The Central Valley in California (USA) covers about 52,000 km2 and is one of the most productive agricultural regions in the world. This agriculture relies heavily on surface-water diversions and groundwater pumpage to meet irrigation water demand. Because the valley is semi-arid and surface-water availability varies substantially, agriculture relies heavily on local groundwater. In the southern two thirds of the valley, the San Joaquin Valley, historic and recent groundwater pumpage has caused significant and extensive drawdowns, aquifer-system compaction and subsidence. During recent drought periods (2007-2009 and 2012-present), groundwater pumping has increased owing to a combination of decreased surface-water availability and land-use changes. Declining groundwater levels, approaching or surpassing historical low levels, have caused accelerated and renewed compaction and subsidence that likely is mostly permanent. The subsidence has caused operational, maintenance, and construction-design problems for water-delivery and floodcontrol canals in the San Joaquin Valley. Planning for the effects of continued subsidence in the area is important for water agencies. As land use, managed aquifer recharge, and surface-water availability continue to vary, long-term groundwater- level and subsidence monitoring and modelling are critical to understanding the dynamics of historical and continued groundwater use resulting in additional water-level and groundwater storage declines, and associated subsidence. Modeling tools such as the Central Valley Hydrologic Model, can be used in the evaluation of management strategies to mitigate adverse impacts due to subsidence while also optimizing water availability. This knowledge will be critical for successful implementation of recent legislation aimed toward sustainable groundwater use.
","language":"English","publisher":"University of California at Davis","doi":"10.1007/s10040-015-1339-x","usgsCitation":"Faunt, C.C., Sneed, M., Traum, J.A., and Brandt, J.T., 2015, Water availability and subsidence in California's Central Valley: San Francisco Estuary and Watershed Science, v. 13, no. 3, 8 p., https://doi.org/10.1007/s10040-015-1339-x.","productDescription":"8 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068386","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":471729,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-015-1339-x","text":"Publisher Index Page"},{"id":381504,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.05810546875,\n 40.70562793820589\n ],\n [\n -122.86010742187499,\n 40.38839687388361\n ],\n [\n -121.95922851562501,\n 37.93553306183642\n ],\n [\n -119.54223632812501,\n 35.074964853989556\n ],\n [\n -118.740234375,\n 35.0120020431607\n ],\n [\n -118.740234375,\n 36.10237644873644\n ],\n [\n -120.728759765625,\n 38.25543637637947\n ],\n [\n -122.05810546875,\n 40.70562793820589\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-17","publicationStatus":"PW","scienceBaseUri":"5614e2afe4b0ba4884c611a8","contributors":{"authors":[{"text":"Faunt, Claudia C. ccfaunt@usgs.gov","contributorId":149018,"corporation":false,"usgs":true,"family":"Faunt","given":"Claudia","email":"ccfaunt@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":576574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sneed, Michelle 0000-0002-8180-382X micsneed@usgs.gov","orcid":"https://orcid.org/0000-0002-8180-382X","contributorId":149052,"corporation":false,"usgs":true,"family":"Sneed","given":"Michelle","email":"micsneed@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":576575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Traum, Jonathan A. 0000-0002-4787-3680 jtraum@usgs.gov","orcid":"https://orcid.org/0000-0002-4787-3680","contributorId":4780,"corporation":false,"usgs":true,"family":"Traum","given":"Jonathan","email":"jtraum@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807111,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brandt, Justin T. 0000-0002-9397-6824 jbrandt@usgs.gov","orcid":"https://orcid.org/0000-0002-9397-6824","contributorId":157,"corporation":false,"usgs":true,"family":"Brandt","given":"Justin","email":"jbrandt@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807112,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159172,"text":"70159172 - 2015 - Statistical models for the analysis and design of digital polymerase chain (dPCR) experiments","interactions":[],"lastModifiedDate":"2016-07-11T15:43:19","indexId":"70159172","displayToPublicDate":"2015-10-05T13:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":761,"text":"Analytical Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Statistical models for the analysis and design of digital polymerase chain (dPCR) experiments","docAbstract":"Statistical methods for the analysis and design of experiments using digital PCR (dPCR) have received only limited attention and have been misused in many instances. To address this issue and to provide a more general approach to the analysis of dPCR data, we describe a class of statistical models for the analysis and design of experiments that require quantification of nucleic acids. These models are mathematically equivalent to generalized linear models of binomial responses that include a complementary, log–log link function and an offset that is dependent on the dPCR partition volume. These models are both versatile and easy to fit using conventional statistical software. Covariates can be used to specify different sources of variation in nucleic acid concentration, and a model’s parameters can be used to quantify the effects of these covariates. For purposes of illustration, we analyzed dPCR data from different types of experiments, including serial dilution, evaluation of copy number variation, and quantification of gene expression. We also showed how these models can be used to help design dPCR experiments, as in selection of sample sizes needed to achieve desired levels of precision in estimates of nucleic acid concentration or to detect differences in concentration among treatments with prescribed levels of statistical power.
","language":"English","publisher":"American Chemical Society","publisherLocation":"Washington, DC","doi":"10.1021/acs.analchem.5b02429","usgsCitation":"Dorazio, R., and Hunter, M., 2015, Statistical models for the analysis and design of digital polymerase chain (dPCR) experiments: Analytical Chemistry, v. 87, no. 21, p. 10886-10893, https://doi.org/10.1021/acs.analchem.5b02429.","productDescription":"8 p.","startPage":"10886","endPage":"10893","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066660","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":310034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"87","issue":"21","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-13","publicationStatus":"PW","scienceBaseUri":"56261492e4b0fb9a11dd7651","contributors":{"authors":[{"text":"Dorazio, Robert 0000-0003-2663-0468 bob_dorazio@usgs.gov","orcid":"https://orcid.org/0000-0003-2663-0468","contributorId":149286,"corporation":false,"usgs":true,"family":"Dorazio","given":"Robert","email":"bob_dorazio@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":577747,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hunter, Margaret 0000-0002-4760-9302 mhunter@usgs.gov","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":140627,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","email":"mhunter@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":577748,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70158597,"text":"70158597 - 2015 - Avian community responses to post-fire forest structure: Implications for fire management in mixed conifer forests","interactions":[],"lastModifiedDate":"2016-01-25T12:39:01","indexId":"70158597","displayToPublicDate":"2015-10-05T13:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":774,"text":"Animal Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Avian community responses to post-fire forest structure: Implications for fire management in mixed conifer forests","docAbstract":"Fire is a natural process and the dominant disturbance shaping plant and animal communities in many coniferous forests of the western US. Given that fire size and severity are predicted to increase in the future, it has become increasingly important to understand how wildlife responds to fire and post-fire management. The Angora Fire burned 1243 hectares of mixed conifer forest in South Lake Tahoe, California. We conducted avian point counts for the first 3 years following the fire in burned and unburned areas to investigate which habitat characteristics are most important for re-establishing or maintaining the native avian community in post-fire landscapes. We used a multi-species occurrence model to estimate how avian species are influenced by the density of live and dead trees and shrub cover. While accounting for variations in the detectability of species, our approach estimated the occurrence probabilities of all species detected including those that were rare or observed infrequently. Although all species encountered in this study were detected in burned areas, species-specific modeling results predicted that some species were strongly associated with specific post-fire conditions, such as a high density of dead trees, open-canopy conditions or high levels of shrub cover that occur at particular burn severities or at a particular time following fire. These results indicate that prescribed fire or managed wildfire which burns at low to moderate severity without at least some high-severity effects is both unlikely to result in the species assemblages that are unique to post-fire areas or to provide habitat for burn specialists. Additionally, the probability of occurrence for many species was associated with high levels of standing dead trees indicating that intensive post-fire harvest of these structures could negatively impact habitat of a considerable proportion of the avian community.
","language":"English","publisher":"Zoological Society of London","publisherLocation":"Cambridge, United Kingdom","doi":"10.1111/acv.12237","usgsCitation":"White, A.M., Manley, P.N., Tarbill, G., Richardson, T., Russell, R.E., Safford, H.D., and Dobrowski, S.Z., 2015, Avian community responses to post-fire forest structure: Implications for fire management in mixed conifer forests: Animal Conservation, 9 p., https://doi.org/10.1111/acv.12237.","productDescription":"9 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055788","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":309571,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Lake Tahoe basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.1,\n 38.8\n ],\n [\n -120.1,\n 38.9\n ],\n [\n -120,\n 38.9\n ],\n [\n -120,\n 38.8\n ],\n [\n -120.1,\n 38.8\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-28","publicationStatus":"PW","scienceBaseUri":"5613911fe4b0ba4884c60f5e","contributors":{"authors":[{"text":"White, Angela M.","contributorId":84255,"corporation":false,"usgs":true,"family":"White","given":"Angela","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":576249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manley, Patricia N.","contributorId":79010,"corporation":false,"usgs":true,"family":"Manley","given":"Patricia","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":576250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tarbill, Gina","contributorId":148953,"corporation":false,"usgs":false,"family":"Tarbill","given":"Gina","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":576251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richardson, T.L.","contributorId":78607,"corporation":false,"usgs":true,"family":"Richardson","given":"T.L.","email":"","affiliations":[],"preferred":false,"id":576253,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Russell, Robin E. 0000-0001-8726-7303 rerussell@usgs.gov","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":3998,"corporation":false,"usgs":true,"family":"Russell","given":"Robin","email":"rerussell@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":576248,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Safford, Hugh D.","contributorId":112922,"corporation":false,"usgs":true,"family":"Safford","given":"Hugh","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":576252,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dobrowski, Solomon Z.","contributorId":8751,"corporation":false,"usgs":true,"family":"Dobrowski","given":"Solomon","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":576254,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70158666,"text":"70158666 - 2015 - Effectiveness of a refuge for Lake Trout in Western Lake Superior II: Simulation of future performance","interactions":[],"lastModifiedDate":"2016-06-01T11:53:35","indexId":"70158666","displayToPublicDate":"2015-10-05T13:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Effectiveness of a refuge for Lake Trout in Western Lake Superior II: Simulation of future performance","docAbstract":"Historically, Lake Superior supported one of the largest and most diverse Lake Trout Salvelinus namaycush fisheries in the Laurentian Great Lakes, but Lake Trout stocks collapsed due to excessive fishery exploitation and predation by Sea Lampreys Petromyzon marinus. Lake Trout stocking, Sea Lamprey control, and fishery regulations, including a refuge encompassing Gull Island Shoal (Apostle Islands region), were used to enable recovery of Lake Trout stocks that used this historically important spawning shoal. Our objective was to determine whether future sustainability of Lake Trout stocks will depend on the presence of the Gull Island Shoal Refuge. We constructed a stochastic age-structured simulation model to assess the effect of maintaining the refuge as a harvest management tool versus removing the refuge. In general, median abundances of age-4, age-4 and older (age-4+), and age-8+ fish collapsed at lower instantaneous fishing mortality rates (F) when the refuge was removed than when the refuge was maintained. With the refuge in place, the F that resulted in collapse depended on the rate of movement into and out of the refuge. Too many fish stayed in the refuge when movement was low (0–2%), and too many fish became vulnerable to fishing when movement was high (≥22%); thus, the refuge was more effective at intermediate rates of movement (10–11%). With the refuge in place, extinction did not occur at any simulated level of F, whereas refuge removal led to extinction at all combinations of commercial F and recreational F. Our results indicate that the Lake Trout population would be sustained by the refuge at all simulated F-values, whereas removal of the refuge would risk population collapse at much lower F (0.700–0.744). Therefore, the Gull Island Shoal Refuge is needed to sustain the Lake Trout population in eastern Wisconsin waters of Lake Superior.
","language":"English","publisher":"American Fisheries Society","publisherLocation":"Lawrence, KS","doi":"10.1080/02755947.2015.1074960","usgsCitation":"Akins, A.L., Hansen, M.J., and Seider, M.J., 2015, Effectiveness of a refuge for Lake Trout in Western Lake Superior II: Simulation of future performance: North American Journal of Fisheries Management, v. 35, no. 5, p. 1003-1018, https://doi.org/10.1080/02755947.2015.1074960.","productDescription":"16 p.","startPage":"1003","endPage":"1018","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065104","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":309581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Minnesota, Wisconsin","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.451416015625,\n 48.77067246880509\n ],\n [\n -86.275634765625,\n 48.436489955944154\n ],\n [\n -85.97900390625,\n 47.931066347509784\n ],\n [\n -85.594482421875,\n 47.87214396888731\n ],\n [\n -85.111083984375,\n 47.938426929481054\n ],\n [\n -84.803466796875,\n 47.938426929481054\n ],\n [\n -84.847412109375,\n 47.81315451752768\n ],\n [\n -85.01220703125,\n 47.58393661978137\n ],\n [\n -84.88037109375,\n 47.45037978769006\n ],\n [\n -84.66064453125,\n 47.30903424774781\n ],\n [\n -84.78149414062499,\n 47.05515408550348\n ],\n [\n -84.78149414062499,\n 46.94276208682137\n ],\n [\n -84.451904296875,\n 46.875213396722685\n ],\n [\n -84.55078125,\n 46.717268685073954\n ],\n [\n -84.638671875,\n 46.53619267489863\n ],\n [\n -84.825439453125,\n 46.48326472915561\n ],\n [\n -84.979248046875,\n 46.626806395355175\n ],\n [\n -84.91333007812499,\n 46.7549166192819\n ],\n [\n -85.067138671875,\n 46.800059446787316\n ],\n [\n -85.50659179687499,\n 46.717268685073954\n ],\n [\n -86.06689453125,\n 46.694667307773116\n ],\n [\n -86.561279296875,\n 46.521075663842836\n ],\n [\n -86.8798828125,\n 46.521075663842836\n ],\n [\n -87.07763671875,\n 46.543749602738565\n ],\n [\n -87.374267578125,\n 46.521075663842836\n ],\n [\n -87.703857421875,\n 46.89023157359399\n ],\n [\n -88.231201171875,\n 46.965259400349275\n ],\n [\n -88.428955078125,\n 46.927758623434435\n ],\n [\n -88.26416015625,\n 47.092565552235705\n ],\n [\n -87.86865234374999,\n 47.30903424774781\n ],\n [\n -87.60498046875,\n 47.42065432071321\n ],\n [\n -87.923583984375,\n 47.50978034953473\n ],\n [\n -88.385009765625,\n 47.47266286861342\n ],\n [\n -88.76953125,\n 47.234489635299184\n ],\n [\n -89.483642578125,\n 46.912750956378915\n ],\n [\n -89.967041015625,\n 46.800059446787316\n ],\n [\n -90.4833984375,\n 46.619261036171515\n ],\n [\n -90.692138671875,\n 46.70973594407157\n ],\n [\n -90.94482421875,\n 46.702202151643455\n ],\n [\n -90.758056640625,\n 46.93526088057719\n ],\n [\n -90.94482421875,\n 46.93526088057719\n ],\n [\n -91.3623046875,\n 46.830133640447386\n ],\n [\n -91.845703125,\n 46.73986059969267\n ],\n [\n -92.054443359375,\n 46.7549166192819\n ],\n [\n -92.076416015625,\n 46.807579571992385\n ],\n [\n -91.395263671875,\n 47.16730970131578\n ],\n [\n -90.911865234375,\n 47.53945544742392\n ],\n [\n -90.1318359375,\n 47.80577611936809\n ],\n [\n -89.58251953125,\n 47.96785877999253\n ],\n [\n -89.26391601562499,\n 48.188063481211415\n ],\n [\n -89.23095703125,\n 48.472921272487824\n ],\n [\n -88.9013671875,\n 48.61838518688487\n ],\n [\n -88.43994140625,\n 48.90083790234088\n ],\n [\n -88.22021484375,\n 49.001843917978526\n ],\n [\n -86.451416015625,\n 48.77067246880509\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-02","publicationStatus":"PW","scienceBaseUri":"56139122e4b0ba4884c60f62","contributors":{"authors":[{"text":"Akins, Andrea L","contributorId":148998,"corporation":false,"usgs":false,"family":"Akins","given":"Andrea","email":"","middleInitial":"L","affiliations":[{"id":17613,"text":"University of Wisconsin - Stevens Point","active":true,"usgs":false}],"preferred":false,"id":576420,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, Michael J. 0000-0001-8522-3876 michaelhansen@usgs.gov","orcid":"https://orcid.org/0000-0001-8522-3876","contributorId":5006,"corporation":false,"usgs":true,"family":"Hansen","given":"Michael","email":"michaelhansen@usgs.gov","middleInitial":"J.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":576419,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seider, Michael J.","contributorId":19452,"corporation":false,"usgs":true,"family":"Seider","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":576421,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70158674,"text":"70158674 - 2015 - Many atolls may be uninhabitable within decades due to climate change","interactions":[],"lastModifiedDate":"2015-10-05T13:32:53","indexId":"70158674","displayToPublicDate":"2015-10-05T13:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Many atolls may be uninhabitable within decades due to climate change","docAbstract":"Observations show global sea level is rising due to climate change, with the highest rates in the tropical Pacific Ocean where many of the world’s low-lying atolls are located. Sea-level rise is particularly critical for low-lying carbonate reef-lined atoll islands; these islands have limited land and water available for human habitation, water and food sources, and ecosystems that are vulnerable to inundation from sea-level rise. Here we demonstrate that sea-level rise will result in larger waves and higher wave-driven water levels along atoll islands’ shorelines than at present. Numerical model results reveal waves will synergistically interact with sea-level rise, causing twice as much land forecast to be flooded for a given value of sea-level rise than currently predicted by current models that do not take wave-driven water levels into account. Atolls with islands close to the shallow reef crest are more likely to be subjected to greater wave-induced run-up and flooding due to sea-level rise than those with deeper reef crests farther from the islands’ shorelines. It appears that many atoll islands will be flooded annually, salinizing the limited freshwater resources and thus likely forcing inhabitants to abandon their islands in decades, not centuries, as previously thought.
","language":"English","publisher":"Nature Publishing Group","publisherLocation":"London, United Kingdom","doi":"10.1038/srep14546","collaboration":"Deltares U.S.A., Hawaii Cooperative Studies Unit","usgsCitation":"Storlazzi, C.D., Elias, E.P., and Berkowitz, P., 2015, Many atolls may be uninhabitable within decades due to climate change: Scientific Reports, v. 5, p. 1-9, https://doi.org/10.1038/srep14546.","productDescription":"Art 14546: 9 p.","startPage":"1","endPage":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063820","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471730,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/srep14546","text":"Publisher Index Page"},{"id":309559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Midway Atoll, Laysan Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -177.33083724975583,\n 28.21986116672942\n ],\n [\n -177.34182357788086,\n 28.213810689875928\n ],\n [\n -177.36448287963867,\n 28.21910487587188\n ],\n [\n -177.37546920776367,\n 28.221978752630008\n ],\n [\n -177.39006042480466,\n 28.217894799357122\n ],\n [\n -177.39967346191406,\n 28.18945405424149\n ],\n [\n -177.38079071044922,\n 28.194598154006137\n ],\n [\n -177.36825942993164,\n 28.197623979398678\n ],\n [\n -177.3625946044922,\n 28.203675373165503\n ],\n [\n -177.352294921875,\n 28.20382665361861\n ],\n [\n -177.32603073120117,\n 28.20231383944811\n ],\n [\n -177.31813430786133,\n 28.20791114488013\n ],\n [\n -177.31985092163086,\n 28.216835971157135\n ],\n [\n -177.33083724975583,\n 28.21986116672942\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -171.7060089111328,\n 25.7989638778495\n ],\n [\n -171.73261642456052,\n 25.732024280088133\n ],\n [\n -171.7624855041504,\n 25.74192073145334\n ],\n [\n -171.7741584777832,\n 25.774696838890705\n ],\n [\n -171.71184539794922,\n 25.800045732089465\n ],\n [\n -171.7060089111328,\n 25.7989638778495\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-25","publicationStatus":"PW","scienceBaseUri":"56139123e4b0ba4884c60f66","contributors":{"authors":[{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":576452,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elias, Edwin P.L.","contributorId":47295,"corporation":false,"usgs":true,"family":"Elias","given":"Edwin","email":"","middleInitial":"P.L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":576453,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berkowitz, Paul pberkowitz@usgs.gov","contributorId":4642,"corporation":false,"usgs":true,"family":"Berkowitz","given":"Paul","email":"pberkowitz@usgs.gov","affiliations":[],"preferred":true,"id":576454,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70158676,"text":"70158676 - 2015 - Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 climate scenarios","interactions":[],"lastModifiedDate":"2015-12-07T11:17:03","indexId":"70158676","displayToPublicDate":"2015-10-05T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2925,"text":"Ocean Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 climate scenarios","docAbstract":"Hindcast and 21st century winds, simulated by General Circulation Models (GCMs), were used to drive global- and regional-scale spectral wind-wave generation models in the Pacific Ocean Basin to assess future wave conditions along the margins of the North American west coast and Hawaiian Islands. Three-hourly winds simulated by four separate GCMs were used to generate an ensemble of wave conditions for a recent historical time-period (1976–2005) and projections for the mid and latter parts of the 21st century under two radiative forcing scenarios (RCP 4.5 and RCP 8.5), as defined by the fifth phase of the Coupled Model Inter-comparison Project (CMIP5) experiments. Comparisons of results from historical simulations with wave buoy and ERA-Interim wave reanalysis data indicate acceptable model performance of wave heights, periods, and directions, giving credence to generating projections. Mean and extreme wave heights are projected to decrease along much of the North American west coast. Extreme wave heights are projected to decrease south of ∼50°N and increase to the north, whereas extreme wave periods are projected to mostly increase. Incident wave directions associated with extreme wave heights are projected to rotate clockwise at the eastern end of the Aleutian Islands and counterclockwise offshore of Southern California. Local spatial patterns of the changing wave climate are similar under the RCP 4.5 and RCP 8.5 scenarios, but stronger magnitudes of change are projected under RCP 8.5. Findings of this study are similar to previous work using CMIP3 GCMs that indicates decreasing mean and extreme wave conditions in the Eastern North Pacific, but differ from other studies with respect to magnitude and local patterns of change. This study contributes toward a larger ensemble of global and regional climate projections needed to better assess uncertainty of potential future wave climate change, and provides model boundary conditions for assessing the impacts of climate change on coastal systems.
","language":"English","publisher":"Elsevier Science Ltd","publisherLocation":"Oxford, United Kingdom","doi":"10.1016/j.ocemod.2015.07.004","collaboration":"Christie Hegermiller, UCSC; Peter Ruggiero, Oregon State University; Maarten van Ormondt, Deltares,","usgsCitation":"Erikson, L., Hegermiller, C., Barnard, P., Ruggiero, P., and van Ormondt, M., 2015, Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 climate scenarios: Ocean Modelling, v. 96, no. 1, p. 171-185, https://doi.org/10.1016/j.ocemod.2015.07.004.","productDescription":"15 p.","startPage":"171","endPage":"185","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-051162","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":309542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -180,\n -3.513421045640032\n ],\n [\n -180,\n 65.2198939361321\n ],\n [\n -78.046875,\n 65.2198939361321\n ],\n [\n -78.046875,\n -3.513421045640032\n ],\n [\n -180,\n -3.513421045640032\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"96","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56139126e4b0ba4884c60f6a","contributors":{"authors":[{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":147149,"corporation":false,"usgs":true,"family":"Erikson","given":"Li H.","email":"lerikson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":576458,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hegermiller, Christie 0000-0002-6383-7508 chegermiller@usgs.gov","orcid":"https://orcid.org/0000-0002-6383-7508","contributorId":149010,"corporation":false,"usgs":true,"family":"Hegermiller","given":"Christie","email":"chegermiller@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":576459,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":576460,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruggiero, Peter","contributorId":15709,"corporation":false,"usgs":false,"family":"Ruggiero","given":"Peter","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":576461,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Ormondt, Martin","contributorId":149011,"corporation":false,"usgs":false,"family":"van Ormondt","given":"Martin","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":576462,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70158675,"text":"70158675 - 2015 - Structural classification of marshes with Polarimetric SAR highlighting the temporal mapping of marshes exposed to oil","interactions":[],"lastModifiedDate":"2016-07-17T23:30:02","indexId":"70158675","displayToPublicDate":"2015-10-05T09:30:00","publicationYear":"2015","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":"Structural classification of marshes with Polarimetric SAR highlighting the temporal mapping of marshes exposed to oil","docAbstract":"Empirical relationships between field-derived Leaf Area Index (LAI) and Leaf Angle Distribution (LAD) and polarimetric synthetic aperture radar (PolSAR) based biophysical indicators were created and applied to map S. alterniflora marsh canopy structure. PolSAR and field data were collected near concurrently in the summers of 2010, 2011, and 2012 in coastal marshes, and PolSAR data alone were acquired in 2009. Regression analyses showed that LAI correspondence with the PolSAR biophysical indicator variables equaled or exceeded those of vegetation water content (VWC) correspondences. In the final six regressor model, the ratio HV/VV explained 49% of the total 77% explained LAI variance, and the HH-VV coherence and phase information accounted for the remainder. HV/HH dominated the two regressor LAD relationship, and spatial heterogeneity and backscatter mechanism followed by coherence information dominated the final three regressor model that explained 74% of the LAD variance. Regression results applied to 2009 through 2012 PolSAR images showed substantial changes in marsh LAI and LAD. Although the direct cause was not substantiated, following a release of freshwater in response to the 2010 Deepwater Horizon oil spill, the fairly uniform interior marsh structure of 2009 was more vertical and dense shortly after the oil spill cessation. After 2010, marsh structure generally progressed back toward the 2009 uniformity; however, the trend was more disjointed in oil impact marshes.
","language":"English","publisher":"Molecular Diversity Preservation International","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs70911295","collaboration":"Amina Rangoonwala USGS, Cathleen E Jones NASA-CalTech","usgsCitation":"Ramsey, E.W., Rangoonwala, A., and Jones, C.E., 2015, Structural classification of marshes with Polarimetric SAR highlighting the temporal mapping of marshes exposed to oil: Remote Sensing, v. 7, no. 9, p. 11295-11321, https://doi.org/10.3390/rs70911295.","productDescription":"27 p.","startPage":"11295","endPage":"11321","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063104","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":471732,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs70911295","text":"Publisher Index Page"},{"id":309543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.5,\n 29\n ],\n [\n -92.5,\n 30\n ],\n [\n -89.5,\n 30\n ],\n [\n -89.5,\n 29\n ],\n [\n -92.5,\n 29\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"9","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-02","publicationStatus":"PW","scienceBaseUri":"56139126e4b0ba4884c60f6e","contributors":{"authors":[{"text":"Ramsey, Elijah W. III 0000-0002-4518-5796 ramseye@usgs.gov","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":2883,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah","suffix":"III","email":"ramseye@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":576455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rangoonwala, Amina 0000-0002-0556-0598 rangoonwalaa@usgs.gov","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":3455,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","email":"rangoonwalaa@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":576456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Cathleen E.","contributorId":11890,"corporation":false,"usgs":true,"family":"Jones","given":"Cathleen","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":576457,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70158670,"text":"70158670 - 2015 - The influence of coral reefs and climate change on wave-driven flooding of tropical coastlines","interactions":[],"lastModifiedDate":"2019-12-11T13:24:59","indexId":"70158670","displayToPublicDate":"2015-10-05T09:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The influence of coral reefs and climate change on wave-driven flooding of tropical coastlines","docAbstract":"A numerical model, XBeach, calibrated and validated on field data collected at Roi-Namur Island on Kwajalein Atoll in the Republic of Marshall Islands, was used to examine the effects of different coral reef characteristics on potential coastal hazards caused by wave-driven flooding and how these effects may be altered by projected climate change. The results presented herein suggest that coasts fronted by relatively narrow reefs with steep fore reef slopes (~1:10 and steeper) and deeper, smoother reef flats are expected to experience the highest wave runup. Wave runup increases for higher water levels (sea level rise), higher waves, and lower bed roughness (coral degradation), which are all expected effects of climate change. Rising sea levels and climate change will therefore have a significant negative impact on the ability of coral reefs to mitigate the effects of coastal hazards in the future.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2015GL064861","usgsCitation":"Quataert, E., Storlazzi, C.D., van Rooijen, A., van Dongeren, A., and Cheriton, O., 2015, The influence of coral reefs and climate change on wave-driven flooding of tropical coastlines: Geophysical Research Letters, v. 42, no. 15, p. 6407-6415, https://doi.org/10.1002/2015GL064861.","productDescription":"9 p.","startPage":"6407","endPage":"6415","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064753","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471733,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gl064861","text":"Publisher Index Page"},{"id":309544,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Republic of the Marshall Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 167.8271484375,\n 7.798078531355303\n ],\n [\n 169.63989257812497,\n 7.798078531355303\n ],\n [\n 169.63989257812497,\n 10.077037154404719\n ],\n [\n 167.8271484375,\n 10.077037154404719\n ],\n [\n 167.8271484375,\n 7.798078531355303\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"15","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-04","publicationStatus":"PW","scienceBaseUri":"56139126e4b0ba4884c60f70","chorus":{"doi":"10.1002/2015gl064861","url":"http://dx.doi.org/10.1002/2015gl064861","publisher":"Wiley-Blackwell","authors":"Quataert Ellen, Storlazzi Curt, van Rooijen Arnold, Cheriton Olivia, van Dongeren Ap","journalName":"Geophysical Research Letters","publicationDate":"8/4/2015","auditedOn":"7/24/2015"},"contributors":{"authors":[{"text":"Quataert, Ellen","contributorId":149000,"corporation":false,"usgs":false,"family":"Quataert","given":"Ellen","affiliations":[{"id":17614,"text":"Delft University of Technology","active":true,"usgs":false}],"preferred":false,"id":576425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":576424,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Rooijen, Arnold","contributorId":149001,"corporation":false,"usgs":false,"family":"van Rooijen","given":"Arnold","email":"","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":576426,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van Dongeren, Ap","contributorId":149002,"corporation":false,"usgs":false,"family":"van Dongeren","given":"Ap","email":"","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":576427,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cheriton, Olivia 0000-0003-3011-9136 ocheriton@usgs.gov","orcid":"https://orcid.org/0000-0003-3011-9136","contributorId":149003,"corporation":false,"usgs":true,"family":"Cheriton","given":"Olivia","email":"ocheriton@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":576428,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70158664,"text":"70158664 - 2015 - Fish assemblages in the Upper Esopus Creek, NY: Current status, variability, and controlling factors","interactions":[],"lastModifiedDate":"2019-12-11T13:20:58","indexId":"70158664","displayToPublicDate":"2015-10-05T09:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2898,"text":"Northeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Fish assemblages in the Upper Esopus Creek, NY: Current status, variability, and controlling factors","docAbstract":"The Upper Esopus Creek receives water diversions from a neighboring basin through the Shandaken Tunnel (the portal) from the Schoharie Reservoir. Although the portal is closed during floods, mean flows and turbidity of portal waters are generally greater than in Esopus Creek above their confluence. These conditions could potentially affect local fish assemblages, yet such effects have not been assessed in this highly regulated stream. We studied water quality, hydrology, temperature, and fish assemblages at 18 sites in the Upper Esopus Creek during 2009–2011 to characterize the effects of the portal input on resident-fish assemblages and to document the status of the fishery resource. In general, fish-community richness increased by 2–3 species at mainstem sites near the portal, and median density and biomass of fish communities at sites downstream of the portal were significantly lower than they were at sites upstream of the portal. Median densities of Salmo trutta (Brown Trout) and all trout species were significantly lower than at mainstem sites downstream from the portal—25.1 fish/0.1 ha and 148.9 fish/0.1 ha, respectively—than at mainstem sites upstream from the portal—68.8 fish/0.1 ha and 357.7 fish/0.1 ha, respectively—yet median biomass for Brown Trout and all trout did not differ between sites from both reaches. The median density of young-of-year Brown Trout at downstream sites (9.3 fish/0.1 ha) was significantly lower than at upstream sites (33.9 fish/0.1 ha). Waters from the portal appeared to adversely affect the density and biomass of young-of-year Brown Trout, but lower temperatures and increased flows also improved habitat quality for mature trout at downstream sites during summer. These findings, and those from companion studies, indicate that moderately turbid waters from the portal had few if any adverse impacts on trout populations and overall fish communities in the Upper Esopus Creek during this study.
","language":"English","publisher":"Eagle Hill Institute","publisherLocation":"Steuben, ME","doi":"10.1656/045.022.0209","collaboration":"Cornell Cooperative Extension of Ulster County; USGS","usgsCitation":"Baldigo, B.P., George, S.D., and Keller, W.T., 2015, Fish assemblages in the Upper Esopus Creek, NY: Current status, variability, and controlling factors: Northeastern Naturalist, v. 22, no. 2, p. 345-371, https://doi.org/10.1656/045.022.0209.","productDescription":"27 p.","startPage":"345","endPage":"371","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-042999","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":309548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Upper Esopus Creek, Catskill Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.16571044921875,\n 41.748775021355044\n ],\n [\n -73.9874267578125,\n 41.748775021355044\n ],\n [\n -73.9874267578125,\n 42.409262623071186\n ],\n [\n -75.16571044921875,\n 42.409262623071186\n ],\n [\n -75.16571044921875,\n 41.748775021355044\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56139122e4b0ba4884c60f64","contributors":{"authors":[{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":576413,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":576414,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keller, Walter T","contributorId":148996,"corporation":false,"usgs":false,"family":"Keller","given":"Walter","email":"","middleInitial":"T","affiliations":[{"id":17612,"text":"Retired Fisheries Manager, NYS Dept of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":576415,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70156829,"text":"70156829 - 2015 - Borehole strainmeter measurements spanning the 2014, Mw6.0 South Napa Earthquake, California: The effect from instrument calibration","interactions":[],"lastModifiedDate":"2015-11-18T16:19:37","indexId":"70156829","displayToPublicDate":"2015-10-05T06:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Borehole strainmeter measurements spanning the 2014, Mw6.0 South Napa Earthquake, California: The effect from instrument calibration","docAbstract":"The 24 August 2014 Mw6.0 South Napa, California earthquake produced significant offsets on 12 borehole strainmeters in the San Francisco Bay area. These strainmeters are located between 24 and 80 km from the source and the observed offsets ranged up to 400 parts-per-billion (ppb), which exceeds their nominal precision by a factor of 100. However, the observed offsets of tidally calibrated strains differ by up to 130 ppb from predictions based on a moment tensor derived from seismic data. The large misfit can be attributed to a combination of poor instrument calibration and better modeling of the strain fit from the earthquake. Borehole strainmeters require in-situ calibration, which historically has been accomplished by comparing their measurements of Earth tides with the strain-tides predicted by a model. Although the borehole strainmeter accurately measure the deformation within the borehole, the long-wavelength strain signals from tides or other tectonic processes recorded in the borehole are modified by the presence of the borehole and the elastic properties of the grout and the instrument. Previous analyses of surface-mounted, strainmeter data and their relationship with the predicted tides suggest that tidal models could be in error by 30%. The poor fit of the borehole strainmeter data from this earthquake can be improved by simultaneously varying the components of the model tides up to 30% and making small adjustments to the point-source model of the earthquake, which reduces the RMS misfit from 130 ppb to 18 ppb. This suggests that relying on tidal models to calibrate borehole strainmeters significantly reduces their accuracy.
","language":"English","publisher":"William Byrd Press for John Hopkins Press","publisherLocation":"Richmond, VA","doi":"10.1002/2015JB012278","usgsCitation":"Langbein, J.O., 2015, Borehole strainmeter measurements spanning the 2014, Mw6.0 South Napa Earthquake, California: The effect from instrument calibration: Journal of Geophysical Research, v. 120, no. 10, p. 7190-7202, https://doi.org/10.1002/2015JB012278.","productDescription":"13 p.","startPage":"7190","endPage":"7202","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065802","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":471734,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012278","text":"Publisher Index Page"},{"id":311547,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Napa","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.54425048828125,\n 37.903032319353656\n ],\n [\n -122.54425048828125,\n 38.48261976950729\n ],\n [\n -122.00729370117188,\n 38.48261976950729\n ],\n [\n -122.00729370117188,\n 37.903032319353656\n ],\n [\n -122.54425048828125,\n 37.903032319353656\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-05","publicationStatus":"PW","scienceBaseUri":"564daf45e4b0112df6c62df0","contributors":{"authors":[{"text":"Langbein, John O. 0000-0002-7821-8101 langbein@usgs.gov","orcid":"https://orcid.org/0000-0002-7821-8101","contributorId":3293,"corporation":false,"usgs":true,"family":"Langbein","given":"John","email":"langbein@usgs.gov","middleInitial":"O.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":570734,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70158030,"text":"ofr20151189 - 2015 - Status and trends of adult Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) sucker populations in Upper Klamath Lake, Oregon, 2014","interactions":[],"lastModifiedDate":"2015-10-05T11:04:29","indexId":"ofr20151189","displayToPublicDate":"2015-10-02T17:00:00","publicationYear":"2015","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":"2015-1189","title":"Status and trends of adult Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) sucker populations in Upper Klamath Lake, Oregon, 2014","docAbstract":"Data from a long-term capture-recapture program were used to assess the status and dynamics of populations of two long-lived, federally endangered catostomids in Upper Klamath Lake, Oregon. Lost River suckers (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) have been captured and tagged with passive integrated transponder (PIT) tags during their spawning migrations in each year since 1995. In addition, beginning in 2005, individuals that had been previously PIT-tagged were re-encountered on remote underwater antennas deployed throughout sucker spawning areas. Captures and remote encounters during the spawning season in spring 2014 were incorporated into capture-recapture analyses of population dynamics.
\nCormack-Jolly-Seber (CJS) open population capture-recapture models were used to estimate annual survival probabilities, and a reverse-time analog of the CJS model was used to estimate recruitment of new individuals into the spawning populations. In addition, data on the size composition of captured fish were examined to provide corroborating evidence of recruitment. Model estimates of survival and recruitment were used to derive estimates of changes in population size over time and to determine the status of the populations through 2013. Separate analyses were conducted for each species and also for each subpopulation of Lost River suckers (LRS). Shortnose suckers (SNS) and one subpopulation of LRS migrate into tributary rivers to spawn, whereas the other LRS subpopulation spawns at groundwater upwelling areas along the eastern shoreline of the lake.
\nIn 2014, we captured, tagged, and released 496 LRS at four lakeshore spawning areas and recaptured an additional 970 individuals that had been tagged in previous years. Across all four areas, the remote antennas detected 6,370 individual LRS during the spawning season. Spawning activity peaked in April and most individuals were encountered at Cinder Flats and Sucker Springs. In the Williamson River, we captured, tagged, and released 3,038 LRS and 267 SNS, and recaptured 762 LRS and 156 SNS that had been tagged in previous years. Remote PIT tag antennas in the traps at the weir on the Williamson River and remote antenna systems that spanned the river at three different locations on the Williamson and Sprague Rivers detected a total of 23,446 LRS and 6,259 SNS. Most LRS passed upstream in the first and second weeks of April when water temperatures were increasing and greater than 10 °C. In contrast, upstream passage for SNS occurred in two pulses, one in early April and one in late April to early May, when water temperatures were increasing and near or greater than 12 °C. Finally, an additional 375 LRS and 884 SNS were captured in trammel net sampling at pre-spawn staging areas in the northeastern part of the lake. Of these, 111 of the LRS and 390 of the SNS had been PIT-tagged in previous years. For LRS captured at the staging areas that had encounter histories that were informative about their spawning location, 79 percent of the fish were members of the subpopulation that spawns in the rivers.
\nCapture-recapture analyses for the LRS subpopulation that spawns at the shoreline areas included encounter histories for more than 13,200 individuals, and analyses for the subpopulation that spawns in the rivers included more than 36,400 encounter histories. With a few exceptions, the survival of males and females in both subpopulations was high (greater than 0.88) between 1999 and 2012. Notably lower survival occurred for both sexes from the rivers in 2000, for males from the shoreline areas in 2002, and for males from the rivers in 2006 and 2012. Between 2001 and 2013, the abundance of males in the lakeshore spawning subpopulation decreased by at least 55 percent and the abundance of females decreased by at least 42 percent. Capture-recapture models suggested that the abundance of both sexes in the river spawning subpopulation of LRS had increased substantially since 2006; increases were mostly due to large estimated recruitment events in 2006 and 2008. We know that the estimates in 2006 are substantially biased in favor of recruitment because of a sampling issue. We are skeptical of the magnitude of recruitment indicated by the 2008 estimates as well because (1) few small individuals that would indicate the presence of new recruits were captured in that year, and (2) recapture probabilities in recruitment models based on just physical recaptures of fish were lower than desired for robust inferences from capture-recapture models. If we assume instead that little or no recruitment occurred for this subpopulation, the abundance of both sexes in the river spawning subpopulation likely has decreased at rates similar to the rates for the lakeshore spawning subpopulation between 2002 and 2013.
\nCapture-recapture analyses for SNS included encounter histories for more than 19,200 individuals. Most annual survival estimates between 2001 and 2012 were high (greater than 0.80), but SNS experienced more years of low survival than either LRS subpopulation. Annual survival of both sexes was relatively low in 2004, 2010, and 2012. In addition, male survival was low in 2002. Capture-recapture models and size composition data indicate that recruitment of new individuals into the SNS spawning population was trivial between 2001 and 2005. Models indicate that more than 10 percent of the population was new recruits in a number of more recent years. As a result, capture-recapture modeling suggests that the abundance of adult spawning SNS was relatively stable between 2006 and 2010. We are skeptical of the estimated recruitment in 2006 because of the known sampling issue. We also are skeptical of the estimated recruitment in other recent years because few small individuals that would indicate the presence of new recruits were captured in any of those years, and recapture probabilities in recruitment models were low. The best-case scenario for SNS, based on capture-recapture recruitment modeling, indicates that the abundance of males in the spawning population decreased by 77 percent and the abundance of females decreased by 73 percent between 2001 and 2013. Decreases in abundance for both sexes likely are greater than these estimates indicate.
\nDespite relatively high survival in most years, we conclude that both species have experienced substantial decreases in the abundance of spawning adults because losses from mortality have not been balanced by recruitment of new individuals. Although capture-recapture data indicate substantial recruitment of new individuals into the spawning populations for SNS and river spawning LRS in some years, size data do not corroborate these estimates. As a result, the status of the endangered sucker populations in Upper Klamath Lake remains worrisome, especially for shortnose suckers. Our monitoring program provides a robust platform for estimating vital population parameters, evaluating the status of the populations, and assessing the effectiveness of conservation and recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151189","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hewitt, D.A., Janney, E.C., Hayes, B.S., and Harris, A.C., 2015, Status and trends of adult Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) sucker populations in Upper Klamath Lake, Oregon, 2014: U.S. Geological Survey Open-File Report 2015-1189, 36 p., https://dx.doi.org/10.3133/ofr20151189.","productDescription":"iv, 36 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065787","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":309534,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1189/ofr20151189.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1189 PDF"},{"id":309533,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1189/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.80610656738281,\n 42.23512673690766\n ],\n [\n -121.81297302246092,\n 42.26511445833756\n ],\n [\n -121.82876586914061,\n 42.279848959767385\n ],\n [\n -121.82258605957031,\n 42.29711950573175\n ],\n [\n -121.81365966796874,\n 42.30879983710443\n ],\n [\n -121.82052612304688,\n 42.34382782918463\n ],\n [\n -121.82327270507812,\n 42.35296235855687\n ],\n [\n -121.80679321289062,\n 42.383401202818526\n ],\n [\n -121.83563232421875,\n 42.42142901536395\n ],\n [\n -121.86447143554686,\n 42.44372793752476\n ],\n [\n -121.87339782714844,\n 42.453354564875475\n ],\n [\n -121.88987731933594,\n 42.449301428414955\n ],\n [\n -121.90086364746094,\n 42.461460050936715\n ],\n [\n -121.91665649414062,\n 42.47361631282951\n ],\n [\n -121.93244934082031,\n 42.47007098029904\n ],\n [\n -121.95716857910155,\n 42.46855149059522\n ],\n [\n -121.97776794433594,\n 42.48323834594139\n ],\n [\n -122.00592041015626,\n 42.49792175437361\n ],\n [\n -122.04711914062499,\n 42.50197174319114\n ],\n [\n -122.05810546875,\n 42.488808320425846\n ],\n [\n -122.06565856933592,\n 42.46753847696729\n ],\n [\n -122.07252502441406,\n 42.46652544694582\n ],\n [\n -122.07870483398436,\n 42.48627657532141\n ],\n [\n -122.08831787109375,\n 42.482731960032936\n ],\n [\n -122.09449768066405,\n 42.46804498583046\n ],\n [\n -122.08145141601561,\n 42.455381034748896\n ],\n [\n -122.04299926757812,\n 42.42548395494743\n ],\n [\n -122.04505920410156,\n 42.416359972082866\n ],\n [\n -122.03201293945311,\n 42.39810802339276\n ],\n [\n -122.00523376464842,\n 42.39151573685182\n ],\n [\n -121.99356079101562,\n 42.40368557113506\n ],\n [\n -122.00042724609374,\n 42.41179748277328\n ],\n [\n -121.98257446289062,\n 42.40622065620649\n ],\n [\n -121.981201171875,\n 42.39405131362432\n ],\n [\n -121.97708129882812,\n 42.37934354245872\n ],\n [\n -121.95854187011717,\n 42.381879610913195\n ],\n [\n -121.95167541503906,\n 42.39455841668649\n ],\n [\n -121.95716857910155,\n 42.42092212947584\n ],\n [\n -121.94618225097656,\n 42.39912215986002\n ],\n [\n -121.94000244140624,\n 42.38035798213233\n ],\n [\n -121.91940307617188,\n 42.370720143531955\n ],\n [\n -121.90086364746094,\n 42.354992073710456\n ],\n [\n -121.92008972167969,\n 42.34941019930749\n ],\n [\n -121.94549560546875,\n 42.34433533786168\n ],\n [\n -121.94961547851562,\n 42.33519955487233\n ],\n [\n -121.94686889648438,\n 42.31590854308647\n ],\n [\n -121.93588256835938,\n 42.28950073090457\n ],\n [\n -121.92283630371092,\n 42.28035698458569\n ],\n [\n -121.92558288574217,\n 42.29711950573175\n ],\n [\n -121.9207763671875,\n 42.30676863078423\n ],\n [\n -121.904296875,\n 42.31083097788356\n ],\n [\n -121.89193725585936,\n 42.31540080499991\n ],\n [\n -121.88987731933594,\n 42.30321386201705\n ],\n [\n -121.87889099121092,\n 42.293056273848215\n ],\n [\n -121.86927795410156,\n 42.277816819534955\n ],\n [\n -121.8548583984375,\n 42.266638876842244\n ],\n [\n -121.84730529785155,\n 42.25495072629938\n ],\n [\n -121.84730529785155,\n 42.245801966774025\n ],\n [\n -121.83769226074219,\n 42.23766862211923\n ],\n [\n -121.8109130859375,\n 42.23105950761338\n ],\n [\n -121.80610656738281,\n 42.23512673690766\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
A new option to write and read antecedent conditions (also referred to as initial conditions) has been developed for the U.S. Geological Survey (USGS) Groundwater and Surface-Water Flow (GSFLOW) numerical, hydrologic simulation code. GSFLOW is an integration of the USGS Precipitation-Runoff Modeling System (PRMS) and USGS Modular Groundwater-Flow Model (MODFLOW), and provides three simulation modes: MODFLOW-only, PRMS-only, and GSFLOW (or coupled). The new capability, referred to as the restart option, can be used for all three simulation modes, such that the results from a pair (or set) of spin-up and restart simulations are nearly identical to results produced from a continuous simulation for the same time period. The restart option writes all results to files at the end of a spin-up simulation that are required to initialize a subsequent restart simulation. Previous versions of GSFLOW have had some capability to save model results for use as antecedent condiitions in subsequent simulations; however, the existing capabilities were not comprehensive or easy to use. The new restart option supersedes the previous methods. The restart option was developed in collaboration with the National Oceanic and Atmospheric Administration, National Weather Service as part of the Integrated Water Resources Science and Services Partnership. The primary focus for the development of the restart option was to support medium-range (7- to 14-day) forecasts of low streamflow conditions made by the National Weather Service for critical water-supply basins in which groundwater plays an important role.
\nThe spin-up simulation should be run for a sufficient length of time necessary to establish antecedent conditions throughout a model domain. Each GSFLOW application can require different lengths of time to account for the hydrologic stresses to propagate through a coupled groundwater and surface-water system. Typically, groundwater hydrologic processes require many years to come into equilibrium with dynamic climate and other forcing (or stress) data, such as precipitation and well pumping, whereas runoff-dominated surface-water processes respond relatively quickly. Use of a spin-up simulation can substantially reduce execution-time requirements for applications where the time period of interest is small compared to the time for hydrologic memory; thus, use of the restart option can be an efficient strategy for forecast and calibration simulations that require multiple simulations starting from the same day.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section D: Ground-water/Surface-water in Book 6U.S. Geological Survey
Office of Groundwater
411 National Center
Reston, VA 20192
Internet: http://water.usgs.gov/ogw/
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Land resources monitoring, modeling, and mapping with remote sensing","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","doi":"10.1201/b19355-96","isbn":"978-1-4822-1795-7","usgsCitation":"Thenkabail, P.S., 2015, Remote sensing of land resources: Monitoring, modeling, and mapping advances over the last 50 years and a vision for the future, chap. 26 of Land resources monitoring, modeling, and mapping with remote sensing, p. 791-828, https://doi.org/10.1201/b19355-96.","productDescription":"41 p.","startPage":"791","endPage":"828","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060648","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":310835,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"563486d2e4b048076347fb54","contributors":{"authors":[{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":525777,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70134262,"text":"70134262 - 2015 - Remote sensing of water resources, disasters, and urban area: monitoring, modeling, and mapping advances over last 50 years and a vision for the future","interactions":[],"lastModifiedDate":"2015-10-30T15:15:16","indexId":"70134262","displayToPublicDate":"2015-10-02T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Remote sensing of water resources, disasters, and urban area: monitoring, modeling, and mapping advances over last 50 years and a vision for the future","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Remote Sensing of Water Resources, Disasters, and Urban Studies","language":"English","publisher":"CRC Press","usgsCitation":"Thenkabail, P.S., 2015, Remote sensing of water resources, disasters, and urban area: monitoring, modeling, and mapping advances over last 50 years and a vision for the future, chap. of Remote Sensing of Water Resources, Disasters, and Urban Studies, v. 3, p. 615-659.","startPage":"615","endPage":"659","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060651","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":310873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"563486d2e4b048076347fb56","contributors":{"authors":[{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":525771,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70154751,"text":"70154751 - 2015 - Landfill leachate as a mirror of today's disposable society: Pharmaceuticals and other contaminants of emerging concern in final leachate from landfills in the conterminous United States","interactions":[],"lastModifiedDate":"2021-06-01T14:43:31.062569","indexId":"70154751","displayToPublicDate":"2015-10-01T17:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Landfill leachate as a mirror of today's disposable society: Pharmaceuticals and other contaminants of emerging concern in final leachate from landfills in the conterminous United States","docAbstract":"Final leachates (leachate after storage or treatment processes) from 22 landfills in 12 states were analyzed for 190 pharmaceuticals and other contaminants of emerging concern (CECs), which were detected in every sample, with the number of CECs ranging from 1 to 58 (median = 22). In total, 101 different CECs were detected in leachate samples, including 43 prescription pharmaceuticals, 22 industrial chemicals, 15 household chemicals, 12 nonprescription pharmaceuticals, 5 steroid hormones, and 4 animal/plant sterols. The most frequently detected CECs were lidocaine (91%, local anesthetic), cotinine (86%, nicotine degradate), carisoprodol (82%, muscle relaxant), bisphenol A (77%, component of plastics and thermal paper), carbamazepine (77%, anticonvulsant), and N,N-diethyltoluamide (68%, insect repellent). Concentrations of CECs spanned 7 orders of magnitude, ranging from 2.0 ng/L (estrone) to 17 200 000 ng/L (bisphenol A). Concentrations of household and industrial chemicals were the greatest (∼1000-1 000 000 ng/L), followed by plant/animal sterols (∼1000-100 000 ng/L), nonprescription pharmaceuticals (∼100-10 000 ng/L), prescription pharmaceuticals (∼10-10 000 ng/L), and steroid hormones (∼10-100 ng/L). The CEC concentrations in leachate from active landfills were significantly greater than those in leachate from closed, unlined landfills (p = 0.05). The CEC concentrations were significantly greater (p < 0.01) in untreated leachate compared with treated leachate. The CEC concentrations were significantly greater in leachate disposed to wastewater treatment plants from modern lined landfills than in leachate released to groundwater from closed, unlined landfills (p = 0.04). The CEC concentrations were significantly greater (p = 0.06) in the fresh leachate (leachate before storage or treatment) reported in a previous study compared with the final leachate sampled for the present study.
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MODIS vegetation indices in crop biomass estimation","docAbstract":"Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. Therefore, we used standard data mining techniques to identify a set of MSBB data from the IKONOS, GeoEye-1, Landsat ETM+, MODIS, WorldView-2 sensors and compared their performance with HNB data from the EO-1 Hyperion sensor in explaining crop biomass variability of four important field crops (rice, alfalfa, cotton, maize). The analysis employed two-band (ratio) vegetation indices (TBVIs) and multiband (additive) vegetation indices (MBVIs) derived from Singular Value Decomposition (SVD) and stepwise regression. Results demonstrated that HNB-derived TBVIs and MBVIs performed better than MSBB-derived TBVIs and MBVIs on a per crop basis and for the pooled data: overall, HNB TBVIs explained 5–31% greater variability when compared with various MSBB TBVIs; and HNB MBVIs explained 3–33% greater variability when compared with various MSBB MBVIs. The performance of MSBB MBVIs and TBVIs improved mildly, by combining spectral information across multiple sensors involving IKONOS, GeoEye-1, Landsat ETM+, MODIS, and WorldView-2. A number of HNBs that advance crop biomass modeling were determined. Based on the highest factor loadings on the first component of the SVD, the “red-edge” spectral range (700–740 nm) centered at 722 nm (bandwidth = 10 nm) stood out prominently, while five additional and distinct portions of the recorded spectral range (400–2500 nm) centered at 539 nm, 758 nm, 914 nm, 1130 nm, 1320 nm (bandwidth = 10 nm) were also important. The best HNB vegetation indices for crop biomass estimation involved 549 and 752 nm for rice (R2 = 0.91); 925 and 1104 nm for alfalfa (R2 = 0.81); 722 and 732 nm for cotton (R2 = 0.97); and 529 and 895 nm for maize (R2 = 0.94). The higher spectral resolution of the EO-1 Hyperion hyperspectral sensor and the ability of users to choose distinct HNBs for improved crop biomass estimation outweigh the benefits that come with higher spatial resolution of MSBBs.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.isprsjprs.2015.08.001","usgsCitation":"Marshall, M.T., and Thenkabail, P.S., 2015, Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation: ISPRS Journal of Photogrammetry and Remote Sensing, v. 108, p. 205-218, https://doi.org/10.1016/j.isprsjprs.2015.08.001.","productDescription":"14 p.","startPage":"205","endPage":"218","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060745","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471737,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2015.08.001","text":"Publisher Index Page"},{"id":313981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.05810546875,\n 40.730608477796636\n ],\n [\n -122.82714843749999,\n 40.3130432088809\n ],\n [\n -122.56347656249999,\n 39.90973623453719\n ],\n [\n -122.62939453125001,\n 39.38526381099774\n ],\n [\n -122.23388671874999,\n 38.496593518947556\n ],\n [\n -121.70654296874999,\n 37.78808138412046\n ],\n [\n -121.17919921875001,\n 37.43997405227057\n ],\n [\n -121.00341796874999,\n 36.96744946416934\n ],\n [\n -120.60791015625,\n 36.4566360115962\n ],\n [\n -120.16845703125,\n 36.01356058518153\n ],\n [\n -119.7509765625,\n 35.35321610123821\n ],\n [\n -119.39941406249999,\n 34.95799531086792\n ],\n [\n -118.93798828125,\n 34.97600151317591\n ],\n [\n -118.63037109375,\n 35.15584570226544\n ],\n [\n -118.63037109375,\n 35.764343479667176\n ],\n [\n -118.89404296875,\n 36.24427318493909\n ],\n [\n -119.39941406249999,\n 36.84446074079564\n ],\n [\n -119.88281249999999,\n 37.23032838760387\n ],\n [\n -120.41015624999999,\n 37.80544394934274\n ],\n [\n -120.84960937499999,\n 38.34165619279595\n ],\n [\n -121.28906250000001,\n 38.89103282648849\n ],\n [\n -121.61865234375,\n 39.52099229357195\n ],\n [\n -121.9921875,\n 39.9434364619742\n ],\n [\n -122.05810546875,\n 40.730608477796636\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"108","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"568e48dee4b0e7a44bc4186b","contributors":{"authors":[{"text":"Marshall, Michael T. mmarshall@usgs.gov","contributorId":5480,"corporation":false,"usgs":true,"family":"Marshall","given":"Michael","email":"mmarshall@usgs.gov","middleInitial":"T.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":581537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":581536,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70158916,"text":"70158916 - 2015 - Mining for metals in society's waste","interactions":[],"lastModifiedDate":"2016-06-17T09:38:56","indexId":"70158916","displayToPublicDate":"2015-10-01T12:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5008,"text":"The Conversation","active":true,"publicationSubtype":{"id":10}},"title":"Mining for metals in society's waste","docAbstract":"Metals are crucial to society and enable our modern standard of living. Look around and you can't help but see products made of metals. For instance, a typical gasoline-powered automobile contains over a ton of iron and steel, 240 pounds of aluminum, 42 pounds of copper, 41 pounds of silicon, 22 pounds of zinc and more than 30 other mineral commodities including titanium, platinum and gold.
\nMetals and minerals are natural resources that human beings have been mining for thousands of years. Contemporary metal mining is dominated by iron ore, copper and gold, with 2 billion tons of iron ore, nearly 20 million tons of copper and 2,000 tons of gold produced every year. Tens to hundreds of tons of other metals that are essential components for electronics, green energy production, and high-technology products are produced annually.
","language":"English","publisher":"The Conversation US","publisherLocation":"Boston, MA","usgsCitation":"Smith, K.S., Plumlee, G.S., and Hageman, P.L., 2015, Mining for metals in society's waste: The Conversation, p. 1-5.","productDescription":"5 p.","startPage":"1","endPage":"5","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068802","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":309709,"type":{"id":15,"text":"Index Page"},"url":"https://theconversation.com/mining-for-metals-in-societys-waste-43766"},{"id":309721,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56164257e4b0ba4884c614ad","contributors":{"authors":[{"text":"Smith, Kathleen S. 0000-0001-8547-9804 ksmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8547-9804","contributorId":182,"corporation":false,"usgs":true,"family":"Smith","given":"Kathleen","email":"ksmith@usgs.gov","middleInitial":"S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":576829,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plumlee, Geoffrey S. 0000-0002-9607-5626 gplumlee@usgs.gov","orcid":"https://orcid.org/0000-0002-9607-5626","contributorId":960,"corporation":false,"usgs":true,"family":"Plumlee","given":"Geoffrey","email":"gplumlee@usgs.gov","middleInitial":"S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":576831,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hageman, Philip L. 0000-0002-3440-2150 phageman@usgs.gov","orcid":"https://orcid.org/0000-0002-3440-2150","contributorId":811,"corporation":false,"usgs":true,"family":"Hageman","given":"Philip","email":"phageman@usgs.gov","middleInitial":"L.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":576830,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70158912,"text":"70158912 - 2015 - Groundwater recharge assessment at local and episodic scale in a soil mantled perched karst aquifer in southern Italy","interactions":[],"lastModifiedDate":"2015-10-07T11:11:03","indexId":"70158912","displayToPublicDate":"2015-10-01T12:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater recharge assessment at local and episodic scale in a soil mantled perched karst aquifer in southern Italy","docAbstract":"Groundwater recharge assessment of karst aquifers, at various spatial and temporal scales, is a major scientific topic of current importance, since these aquifers play an essential role for both socio-economic development and fluvial ecosystems.
\nIn this study, groundwater recharge was estimated at local and episodic scales in a representative perched karst aquifer in a region of southern Italy with a Mediterranean climate. The research utilized measurements of precipitation, air temperature, soil water content, and water-table depth, obtained in 2008 at the Acqua della Madonna test area (Terminio Mount karst aquifer, Campania region). At this location the aquifer is overlain by ash-fall pyroclastic soils. The Episodic Master Recession (EMR) method, an improved version of the Water Table Fluctuation (WTF) method, was applied to estimate the amount of recharge generated episodically by individual rainfall events. The method also quantifies the amount of precipitation generating each recharge episode, thus permitting calculation of the Recharge to the Precipitation Ratio (RPR) on a storm-by-storm basis.
\nDepending on the seasonally varying air temperature, evapotranspiration, and precipitation patterns, calculated values of RPR varied between 35% and 97% among the individual episodes. A multiple linear correlation of the RPR with both the average intensity of recharging rainfall events and the antecedent soil water content was calculated. Given the relatively easy measurability of precipitation and soil water content, such an empirical model would have great hydrogeological and practical utility. It would facilitate short-term forecasting of recharge in karst aquifers of the Mediterranean region and other aquifers with similar hydrogeological characteristics. By establishing relationships between the RPR and climate-dependent variables such as average storm intensity, it would facilitate prediction of climate-change effects on groundwater recharge. The EMR methodology could further be applied to other aquifers for evaluating the relationship of recharge to various hydrometeorological and hydrogeological processes.
","language":"English","publisher":"European Geophysical Society","publisherLocation":"New York, NY","doi":"10.1016/j.jhydrol.2015.08.032","usgsCitation":"Allocca, V., De Vita, P., Manna, F., and Nimmo, J.R., 2015, Groundwater recharge assessment at local and episodic scale in a soil mantled perched karst aquifer in southern Italy: Journal of Hydrology, v. 529, no. 3, p. 843-853, https://doi.org/10.1016/j.jhydrol.2015.08.032.","productDescription":"11 p.","startPage":"843","endPage":"853","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068702","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":309722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"529","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5616423ee4b0ba4884c61494","contributors":{"authors":[{"text":"Allocca, V.","contributorId":149077,"corporation":false,"usgs":false,"family":"Allocca","given":"V.","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":576822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Vita, P.","contributorId":26207,"corporation":false,"usgs":true,"family":"De Vita","given":"P.","affiliations":[],"preferred":false,"id":576821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Manna, F.","contributorId":149078,"corporation":false,"usgs":false,"family":"Manna","given":"F.","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":576823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":576820,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70148003,"text":"70148003 - 2015 - Ground motion-simulations of 1811-1812 New Madrid earthquakes, central United States","interactions":[],"lastModifiedDate":"2016-01-29T10:55:09","indexId":"70148003","displayToPublicDate":"2015-10-01T12:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Ground motion-simulations of 1811-1812 New Madrid earthquakes, central United States","docAbstract":"We performed a suite of numerical simulations based on the 1811–1812 New Madrid seismic zone (NMSZ) earthquakes, which demonstrate the importance of 3D geologic structure and rupture directivity on the ground‐motion response throughout a broad region of the central United States (CUS) for these events. Our simulation set consists of 20 hypothetical earthquakes located along two faults associated with the current seismicity trends in the NMSZ. The hypothetical scenarios range in magnitude from M 7.0 to 7.7 and consider various epicenters, slip distributions, and rupture characterization approaches. The low‐frequency component of our simulations was computed deterministically up to a frequency of 1 Hz using a regional 3D seismic velocity model and was combined with higher‐frequency motions calculated for a 1D medium to generate broadband synthetics (0–40 Hz in some cases). For strike‐slip earthquakes located on the southwest–northeast‐striking NMSZ axial arm of seismicity, our simulations show 2–10 s period energy channeling along the trend of the Reelfoot rift and focusing strong shaking northeast toward Paducah, Kentucky, and Evansville, Indiana, and southwest toward Little Rock, Arkansas. These waveguide effects are further accentuated by rupture directivity such that an event with a western epicenter creates strong amplification toward the northeast, whereas an eastern epicenter creates strong amplification toward the southwest. These effects are not as prevalent for simulations on the reverse‐mechanism Reelfoot fault, and large peak ground velocities (>40 cm/s) are typically confined to the near‐source region along the up‐dip projection of the fault. Nonetheless, these basin response and rupture directivity effects have a significant impact on the pattern and level of the estimated intensities, which leads to additional uncertainty not previously considered in magnitude estimates of the 1811–1812 sequence based only on historical reports.
\nThe region covered by our simulation domain encompasses a large portion of the CUS centered on the NMSZ, including several major metropolitan areas. Based on our simulations, more than eight million people living and working near the NMSZ would experience potentially damaging ground motion and modified Mercalli intensities ranging from VI to VIII if a repeat of the 1811–1812 earthquakes occurred today. Moreover, the duration of strong ground shaking in the greater Memphis metropolitan area could last from 30 to more than 60 s, depending on the magnitude and epicenter.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Bulletin of the Seismological Society of America","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Seismological Society of America","publisherLocation":"Stanford","doi":"10.1785/0120140330","usgsCitation":"Ramirez-Guzman, L., Graves, R., Olsen, K., Boyd, O.S., Cramer, C.H., Hartzell, S.H., Ni, S., Somerville, P.G., Williams, R., and Zhong, J., 2015, Ground motion-simulations of 1811-1812 New Madrid earthquakes, central United States: Bulletin of the Seismological Society of America, v. 105, no. 4, p. 1961-1988, https://doi.org/10.1785/0120140330.","productDescription":"28 p.","startPage":"1961","endPage":"1988","numberOfPages":"28","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065550","costCenters":[{"id":300,"text":"Geologic Hazards Science 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Sex, size, and diel periods had no effect on thermal occupancy of adult walleye in either lake. Thermal occupancy differed between lakes and seasons. Walleye from Lake Erie generally experienced higher temperatures throughout the spring and summer months than did walleye in Lake Huron, due to limnological differences between the lakes. Tagged walleye that remained in Saginaw Bay, Lake Huron (i.e., adjacent to the release location), as opposed to those migrating to the main basin of Lake Huron, experienced higher temperatures, and thus accumulated more thermal units (the amount of temperature units amassed over time) throughout the year. Walleye that migrated toward the southern end of Lake Huron occupied higher temperatures than those that moved toward the north. Consequently, walleye that emigrated from Saginaw Bay experienced thermal environments that were more favorable for growth as they spent more time within their thermal optimas than those that remained in Saginaw Bay. Results presented in this paper provide information on the thermal experience of wild fish in a large lake, and could be used to refine sex- and lake-specific bioenergetics models of walleye in the Great Lakes to enable the testing of ecological hypotheses.
","language":"English","publisher":"Elsevier Science","publisherLocation":"New York, NY","doi":"10.1016/j.jtherbio.2015.08.009","usgsCitation":"Peat, T., Hayden, T.A., Gutowsky, L.F., Vandergoot, C.S., Fielder, D., Madenjian, C.P., Murchie, K.J., Dettmers, J.M., Krueger, C., and Cooke, S., 2015, Seasonal thermal ecology of adult walleye (Sander vitreus) in Lake Huron and Lake Erie: Journal of Thermal Biology, v. 53, p. 98-106, https://doi.org/10.1016/j.jtherbio.2015.08.009.","productDescription":"9 p.","startPage":"98","endPage":"106","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062613","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":310998,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"563b3a47e4b0d6133fe75c6d","contributors":{"authors":[{"text":"Peat, Tyler B","contributorId":149695,"corporation":false,"usgs":false,"family":"Peat","given":"Tyler B","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":579180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayden, Todd A. 0000-0002-0451-0425 thayden@usgs.gov","orcid":"https://orcid.org/0000-0002-0451-0425","contributorId":5987,"corporation":false,"usgs":true,"family":"Hayden","given":"Todd","email":"thayden@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":579181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gutowsky, Lee F G","contributorId":149696,"corporation":false,"usgs":false,"family":"Gutowsky","given":"Lee","email":"","middleInitial":"F G","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":579182,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vandergoot, Christopher S.","contributorId":71849,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":579183,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fielder, David G.","contributorId":85434,"corporation":false,"usgs":true,"family":"Fielder","given":"David G.","affiliations":[],"preferred":false,"id":579184,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":579179,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Murchie, Karen J","contributorId":149697,"corporation":false,"usgs":false,"family":"Murchie","given":"Karen","email":"","middleInitial":"J","affiliations":[{"id":17787,"text":"College of The Bahamas","active":true,"usgs":false}],"preferred":false,"id":579185,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dettmers, John M.","contributorId":27395,"corporation":false,"usgs":true,"family":"Dettmers","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":579186,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Krueger, Charles C.","contributorId":67821,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles C.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":579187,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cooke, Steven J.","contributorId":56132,"corporation":false,"usgs":false,"family":"Cooke","given":"Steven J.","affiliations":[{"id":36574,"text":"Carleton University, Ottawa, Ontario","active":true,"usgs":false}],"preferred":false,"id":579188,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70170275,"text":"70170275 - 2015 - Spatial occupancy models for predicting metapopulation dynamics and viability following reintroduction","interactions":[],"lastModifiedDate":"2016-04-21T12:47:48","indexId":"70170275","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial occupancy models for predicting metapopulation dynamics and viability following reintroduction","docAbstract":"Intrinsic and extrinsic factors affect vital rates and population-level processes, and understanding these factors is paramount to devising successful management plans for wildlife species. For example, birds time migration in response, in part, to local and broadscale climate fluctuations to initiate breeding upon arrival to nesting territories, and prolonged inclement weather early in the breeding season can inhibit egg-laying and reduce productivity. Also, density-dependent regulation occurs in raptor populations, as territory size is related to resource availability. Arctic Peregrine Falcons (Falco peregrinus tundrius; hereafter Arctic peregrine) have a limited and northern breeding distribution, including the Colville River Special Area (CRSA) in the National Petroleum Reserve–Alaska, USA. We quantified influences of climate, topography, nest productivity, prey habitat, density dependence, and interspecific competition affecting Arctic peregrines in the CRSA by applying the Dail-Madsen model to estimate abundance and vital rates of adults on nesting cliffs from 1981 through 2002. Arctic peregrine abundance increased throughout the 1980s, which spanned the population's recovery from DDT-induced reproductive failure, until exhibiting a stationary trend in the 1990s. Apparent survival rate (i.e., emigration; death) was negatively correlated with the number of adult Arctic peregrines on the cliff the previous year, suggesting effects of density-dependent population regulation. Apparent survival and arrival rates (i.e., immigration; recruitment) were higher during years with earlier snowmelt and milder winters, and apparent survival was positively correlated with nesting season maximum daily temperature. Arrival rate was positively correlated with average Arctic peregrine productivity along a cliff segment from the previous year and initial abundance was positively correlated with cliff height. Higher cliffs with documented higher productivity (presumably indicative of higher-quality habitat), are a priority for continued protection from potential nearby development and disturbance to minimize population-level impacts. Climate change may affect Arctic peregrines in multiple ways, including through access to more snow-free nest sites and a lengthened breeding season that may increase likelihood of nest success. Our work provides insight into factors affecting a population during and after recovery, and demonstrates how the Dail-Madsen model can be used for any unmarked population with multiple years of abundance data collected through repeated surveys.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/14-1591.1","usgsCitation":"Bruggeman, J.E., Swem, T., Andersen, D., Kennedy, P.L., and Nigro, D.A., 2015, Dynamics of a recovering Arctic bird population: the importance of climate, density dependence, and site quality: Ecological Applications, v. 25, no. 7, p. 1932-1943, https://doi.org/10.1890/14-1591.1.","productDescription":"12 p.","startPage":"1932","endPage":"1943","ipdsId":"IP-055304","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":340549,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.5107421875,\n 68.73638345287264\n ],\n [\n -149.94140625,\n 68.73638345287264\n ],\n [\n -149.94140625,\n 70.56149224990756\n ],\n [\n -158.5107421875,\n 70.56149224990756\n ],\n [\n -158.5107421875,\n 68.73638345287264\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59030327e4b0e862d230f735","contributors":{"authors":[{"text":"Bruggeman, Jason E.","contributorId":18983,"corporation":false,"usgs":false,"family":"Bruggeman","given":"Jason","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":693305,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swem, Ted","contributorId":64463,"corporation":false,"usgs":true,"family":"Swem","given":"Ted","affiliations":[],"preferred":false,"id":693306,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":2168,"corporation":false,"usgs":true,"family":"Andersen","given":"David E.","email":"dea@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":true,"id":693219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Patricia L.","contributorId":172826,"corporation":false,"usgs":false,"family":"Kennedy","given":"Patricia","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":693307,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nigro, Debora A.","contributorId":10628,"corporation":false,"usgs":false,"family":"Nigro","given":"Debora","email":"","middleInitial":"A.","affiliations":[{"id":12934,"text":"Bureau of Land Management, Arctic Field Office","active":true,"usgs":false}],"preferred":false,"id":693308,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70173619,"text":"70173619 - 2015 - Climate, water use, and land surface transformation in an irrigation intensive watershed - streamflow responses from 1950 through 2010","interactions":[],"lastModifiedDate":"2020-02-26T17:54:22","indexId":"70173619","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Climate, water use, and land surface transformation in an irrigation intensive watershed - streamflow responses from 1950 through 2010","docAbstract":"Climatic variability and land surface change have a wide range of effects on streamflow and are often difficult to separate. We analyzed long-term records of climate, land use and land cover, and re-constructed the water budget based on precipitation, groundwater levels, and water use from 1950 through 2010 in the Cimarron–Skeleton watershed and a portion of the Cimarron–Eagle Chief watershed in Oklahoma, an irrigation-intensive agricultural watershed in the Southern Great Plains, USA. Our results show that intensive irrigation through alluvial aquifer withdrawal modifies climatic feedback and alters streamflow response to precipitation. Increase in consumptive water use was associated with decreases in annual streamflow, while returning croplands to non-irrigated grasslands was associated with increases in streamflow. Along with groundwater withdrawal, anthropogenic-induced factors and activities contributed nearly half to the observed variability of annual streamflow. Streamflow was more responsive to precipitation during the period of intensive irrigation between 1965 and 1984 than the period of relatively lower water use between 1985 and 2010. The Cimarron River is transitioning from a historically flashy river to one that is more stable with a lower frequency of both high and low flow pulses, a higher baseflow, and an increased median flow due in part to the return of cropland to grassland. These results demonstrated the interrelationship among climate, land use, groundwater withdrawal and streamflow regime and the potential to design agricultural production systems and adjust irrigation to mitigate impact of increasing climate variability on streamflow in irrigation intensive agricultural watershed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2015.07.007","usgsCitation":"Dale, J., Zou, C., Andrews, W.J., Long, J.M., Liang, Y., and Qiao, L., 2015, Climate, water use, and land surface transformation in an irrigation intensive watershed - streamflow responses from 1950 through 2010: Agricultural Water Management, v. 160, p. 144-152, https://doi.org/10.1016/j.agwat.2015.07.007.","productDescription":"9 p.","startPage":"144","endPage":"152","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062619","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":323211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.5311279296875,\n 35.98689628443789\n ],\n [\n -97.701416015625,\n 35.58138418324621\n ],\n [\n -97.811279296875,\n 35.49198366469642\n ],\n [\n -98.7506103515625,\n 35.88459964717596\n ],\n [\n -99.4647216796875,\n 36.213255233061844\n ],\n [\n -99.5526123046875,\n 36.461054075054314\n ],\n [\n -99.11865234374999,\n 36.59347887826919\n ],\n [\n -98.3056640625,\n 36.4477991295848\n ],\n [\n -97.525634765625,\n 36.06686213257888\n ],\n [\n -97.52014160156249,\n 36.02244668175846\n ],\n [\n -97.5311279296875,\n 35.98689628443789\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"160","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5757f031e4b04f417c24da38","contributors":{"authors":[{"text":"Dale, Joseph","contributorId":171495,"corporation":false,"usgs":false,"family":"Dale","given":"Joseph","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":637689,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zou, Chris B.","contributorId":31657,"corporation":false,"usgs":true,"family":"Zou","given":"Chris B.","affiliations":[],"preferred":false,"id":637690,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andrews, William J. 0000-0003-4780-8835 wandrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4780-8835","contributorId":328,"corporation":false,"usgs":true,"family":"Andrews","given":"William","email":"wandrews@usgs.gov","middleInitial":"J.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":637691,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":637692,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liang, Ye","contributorId":171496,"corporation":false,"usgs":false,"family":"Liang","given":"Ye","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":637693,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Qiao, Lei","contributorId":171497,"corporation":false,"usgs":false,"family":"Qiao","given":"Lei","email":"","affiliations":[],"preferred":false,"id":637694,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70173446,"text":"70173446 - 2015 - Spatial and temporal movement dynamics of brook Salvelinus fontinalis and brown trout Salmo trutta","interactions":[],"lastModifiedDate":"2016-06-20T13:03:17","indexId":"70173446","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal movement dynamics of brook Salvelinus fontinalis and brown trout Salmo trutta","docAbstract":"Native eastern brook trout Salvelinus fontinalis and naturalized brown trout Salmo trutta occur sympatrically in many streams across the brook trout’s native range in the eastern United States. Understanding within- among-species variability in movement, including correlates of movement, has implications for management and conservation. We radio tracked 55 brook trout and 45 brown trout in five streams in a north-central Pennsylvania, USA watershed to quantify the movement of brook trout and brown trout during the fall and early winter to (1) evaluate the late-summer, early winter movement patterns of brook trout and brown trout, (2) determine correlates of movement and if movement patterns varied between brook trout and brown trout, and (3) evaluate genetic diversity of brook trout within and among study streams, and relate findings to telemetry-based observations of movement. Average total movement was greater for brown trout (mean ± SD = 2,924 ± 4,187 m) than for brook trout (mean ± SD = 1,769 ± 2,194 m). Although there was a large amount of among-fish variability in the movement of both species, the majority of movement coincided with the onset of the spawning season, and a threshold effect was detected between stream flow and movement: where movement increased abruptly for both species during positive flow events. Microsatellite analysis of brook trout revealed consistent findings to those found using radio-tracking, indicating a moderate to high degree of gene flow among brook trout populations. Seasonal movement patterns and the potential for relatively large movements of brook and brown trout highlight the importance of considering stream connectivity when restoring and protecting fish populations and their habitats.
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