":[],"preferred":false,"id":644191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keszthelyi, Laszlo P. 0000-0003-1879-4331 laz@usgs.gov","orcid":"https://orcid.org/0000-0003-1879-4331","contributorId":227,"corporation":false,"usgs":true,"family":"Keszthelyi","given":"Laszlo","email":"laz@usgs.gov","middleInitial":"P.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":644190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McEwen, Alfred S.","contributorId":61657,"corporation":false,"usgs":false,"family":"McEwen","given":"Alfred","email":"","middleInitial":"S.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":644192,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70202096,"text":"70202096 - 2016 - A cellular automata downscaling based 1 km global land use datasets (2010–2100)","interactions":[],"lastModifiedDate":"2019-02-11T11:04:21","indexId":"70202096","displayToPublicDate":"2016-11-01T11:04:14","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5802,"text":"Science Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"A cellular automata downscaling based 1 km global land use datasets (2010–2100)","docAbstract":"<div id=\"abstracts\" class=\"Abstracts\"><div id=\"ab0005\" class=\"abstract author\"><div id=\"abs0005\"><p id=\"sp0055\"><span>Global climate and environmental change&nbsp;studies require detailed&nbsp;land-use&nbsp;and&nbsp;land-cover(LULC) information about the past, present, and future. In this paper, we discuss a methodology for downscaling coarse-resolution (i.e., half-degree) future land use scenarios to finer (i.e., 1</span>&nbsp;<span>km) resolutions at the&nbsp;global scale&nbsp;using a grid-based spatially explicit&nbsp;cellular automata&nbsp;(CA) model. We account for spatial heterogeneity from&nbsp;topography, climate, soils, and socioeconomic variables. The model uses a global 30</span>&nbsp;<span>m land cover map (2010) as the base input, a variety of biogeographic and socioeconomic variables, and an&nbsp;empirical analysis&nbsp;to downscale coarse-resolution land use information (specifically urban, crop and pasture). The output of this model offers the most current and finest-scale future LULC dynamics from 2010 to 2100 (with four representative concentration pathway (RCP) scenarios—RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) at a 1</span>&nbsp;<span>km resolution within a globally consistent framework. The data are freely available for download, and will enable researchers to study the impacts of LULC change at the&nbsp;local scale.</span></p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1007/s11434-016-1148-1","usgsCitation":"Li, X., Yu, L., Sohl, T.L., Clinton, N., Li, W., Zhu, Z., Liu, X., and Gong, P., 2016, A cellular automata downscaling based 1 km global land use datasets (2010–2100): Science Bulletin, v. 61, no. 21, p. 1651-1661, https://doi.org/10.1007/s11434-016-1148-1.","productDescription":"11 p.","startPage":"1651","endPage":"1661","ipdsId":"IP-088252","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":361126,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"21","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Xuecao","contributorId":169731,"corporation":false,"usgs":false,"family":"Li","given":"Xuecao","email":"","affiliations":[{"id":25577,"text":"Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":756907,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yu, Le","contributorId":213081,"corporation":false,"usgs":false,"family":"Yu","given":"Le","email":"","affiliations":[],"preferred":false,"id":756908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","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":756872,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clinton, Nicholas","contributorId":213082,"corporation":false,"usgs":false,"family":"Clinton","given":"Nicholas","email":"","affiliations":[],"preferred":false,"id":756909,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Wenyu","contributorId":213083,"corporation":false,"usgs":false,"family":"Li","given":"Wenyu","email":"","affiliations":[],"preferred":false,"id":756910,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"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":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":756911,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Liu, Xiaoping","contributorId":213084,"corporation":false,"usgs":false,"family":"Liu","given":"Xiaoping","email":"","affiliations":[],"preferred":false,"id":756912,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gong, Peng","contributorId":102393,"corporation":false,"usgs":true,"family":"Gong","given":"Peng","affiliations":[],"preferred":false,"id":756913,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
]}