{"pageNumber":"316","pageRowStart":"7875","pageSize":"25","recordCount":40783,"records":[{"id":70254786,"text":"70254786 - 2019 - Strategic conservation for lesser prairie-chickens among landscapes of varying anthropogenic influence","interactions":[],"lastModifiedDate":"2024-06-07T14:20:41.927474","indexId":"70254786","displayToPublicDate":"2019-08-24T09:10:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Strategic conservation for lesser prairie-chickens among landscapes of varying anthropogenic influence","docAbstract":"<p><span>For millennia grasslands have provided a myriad of ecosystem services and have been coupled with human resource use. The loss of 46% of grasslands worldwide necessitates the need for conservation that is spatially, temporally, and socioeconomically strategic. In the Southern Great Plains of the United States, conversion of native grasslands to cropland, woody encroachment, and establishment of vertical anthropogenic features have made large intact grasslands rare for lesser prairie-chickens (</span><i>Tympanuchus pallidicinctus</i><span>). However, it remains unclear how the spatial distribution of grasslands and anthropogenic features constrain populations and influence conservation. We estimated the distribution of lesser prairie-chickens using data from individuals marked with&nbsp;GPS&nbsp;transmitters in Kansas and Colorado,&nbsp;USA, and empirically derived relationships with anthropogenic structure densities and grassland composition. Our model suggested decreased probability of use in 2-km radius (12.6 km</span><sup>2</sup><span>) landscapes that had greater than two vertical features, two oil wells, 8 km of county roads, and 0.15 km of major roads or transmission lines. Predicted probability of use was greatest in 5-km radius landscapes that were 77% grassland. Based on our model predictions, ~10% of the current expected lesser prairie-chicken distribution was available as habitat. We used our estimated species distribution to provide spatially explicit prescriptions for&nbsp;CRP&nbsp;enrollment and tree removal in locations most likely to benefit lesser prairie-chickens. Spatially incentivized&nbsp;CRP&nbsp;sign up has the potential to provide 4189 km</span><sup>2</sup><span>&nbsp;of additional habitat and strategic application of tree removal has the potential to restore 1154 km</span><sup>2</sup><span>. Tree removal and CRP enrollment are conservation tools that can align with&nbsp;landowner&nbsp;goals and are much more likely to be effective on privately owned working lands.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2019.108213","usgsCitation":"Sullins, D.S., Haukos, D.A., Lautenbach, J.M., Lautenbach, J., Robinson, S.G., Rice, M.B., Sandercock, B.K., Kraft, J.D., Plumb, R.T., Reitz, J., Hutchinson, J.M., and Hagen, C., 2019, Strategic conservation for lesser prairie-chickens among landscapes of varying anthropogenic influence: Biological Conservation, v. 238, 108213, 10 p., https://doi.org/10.1016/j.biocon.2019.108213.","productDescription":"108213, 10 p.","ipdsId":"IP-105616","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467342,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2019.108213","text":"Publisher Index Page"},{"id":429644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -103.72145468354752,\n              39.86812103477851\n            ],\n            [\n              -103.72145468354752,\n              37.00372049543908\n            ],\n            [\n              -97.7109610420592,\n              37.00372049543908\n            ],\n            [\n              -97.7109610420592,\n              39.86812103477851\n            ],\n            [\n              -103.72145468354752,\n              39.86812103477851\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"238","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sullins, Daniel S.","contributorId":166689,"corporation":false,"usgs":false,"family":"Sullins","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":902538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lautenbach, Joseph M.","contributorId":172788,"corporation":false,"usgs":false,"family":"Lautenbach","given":"Joseph","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":902540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lautenbach, Jonathan","contributorId":272579,"corporation":false,"usgs":false,"family":"Lautenbach","given":"Jonathan","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":902541,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robinson, Samantha G.","contributorId":172786,"corporation":false,"usgs":false,"family":"Robinson","given":"Samantha","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":902542,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rice, Mindy B.","contributorId":214399,"corporation":false,"usgs":false,"family":"Rice","given":"Mindy","email":"","middleInitial":"B.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":902543,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sandercock, Brett K.","contributorId":95816,"corporation":false,"usgs":true,"family":"Sandercock","given":"Brett","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":902544,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kraft, John D.","contributorId":172789,"corporation":false,"usgs":false,"family":"Kraft","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":902545,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Plumb, Reid T.","contributorId":172787,"corporation":false,"usgs":false,"family":"Plumb","given":"Reid","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":902546,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reitz, Jonathan H.","contributorId":337597,"corporation":false,"usgs":false,"family":"Reitz","given":"Jonathan H.","affiliations":[{"id":40103,"text":"cdpw","active":true,"usgs":false}],"preferred":false,"id":902547,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hutchinson, J. M. Shawn","contributorId":337599,"corporation":false,"usgs":false,"family":"Hutchinson","given":"J.","email":"","middleInitial":"M. Shawn","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":902548,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hagen, Christian A.","contributorId":279696,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":902549,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70223799,"text":"70223799 - 2019 - Influence of climate change and postdelisting management on long-term population viability of the conservation-reliant Kirtland's Warbler","interactions":[],"lastModifiedDate":"2021-09-08T12:39:32.771645","indexId":"70223799","displayToPublicDate":"2019-08-24T07:36:04","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Influence of climate change and postdelisting management on long-term population viability of the conservation-reliant Kirtland's Warbler","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Rapid global climate change is resulting in novel abiotic and biotic conditions and interactions. Identifying management strategies that maximize probability of long-term persistence requires an understanding of the vulnerability of species to environmental changes. We sought to quantify the vulnerability of Kirtland's Warbler (<i>Setophaga kirtlandii</i>), a rare Neotropical migratory songbird that breeds almost exclusively in the Lower Peninsula of Michigan and winters in the Bahamian Archipelago, to projected environmental changes on the breeding and wintering grounds. We developed a population-level simulation model that incorporates the influence of annual environmental conditions on the breeding and wintering grounds, and parameterized the model using empirical relationships. We simulated independent and additive effects of reduced breeding grounds habitat quantity and quality, and wintering grounds habitat quality, on population viability. Our results indicated the Kirtland's Warbler population is stable under current environmental and management conditions. Reduced breeding grounds habitat quantity resulted in reductions of the stable population size, but did not cause extinction under the scenarios we examined. In contrast, projected large reductions in wintering grounds precipitation caused the population to decline, with risk of extinction magnified when breeding habitat quantity or quality also decreased. Our study indicates that probability of long-term persistence for Kirtland's Warbler will depend on climate change impacts to wintering grounds habitat quality and contributes to the growing literature documenting the importance of considering the full annual cycle for understanding population dynamics of migratory species.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5547","usgsCitation":"Brown, D., Donner, D., Ribic, C., and Bocetti, C., 2019, Influence of climate change and postdelisting management on long-term population viability of the conservation-reliant Kirtland's Warbler: Ecology and Evolution, v. 9, no. 18, p. 10263-10276, https://doi.org/10.1002/ece3.5547.","productDescription":"14 p.","startPage":"10263","endPage":"10276","ipdsId":"IP-105478","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5547","text":"Publisher Index Page"},{"id":388938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bahamas, United States","state":"Michigan","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -79.07958984375,\n              23.60426184707018\n            ],\n            [\n              -75.43212890625,\n              23.60426184707018\n            ],\n            [\n              -75.43212890625,\n              27.039556602163195\n            ],\n            [\n              -79.07958984375,\n              27.039556602163195\n            ],\n            [\n              -79.07958984375,\n              23.60426184707018\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -86.15478515625,\n              43.5326204268101\n            ],\n            [\n              -83.49609375,\n              43.5326204268101\n            ],\n            [\n              -83.49609375,\n              45.644768217751924\n            ],\n            [\n              -86.15478515625,\n              45.644768217751924\n            ],\n            [\n              -86.15478515625,\n              43.5326204268101\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"9","issue":"18","noUsgsAuthors":false,"publicationDate":"2019-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Brown, Donald J.","contributorId":265421,"corporation":false,"usgs":false,"family":"Brown","given":"Donald J.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":822723,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Donner, Deahn M.","contributorId":265422,"corporation":false,"usgs":false,"family":"Donner","given":"Deahn M.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":822724,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ribic, Christine 0000-0003-2583-1778 caribic@usgs.gov","orcid":"https://orcid.org/0000-0003-2583-1778","contributorId":147952,"corporation":false,"usgs":true,"family":"Ribic","given":"Christine","email":"caribic@usgs.gov","affiliations":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bocetti, Carol I.","contributorId":265423,"corporation":false,"usgs":false,"family":"Bocetti","given":"Carol I.","affiliations":[{"id":18003,"text":"California University of Pennsylvania","active":true,"usgs":false}],"preferred":false,"id":822725,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70228326,"text":"70228326 - 2019 - Temporally adaptive acoustic sampling to maximize detection across a suite of focal wildlife species","interactions":[],"lastModifiedDate":"2022-02-09T20:12:38.615453","indexId":"70228326","displayToPublicDate":"2019-08-22T14:03:42","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Temporally adaptive acoustic sampling to maximize detection across a suite of focal wildlife species","docAbstract":"<ol class=\"\"><li>Acoustic recordings of the environment can produce species presence–absence data for characterizing populations of sound-producing wildlife over multiple spatial scales. If a species is present at a site but does not vocalize during a scheduled audio recording survey, researchers may incorrectly conclude that the species is absent (“false negative”). The risk of false negatives is compounded when audio devices have sampling constraints, do not record continuously, and must be manually scheduled to operate at pre-selected times of day, particularly when research programs target multiple species with acoustic availability that varies across temporal conditions.</li><li>We developed a temporally adaptive acoustic sampling algorithm to maximize detection probabilities for a suite of focal species amid sampling constraints. The algorithm combines user-supplied species vocalization models with site-specific weather forecasts to set an optimized sampling schedule for the following day. To test our algorithm, we simulated hourly vocalization probabilities for a suite of focal species in a hypothetical monitoring area for the year 2016. We conducted a factorial experiment that sampled from the 2016 acoustic environment to compare the probability of acoustic detection by a fixed (stationary) schedule versus a temporally adaptive optimized schedule under several sampling efforts and monitoring durations.</li><li>We found that over the course of a study season, the probability of acoustically capturing a focal species (given presence) at least once via automated acoustic monitoring was greater (and acoustic capture occurred earlier in the season) when using the temporally adaptive optimized schedule as compared to a fixed schedule.</li><li>The advantages of a temporally adaptive optimized acoustic sampling schedule are magnified when a study duration is short, sampling effort is low, and/or species acoustic availability is minimal. This methodology presents the opportunity to maximize acoustic monitoring sampling efforts amid constraints.</li></ol>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5579","usgsCitation":"Balantic, C., and Donovan, T.M., 2019, Temporally adaptive acoustic sampling to maximize detection across a suite of focal wildlife species: Ecology and Evolution, v. 9, no. 18, p. 10582-10600, https://doi.org/10.1002/ece3.5579.","productDescription":"19 p.","startPage":"10582","endPage":"10600","ipdsId":"IP-098225","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467346,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5579","text":"Publisher Index Page"},{"id":395723,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Sonoran Desert","volume":"9","issue":"18","noUsgsAuthors":false,"publicationDate":"2019-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Balantic, Cathleen","contributorId":275168,"corporation":false,"usgs":false,"family":"Balantic","given":"Cathleen","affiliations":[{"id":56735,"text":"University of Vemont","active":true,"usgs":false}],"preferred":false,"id":833763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833764,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70204969,"text":"70204969 - 2019 - Tsunamis: Stochastic models of generation, propagation, and occurrence","interactions":[],"lastModifiedDate":"2019-08-28T13:55:46","indexId":"70204969","displayToPublicDate":"2019-08-22T13:52:06","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"title":"Tsunamis: Stochastic models of generation, propagation, and occurrence","docAbstract":"The devastating consequences of the 2004 Indian Ocean and 2011 Tohoku-oki tsunamis have led to increased research into many different aspects of the tsunami phenomenon.  In this paper, we review research related to the observed complexity and uncertainty associated with tsunami generation, propagation, and occurrence described and analyzed using a variety of stochastic models. In each case, tsunamis generated by earthquakes are primarily considered. Stochastic models are developed from the physical theories that govern tsunami evolution combined with empirical models fitted to seismic and tsunami observations, as well as tsunami catalogs.  These stochastic models are key to providing probabilistic forecasts and hazard assessments for tsunamis.  The stochastic methods described here are similar to those described for earthquakes (Vere-Jones, 2013) and volcanoes (Bebbington, 2013) in this Encyclopedia.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of complexity and systems science","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-642-27737-5_595-2","usgsCitation":"Geist, E.L., David Oglesby, and Ryan, K., 2019, Tsunamis: Stochastic models of generation, propagation, and occurrence, chap. <i>of</i> Encyclopedia of complexity and systems science, 30 p., https://doi.org/10.1007/978-3-642-27737-5_595-2.","productDescription":"30 p.","ipdsId":"IP-105439","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":367024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"2nd edition","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150 egeist@usgs.gov","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":1956,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"egeist@usgs.gov","middleInitial":"L.","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":769324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"David Oglesby","contributorId":218469,"corporation":false,"usgs":false,"family":"David Oglesby","affiliations":[{"id":6984,"text":"UC Riverside","active":true,"usgs":false}],"preferred":false,"id":769325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryan, Kenny","contributorId":218470,"corporation":false,"usgs":false,"family":"Ryan","given":"Kenny","email":"","affiliations":[{"id":39852,"text":"Air Force Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":769326,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70215267,"text":"70215267 - 2019 - Paleoclimate of the subtropical Andes during the latest Miocene, Lauca Basin, Chile","interactions":[],"lastModifiedDate":"2020-10-14T14:04:19.848137","indexId":"70215267","displayToPublicDate":"2019-08-22T08:57:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2996,"text":"Palaeogeography, Palaeoclimatology, Palaeoecology","printIssn":"0031-0182","active":true,"publicationSubtype":{"id":10}},"title":"Paleoclimate of the subtropical Andes during the latest Miocene, Lauca Basin, Chile","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0045\">Uplift of the Andean Cordillera during the Miocene and Pliocene produced large-scale changes in regional atmospheric circulation that impacted local ecosystems. The Lauca Basin (northern Chilean Altiplano) contains variably fluvial and lacustrine sedimentary sequences spanning the interval from 8.7 to 2.3 Ma. Field samples were collected from paleo-lacustrine sediments in the basin. Sediments were dated using detrital zircon geochronology on volcanic tuffs, yielding an age range between ~5.57 and 5.44 Ma. These new age constraints provided an opportunity to evaluate changes in the Lauca Basin ecosystem across this dynamic Miocene-Pliocene transition. We employed multiple proxies (lithofacies analysis, diatoms, pollen, and oxygen stable isotopes of authigenic carbonates) to interpret ancient lacustrine and terrestrial paleoenvironments. Alternations among mudstone, carbonate, and evaporitic facies indicate lake-level variability through time. The diatom assemblage is characterized by meso- to hypersaline and alkaline-tolerant taxa typical of shallow lakes. The δ<sup>18</sup>O values ranged from −8.96 to −2.22‰ indicating fluctuations in water balance. Pollen taxa in the outcrop are typical of a transitional stage between seasonal cloud forest and open grassland. Together, these proxies indicate that the Lauca paleolake sediments were deposited under a wetter-than-modern climate with high temporal variability. Our results refine previous studies in the Lauca Basin and are consistent with other regional studies suggesting that the South American summer monsoon at the Miocene-Pliocene transition was more intense than it is at present.</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.palaeo.2019.109336","usgsCitation":"Feitl, M., Kern, A., Jones, A., Fritz, S., Baker, P.E., R.M., J., Salenbien, W., and Willard, D.A., 2019, Paleoclimate of the subtropical Andes during the latest Miocene, Lauca Basin, Chile: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 534, 109336, 14 p., https://doi.org/10.1016/j.palaeo.2019.109336.","productDescription":"109336, 14 p.","ipdsId":"IP-105895","costCenters":[{"id":24693,"text":"Climate Research and Development","active":true,"usgs":true}],"links":[{"id":467348,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.palaeo.2019.109336","text":"Publisher Index Page"},{"id":379358,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -72.685546875,\n              -29.036960648558257\n            ],\n            [\n              -66.3134765625,\n              -29.036960648558257\n            ],\n            [\n              -66.3134765625,\n              -16.93070509876553\n            ],\n            [\n              -72.685546875,\n              -16.93070509876553\n            ],\n            [\n              -72.685546875,\n              -29.036960648558257\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"534","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Feitl, Melina","contributorId":243038,"corporation":false,"usgs":false,"family":"Feitl","given":"Melina","email":"","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":801399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kern, Andrea","contributorId":243039,"corporation":false,"usgs":false,"family":"Kern","given":"Andrea","affiliations":[{"id":48623,"text":"University of Sao Paulo","active":true,"usgs":false}],"preferred":false,"id":801400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Amanda","contributorId":243040,"corporation":false,"usgs":false,"family":"Jones","given":"Amanda","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":801401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fritz, Sherilyn","contributorId":205233,"corporation":false,"usgs":false,"family":"Fritz","given":"Sherilyn","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":801402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baker, Paul E.","contributorId":176810,"corporation":false,"usgs":false,"family":"Baker","given":"Paul","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":801403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"R.M., Joeckel .","contributorId":243041,"corporation":false,"usgs":false,"family":"R.M.","given":"Joeckel","email":"","middleInitial":".","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":801404,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Salenbien, Wout","contributorId":243042,"corporation":false,"usgs":false,"family":"Salenbien","given":"Wout","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":801405,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Willard, Debra A. 0000-0003-4878-0942 dwillard@usgs.gov","orcid":"https://orcid.org/0000-0003-4878-0942","contributorId":2076,"corporation":false,"usgs":true,"family":"Willard","given":"Debra","email":"dwillard@usgs.gov","middleInitial":"A.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":24693,"text":"Climate Research and Development","active":true,"usgs":true}],"preferred":true,"id":801406,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70206869,"text":"70206869 - 2019 - Mechanisms of methane hydrate formation in geological systems","interactions":[],"lastModifiedDate":"2020-02-06T11:01:38","indexId":"70206869","displayToPublicDate":"2019-08-22T07:02:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3283,"text":"Reviews of Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Mechanisms of methane hydrate formation in geological systems","docAbstract":"Natural gas hydrates are ice-like mixtures of gas (mostly methane) and water that are widely found in sediments along the world’s continental margins and within and beneath permafrost in a near-surface depth interval where the pressure is sufficiently high and temperature sufficiently low for gas hydrate to be stable. Beneath this interval, gas hydrate is not stable and free gas may be present. This paper reviews the multiple quantitative models that have proposed to describe the genesis of gas hydrate in geological systems. We emphasize the importance of coupling multi-phase flow (vapor and liquid) and multicomponent reactive transport with geological history to describe the dynamical processes of gas hydrate formation and evolution in geological systems. By understanding the generation and evolution of gas hydrate through time, we will better understand their role in the carbon cycle, their potential to contribute to climate change and geohazards, and how to design optimal strategies for the environmentally safe production of gas from hydrate reservoirs.","language":"English","publisher":"AGU","doi":"10.1029/2018RG000638","usgsCitation":"Kehua You, Flemings, P.B., Alberto Malinverno, Collett, T., and Darnell, K., 2019, Mechanisms of methane hydrate formation in geological systems: Reviews of Geophysics, v. 57, no. 4, p. 1146-1196, https://doi.org/10.1029/2018RG000638.","productDescription":"51 p.","startPage":"1146","endPage":"1196","ipdsId":"IP-106750","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":467350,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018rg000638","text":"Publisher Index Page"},{"id":369608,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Kehua You","contributorId":220889,"corporation":false,"usgs":false,"family":"Kehua You","affiliations":[{"id":29861,"text":"The University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":776108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flemings, Peter B.","contributorId":220890,"corporation":false,"usgs":false,"family":"Flemings","given":"Peter","email":"","middleInitial":"B.","affiliations":[{"id":29861,"text":"The University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":776109,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alberto Malinverno","contributorId":220891,"corporation":false,"usgs":false,"family":"Alberto Malinverno","affiliations":[{"id":40291,"text":"Lamont-Doherty Earth Observatory of Columbia University","active":true,"usgs":false}],"preferred":false,"id":776110,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220806,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":776107,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Darnell, Kristopher","contributorId":220892,"corporation":false,"usgs":false,"family":"Darnell","given":"Kristopher","email":"","affiliations":[{"id":40292,"text":"Slingshot Aerospace","active":true,"usgs":false}],"preferred":false,"id":776111,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70205456,"text":"70205456 - 2019 - Small ponds in headwater catchments are a dominant influence on regional nutrient and sediment budgets","interactions":[],"lastModifiedDate":"2020-09-01T13:56:45.587579","indexId":"70205456","displayToPublicDate":"2019-08-21T18:33:18","publicationYear":"2019","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":"Small ponds in headwater catchments are a dominant influence on regional nutrient and sediment budgets","docAbstract":"<p><span>Small ponds—farm ponds, detention ponds, or impoundments below 0.01 km</span><sup>2</sup><span>—serve important human needs throughout most large river basins. Yet the role of small ponds in regional nutrient and sediment budgets is essentially unknown, currently making it impossible to evaluate their management potential to achieve water quality objectives. Here we used new hydrography data sets and found that small ponds, depending on their spatial position within both their local catchments and the larger river network, can dominate the retention of nitrogen, phosphorus, and sediment compared to rivers, lakes, and reservoirs. Over 300,000 small ponds are collectively responsible for 34%, 69%, and 12% of the mean annual retention of nitrogen, phosphorus, and sediment in the Northeastern United States, respectively, with a dominant influence in headwater catchments (54%, 85%, and 50%, respectively). Small ponds play a critical role among the many aquatic features in long‐term nutrient and sediment loading to downstream waters.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL083937","usgsCitation":"Schmadel, N., Harvey, J., Schwarz, G., Alexander, R., Gomez-Velez, J., Scott, D., and Ator, S., 2019, Small ponds in headwater catchments are a dominant influence on regional nutrient and sediment budgets: Geophysical Research Letters, v. 46, no. 16, p. 9669-9677, https://doi.org/10.1029/2019GL083937.","productDescription":"9 p.","startPage":"9669","endPage":"9677","ipdsId":"IP-109711","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction 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,{"id":70128734,"text":"tm6A52 - 2019 - SUTRA, a model for saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of generalized boundary conditions, a modified implementation of specified pressures and concentrations or temperatures, and the lake capability","interactions":[],"lastModifiedDate":"2019-08-23T09:31:13","indexId":"tm6A52","displayToPublicDate":"2019-08-21T13:45:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-A52","displayTitle":"SUTRA, a Model for Saturated-Unsaturated, Variable-Density Groundwater Flow with Solute or Energy Transport—Documentation of Generalized Boundary Conditions, a Modified Implementation of Specified Pressures and Concentrations or Temperatures, and the Lake Capability","title":"SUTRA, a model for saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of generalized boundary conditions, a modified implementation of specified pressures and concentrations or temperatures, and the lake capability","docAbstract":"Version 3.0 of the SUTRA groundwater modeling program offers three new capabilities: generalized boundary conditions, a modified implementation of specified pressures and concentrations or temperatures, and lakes. Two new types of “generalized” boundary conditions facilitate simulation of a wide range of hydrologic processes that interact with the groundwater model, such as rivers, drains, and evapotranspiration. For generalized-flow boundary conditions, gain (inflow) or loss (outflow) of fluid mass varies linearly with pressure, subject to optional upper and lower limits on flow and (or) pressure. For generalized-transport boundary conditions, gain or loss of solute mass or energy varies linearly with concentration or temperature, respectively. Two of the original types of SUTRA boundary conditions—specified-pressure and specified-concentration or temperature—have been modified such that user-specified, conductance-like factors (known as GNUP and GNUU in previous versions of SUTRA) are no longer required. The new lake capability works with all types of SUTRA boundary conditions, including the new generalized boundary conditions, to enable simulation of the interaction of groundwater flow and transport with lake water “ponded” on the surface of a three-dimensional model. SUTRA uses the topography of the top surface of the model, or, optionally, user-specified lake-bottom elevations, to identify potential lakes automatically. Increases and decreases in lake stage can cause lakes to coalesce and divide, respectively. The lake capability may be used with saturated or unsaturated flow and solute or energy transport.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Groundwater in Book 6 <i>Modeling Techniques</i>","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A52","collaboration":"Prepared in cooperation with the Strategic Environmental Research and Development Program","usgsCitation":"Provost, A.M., and Voss, C.I., 2019, SUTRA, a model for saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of generalized boundary conditions, a modified implementation of specified pressures and concentrations or temperatures, and the lake capability: U.S. Geological Survey Techniques and Methods, book 6, chap. A52, 62 p., https://doi.org/10.3133/tm6A52.","productDescription":"viii, 62 p.","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-058173","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":437362,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PPEHHM","text":"USGS data release","linkHelpText":"SUTRA 3"},{"id":364789,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a52/tm6a52.pdf","text":"Report","size":"4.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-A52"},{"id":364788,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a52/coverthb.jpg"}],"publicComments":"This report is Chapter 52 of Section A: Groundwater in Book 6 <i>Modeling Techniques</i>","contact":"<p>Director, Earth System Processes Division<br>U.S. Geological Survey<br>Mail Stop 411<br>12201 Sunrise Valley Drive<br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Chapter 1. Generalized Boundary Conditions</li><li>Chapter 2. Modified Implementation of Specified Pressures and Concentrations or Temperatures</li><li>Chapter 3. Lake Capability</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. List of Symbols</li><li>Appendix 2. Flow Across a Conductive Layer</li><li>Appendix 3. Input Data List</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2019-08-21","noUsgsAuthors":false,"publicationDate":"2019-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Provost, Alden M. 0000-0002-4443-1107 aprovost@usgs.gov","orcid":"https://orcid.org/0000-0002-4443-1107","contributorId":138757,"corporation":false,"usgs":true,"family":"Provost","given":"Alden","email":"aprovost@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":false,"id":764514,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voss, Clifford I. 0000-0001-5923-2752 cvoss@usgs.gov","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":1559,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford","email":"cvoss@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":764515,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70204559,"text":"ds1115 - 2019 - Catalog of earthquake parameters and description of seismograph and infrasound stations at Alaskan volcanoes—January 1, 2013, through December 31, 2017","interactions":[],"lastModifiedDate":"2019-08-21T15:23:24","indexId":"ds1115","displayToPublicDate":"2019-08-21T09:34:53","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1115","displayTitle":"Catalog of Earthquake Parameters and Description of Seismograph and Infrasound Stations at Alaskan Volcanoes—January 1, 2013, through December 31, 2017","title":"Catalog of earthquake parameters and description of seismograph and infrasound stations at Alaskan volcanoes—January 1, 2013, through December 31, 2017","docAbstract":"<div>Between January 1, 2013, and December 31, 2017, the Alaska Volcano Observatory (AVO) located a total of 28,172 earthquakes at volcanoes in Alaska. The annual totals are 3,840, 5,819, 5,297, 6,151, and 7,065 earthquakes for the years 2013 through 2017, respectively. This represents an average of 5,634 earthquakes per year, which is comparable to the yearly number of earthquakes AVO located in the previous decade when AVO monitored a similar number of volcanoes. During the reporting period, there was significant seismic activity at 20 of the 34 volcanoes monitored by a seismograph network (Akutan Peak, Aniakchak Crater, Augustine, Mount Cerberus, Mount Cleveland, Fourpeaked Mountain, Mount Gareloi, Great Sitkin, Ilimana, Kanaga, Korovin, Makushin, Mount Martin, Okmok Caldera, Pavlof, Shishaldin, Mount Spurr, Tanaga, Ugashik-Peulik, and Mount Veniaminof) and two volcanoes without a monitoring network (Mount Recheshnoi and Bogoslof Island). Instrumentation highlights for this period include the establishment of a new subnetwork on Mount Cleveland, an accelerated transition from analog to digital telemetry at most subnetworks, and an increased number of broadband and infrasound sensors throughout the AVO network. The operational highlight was the return of seismic monitoring at Korovin and Ugashik-Peulik Volcanoes following network repairs. This catalog includes hypocenters, magnitudes, and statistics of the earthquakes located in 2013–17, along with the associated station parameters, and velocity models.</div>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1115","usgsCitation":"Dixon, J.P., Stihler S.D., Haney, M.M., Lyons, J.J., Ketner, D.M., Mulliken, K.M., Parker, T., and Power, J.A., 2019, Catalog of earthquake parameters and description of seismograph and infrasound stations at Alaskan volcanoes—January 1, 2013, through December 31, 2017: U.S. Geological Survey Data Series 1115, 92 p., https://doi.org/10.3133/ds1115.","productDescription":"Report: xi, 92 p.; Datasets; Metadata; Read Me","numberOfPages":"92","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099710","costCenters":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science 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href=\"mailto:tlmurray@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:tlmurray@usgs.gov\">Director</a>,<br><a href=\"https://volcanoes.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://volcanoes.usgs.gov/\">Volcano Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>4210 University Drive<br>Anchorage, AK 99508</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Instrumentation</li><li>Data Acquisition and Processing</li><li>Seismic-Velocity Models</li><li>Seismicity</li><li>Summary</li><li>References Cited</li><li>Appendixes</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2019-08-21","noUsgsAuthors":false,"publicationDate":"2019-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Dixon, James P. 0000-0002-8478-9971 jpdixon@usgs.gov","orcid":"https://orcid.org/0000-0002-8478-9971","contributorId":3163,"corporation":false,"usgs":true,"family":"Dixon","given":"James","email":"jpdixon@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":767561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stihler, Scott D. 0000-0002-3585-7050","orcid":"https://orcid.org/0000-0002-3585-7050","contributorId":215242,"corporation":false,"usgs":false,"family":"Stihler","given":"Scott","email":"","middleInitial":"D.","affiliations":[{"id":39214,"text":"Alaska Volcano Observatory, UAFGI.","active":true,"usgs":false}],"preferred":false,"id":767562,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":767563,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":767564,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ketner, Dane M. 0000-0002-1610-0773","orcid":"https://orcid.org/0000-0002-1610-0773","contributorId":217809,"corporation":false,"usgs":true,"family":"Ketner","given":"Dane","email":"","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":767565,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mulliken, Katherine M. 0000-0003-4190-5060","orcid":"https://orcid.org/0000-0003-4190-5060","contributorId":217810,"corporation":false,"usgs":false,"family":"Mulliken","given":"Katherine","email":"","middleInitial":"M.","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":767566,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Parker, Thomas 0000-0002-3006-5652 tparker@usgs.gov","orcid":"https://orcid.org/0000-0002-3006-5652","contributorId":215241,"corporation":false,"usgs":true,"family":"Parker","given":"Thomas","email":"tparker@usgs.gov","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":767568,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Power, John 0000-0002-7233-4398","orcid":"https://orcid.org/0000-0002-7233-4398","contributorId":215240,"corporation":false,"usgs":true,"family":"Power","given":"John","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":767567,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70204870,"text":"70204870 - 2019 - A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent","interactions":[],"lastModifiedDate":"2019-08-26T09:30:13","indexId":"70204870","displayToPublicDate":"2019-08-21T09:33:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent","docAbstract":"Harmful algal blooms (HABs) have been increasing in intensity across many waterbodies worldwide, including the western basin of Lake Erie. Substantial efforts have been made to track these blooms using in situ sampling and remote sensing. However, such measurements do not fully capture HAB spatial and temporal dynamics due to the limitations of discrete shipboard sampling over large areas and the effects of clouds and winds on remote sensing estimates. To address these limitations, we develop a space-time geostatistical modeling framework to improve estimates of HAB timing, extent, and intensity using five independent sets of chlorophyll a (chl-a) data sampled from June to October, 2008 to 2017. Based on the Bayesian information criterion for model selection, trend variables explain bloom northerly and easterly expansion from Maumee Bay, wind effects over depth, and variability among sampling methods. Cross validation results indicate the model can estimate daily, location-specific chl-a concentrations with reasonable accuracy (R2 = 55%) between monitoring cruises. Conditional simulations provide probabilistic estimates of algal biomass and surface areal extent, which are compared to remote sensing estimates. The simulations also provide, for the first time, comprehensive estimates of overall bloom biomass based on depth-integrated concentrations, with quantified uncertainties. These estimates enhance our understanding of HAB variability and can inform HAB monitoring network design, predictive modeling, and management.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.133776","usgsCitation":"Fang, S., Giudice, D.D., Scavia, D., Binding, C.E., Bridgeman, T.B., Chaffin, J.D., Evans, M.A., Guinness, J., Johengen, T.H., and Obenour, D.R., 2019, A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent: Science of the Total Environment, v. 695, 133776, 12 p., https://doi.org/10.1016/j.scitotenv.2019.133776.","productDescription":"133776, 12 p.","ipdsId":"IP-107890","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":467354,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.133776","text":"Publisher Index 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,{"id":70203792,"text":"ofr20191064 - 2019 - Molecular identification of fecal contamination in the Elks Run Watershed, Jefferson County, West Virginia, 2016–17","interactions":[],"lastModifiedDate":"2024-03-04T19:35:54.980435","indexId":"ofr20191064","displayToPublicDate":"2019-08-20T15:30:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1064","displayTitle":"Molecular Identification of Fecal Contamination in the Elks Run Watershed, Jefferson County, West Virginia, 2016–17","title":"Molecular identification of fecal contamination in the Elks Run Watershed, Jefferson County, West Virginia, 2016–17","docAbstract":"<p>The U.S. Geological Survey conducted a study using modern methods of molecular analysis aimed at attempting to identify the source(s) of fecal contamination that had been identified in previous studies conducted by the West Virginia Conservation Agency in the Elk Run watershed, Jefferson County, West Virginia. Water samples from multiple sites showing elevated fecal coliform counts were analyzed using molecular markers associated with general mammalian fecal contamination (AllBac), human <i>Bacteroides</i> (HF183), bovine <i>Bacteroides</i> (BoBac), and human polyomavirus (HPyV). Samples were also analyzed by quantitative polymerase chain reaction (qPCR) for human and bovine cytochrome b (mitochondrial DNA marker). A headwater site (Elk Branch at Shenandoah Junction) was found to be severely affected by both human and bovine contamination in May 2017. Although many of the molecular marker levels as well as <i>Escherichia coli</i> numbers had declined by a repeat sampling in June 2017, total coliform bacterial numbers remained high. Examination of the data indicated that this site had probably been affected by two separate contamination events, an influx of bovine contamination close to the time of the May sampling and a human contamination event that had occurred earlier. Samples from all sites contained bovine mitochondrial DNA, whereas only one revealed relatively high levels of human mitochondrial DNA. The Elk Run watershed appears to be widely affected by bovine influences with human influence episodically playing a role. Surface runoff caused by rain events exacerbates both.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191064","usgsCitation":"Schill, W.B., and Iwanowicz, D.D., 2019, Molecular identification of fecal contamination in the Elks Run watershed, Jefferson County, West Virginia, 2016–17: U.S. Geological Survey Open-File Report 2019–1064, 9 p., https://doi.org/10.3133/ofr20191064.","productDescription":"9 p.","onlineOnly":"Y","ipdsId":"IP-092227","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":366675,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1064/ofr20191064.pdf","text":"Report","size":"6.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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<a href=\"https://www.usgs.gov/centers/eesc\" data-mce-href=\"https://www.usgs.gov/centers/eesc\">Eastern Ecological Science Center</a><br>U.S. Geological Survey<br>11649 Leetown Road<br>Kearneysville, WV 25430</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2019-08-20","noUsgsAuthors":false,"publicationDate":"2019-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Schill, W. Bane 0000-0002-9217-984X","orcid":"https://orcid.org/0000-0002-9217-984X","contributorId":213903,"corporation":false,"usgs":true,"family":"Schill","given":"W. Bane","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":764147,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iwanowicz, Deborah D. 0000-0002-9613-8594","orcid":"https://orcid.org/0000-0002-9613-8594","contributorId":216201,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Deborah D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":764148,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70204861,"text":"70204861 - 2019 - Spatial distribution of water level impact to back-barrier bays","interactions":[],"lastModifiedDate":"2021-09-17T11:49:09.164716","indexId":"70204861","displayToPublicDate":"2019-08-20T14:48:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2824,"text":"Natural Hazards and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Spatial distribution of water level impact to back-barrier bays","docAbstract":"Water level in semi-enclosed bays, landward of barrier islands, is mainly driven by offshore sea level fluctuations that are modulated by bay geometry and bathymetry, causing spatial variability in the ensuing response (transfer). Local wind setup can have a secondary role that depends on wind speed, fetch, and relative orientation of the wind direction and the bay. Inlet geometry and bathymetry primarily regulate the magnitude of the transfer between open ocean and bay. Tides and short-period offshore oscillations are more damped in the bays than longer-lasting offshore fluctuations, such as storm surge and sea level rise. We compare observed and modeled water levels at stations in a mid-Atlantic bay (Barnegat Bay) with offshore water level proxies. Observed water levels in Barnegat Bay are compared and combined with model results from the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system to evaluate the spatial structure of the water level transfer. Analytical models based on the dimensional characteristics of the bay are used to combine the observed data and the numerical model results in a physically consistent approach. Model water level transfers match observed values at locations inside the Bay in the storm frequency band (transfers ranging from 70-100%) and tidal frequencies (10-55%). The contribution of frequency-dependent local setup caused by wind acting along the bay is also considered. The approach provides transfer estimates for locations inside the Bay where observations were not available resulting in a complete spatial characterization. The approach allows for the study of the Bay response to alternative forcing scenarios (landscape changes, future storms, and rising sea level). Detailed spatial estimates of water level transfer can inform decisions on inlet management and contribute to the assessment of current and future flooding hazard in back-barrier bays and along mainland shorelines.","language":"English","publisher":"European Geoscience Union","doi":"10.5194/nhess-19-1823-2019","usgsCitation":"Aretxabaleta, A., Ganju, N., Defne, Z., and Signell, R.P., 2019, Spatial distribution of water level impact to back-barrier bays: Natural Hazards and Earth System Sciences, v. 19, no. 8, p. 1823-1838, https://doi.org/10.5194/nhess-19-1823-2019.","productDescription":"16 p.","startPage":"1823","endPage":"1838","ipdsId":"IP-102040","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467356,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-19-1823-2019","text":"Publisher Index Page"},{"id":366748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"8","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":768781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":768782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Defne, Zafer 0000-0003-4544-4310 zdefne@usgs.gov","orcid":"https://orcid.org/0000-0003-4544-4310","contributorId":5520,"corporation":false,"usgs":true,"family":"Defne","given":"Zafer","email":"zdefne@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":768783,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Signell, Richard P. 0000-0003-0682-9613 rsignell@usgs.gov","orcid":"https://orcid.org/0000-0003-0682-9613","contributorId":140906,"corporation":false,"usgs":true,"family":"Signell","given":"Richard","email":"rsignell@usgs.gov","middleInitial":"P.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":768784,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70222960,"text":"70222960 - 2019 - Evaluating the temperature difference parameter in the SSEBop model with satellite observed land surface temperature data","interactions":[],"lastModifiedDate":"2021-08-10T13:19:36.379901","indexId":"70222960","displayToPublicDate":"2019-08-20T08:11:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the temperature difference parameter in the SSEBop model with satellite observed land surface temperature data","docAbstract":"<p><span>The Operational Simplified Surface Energy Balance (SSEBop) model uses the principle of satellite psychrometry to produce spatially explicit actual evapotranspiration (ETa) with remotely sensed and weather data. The temperature difference (</span><span class=\"html-italic\">dT</span><span>) in the model is a predefined parameter quantifying the difference between surface temperature at bare soil and air temperature at canopy level. Because&nbsp;</span><span class=\"html-italic\">dT</span><span>&nbsp;is derived from the average-sky net radiation based primarily on climate data, validation of the&nbsp;</span><span class=\"html-italic\">dT</span><span>&nbsp;estimation is critical for assuring a high-quality ETa product. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) data to evaluate the SSEBop&nbsp;</span><span class=\"html-italic\">dT</span><span>&nbsp;estimation for the conterminous United States. MODIS data (2008–2017) were processed to compute the 10-year average land surface temperature (LST) and normalized difference vegetation index (NDVI) at 1 km resolution and 8-day interval. The observed&nbsp;</span><span class=\"html-italic\">dT</span><span>&nbsp;(</span><span class=\"html-italic\">dT<sub>o</sub></span><span>) was computed from the LST difference between hot (NDVI &lt; 0.25) and cold (NDVI &gt; 0.7) pixels within each 2° × 2° sampling block. There were enough hot and cold pixels within each block to create&nbsp;</span><span class=\"html-italic\">dT<sub>o</sub></span><span>&nbsp;timeseries in the West Coast and South-Central regions. The comparison of&nbsp;</span><span class=\"html-italic\">dT<sub>o</sub></span><span>&nbsp;and modeled&nbsp;</span><span class=\"html-italic\">dT</span><span>&nbsp;(</span><span class=\"html-italic\">dT<sub>m</sub></span><span>) showed high agreement, with a bias of 0.8 K and a correlation coefficient of 0.88 on average. This study concludes that the&nbsp;</span><span class=\"html-italic\">dT<sub>m</sub></span><span>&nbsp;estimation from the SSEBop model is reliable, which further assures the accuracy of the ETa estimation.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs11161947","usgsCitation":"Ji, L., Senay, G.B., Velpuri, N., and Kagone, S., 2019, Evaluating the temperature difference parameter in the SSEBop model with satellite observed land surface temperature data: Remote Sensing, v. 11, no. 6, 1947, 16 p., https://doi.org/10.3390/rs11161947.","productDescription":"1947, 16 p.","ipdsId":"IP-108754","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467358,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11161947","text":"Publisher Index Page"},{"id":387802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -94.64,\n                48.84\n              ],\n              [\n                -94.32914,\n                48.67074\n              ],\n              [\n                -93.63087,\n                48.60926\n              ],\n              [\n                -92.61,\n                48.45\n              ],\n              [\n                -91.64,\n                48.14\n              ],\n              [\n                -90.83,\n                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     [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":820916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":820917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Velpuri, Naga Manohar  0000-0002-6370-1926","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":216911,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga Manohar ","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":820918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":210980,"corporation":false,"usgs":true,"family":"Kagone","given":"Stefanie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":820919,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204349,"text":"sir20195069 - 2019 - Estimates of long-term mean daily streamflow and annual nutrient and suspended-sediment loads considered for use in regional SPARROW models of the Conterminous United States,  2012 base year","interactions":[],"lastModifiedDate":"2019-12-05T09:57:02","indexId":"sir20195069","displayToPublicDate":"2019-08-19T15:45:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5069","displayTitle":"Estimates of Long-Term Mean Daily Streamflow and Annual Nutrient and Suspended-Sediment Loads Considered for Use in Regional SPARROW Models of the Conterminous United States,  2012 Base Year","title":"Estimates of long-term mean daily streamflow and annual nutrient and suspended-sediment loads considered for use in regional SPARROW models of the Conterminous United States,  2012 base year","docAbstract":"<p>Streamflow, nutrient, and sediment concentration data needed to estimate long-term mean daily streamflow and annual constituent loads were compiled from Federal, State, Tribal, and regional agencies, universities, and nongovernmental organizations. The streamflow and loads are used to develop Spatially Referenced Regressions on Watershed Attributes (SPARROW) models. SPARROW models help describe the distribution, sources, and transport of streamflow, nutrients, and sediment in streams throughout five regions of the conterminous United States. After the data were screened, approximately 5,200 streamflow, 3,000 sediment, and 3,300 nutrient sites, sampled by 137 agencies and organizations were identified as having suitable data for calculating the long-term mean daily streamflow and annual nutrient and sediment loads required for SPARROW model estimation. These sites are representative of a wide range in terms of watershed size, contaminant source types, and land-use and other important watershed characteristics. The methods used to estimate long-term mean annual loads include the Beale ratio estimator and Fluxmaster regression method with Kalman smoothing.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195069","collaboration":" ","usgsCitation":"Saad, D.A., Schwarz, G.E., Argue, D.M., Anning, D.W., Ator, S.W., Hoos, A.B., Preston, S.D., Robertson, D.M., and Wise, D.R., 2019, Estimates of long-term mean daily streamflow and annual nutrient and suspended-sediment loads considered for use in regional SPARROW models of the conterminous United States, 2012 base year: U.S. Geological Survey Scientific Investigations Report 2019–5069, 51 p., https://doi.org/10.3133/sir20195069.","productDescription":"Report: vii, 51 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-081781","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":437366,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DN436B","text":"USGS data release","linkHelpText":"Water-quality and streamflow datasets used for estimating long-term mean daily streamflow and annual loads to be considered for use in regional streamflow, nutrient and sediment SPARROW models, United States, 1999-2014"},{"id":366624,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5069/sir20195069.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5069"},{"id":366634,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DN436B ","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-Quality and Streamflow Datasets Used for Estimating Long-Term Mean Daily Streamflow and Annual Loads to be Considered for Use in Regional Streamflow, Nutrient and Sediment SPARROW Models, United States, 1999-2014"},{"id":366623,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5069/coverthb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -94.64,\n                48.84\n              ],\n              [\n                -94.32914,\n                48.67074\n              ],\n              [\n                -93.63087,\n                48.60926\n              ],\n              [\n                -92.61,\n                48.45\n              ],\n    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               44.6159\n              ],\n              [\n                -123.89893,\n                45.52341\n              ],\n              [\n                -124.07963,\n                46.86475\n              ],\n              [\n                -124.39567,\n                47.72017\n              ],\n              [\n                -124.68721,\n                48.18443\n              ],\n              [\n                -124.5661,\n                48.37971\n              ],\n              [\n                -123.12,\n                48.04\n              ],\n              [\n                -122.58736,\n                47.096\n              ],\n              [\n                -122.34,\n                47.36\n              ],\n              [\n                -122.5,\n                48.18\n              ],\n              [\n                -122.84,\n                49\n              ],\n              [\n                -120,\n                49\n              ],\n              [\n                -117.03121,\n                49\n              ],\n              [\n                -116.04818,\n                49\n              ],\n              [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_wi@usgs.gov\" data-mce-href=\"mailto:dc_wi@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/umid-water\" data-mce-href=\"https://www.usgs.gov/centers/umid-water\">Upper Midwest Water Science Center</a><br>U.S. Geological Survey <br>8505 Research Way<br>Middleton, WI 53562</p>","tableOfContents":"<ul><li>Foreword</li><li>Abstract</li><li>Introduction</li><li>Streamflow and Water-Quality Data Used to Estimate Long-Term Mean Daily Streamflow and Annual Loads</li><li>Methods for Estimating Long-Term Mean Daily Streamflows and Annual Loads</li><li>Final Streamflow and Load Estimates Considered for Use in the 2012 Regional SPARROW Models</li><li>Streamflow and Constituent Yields for Sites Considered for Use in the 2012 Regional SPARROW Models</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Sampling Agencies Associated with Water-Quality Data Used To Compute Mean Annual Load Estimates Considered for Use in 2012 Regional SPARROW Models</li><li>Appendix 2. A Kalman-Smoothing Estimate of Water-Quality Loads Based on Simulated Maximum Likelihood Estimation for Censored Data: The Fluxmaster-K Algorithm</li><li>Appendix 3. Derivation of Regularity Conditions Used to Evaluate the Covariance Matrix and Asymptotic Efficiency of the Estimates Produced by the Fluxmaster-K Algorithm</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2019-08-19","noUsgsAuthors":false,"publicationDate":"2019-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Saad, David A. 0000-0001-6559-6181","orcid":"https://orcid.org/0000-0001-6559-6181","contributorId":217251,"corporation":false,"usgs":true,"family":"Saad","given":"David A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766459,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":217253,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":false,"id":766461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Argue, Denise M. 0000-0002-1096-5362","orcid":"https://orcid.org/0000-0002-1096-5362","contributorId":217252,"corporation":false,"usgs":true,"family":"Argue","given":"Denise","email":"","middleInitial":"M.","affiliations":[],"preferred":true,"id":766460,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anning, David W. 0000-0002-4470-3387","orcid":"https://orcid.org/0000-0002-4470-3387","contributorId":217254,"corporation":false,"usgs":true,"family":"Anning","given":"David","email":"","middleInitial":"W.","affiliations":[],"preferred":true,"id":766462,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ator, Scott A. 0000-0002-9186-4837","orcid":"https://orcid.org/0000-0002-9186-4837","contributorId":217255,"corporation":false,"usgs":true,"family":"Ator","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":true,"id":766463,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hoos, Anne B. 0000-0001-9845-7831","orcid":"https://orcid.org/0000-0001-9845-7831","contributorId":217256,"corporation":false,"usgs":true,"family":"Hoos","given":"Anne B.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766464,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Preston, Stephen D. 0000-0003-1515-6692","orcid":"https://orcid.org/0000-0003-1515-6692","contributorId":217257,"corporation":false,"usgs":true,"family":"Preston","given":"Stephen D.","affiliations":[],"preferred":true,"id":766465,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766466,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wise, Daniel 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":217259,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel","email":"","affiliations":[],"preferred":true,"id":766467,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70202980,"text":"fs20193017 - 2019 - Water-quality and geochemical variability in the Little Arkansas River and Equus Beds aquifer, south-central Kansas, 2001–16","interactions":[],"lastModifiedDate":"2019-08-19T15:06:11","indexId":"fs20193017","displayToPublicDate":"2019-08-19T10:37:31","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3017","displayTitle":"Water-Quality and Geochemical Variability in the Little Arkansas River and <i>Equus</i> Beds Aquifer, South-Central Kansas, 2001–16","title":"Water-quality and geochemical variability in the Little Arkansas River and Equus Beds aquifer, south-central Kansas, 2001–16","docAbstract":"<p><span>This fact sheet describes water quality and geochemistry of the Little Arkansas River and</span> <i>Equus</i><span> Beds aquifer during 2001 through 2016 as part of the City of Wichita’s </span><i>Equus</i><span> Beds aquifer storage and recovery project in south-central Kansas. The</span> <i>Equus</i><span> Beds </span>aquifer storage and recovery<span> project was developed to help meet future water demand by pumping water out of the Little Arkansas River (during above-base-flow conditions), treating it using National Primary Drinking Water Regulations as a guideline, and injecting it into the aquifer for later use. Water-quality data were collected and analyzed by the U.S.&nbsp;Geological Survey from 2&nbsp;Little Arkansas River surface-water sites and 63&nbsp;</span><i>Equus</i><span> Beds groundwater sites, including 38&nbsp;areal assessment index wells, each of which has a shallow well and a deep well. About 4,700&nbsp;surface and groundwater samples were collected and analyzed for more than 300&nbsp;water-quality constituents. About 1,300&nbsp;groundwater chemistry samples were geochemically modeled. </span>Constituents of concern in the <i>Equus</i> Beds aquifer exceeded their respective Federal criteria throughout the study period and included chloride, sulfate, nitrate plus nitrite, <i>Escherichia coli</i> (<i>E. coli</i>), total coliforms, and dissolved iron and arsenic species.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193017","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., Klager, B.J., and Ziegler, A.C., 2019, Water-quality and geochemical variability in the Little Arkansas River and <i>Equus</i> Beds aquifer, south-central Kansas, 2001–16: U.S. Geological Survey Fact Sheet 2019–3017, 6 p., https://doi.org/10.3133/fs20193017.","productDescription":"Report: 6 p.; Companion Files","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-097042","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":364768,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3017/coverthb.jpg"},{"id":364769,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3017/fs20193017.pdf","text":"Report","size":"5.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3017"},{"id":364770,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026.pdf","text":"SIR 2019–5026","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5026","linkHelpText":" – Water-Quality and Geochemical Variability in the Little Arkansas River and <i>Equus</i> Beds Aquifer, South-Central Kansas, 2001–16"},{"id":364797,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026_appendix01.xlsx","text":"SIR 2019–5026 Appendix Tables","size":"236 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5026 Appendix Tables","linkHelpText":"– Table 1.1 through Table 1.14"}],"country":"United States","state":"Kansas","otherGeospatial":"Equus Beds Aquifer, Little Arkansas River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.83462524414062,\n              37.884608857503785\n            ],\n            [\n              -97.82844543457031,\n              37.85859141570558\n            ],\n            [\n              -97.76664733886719,\n              37.79296501804014\n            ],\n            [\n              -97.57919311523438,\n              37.66805980224121\n            ],\n            [\n              -97.33749389648438,\n              37.684907136008846\n            ],\n            [\n              -97.33062744140625,\n              37.74248523826606\n            ],\n            [\n              -97.35397338867188,\n              37.859675659210005\n            ],\n            [\n              -97.34230041503906,\n              38.03619406237626\n            ],\n            [\n              -97.3443603515625,\n              38.17829073458205\n            ],\n            [\n              -97.40684509277344,\n              38.17613163876633\n            ],\n            [\n              -97.8826904296875,\n              38.171273439283084\n            ],\n            [\n              -97.89985656738281,\n              38.149137543764894\n            ],\n            [\n              -97.83462524414062,\n              37.884608857503785\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:%20dc_ks@usgs.gov\" data-mce-href=\"mailto:%20dc_ks@usgs.gov\">Director</a>, <a href=\"https://ks.water.usgs.gov\" data-mce-href=\"https://ks.water.usgs.gov\">Kansas Water Science Center</a> <br>U.S. Geological Survey<br>1217 Biltmore Dr. <br>Lawrence, KS 66049</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Water Quality of the Little Arkansas River and <em>Equus</em> Beds Aquifer</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-08-19","noUsgsAuthors":false,"publicationDate":"2019-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Stone, Mandy L. 0000-0002-6711-1536","orcid":"https://orcid.org/0000-0002-6711-1536","contributorId":214749,"corporation":false,"usgs":true,"family":"Stone","given":"Mandy L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":760681,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klager, Brian J. 0000-0001-8361-6043","orcid":"https://orcid.org/0000-0001-8361-6043","contributorId":214750,"corporation":false,"usgs":true,"family":"Klager","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":760682,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ziegler, Andrew C. 0000-0003-4368-6287 aziegler@usgs.gov","orcid":"https://orcid.org/0000-0003-4368-6287","contributorId":214751,"corporation":false,"usgs":true,"family":"Ziegler","given":"Andrew","email":"aziegler@usgs.gov","middleInitial":"C.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":760683,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70217322,"text":"70217322 - 2019 - Development and implementation of an empirical habitat change model and decision support tool for estuarine ecosystems","interactions":[],"lastModifiedDate":"2021-01-19T12:55:34.568699","indexId":"70217322","displayToPublicDate":"2019-08-19T07:07:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Development and implementation of an empirical habitat change model and decision support tool for estuarine ecosystems","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0005\" class=\"abstract author\"><div id=\"abst0005\"><p id=\"spar0075\">Widespread land use change in coastal ecosystems has led to a decline in the amount of habitat available for fish and wildlife, lower production of ecosystem goods and services, and loss of recreational and aesthetic value. This has prompted global efforts to restore the natural hydrologic regimes of developed shorelines, especially resource-rich estuaries, but the resilience of these restored ecosystems in the face of accelerated sea-level rise (SLR) remains uncertain. We implemented a<span>&nbsp;</span><u>Mo</u>nitoring-based<span>&nbsp;</span><u>S</u>imulation of<span>&nbsp;</span><u>A</u>ccretion<span>&nbsp;</span><u>i</u>n<span>&nbsp;</span><u>C</u>oastal E<u>s</u>tuaries (MOSAICS) in R statistical software to address uncertainty in the resilience of modified estuarine habitats, using the Nisqually River Delta in the Pacific Northwest USA as a case study. MOSAICS is a spatially explicit model with a numerical foundation that uses empirical monitoring datasets to forecast habitat change in response to rising tidal levels. Because it accounts for the crucial ecomorphodynamic feedbacks between tidal inundation, vegetative growth, and sediment accretion, MOSAICS can be used to determine whether alternative management scenarios, such as enhanced sediment inputs, will bolster estuarine resilience to SLR. Under moderate SLR (0.62 m), the model predicted that a two-fold increase in mean daily suspended sediment during the rainy season was sufficient to maintain Nisqually’s emergent marshes through 2100, but under high SLR (1.35 m) MOSAICS indicated that greater sediment additions would be necessary to prevent submergence. A comparison between a restored marsh with subsided and high-elevation areas and a relict marsh demonstrated that the subsided restoration area was highly susceptible to SLR. Findings from the MOSAICS model highlight the importance of a site’s initial elevation, capacity for producing above and belowground biomass, and suspended sediment availability when considering management actions in estuaries and other coastal ecosystems.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2019.108722","usgsCitation":"Davis, M.J., Woo, I., and De La Cruz, S.E., 2019, Development and implementation of an empirical habitat change model and decision support tool for estuarine ecosystems: Ecological Modelling, v. 410, 108722, 18 p., https://doi.org/10.1016/j.ecolmodel.2019.108722.","productDescription":"108722, 18 p.","ipdsId":"IP-109169","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":467361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2019.108722","text":"Publisher Index Page"},{"id":382246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.79968261718749,\n              46.99524110694593\n            ],\n            [\n              -122.58132934570311,\n              46.99524110694593\n            ],\n            [\n              -122.58132934570311,\n              47.13835880864309\n            ],\n            [\n              -122.79968261718749,\n              47.13835880864309\n            ],\n            [\n              -122.79968261718749,\n              46.99524110694593\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"410","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808360,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70204989,"text":"70204989 - 2019 - Incorporating uncertainty and risk into decision making to reduce nitrogen inputs to impaired waters","interactions":[],"lastModifiedDate":"2019-08-28T12:09:20","indexId":"70204989","displayToPublicDate":"2019-08-18T12:02:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating uncertainty and risk into decision making to reduce nitrogen inputs to impaired waters","docAbstract":"This article aims to understand decision making under uncertainty and risk, with a case study on Cape Cod, Massachusetts. Decision makers need to consider imperfect information on the cost and effectiveness of advanced nitrogen-removing on-site wastewater treatment systems as options to mitigate water quality degradation. Research included modeling nitrogen load reduction to impaired coastal waters from seven treatment system technologies and eliciting expert knowledge on their costs. Predictions of nitrogen load removal and cost for each technology incorporated variation in effectiveness and uncertainty in household water use, costs, and expert confidence in costs. The predictions were evaluated using the Pareto efficiency concept to reveal tradeoffs between cost and effectiveness. The stochastic dominance index was used to identify preferred technologies for risk-averse decision making, assuming no further learning is possible. Lastly, the predictions were combined into a cost-effectiveness metric to estimate the expected payoff of implementing the best treatment system in the face of uncertainty and the expected payoff of learning which treatment systems are most cost-effective over time. The expected value of perfect information was calculated as the difference between the expected payoffs. Three technologies revealed Pareto efficient tradeoffs between cost and effectiveness, whereas one technology was the preferred risk-averse option in the absence of future learning. There was a high expected value of perfect information, which could motivate adaptive management on Cape Cod. This research demonstrated decision analysis methods to guide future research and decision making toward meeting water quality objectives and reducing uncertainty.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2019.109380","usgsCitation":"Martin, D.M., and Johnson, F., 2019, Incorporating uncertainty and risk into decision making to reduce nitrogen inputs to impaired waters: Journal of Environmental Management, v. 249, 109380, 9 p., https://doi.org/10.1016/j.jenvman.2019.109380.","productDescription":"109380, 9 p.","ipdsId":"IP-103926","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467362,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7901712","text":"External Repository"},{"id":367010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -70.48004150390625,\n              41.79691191119474\n            ],\n            [\n              -70.6640625,\n              41.713930073371294\n            ],\n            [\n              -70.68878173828125,\n              41.572306568724365\n            ],\n            [\n              -70.96343994140625,\n              41.44684402008925\n            ],\n            [\n              -70.97305297851562,\n              41.395354710280166\n            ],\n            [\n              -70.53085327148438,\n              41.52811390935743\n            ],\n            [\n              -70.41412353515625,\n              41.558949183362564\n            ],\n            [\n              -70.301513671875,\n              41.61646901513335\n            ],\n            [\n              -70.23422241210938,\n              41.58771550500517\n            ],\n            [\n              -70.03097534179688,\n              41.64623592868676\n            ],\n            [\n              -70.04470825195312,\n              41.51680395810118\n            ],\n            [\n              -69.98016357421875,\n              41.509605687197975\n            ],\n            [\n              -69.87442016601561,\n              41.712904935827744\n            ],\n            [\n              -69.94583129882812,\n              41.95540515378059\n            ],\n            [\n              -70.06668090820312,\n              42.07783959017503\n            ],\n            [\n              -70.22598266601562,\n              42.09618442380296\n            ],\n            [\n              -70.26443481445311,\n              42.07783959017503\n            ],\n            [\n              -70.21499633789062,\n              41.9942015603157\n            ],\n            [\n              -70.13534545898438,\n              42.02685388718981\n            ],\n            [\n              -70.09689331054688,\n              41.89716623689334\n            ],\n            [\n              -70.05157470703125,\n              41.87160608117939\n            ],\n            [\n              -70.0543212890625,\n              41.80919639152055\n            ],\n            [\n              -70.28366088867188,\n              41.76721469421018\n            ],\n            [\n              -70.48004150390625,\n              41.79691191119474\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"249","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Martin, David M. 0000-0002-1514-5734","orcid":"https://orcid.org/0000-0002-1514-5734","contributorId":210575,"corporation":false,"usgs":false,"family":"Martin","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":769462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Fred 0000-0002-5854-3695","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":218516,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":769461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70205015,"text":"70205015 - 2019 - Air pollution and respiratory hospital admissions in Shiraz, Iran, 2009 to 2015","interactions":[],"lastModifiedDate":"2019-12-22T14:56:07","indexId":"70205015","displayToPublicDate":"2019-08-16T11:37:50","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":924,"text":"Atmospheric Environment","active":true,"publicationSubtype":{"id":10}},"title":"Air pollution and respiratory hospital admissions in Shiraz, Iran, 2009 to 2015","docAbstract":"<div id=\"abssec0010\"><p id=\"abspara0010\">Air pollution has been identified as one of the most challenging health issues in urban areas worldwide.</p><p id=\"abspara0015\"><span>The aim of this study was to investigate the association between short-term exposure to ambient air pollution and&nbsp;respiratory disease&nbsp;over a long-term period in Shiraz, one of the largest cities in Iran. Methods: hospital admissions due to respiratory diseases (asthma,&nbsp;pneumonia, chronic obstructive&nbsp;pulmonary disease(COPD) and pleural effusion) in residents of Shiraz between March 21, 2009 and March 20, 2015 were included. Demographics for each patient and&nbsp;meta data&nbsp;to include principal meteorological variables (temperature and relative humidity) and five ambient&nbsp;pollutants&nbsp;(CO, O</span><sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, and PM<sub>10</sub>) were also included. Statistical analysis was performed by Poisson regression in single-pollutant generalized linear model with principal component analysis (GLPCA) to analyze the relationship between pollutants and hospital admissions at the 0–9 cumulative lag day period. Pearson correlation test was used to determine the relationship between different pollutants, temperature and humidity.</p></div><div id=\"abssec0015\"><h3 id=\"sectitle0015\" class=\"u-h4 u-margin-m-top u-margin-xs-bottom\">Results</h3><p id=\"abspara0020\">It was found that the highest increase in asthma admission was related to PM<sub>10</sub>(relative risk (RR) = 1.31, 95% CI = 1.17, 1.47). For COPD, the rate of hospital visits significantly increased with the increase in NO<sub>2</sub><span>&nbsp;</span>concentration (RR = 1.17, 95% CI = 1.09.1.27). In the children's hospital, O<sub>3</sub><span>&nbsp;</span>(RR = 1.25, 95% CI = 1.06, 1.47) and SO<sub>2</sub>(RR = 1.17, 95% CI = 1.07, 1.28) affected the asthma admissions and all contaminants (highest RR observed was for NO<sub>2</sub><span>&nbsp;(RR = 1.28 95% CI = 1.18, 1.40) affected pneumonia admissions on cumulative lag days of 0–9. Conclusions: These data confirm an association between ambient&nbsp;air pollutants&nbsp;and hospital admissions due to respiratory disease.</span></p></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.atmosenv.2019.04.030","usgsCitation":"Soleimani, Z., Darvishi Boloorani, A., Khalifeh, R., Teymouri, P., Mesdaghinia, A., and Griffin, D.W., 2019, Air pollution and respiratory hospital admissions in Shiraz, Iran, 2009 to 2015: Atmospheric Environment, v. 209, p. 233-239, https://doi.org/10.1016/j.atmosenv.2019.04.030.","productDescription":"7 p.","startPage":"233","endPage":"239","ipdsId":"IP-098429","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":367007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Iran","city":"Shiraz","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[53.9216,37.19892],[54.8003,37.39242],[55.51158,37.96412],[56.18037,37.93513],[56.61937,38.12139],[57.33043,38.02923],[58.43615,37.52231],[59.23476,37.41299],[60.37764,36.52738],[61.12307,36.4916],[61.21082,35.65007],[60.80319,34.4041],[60.52843,33.67645],[60.9637,33.52883],[60.53608,32.98127],[60.86365,32.18292],[60.94194,31.54807],[61.69931,31.37951],[61.78122,30.73585],[60.87425,29.82924],[61.36931,29.30328],[61.77187,28.69933],[62.72783,28.25964],[62.75543,27.37892],[63.2339,27.21705],[63.31663,26.75653],[61.87419,26.23997],[61.49736,25.07824],[59.61613,25.38016],[58.52576,25.60996],[57.39725,25.7399],[56.97077,26.96611],[56.49214,27.1433],[55.72371,26.96463],[54.71509,26.48066],[53.4931,26.81237],[52.4836,27.58085],[51.52076,27.86569],[50.85295,28.81452],[50.11501,30.14777],[49.57685,29.98572],[48.94133,30.31709],[48.56797,29.92678],[48.01457,30.45246],[48.0047,30.98514],[47.68529,30.98485],[47.8492,31.70918],[47.33466,32.46916],[46.10936,33.01729],[45.41669,33.9678],[45.64846,34.74814],[46.15179,35.09326],[46.07634,35.67738],[45.42062,35.97755],[44.77267,37.17045],[44.22576,37.97158],[44.4214,38.28128],[44.10923,39.42814],[44.79399,39.713],[44.95269,39.33576],[45.45772,38.87414],[46.14362,38.7412],[46.50572,38.77061],[47.68508,39.50836],[48.0601,39.58224],[48.35553,39.28876],[48.01074,38.79401],[48.63438,38.27038],[48.88325,38.32025],[49.19961,37.58287],[50.14777,37.37457],[50.84235,36.87281],[52.26402,36.70042],[53.82579,36.96503],[53.9216,37.19892]]]},\"properties\":{\"name\":\"Iran\"}}]}","volume":"209","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Soleimani, Zahra","contributorId":218554,"corporation":false,"usgs":false,"family":"Soleimani","given":"Zahra","email":"","affiliations":[{"id":39869,"text":"Tehran University of Medical Sciences","active":true,"usgs":false}],"preferred":false,"id":769556,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Darvishi Boloorani, Ali","contributorId":218553,"corporation":false,"usgs":false,"family":"Darvishi Boloorani","given":"Ali","email":"","affiliations":[{"id":39868,"text":"Semnan University of Medical Sciences","active":true,"usgs":false}],"preferred":false,"id":769555,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Khalifeh, Reza","contributorId":218555,"corporation":false,"usgs":false,"family":"Khalifeh","given":"Reza","email":"","affiliations":[{"id":39870,"text":"Tehran University","active":true,"usgs":false}],"preferred":false,"id":769557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teymouri, Pari","contributorId":218556,"corporation":false,"usgs":false,"family":"Teymouri","given":"Pari","email":"","affiliations":[{"id":39871,"text":"Tabriz University of Medical Sciences","active":true,"usgs":false}],"preferred":false,"id":769558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mesdaghinia, Alireza","contributorId":218557,"corporation":false,"usgs":false,"family":"Mesdaghinia","given":"Alireza","email":"","affiliations":[{"id":39869,"text":"Tehran University of Medical Sciences","active":true,"usgs":false}],"preferred":false,"id":769559,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":769554,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70204786,"text":"70204786 - 2019 - Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests","interactions":[],"lastModifiedDate":"2019-08-19T13:52:04","indexId":"70204786","displayToPublicDate":"2019-08-16T06:59:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests","docAbstract":"Abstract \nWildfires raise risks of floods, debris flows, major geomorphologic and sedimentologic change, and water quality and quantity shifts. A principal control on the magnitude of these changes is field-saturated hydraulic conductivity (Kfs), which dictates surface runoff generation and is a key input into numerical models. This work synthesizes 73 Kfs datasets from the literature in the first year following fire at the plot scale (≤ 10 m2). A meta-analysis using a random effects analysis showed significant differences between burned and unburned Kfs. The reductions in Kfs after fire, expressed by the ratio of Kfs Burned / Kfs Unburned, were 0.46 (95% confidence interval of 0.31-0.70) combining wildfire and prescribed fire and 0.3 (95% confidence interval of 0.13-0.71) for wildfire.  No significant differences for Kfs were observed between wildfire and prescribed fire or moderate and high fire severity. Both Kfs magnitude and variability depended more on measurement method than measurement support area at the plot scale, with methods applying head ≥0.5 cm producing larger estimates of Kfs. It is recommended that post-fire efforts to characterize Kfs for modeling or process-based interpretations use methods that reflect the dominant infiltration processes: tension infiltrometers and simulated rainfall methods when soil matrix flow dominates and ponded head methods when macropore flow is critical.","language":"English","publisher":"Wiley","doi":"10.1002/esp.4621","usgsCitation":"Ebel, B.A., 2019, Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests: Earth Surface Processes and Landforms, v. 44, no. 10, p. 1945-1956, https://doi.org/10.1002/esp.4621.","productDescription":"12 p.","startPage":"1945","endPage":"1956","ipdsId":"IP-101329","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":366581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Ebel, Brian A. 0000-0002-5413-3963 bebel@usgs.gov","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":218151,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian","email":"bebel@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":768477,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70227922,"text":"70227922 - 2019 - Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context","interactions":[],"lastModifiedDate":"2022-02-03T12:08:31.632737","indexId":"70227922","displayToPublicDate":"2019-08-15T14:38:12","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context","docAbstract":"Hybridization can profoundly affect the genomic composition and phenotypes of closely related species, and provides an opportunity to identify mechanisms that maintain reproductive isolation between species. Recent evidence suggests that hybridization outcomes within a species pair can vary across locations. However, we still don’t know how extensive variation in outcomes of hybridization is across geographic replicates, and what mechanisms drive that variation. In this study, we described hybridization outcomes across 27 locations in the North Fork Shoshone River basin (Wyoming, USA) where native Yellowstone cutthroat trout and introduced rainbow trout co-occur. We used genomic data and hierarchical Bayesian models to precisely identify ancestry of hybrid individuals. Hybridization outcomes varied across locations. In some locations, only rainbow trout and advanced backcrossed hybrids towards rainbow trout were present, while other locations had a broader range of ancestry, including both parental species and first-generation hybrids. Using an individual-based simulation, we found that outcomes of hybridization in the North Fork Shoshone River basin deviate substantially from what we would expect under assumptions of random mating and no selection against hybrids. Since this implies that some mechanisms of reproductive isolation function to maintain parental taxa and a diversity of hybrid types, we then modeled hybridization outcomes as a function of environmental variables and stocking history that are likely to affect prezygotic barriers to hybridization. Variables associated with history of fish stocking were the strongest predictors of hybridization outcomes, followed by environmental variables that might affect overlap in spawning time and location.","language":"English","publisher":"Wiley","doi":"10.1111/mec.15175","usgsCitation":"Mandeville, E., Walters, A.W., Nordberg, B.J., Higgins, K.H., Burckhardt, J.C., and Wagner, C.E., 2019, Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context: Molecular Ecology, v. 28, no. 16, p. 3738-3755, https://doi.org/10.1111/mec.15175.","productDescription":"18 p.","startPage":"3738","endPage":"3755","ipdsId":"IP-099309","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467366,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6775767","text":"External Repository"},{"id":395304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Buffalo Bill Reservoir, North Fork Shoshone River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.57077026367188,\n              44.26683800273895\n            ],\n            [\n              -108.96102905273438,\n              44.26683800273895\n            ],\n            [\n              -108.96102905273438,\n              44.6579085850145\n            ],\n            [\n              -109.57077026367188,\n              44.6579085850145\n            ],\n            [\n              -109.57077026367188,\n              44.26683800273895\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"28","issue":"16","noUsgsAuthors":false,"publicationDate":"2019-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mandeville, Elizabeth G.","contributorId":270691,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth G.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":832577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nordberg, Brittany J.","contributorId":270690,"corporation":false,"usgs":false,"family":"Nordberg","given":"Brittany","email":"","middleInitial":"J.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":832579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Higgins, Karly H.","contributorId":273111,"corporation":false,"usgs":false,"family":"Higgins","given":"Karly","email":"","middleInitial":"H.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":832580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burckhardt, Jason C.","contributorId":270692,"corporation":false,"usgs":false,"family":"Burckhardt","given":"Jason","email":"","middleInitial":"C.","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":832581,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Catherine E.","contributorId":270693,"corporation":false,"usgs":false,"family":"Wagner","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":832582,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70204253,"text":"sir20195068 - 2019 -  Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018","interactions":[],"lastModifiedDate":"2019-08-16T06:55:10","indexId":"sir20195068","displayToPublicDate":"2019-08-15T13:46:58","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5068","displayTitle":"Flood-Inundation Maps for Joachim Creek, De Soto, Missouri, 2018","title":" Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018","docAbstract":"<p>Digital flood-inundation maps for a 6.7-mile reach of Joachim Creek, De Soto, Missouri, were created by the U.S. Geological Survey (USGS) in cooperation with the city of De Soto and Jefferson County, Missouri. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at <a href=\"https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program\" data-mce-href=\"https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program\">https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program</a>, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at <a href=\"https://waterdata.usgs.gov/nwis\" data-mce-href=\"https://waterdata.usgs.gov/nwis\">https://waterdata.usgs.gov/nwis</a> or the National Weather Service Advanced Hydrologic Prediction Service at <a data-mce-href=\"https://water.weather.gov/ahps2/hydrograph.php?wfo=lsx&amp;gage=desm7\" href=\"https://water.weather.gov/ahps2/hydrograph.php?wfo=lsx&amp;gage=desm7\">https://water.weather.gov/ahps2/hydrograph.php?wfo=lsx&amp;gage=desm7</a>, which also forecasts flood hydrographs at this site (site DESM7).</p><p>Flood profiles were computed for the stream reach using a one-dimensional model for simulation of water-surface profiles with steady-state (gradually varied) or unsteady-state flow computation options. The model was calibrated by using the theoretical stage-discharge relation at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500), and documented high-water marks from the flood of April 18, 2013.</p><p>The hydraulic model was then used to compute 10 water surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum. The profiles ranged from 8.0 ft, or near bankfull, to 17.0 ft, which exceeds the stage that corresponds to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.60-ft vertical accuracy and 1.97-ft horizontal resolution) to delineate the area flooded at each water level.</p><p>The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures and for post-flood recovery efforts.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195068","collaboration":"Prepared in cooperation with the city of De Soto, Missouri, and Jefferson County, Missouri","usgsCitation":"Heimann, D.C., Voss, J.D., and Rydlund, P.H., Jr., 2019, Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5068, 10 p., https://doi.org/10.3133/sir20195068.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-105218","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":366556,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5068/sir20195068.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5068"},{"id":366557,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MD7KUL","text":"USGS data release","linkHelpText":"Geospatial datasets for the flood-inundation study of Joachim Creek, De Soto, Missouri, 2018"},{"id":366555,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5068/coverthb.jpg"}],"country":"United States","state":"Missouri","county":"Jefferson County","city":"De 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<a data-mce-href=\"https://www.usgs.gov/centers/cm-water\" href=\"https://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a> <br>U.S. Geological Survey<br>1400 Independence Road <br>Rolla, MO 65401</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Creation of Flood-Inundation-Map Library</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-08-15","noUsgsAuthors":false,"publicationDate":"2019-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Heimann, David C. 0000-0003-0450-2545 dheimann@usgs.gov","orcid":"https://orcid.org/0000-0003-0450-2545","contributorId":3822,"corporation":false,"usgs":true,"family":"Heimann","given":"David","email":"dheimann@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science 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Jr. 0000-0001-9461-9944 prydlund@usgs.gov","orcid":"https://orcid.org/0000-0001-9461-9944","contributorId":3840,"corporation":false,"usgs":true,"family":"Rydlund","given":"Paul","suffix":"Jr.","email":"prydlund@usgs.gov","middleInitial":"H.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768432,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70204034,"text":"sir20195064 - 2019 - Estimated groundwater recharge from a water-budget model incorporating selected climate projections, Island of Maui, Hawai‘i","interactions":[],"lastModifiedDate":"2019-08-16T06:52:34","indexId":"sir20195064","displayToPublicDate":"2019-08-15T10:51:50","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5064","displayTitle":"Estimated Groundwater Recharge from a Water-Budget Model Incorporating Selected Climate Projections, Island of Maui, Hawai‘i","title":"Estimated groundwater recharge from a water-budget model incorporating selected climate projections, Island of Maui, Hawai‘i","docAbstract":"<p><span>Demand for freshwater on the Island of Maui is expected to increase by 45 percent between 2015 and 2035. Groundwater availability on Maui is affected by changes in climate and agricultural irrigation. To evaluate the availability of fresh groundwater under projected future climate conditions and changing agricultural irrigation practices, estimates of groundwater recharge are needed. A water-budget model with a daily computation interval was used to estimate the spatial distribution of recharge on Maui for one present-day and two future-climate scenarios. All three scenarios used 2017 land cover. The two future-climate scenarios, including one wetter than the present-day scenario and one drier than the present-day scenario, were developed using available high-resolution downscaled climate projections. The drier future scenario was developed using projections for a Representative Concentration Pathway warming scenario during 2071–99 with total radiative forcing of 8.5 Watts per square meter by the year 2100 (RCP8.5 2071–99 scenario), whereas the wetter future scenario was developed using projections for a “Special Report on Emissions Scenarios” A1B emission scenario during 2080–99 (A1B 2080–99 scenario). For the RCP8.5 2071–99 scenario, projected mean annual recharge decrease for Maui is about 172 million gallons per day, or about 14 percent less than present-day recharge, which is estimated to be 1,232 million gallons per day. Recharge for the RCP8.5 2071–99 scenario is projected to decrease in 22 of Maui’s 25 aquifer systems, which are defined by the Hawaiʻi Commission on Water Resource Management. For the A1B 2080–99 future scenario, projected mean annual recharge increase for Maui is about 144 million gallons per day, or about 12 percent more than present-day recharge. Recharge for the A1B 2080–99 scenario is projected to increase in 17 of Maui’s 25 aquifer systems. Between the two future scenarios, a total of 11 aquifer systems show similar direction in drying (Kahului, Kama‘ole, Lualaʻilua, Makawao, Olowalu, Pāʻia, Ukumehame, Waikapū) or wetting (Honopou, Kawaipapa, and Waikamoi) changes for recharge. Selectively modifying the climate inputs for the A1B 2080–99 scenario indicates that the projected changes in rainfall account for most of the projected changes in recharge for Maui’s 25 aquifer systems. However,&nbsp;</span><span>projected changes in reference evapotranspiration and forest-canopy evaporation also can account for a substantial part of the projected changes in recharge where changes in reference evapotranspiration are relatively large and where changes in forest-canopy evaporation extend across large forested areas. Projected changes in daily rainfall frequency have a relatively small but non-negligible impact on recharge estimates.</span></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195064","collaboration":"Prepared in cooperation with the County of Maui Department of Water Supply and the Pacific Regional Integrated Sciences and Assessments Program","usgsCitation":"Mair, A., Johnson A.G., Rotzoll, Kolja, and Oki, D.S., 2019, Estimated groundwater recharge from a water-budget model incorporating selected climate projections, Island of Maui, Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2019–5064, 46 p., https://doi.org/10.3133/sir20195064.","productDescription":"Report: vi, 46 p., 3 data releases","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-100732","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":366544,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98W9ABX","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for projected climate conditions, CMIP5 RCP8.5 2071-99 scenario rainfall and 2017 land cover"},{"id":366541,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5064/sir20195064.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5064"},{"id":366542,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91WSOFO","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for average climate conditions, 1978-2007 rainfall and 2017 land cover"},{"id":366540,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5064/coverthb.jpg"},{"id":366543,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9437T2F","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for projected climate conditions, CMIP3 A1B 2080-99 scenario climate and 2017 land cover"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -156.7474365234375,\n              20.52478875041428\n            ],\n            [\n              -155.8685302734375,\n              20.52478875041428\n            ],\n            [\n              -155.8685302734375,\n              21.099875492701216\n            ],\n            [\n              -156.7474365234375,\n              21.099875492701216\n            ],\n            [\n              -156.7474365234375,\n              20.52478875041428\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://mail.google.com/mail/?view=cm&amp;fs=1&amp;tf=1&amp;to=dc_hi@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:dc_hi@usgs.gov\">Director</a>,<br><a href=\"http://hi.water.usgs.gov/\" data-mce-href=\"http://hi.water.usgs.gov/\">Pacific Islands Water Science Center</a><br><a href=\"https://www.usgs.gov/\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>Inouye Regional Center<br>1845 Wasp Blvd., B176<br>Honolulu, HI 96818</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Study Area</li><li>Future-Climate Scenarios</li><li>Water-Budget Model</li><li>Water-Budget and Groundwater-Recharge Estimates</li><li>Study Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2019-08-15","noUsgsAuthors":false,"publicationDate":"2019-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Mair, Alan 0000-0003-0302-6647 dmair@usgs.gov","orcid":"https://orcid.org/0000-0003-0302-6647","contributorId":4975,"corporation":false,"usgs":true,"family":"Mair","given":"Alan","email":"dmair@usgs.gov","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Adam G. 0000-0003-2448-5746 ajohnson@usgs.gov","orcid":"https://orcid.org/0000-0003-2448-5746","contributorId":4752,"corporation":false,"usgs":true,"family":"Johnson","given":"Adam","email":"ajohnson@usgs.gov","middleInitial":"G.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765211,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rotzoll, Kolja 0000-0002-5910-888X","orcid":"https://orcid.org/0000-0002-5910-888X","contributorId":201087,"corporation":false,"usgs":false,"family":"Rotzoll","given":"Kolja","affiliations":[],"preferred":false,"id":765212,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oki, Delwyn S. 0000-0002-6913-8804","orcid":"https://orcid.org/0000-0002-6913-8804","contributorId":207735,"corporation":false,"usgs":true,"family":"Oki","given":"Delwyn S.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765213,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70211576,"text":"70211576 - 2019 - Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future","interactions":[],"lastModifiedDate":"2020-07-31T15:01:25.745893","indexId":"70211576","displayToPublicDate":"2019-08-15T09:24:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future","docAbstract":"<p><span>The Rio Grande Cutthroat Trout&nbsp;</span><i>Oncorhynchus clarkii virginalis<span>&nbsp;</span></i><span>(RGCT ) occupies just 12% of its ancestral range. As the southernmost subspecies of Cutthroat Trout, we expect a warming climate to bring additional stressors to RGCT populations, such as increased stream temperatures, reduced streamflows, and increased incidence of wildfire. We developed a Bayesian network (BN ) model using site‐specific data, empirical research, and expert knowledge to estimate the probability of persistence for each of the 121 remaining RGCT conservation populations and to rank the severity of the threats they face. These inputs quantified the genetic risks (e.g., inbreeding risk and hybridization risk), population demographics (disease risk, habitat suitability, and survival), and probability of stochastic disturbances (stream drying risk and wildfire risk) in an uncertain future. We also created stream temperature and base flow discharge models coupled with regionally downscaled climate projections to predict future abiotic conditions at short‐term (2040s) and long‐term (2080s) time horizons. In the absence of active management, we predicted a decrease in the average probability of population persistence from 0.53 (current) to 0.31 (2040s) and 0.26 (2080s). Only 11% of these populations were predicted to have a greater than 75% chance of persisting to the 2080s. Threat of invasion by nonnative trout had the strongest effect on population persistence. Of the 78 populations that are already invaded or lacking complete barriers, 60% were estimated to be extirpated by 2080 and the remainder averaged only a 10% chance of persistence. In contrast, the effects of increased stream temperatures were predicted to affect the future persistence of only 9% of the 121 RGCT populations remaining, as most have been restricted to high‐elevation habitats that are cold enough to buffer against some stream warming. Our BN model provides a framework for evaluating threats and will be useful to guide management actions that are likely to provide the most benefit for long‐term conservation.</span></p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10320","usgsCitation":"Zeigler, M.P., Rogers, K., Roberts, J., Todd, A., and Fausch, K., 2019, Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future: North American Journal of Fisheries Management, v. 39, no. 5, p. 819-848, https://doi.org/10.1002/nafm.10320.","productDescription":"30 p.","startPage":"819","endPage":"848","ipdsId":"IP-080586","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":376949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Colorado","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.677001953125,\n              33.578014746143985\n            ],\n            [\n              -103.7548828125,\n              33.578014746143985\n            ],\n            [\n              -103.7548828125,\n              38.47079371120379\n            ],\n            [\n              -107.677001953125,\n              38.47079371120379\n            ],\n            [\n              -107.677001953125,\n              33.578014746143985\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"39","issue":"5","noUsgsAuthors":false,"publicationDate":"2019-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeigler, Mathew P.","contributorId":91006,"corporation":false,"usgs":true,"family":"Zeigler","given":"Mathew","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":794698,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogers, Kevin B.","contributorId":220104,"corporation":false,"usgs":false,"family":"Rogers","given":"Kevin B.","affiliations":[],"preferred":false,"id":794699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794673,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Todd, Andrew atodd@usgs.gov","contributorId":149790,"corporation":false,"usgs":true,"family":"Todd","given":"Andrew","email":"atodd@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":794700,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fausch, Kurt D. 0000-0001-5825-7560","orcid":"https://orcid.org/0000-0001-5825-7560","contributorId":198488,"corporation":false,"usgs":false,"family":"Fausch","given":"Kurt D.","affiliations":[],"preferred":false,"id":794701,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70207595,"text":"70207595 - 2019 - Predicting surf zone injuries along the Delaware coast using a Bayesian network","interactions":[],"lastModifiedDate":"2019-12-30T16:30:44","indexId":"70207595","displayToPublicDate":"2019-08-14T16:28:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Predicting surf zone injuries along the Delaware coast using a Bayesian network","docAbstract":"Personnel at Beebe Healthcare in Lewes, Delaware, collected surf zone injury (SZI) data for eight summer seasons from 2010 through 2017. Data include, but are not limited to, time of injury, gender, age, and activity. More than 2000 SZI events, including 196 spinal injuries and 6 fatalities, occurred at the five most populated beaches along the 25 miles of Atlantic-fronting coast. SZI are predominantly wave related incidents associated with wading (50.1%), body surfing (18.4%), and body boarding (13.3%). The episodic nature of SZI indicate the importance of linking the environmental conditions and human behavior in the surf zone to predict days with high injury rates. Higher order statistics are necessary to effectively consider all associated factors related to SZI. Two Bayesian networks (BN) were constructed to model SZI and predict changes in injury rate (proportion of injuries to bathers) and injury likelihood (probability of at least one injury occurrence) on an hourly basis. The models incorporate environmental data collected by weather stations, wave gauges, and researcher personnel on the beach. The models include prior (e.g., historic) information to infer relationships between provided parameters. Sensitivity analysis determined the most influential parameters related to injury rates were significant wave height, foreshore slope, and water temperature. Exposure parameters (e.g., air temperature) influenced the number of people in the water, resulting in strong correlation between injury likelihood and the related meteorological conditions (variance reduction > 0.4%). Log likelihood ratio (LLR) scores indicate the network predicts SZI likelihood during any specified hour with more skill than prior predictions with the best performing model improving prediction 69.1% of the time (LLR = 69.1%). An alternative BN predicting injury rate performed worse with the prior probability model out predicting the injury rate network (positive LLR = 36.7%). Issues persist with predicting SZI that have an LLR ≪ -1 (< 5% of 2017 injuries) and occur in conditions different than when most other SZI occur. Better understanding of SZI will improve awareness techniques to both educate beachgoers and assist beach patrol decision making during high risk conditions.","language":"English","publisher":"Springer","doi":"10.1007/s11069-019-03697-y","usgsCitation":"Doelp, M., Puleo, J., and Plant, N.G., 2019, Predicting surf zone injuries along the Delaware coast using a Bayesian network: Natural Hazards, v. 98, no. 2, p. 379-401, https://doi.org/10.1007/s11069-019-03697-y.","productDescription":"22 p.","startPage":"379","endPage":"401","ipdsId":"IP-100096","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":370880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-75.564927,39.583248],[-75.576271,39.588144],[-75.578719,39.591504],[-75.579615,39.598656],[-75.565823,39.590608],[-75.564927,39.583248]]],[[[-75.55587,39.605824],[-75.561934,39.605216],[-75.567694,39.613744],[-75.571759,39.623584],[-75.570798,39.626768],[-75.559446,39.629812],[-75.559102,39.629056],[-75.559614,39.624208],[-75.558446,39.617296],[-75.556878,39.612144],[-75.557502,39.609184],[-75.556734,39.606688],[-75.55587,39.605824]]],[[[-75.594846,39.837286],[-75.593666,39.837455],[-75.593082,39.8375],[-75.5799,39.838522],[-75.579849,39.838526],[-75.570464,39.839007],[-75.539346,39.838211],[-75.518444,39.836311],[-75.498843,39.833312],[-75.481242,39.829112],[-75.463341,39.823812],[-75.45374,39.820312],[-75.428038,39.809212],[-75.415041,39.801786],[-75.405337,39.796213],[-75.437938,39.783413],[-75.440909,39.780831],[-75.448639,39.774113],[-75.448135,39.773969],[-75.447339,39.773313],[-75.452339,39.769013],[-75.459439,39.765813],[-75.463339,39.761213],[-75.463039,39.758313],[-75.466249,39.750769],[-75.466263,39.750737],[-75.469239,39.743613],[-75.474168,39.735473],[-75.475384,39.731057],[-75.47544,39.728713],[-75.47724,39.724713],[-75.477432,39.720561],[-75.476888,39.718337],[-75.47764,39.715013],[-75.47894,39.713813],[-75.481741,39.714546],[-75.483141,39.715513],[-75.485241,39.715813],[-75.488553,39.714833],[-75.491341,39.711113],[-75.496241,39.701413],[-75.504042,39.698313],[-75.507162,39.696961],[-75.509042,39.694513],[-75.509742,39.686113],[-75.529744,39.692613],[-75.562246,39.656712],[-75.587147,39.651012],[-75.611969,39.621968],[-75.613153,39.62096],[-75.613377,39.620288],[-75.614065,39.61832],[-75.614929,39.615952],[-75.614273,39.61464],[-75.613345,39.613056],[-75.613665,39.61256],[-75.613233,39.607408],[-75.613477,39.606861],[-75.613473,39.606832],[-75.613793,39.606192],[-75.611905,39.597568],[-75.611873,39.597408],[-75.60464,39.58992],[-75.603584,39.58896],[-75.592224,39.583568],[-75.591984,39.583248],[-75.587744,39.580672],[-75.5872,39.580256],[-75.586608,39.57888],[-75.586016,39.578448],[-75.571599,39.567728],[-75.570783,39.56728],[-75.563034,39.56224],[-75.564649,39.559922],[-75.565636,39.558509],[-75.569359,39.540589],[-75.569418,39.539124],[-75.570362,39.527223],[-75.560728,39.520472],[-75.566933,39.508273],[-75.576436,39.509195],[-75.587729,39.496353],[-75.587729,39.495369],[-75.593068,39.479186],[-75.593068,39.477996],[-75.589901,39.462022],[-75.589439,39.460812],[-75.580185,39.450786],[-75.578914,39.44788],[-75.570985,39.442486],[-75.57183,39.438897],[-75.55589,39.430351],[-75.538512,39.416502],[-75.535977,39.409384],[-75.523583,39.391583],[-75.521682,39.387871],[-75.512996,39.366153],[-75.512372,39.365656],[-75.511788,39.365191],[-75.505276,39.359169],[-75.494158,39.354613],[-75.491797,39.351845],[-75.494122,39.34658],[-75.493148,39.345527],[-75.491688,39.343963],[-75.490377,39.342818],[-75.479845,39.337472],[-75.479963,39.336577],[-75.469324,39.33082],[-75.460423,39.328236],[-75.439027,39.313384],[-75.436936,39.309379],[-75.435551,39.297546],[-75.435374,39.296676],[-75.427953,39.285049],[-75.408376,39.264698],[-75.402964,39.254626],[-75.404823,39.245898],[-75.405927,39.243631],[-75.405716,39.223834],[-75.404745,39.222666],[-75.396892,39.216141],[-75.393015,39.204512],[-75.39479,39.188354],[-75.398584,39.186616],[-75.400144,39.186456],[-75.408266,39.174625],[-75.410625,39.156246],[-75.401193,39.088762],[-75.402035,39.066885],[-75.400294,39.065645],[-75.395806,39.059211],[-75.396277,39.057884],[-75.387914,39.051174],[-75.379873,39.04879],[-75.345763,39.024857],[-75.34089,39.01996],[-75.318354,38.988191],[-75.314951,38.980775],[-75.311607,38.967637],[-75.312546,38.951065],[-75.312546,38.94928],[-75.311923,38.945917],[-75.311882,38.945698],[-75.311542,38.944633],[-75.302552,38.939002],[-75.312282,38.924594],[-75.304078,38.91316],[-75.263115,38.877351],[-75.232029,38.844254],[-75.205329,38.823386],[-75.190552,38.806861],[-75.160748,38.791224],[-75.159022,38.790193],[-75.134022,38.782242],[-75.113331,38.782998],[-75.097103,38.788703],[-75.093654,38.793992],[-75.097197,38.803101],[-75.093805,38.803812],[-75.089473,38.797198],[-75.082153,38.772157],[-75.080217,38.750112],[-75.079221,38.738238],[-75.06551,38.66103],[-75.065217,38.632394],[-75.06192,38.608869],[-75.061259,38.608602],[-75.060478,38.608012],[-75.060032,38.607709],[-75.049748,38.486387],[-75.048939,38.451263],[-75.049268,38.451264],[-75.05251,38.451273],[-75.053483,38.451274],[-75.064719,38.451289],[-75.066327,38.451291],[-75.069909,38.451276],[-75.070356,38.451276],[-75.085814,38.451258],[-75.088281,38.451256],[-75.089649,38.451254],[-75.141894,38.451196],[-75.185413,38.451013],[-75.252723,38.451397],[-75.26035,38.451492],[-75.341247,38.45197],[-75.34125,38.45197],[-75.355797,38.452008],[-75.371054,38.452107],[-75.393563,38.452114],[-75.394786,38.45216],[-75.41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 \"}}]}","volume":"98","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Doelp, Matthew","contributorId":221558,"corporation":false,"usgs":false,"family":"Doelp","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":778659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Puleo, Jack A.","contributorId":108287,"corporation":false,"usgs":true,"family":"Puleo","given":"Jack A.","affiliations":[],"preferred":false,"id":778660,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":778661,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70204738,"text":"sir20195082 - 2019 - Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotelluric surveys","interactions":[],"lastModifiedDate":"2019-10-07T16:51:39","indexId":"sir20195082","displayToPublicDate":"2019-08-14T09:51:12","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5082","displayTitle":"Characterization of Big Chino Subbasin Hydrogeology near Paulden, Arizona, Using Controlled Source Audio-Frequency Magnetotelluric Surveys","title":"Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotelluric surveys","docAbstract":"<p class=\"m_1329192573577678645m_7553462186234747730gmail-p1\">The Big Chino subbasin is located in central-northwest Arizona in the transition zone between the Colorado Plateau and the Basin and Range Province. The controlled source audio-frequency magnetotelluric (CSAMT) geophysical method, a low-impact, non-intrusive, electrical resistance sounding technique, was used to evaluate the subsurface hydrogeology of the southern third of the Big Chino subbasin. The Big Chino subbasin is a northwest-trending, late Tertiary graben bordered by the Big Chino Fault along its northeast flank where there is as much as 1,100 meters of displacement. The main water-bearing stratigraphic unit of the basin is Tertiary alluvial-fill sediment. The Devonian Martin Formation provides water to wells near Drake and the Mississippian Redwall Limestone provides water to wells east of the basin and in the Paulden area.</p><p class=\"m_1329192573577678645m_7553462186234747730gmail-p1\">The purpose of the CSAMT surveys was to improve the conceptual model of the aquifer by constraining the basin geometry and identifying stratigraphic units and their subsurface extents. CSAMT methods were used to map the subsurface along 100 kilometers (62 miles) of survey lines across the southern third of the subbasin. Of 21 survey lines, 14 were west of the town of Paulden and another 7 were east of Paulden. Data were cleaned and prepared for entry into Zonge SCS2D software and then inverted to provide a two-dimensional resistivity profile for each survey line. Final inversion models representing the best fit to measured data were compared to driller’s logs or borehole data where present.</p><p class=\"m_1329192573577678645m_7553462186234747730gmail-p1\">Data from the CSAMT lines west and north of Paulden are consistent with thicker alluvial basin deposits that range from 100 meters thick to a few hundred meters thick. Data from the CSAMT lines east of Paulden are consistent with thinner alluvial and basalt deposits overlying Paleozoic Martin Formation and Redwall Limestone, Tapeats Sandstone, and Precambrian granite and schist.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195082","collaboration":"Prepared in cooperation with the City of Prescott, the Town of Prescott Valley, and Salt River Project","usgsCitation":"Macy, J.P., Gungle, B., and Mason, J.P., 2019, Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotellursurveys: U.S. Geological Survey Scientific Investigations Report 2019–5082, 39 p., https://doi.org/10.3133/sir20195082.\nic ","productDescription":"vii, 39 p.","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-098264","costCenters":[{"id":128,"text":"Arizona Water Science 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href=\"mailto:leenhout@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:leenhout@usgs.gov\">Director</a>,<br><a href=\"https://az.water.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://az.water.usgs.gov/\">Arizona Water Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>520 N. Park Avenue<br>Tucson, AZ 85719</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Methods</li><li>Results</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2019-08-14","noUsgsAuthors":false,"publicationDate":"2019-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Macy, Jamie P. 0000-0003-3443-0079 jpmacy@usgs.gov","orcid":"https://orcid.org/0000-0003-3443-0079","contributorId":2173,"corporation":false,"usgs":true,"family":"Macy","given":"Jamie","email":"jpmacy@usgs.gov","middleInitial":"P.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gungle, Bruce 0000-0001-6406-1206 bgungle@usgs.gov","orcid":"https://orcid.org/0000-0001-6406-1206","contributorId":2237,"corporation":false,"usgs":true,"family":"Gungle","given":"Bruce","email":"bgungle@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mason, Jon P. 0000-0003-0576-5494 jmason@usgs.gov","orcid":"https://orcid.org/0000-0003-0576-5494","contributorId":196854,"corporation":false,"usgs":true,"family":"Mason","given":"Jon","email":"jmason@usgs.gov","middleInitial":"P.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":false,"id":768255,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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