{"pageNumber":"506","pageRowStart":"12625","pageSize":"25","recordCount":165387,"records":[{"id":70219015,"text":"70219015 - 2021 - Migrating whooping cranes avoid wind-energy infrastructure when selecting stopover habitat","interactions":[],"lastModifiedDate":"2022-08-16T17:30:43.168536","indexId":"70219015","displayToPublicDate":"2021-03-07T07:25:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Migrating whooping cranes avoid wind-energy infrastructure when selecting stopover habitat","docAbstract":"Electricity generation from renewable-energy sources has increased dramatically worldwide in recent decades. Risks associated with wind-energy infrastructure are not well understood for endangered whooping cranes or other vulnerable crane populations. From 2010 to 2016, we monitored 57 whooping cranes with remote-telemetry devices in the United States Great Plains to determine potential changes in migration distribution (i.e., avoidance) caused by presence of wind-energy infrastructure. During our study, the number of wind towers tripled in the whooping crane migration corridor and quadrupled in the corridor’s center. Median distance of whooping crane locations from nearest wind tower was 52.1 km, and 99% of locations were >4.3 km from wind towers. A habitat selection analysis revealed that whooping cranes used areas ≤5.0 km (95% CI = 4.8–5.4) from towers less than expected (i.e., zone of influence) and that whooping cranes were 20 times (95% CI: 14–64) more likely to use areas outside compared to adjacent to towers. Eighty percent of whooping crane locations and 20% of wind towers were located in areas with the highest relative probability of whooping crane use based on our model, which comprised 20% of the study area. Whooping cranes selected for these places, whereas developers constructed wind infrastructure at random relative to desirable whooping crane habitat. As of early 2020, 4.6% of the study area and 5.0% of the highest-selected whooping crane habitat were within the collective zone of influence. The affected area equates to habitat loss ascribed to wind-energy infrastructure; losses from other disturbances have not been quantified. Continued growth of the whooping crane population during this period of wind infrastructure construction suggests no immediate population-level consequences. Chronic or lag effects of habitat loss are unknown but possible for long-lived species. Preferentially constructing future wind infrastructure outside of the migration corridor or inside of the corridor at sites with low probability of whooping crane use would allow for continued wind-energy development in the Great Plains with minimal additional risk to highly selected habitat that supports recovery of this endangered species.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2324","usgsCitation":"Pearse, A.T., Metzger, K.L., Brandt, D.A., Shaffer, J.A., Bidwell, M.T., and Harrell, W.C., 2021, Migrating whooping cranes avoid wind-energy infrastructure when selecting stopover habitat: Ecological Applications, v. 31, no. 5, e02324, 14 p., https://doi.org/10.1002/eap.2324.","productDescription":"e02324, 14 p.","ipdsId":"IP-121053","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":436471,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SVV311","text":"USGS data release","linkHelpText":"Whooping crane migration habitat selection data and distance to wind-energy infrastructure, 2010-2016"},{"id":384499,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, Montana, Nebraska, North Dakota, Oklahoma, South Dakota, Texas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -95.0537109375,\n              28.998531814051795\n            ],\n            [\n              -96.0205078125,\n              38.272688535980976\n            ],\n            [\n              -97.91015624999999,\n              48.22467264956519\n            ],\n            [\n              -98.4375,\n              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apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":812470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Metzger, Kristine L.","contributorId":147144,"corporation":false,"usgs":false,"family":"Metzger","given":"Kristine","email":"","middleInitial":"L.","affiliations":[{"id":16794,"text":"USFWS, Div of Biol Serv, Albuquerque, NM","active":true,"usgs":false}],"preferred":false,"id":812471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, David A. 0000-0001-9786-307X dbrandt@usgs.gov","orcid":"https://orcid.org/0000-0001-9786-307X","contributorId":149929,"corporation":false,"usgs":true,"family":"Brandt","given":"David","email":"dbrandt@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":812472,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shaffer, Jill A. 0000-0003-3172-0708 jshaffer@usgs.gov","orcid":"https://orcid.org/0000-0003-3172-0708","contributorId":3184,"corporation":false,"usgs":true,"family":"Shaffer","given":"Jill","email":"jshaffer@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":812525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bidwell, Mark T.","contributorId":202007,"corporation":false,"usgs":false,"family":"Bidwell","given":"Mark","email":"","middleInitial":"T.","affiliations":[{"id":36318,"text":"CWS","active":true,"usgs":false}],"preferred":false,"id":812474,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harrell, Wade C.","contributorId":147143,"corporation":false,"usgs":false,"family":"Harrell","given":"Wade","email":"","middleInitial":"C.","affiliations":[{"id":16793,"text":"USFWS, Ecological Services, Austwell, TX","active":true,"usgs":false}],"preferred":false,"id":812475,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70219479,"text":"70219479 - 2021 - Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom","interactions":[],"lastModifiedDate":"2021-04-12T11:50:22.717788","indexId":"70219479","displayToPublicDate":"2021-03-07T07:20:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom","docAbstract":"<p>Regional flood frequency analysis (RFFA) methods are essential tools to assess flood hazard and plan interventions for its mitigation. They are used to estimate flood quantiles when the at‐site record of streamflow data is not available or limited. One commonly used RFFA method is the index flood method (IFM), which assumes that peak floods satisfy the simple scaling hypothesis.</p><p>In this work we present an integrated approach to assess the spatial scaling behavior of floods in the United Kingdom (UK) for 540 catchments, where the IFM is currently used operationally. This assessment employs product moments, probability weighted moments, and quantile analysis, and is applied to two different types of “hydrologically homogeneous” UK regions: geographical regions as defined in the Flood Studies Report (NERC, 1975) and pooling‐groups as defined in the updated Flood Estimation Handbook (FEH; Institute of Hydrology, 1999). To understand which variables play a significant role in the flood‐peak generating mechanism, the assessment approach considers scaling not only of drainage area alone but also of other hydro‐geomorphological variables. Results provided by the different methodologies consistently showed that only part (ranging from 30% to 70%) of the peak flow variability is explained by drainage area alone; this fraction increases (up to 80%–95%) when multiple regression is used. Supported by the peak flow spatial scaling assessment, we compared the proposed approach for peak flow quantile estimation with the current FEH method in ungauged catchments. The quantile regression method based on the pooling‐group outperforms the current FEH‐ungauged method, providing a 14% relative improvement in root mean square error over the entire country.</p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028076","usgsCitation":"Formetta, G., Over, T.M., and Stewart, E., 2021, Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom: Water Resources Research, v. 57, no. 4, e2020WR028076, 21 p., https://doi.org/10.1029/2020WR028076.","productDescription":"e2020WR028076, 21 p.","ipdsId":"IP-119682","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":453168,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://nora.nerc.ac.uk/id/eprint/529960/1/N529960PP.pdf","text":"External Repository"},{"id":384966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Kingdom","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -5.712890625,\n              49.61070993807422\n            ],\n            [\n              -2.28515625,\n              50.064191736659104\n            ],\n            [\n              1.669921875,\n              50.84757295365389\n            ],\n            [\n              2.3291015625,\n              52.32191088594773\n            ],\n            [\n              0.9228515625,\n              54.826007999094955\n            ],\n            [\n              -0.2197265625,\n              55.85064987433714\n            ],\n            [\n              -0.791015625,\n              57.231502991478926\n            ],\n            [\n              -1.142578125,\n              57.938183012205315\n            ],\n            [\n              -2.548828125,\n              58.63121664342478\n            ],\n            [\n              -4.130859375,\n              59.153403092050375\n            ],\n            [\n              -6.767578125,\n              58.97266715450153\n            ],\n            [\n              -8.1298828125,\n              56.24334992410525\n            ],\n            [\n              -7.9541015625,\n              54.521081495443596\n            ],\n            [\n              -7.0751953125,\n              54.059387886623576\n            ],\n            [\n              -5.888671875,\n              53.409531853086435\n            ],\n            [\n              -5.6689453125,\n              51.56341232867588\n            ],\n            [\n              -6.1083984375,\n              50.233151832472245\n            ],\n            [\n              -6.064453125,\n              49.55372551347579\n            ],\n            [\n              -5.712890625,\n              49.61070993807422\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"57","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Formetta, Giuseppe 0000-0002-0252-1462","orcid":"https://orcid.org/0000-0002-0252-1462","contributorId":210296,"corporation":false,"usgs":false,"family":"Formetta","given":"Giuseppe","email":"","affiliations":[{"id":38100,"text":"Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO","active":true,"usgs":false}],"preferred":false,"id":813730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Elizabeth","contributorId":257050,"corporation":false,"usgs":false,"family":"Stewart","given":"Elizabeth","email":"","affiliations":[{"id":51971,"text":"UK Centre for Ecology & Hydrology","active":true,"usgs":false}],"preferred":false,"id":813732,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70218721,"text":"70218721 - 2021 - Prioritizing landscapes for grassland bird conservation with hierarchical community models","interactions":[],"lastModifiedDate":"2021-04-08T15:11:47.181072","indexId":"70218721","displayToPublicDate":"2021-03-06T07:56:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing landscapes for grassland bird conservation with hierarchical community models","docAbstract":"<h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Context</h3><p>Given widespread population declines of birds breeding in North American grasslands, management that sustains wildlife while supporting rancher livelihoods is needed. However, management effects vary across landscapes, and identifying areas with the greatest potential bird response to conservation is a pressing research need.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Objectives</h3><p>We developed a hierarchical modeling approach to study grassland bird response to habitat factors at multiple scales and levels. We then identified areas to prioritize for implementing a bird-friendly ranching program.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Methods</h3><p>Using bird survey data from grassland passerine species and 175 sites (2009–2018) across northeast Wyoming, USA, we fit hierarchical community distance sampling models and evaluated drivers of site-level density and regional-level distribution. We then created spatially-explicit predictions of bird density and distribution for the study area and predicted outcomes from pasture-scale management scenarios.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Results</h3><p>Cumulative overlap of species distributions revealed areas with greater potential community response to management. Within each species’ potential regional-level distribution, the grassland bird community generally responded negatively to cropland cover and vegetation productivity at local scales (up to 10&nbsp;km of survey sites). Multiple species declined with increasing bare ground and litter cover, shrub cover, and grass height measured within sites.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Conclusions</h3><p>We demonstrated a novel approach to multi-scale and multi-level prioritization for grassland bird conservation based on hierarchical community models and extensive population monitoring. Pasture-scale management scenarios also suggested the examined community may benefit from less bare ground cover and shorter grass height. Our approach could be extended to other bird guilds in this region and beyond.</p>","language":"English","publisher":"Springer","doi":"10.1007/s10980-021-01211-z","usgsCitation":"Monroe, A.P., Edmunds, D.R., Aldridge, C.L., Holloran, M.J., Assal, T.J., and Holloran, A., 2021, Prioritizing landscapes for grassland bird conservation with hierarchical community models: Landscape Ecology, v. 36, p. 1023-1038, https://doi.org/10.1007/s10980-021-01211-z.","productDescription":"16 p.","startPage":"1023","endPage":"1038","ipdsId":"IP-122010","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":453170,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-021-01211-z","text":"Publisher Index Page"},{"id":384245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bird Conservation Region 17","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.5341796875,\n              45.02695045318546\n            ],\n            [\n              -107.05078125,\n              44.715513732021336\n            ],\n            [\n              -106.69921875,\n              44.37098696297173\n            ],\n            [\n              -106.61132812499999,\n              43.78695837311561\n            ],\n            [\n              -106.435546875,\n              43.052833917627936\n            ],\n            [\n              -105.57861328125,\n              42.79540065303723\n            ],\n            [\n              -104.83154296875,\n              42.4234565179383\n            ],\n            [\n              -104.0185546875,\n              42.52069952914966\n            ],\n            [\n              -104.0625,\n              45.042478050891546\n            ],\n            [\n              -107.5341796875,\n              45.02695045318546\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"36","noUsgsAuthors":false,"publicationDate":"2021-03-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Monroe, Adrian Pierre-Frederic 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":254952,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian","email":"amonroe@usgs.gov","middleInitial":"Pierre-Frederic","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":811526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holloran, Matthew J 0000-0001-5244-770X","orcid":"https://orcid.org/0000-0001-5244-770X","contributorId":254954,"corporation":false,"usgs":false,"family":"Holloran","given":"Matthew","email":"","middleInitial":"J","affiliations":[{"id":51367,"text":"Operational Conservation LLC","active":true,"usgs":false}],"preferred":false,"id":811527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Assal, Timothy J","contributorId":238085,"corporation":false,"usgs":false,"family":"Assal","given":"Timothy","email":"","middleInitial":"J","affiliations":[{"id":18142,"text":"Kent State University","active":true,"usgs":false}],"preferred":false,"id":811528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holloran, Alison G","contributorId":254955,"corporation":false,"usgs":false,"family":"Holloran","given":"Alison G","affiliations":[{"id":51369,"text":"Audubon Rockies","active":true,"usgs":false}],"preferred":false,"id":811529,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70222570,"text":"70222570 - 2021 - Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data","interactions":[],"lastModifiedDate":"2021-08-05T12:13:48.433134","indexId":"70222570","displayToPublicDate":"2021-03-06T07:09:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3071,"text":"Physics of the Earth and Planetary Interiors","active":true,"publicationSubtype":{"id":10}},"title":"Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data","docAbstract":"<p id=\"sp0135\">We use combined teleseismic and strong motion data sets to investigate finite-fault slip models for a double of earthquakes that occurred on August 11, 2012, in northwestern Iran near the cities of Ahar and Varzaghan. The data include teleseismic P-waveforms retrieved from broadband seismic stations located between 30°–94° from the earthquakes and local strong motion data recorded by the Iran Strong Motion Network, installed and operated by the Building and Housing Research Centre. We first invert teleseismic P-waveforms and local strong motion data separately. For the first event (12:23 UTC), the teleseismic broadband inversion yields a somewhat deeper and simpler distribution of slip than the local strong motion inversion. The strong motion inversion results in a more complex distribution because of higher frequency content but can also be influenced by complexities in the propagation path. For the second event (12:34 UTC), the slip distribution from strong motion data is more similar to the teleseismic result and shows a simple slip area with a small relative movement to the west. To resolve the differences between the results of these two data sets and obtain a better constrained slip model, we perform a joint inversion of teleseismic broadband and local strong motion data.</p><p id=\"sp0140\">The joint inversion for the first event shows two asperities with a maximum slip of 3.9&nbsp;m up- dip from the hypocenter and extending to the west between depths of 1 and 5&nbsp;km. A second narrower high-slip area is seen just above the hypocenter from 6 to 10&nbsp;km depth. The total moment for this earthquake is calculated to be M<sub>o</sub>&nbsp;=&nbsp;3.8&nbsp;×&nbsp;10<sup>25</sup>&nbsp;dyn-cm (3.8&nbsp;×&nbsp;10<sup>18</sup>&nbsp;N.m) (M<sub>w</sub><span>&nbsp;6.4). For the second event, the results of the joint inversion show a simple slip distribution that is mainly confined in a single patch around the hypocenter with a depth range from about 10 to 13&nbsp;km and maximum slip of 1.9&nbsp;m. We compute a total&nbsp;<a class=\"topic-link\" title=\"Learn more about seismic moment from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/seismic-moment\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/seismic-moment\">seismic moment</a>&nbsp;of M</span><sub>o</sub>&nbsp;=&nbsp;1.6&nbsp;×&nbsp;10<sup>25</sup>&nbsp;dyn-cm (1.6&nbsp;×&nbsp;10<sup>18</sup>&nbsp;N.m) (M<sub>w</sub><span>&nbsp;</span>6.1) for the second event. The largest stress drops for the first event occur above the hypocenter with an average stress drop over the rupture area of 120&nbsp;bar (12 Mpa). For the second event, the maximum stress drop occurs at the reported focal depth with an average stress drop over the rupture area of 80&nbsp;bar (8 Mpa).</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.pepi.2021.106688","usgsCitation":"Saltanatpouri, A., Hartzell, S.H., Rahimi, H., Rouhollahi, R., and Amiri Fard, R., 2021, Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data: Physics of the Earth and Planetary Interiors, v. 313, 106688, 15 p., https://doi.org/10.1016/j.pepi.2021.106688.","productDescription":"106688, 15 p.","ipdsId":"IP-124948","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":387702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","otherGeospatial":"East Anatolian Fault","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              42.626953125,\n              39.65011210186371\n            ],\n            [\n              43.1982421875,\n              39.65011210186371\n            ],\n            [\n              43.1982421875,\n              39.94975340768179\n            ],\n            [\n              42.626953125,\n              39.94975340768179\n            ],\n            [\n              42.626953125,\n              39.65011210186371\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"313","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Saltanatpouri, Atefeh","contributorId":261761,"corporation":false,"usgs":false,"family":"Saltanatpouri","given":"Atefeh","email":"","affiliations":[{"id":52998,"text":"Institute of Geophysics, University of Tehran, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rahimi, Habib","contributorId":261762,"corporation":false,"usgs":false,"family":"Rahimi","given":"Habib","email":"","affiliations":[{"id":52998,"text":"Institute of Geophysics, University of Tehran, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rouhollahi, Rahmatollah","contributorId":261763,"corporation":false,"usgs":false,"family":"Rouhollahi","given":"Rahmatollah","email":"","affiliations":[{"id":53001,"text":"Babol Noshirvani University of Technology, Babol, Iran","active":true,"usgs":false}],"preferred":false,"id":820606,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Amiri Fard, Rouholla","contributorId":261764,"corporation":false,"usgs":false,"family":"Amiri Fard","given":"Rouholla","email":"","affiliations":[{"id":53002,"text":"International Institute of Earthquake Engineering and Seismology, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820607,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70219437,"text":"70219437 - 2021 - Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk","interactions":[],"lastModifiedDate":"2021-04-06T11:58:58.749804","indexId":"70219437","displayToPublicDate":"2021-03-06T06:53:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk","docAbstract":"<p><span>Toxic cyanobacterial blooms, often containing multiple toxins, are a serious public health issue. However, there are no known models that predict a cyanotoxin mixture (anatoxin-a, microcystin, saxitoxin). This paper presents two cyanotoxin mixture models (MIX) and compares them to two microcystin (MC) models from data collected in 2016–2017 from three recurring cyanobacterial bloom locations in Kabetogama Lake, Voyageurs National Park (Minnesota, USA). Models include those using near-real-time environmental variables (readily available) and those using additional comprehensive variables (based on laboratory analyses). Comprehensive models (R</span><sup>2</sup><span>&nbsp;=&nbsp;0.87 MC; R</span><sup>2</sup><span>&nbsp;=&nbsp;0.86 MIX) explained more variability than the environmental models (R</span><sup>2</sup><span>&nbsp;=&nbsp;0.58 MC; R</span><sup>2</sup><span>&nbsp;=&nbsp;0.57 MIX). Although neither MIX model was a better fit than the MC models, the MIX models produced no false negatives in the calibration dataset, indicating that all observations above regulatory guidelines were simulated by the MIX models. This is the first known use of Virtual Beach software for a cyanotoxin mixture model, and the methods used in this paper may be applicable to other lakes or beaches.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2021.125560","usgsCitation":"Christensen, V., Stelzer, E., Eikenberry, B., Olds, H., LeDuc, J.F., Maki, R., Norland, J.E., and Khan, E., 2021, Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk: Journal of Hazardous Materials, v. 415, 125560, 13 p., https://doi.org/10.1016/j.jhazmat.2021.125560.","productDescription":"125560, 13 p.","ipdsId":"IP-123013","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436472,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X7EO1K","text":"USGS data release","linkHelpText":"Data and model archive for multiple linear regression models for prediction of weighted cyanotoxin mixture concentrations and microcystin concentrations at three recurring bloom sites in Kabetogama Lake in Minnesota"},{"id":384883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"Minnesota","otherGeospatial":"Kabetogama Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -93.3453369140625,\n              48.21735290928554\n            ],\n            [\n              -92.48291015625,\n              48.21735290928554\n            ],\n            [\n              -92.48291015625,\n              48.622016428468385\n            ],\n            [\n              -93.3453369140625,\n              48.622016428468385\n            ],\n            [\n              -93.3453369140625,\n              48.21735290928554\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"415","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stelzer, Erin A. 0000-0001-7645-7603","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":220549,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813549,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eikenberry, Barbara C. Scudder 0000-0001-8058-1201 beikenberry@usgs.gov","orcid":"https://orcid.org/0000-0001-8058-1201","contributorId":172148,"corporation":false,"usgs":true,"family":"Eikenberry","given":"Barbara C. Scudder","email":"beikenberry@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":813550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olds, Hayley T. 0000-0002-6701-6459 htemplar@usgs.gov","orcid":"https://orcid.org/0000-0002-6701-6459","contributorId":5002,"corporation":false,"usgs":true,"family":"Olds","given":"Hayley T.","email":"htemplar@usgs.gov","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":false,"id":813551,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LeDuc, Jaime F.","contributorId":190132,"corporation":false,"usgs":false,"family":"LeDuc","given":"Jaime","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":813552,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maki, Ryan P.","contributorId":190131,"corporation":false,"usgs":false,"family":"Maki","given":"Ryan P.","affiliations":[],"preferred":false,"id":813553,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Norland, Jack E.","contributorId":214257,"corporation":false,"usgs":false,"family":"Norland","given":"Jack","email":"","middleInitial":"E.","affiliations":[{"id":39001,"text":"School of Natural Resources Sciences, North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":813554,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Khan, Eakalak","contributorId":220550,"corporation":false,"usgs":false,"family":"Khan","given":"Eakalak","email":"","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":813555,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70221894,"text":"70221894 - 2021 - Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine","interactions":[],"lastModifiedDate":"2021-07-13T18:35:29.258188","indexId":"70221894","displayToPublicDate":"2021-03-05T13:30:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3892,"text":"Ecohydrology & Hydrobiology","active":true,"publicationSubtype":{"id":10}},"title":"Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine","docAbstract":"<p id=\"spara016\">Dissolved organic carbon<span>&nbsp;</span>(DOC) is an important component of the carbon cycle as a measure of the hydrological transport of carbon between terrestrial carbon pools into soil pools and eventually into streams. As a result, changes in DOC in rivers and streams may indicate alterations in the storage of terrestrial carbon. Exploring the complex interactions between biogeochemical cycling and hydrologic processes, as well as the micro-climate variabilities that impact the rate of DOC fluxes, are challenging because the information is not readily available from in-situ measurements or from empirical models alone. This is particularly true of large-scale watersheds. The Penobscot Watershed is the largest watershed of the Gulf of Maine and the second largest in New England. Its typical soils, with high organic matter and a large forested and wetland landscape, result in higher DOC fluxes than what has been observed previously for most rivers in the northern temperate or boreal zones (Hope et&nbsp;al., 1994; Mulholland, 1997; Aitkenhead and McDowell, 2000).</p><p id=\"spara017\"><span>In this study, we emphasized the simulation of&nbsp;streamflow&nbsp;and DOC fluxes from the Penobscot Watershed (and several tributaries within the Penobscot Watershed) using the spatially distributed process-based Regional Hydro-Ecological Simulation System (RHESSys) model. Simulated results were evaluated using field measurements (streamflow, DOC fluxes) and remotely sensed products (Net Primary Production (NPP) and Leaf Area Index (LAI) from&nbsp;Moderate Resolution Imaging Spectroradiometer&nbsp;(MODIS). The average DOC flux for the Penobscot Watershed during 2004-2012 using the RHESSys model was 69 kg C/ha/year. The RHESSys simulated DOC flux is shown to correlate well with observed values, as well as with results previously reported from the empirical Load Estimator (LOADEST) model (71 kg C/ha/year) for 2004-2007 (</span>Huntington and Aiken, 2013).</p><p id=\"spara018\">Our simulated results also show a temporal variation in the amount of DOC flux, indicating that the antecedent DOC concentration from one year can impact the DOC export in following years. Thus, DOC concentration is positively correlated with streamflow and antecedent precipitation, in agreement with previous studies (Ågren et&nbsp;al., 2010;<span>&nbsp;</span>Huntington and Aiken, 2013;<span>&nbsp;</span>Tian et&nbsp;al., 2013<span>). The successful application of the rigorous RHESSys model in the Penobscot Watershed makes it a reasonable platform to test future scenarios impacting the hydrology and&nbsp;biogeochemistry&nbsp;within similar large complex watersheds.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecohyd.2021.02.005","usgsCitation":"Rouhani, S., Schaaf, C.B., Huntington, T., and Choate, J., 2021, Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine: Ecohydrology & Hydrobiology, v. 21, no. 23-24, p. 256-270, https://doi.org/10.1016/j.ecohyd.2021.02.005.","productDescription":"15 p.","startPage":"256","endPage":"270","ipdsId":"IP-106391","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":453173,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecohyd.2021.02.005","text":"Publisher Index Page"},{"id":387156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -68.73046875,\n              44.48866833139464\n            ],\n            [\n              -67.576904296875,\n              45.57560020947802\n            ],\n            [\n              -68.5986328125,\n              46.255846818480315\n            ],\n            [\n              -70.15869140625,\n              46.430285240839964\n            ],\n            [\n              -70.37841796875,\n              45.78284835197676\n            ],\n            [\n              -69.43359375,\n              45.874712248904764\n            ],\n            [\n              -69.60937499999999,\n              45.36758436884978\n            ],\n            [\n              -70.11474609375,\n              45.213003555993964\n            ],\n            [\n              -69.345703125,\n              44.6061127451739\n            ],\n            [\n              -68.73046875,\n              44.48866833139464\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"21","issue":"23-24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rouhani, Shabnam","contributorId":260994,"corporation":false,"usgs":false,"family":"Rouhani","given":"Shabnam","email":"","affiliations":[{"id":52735,"text":"University of Massachusetts, Boston, MA","active":true,"usgs":false}],"preferred":false,"id":819233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaaf, Crystal B.","contributorId":149538,"corporation":false,"usgs":false,"family":"Schaaf","given":"Crystal","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":819234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huntington, Thomas G. 0000-0002-9427-3530","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":218737,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choate, Janet","contributorId":260995,"corporation":false,"usgs":false,"family":"Choate","given":"Janet","email":"","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":819236,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70218748,"text":"70218748 - 2021 - Population density, distribution, and trends of landbirds in the National Park of American Samoa, Ta‘ū and Tutuila Units (2011–2018)","interactions":[],"lastModifiedDate":"2021-03-29T17:21:00.056125","indexId":"70218748","displayToPublicDate":"2021-03-05T07:55:31","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":273,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":4}},"title":"Population density, distribution, and trends of landbirds in the National Park of American Samoa, Ta‘ū and Tutuila Units (2011–2018)","docAbstract":"The National Park of American Samoa (NPSA) was surveyed for landbirds from June through July, 2018. Surveys were conducted using point-transect distance sampling methods to estimate bird densities. This information provides the second datum in the time-series of landbird monitoring for long-term trends in landbird distribution, density, and abundance within NPSA. The Ta‘ū Unit and Tutuila Unit, each on separate islands, were first surveyed in 2011 and we tested for changes in densities between each survey year.","language":"English","publisher":"National Park Service","doi":"10.36967/nrr-2284409","usgsCitation":"Judge, S., Camp, R.J., Vaivai, V., and Hart, P.J., 2021, Population density, distribution, and trends of landbirds in the National Park of American Samoa, Ta‘ū and Tutuila Units (2011–2018): Natural Resource Report, viii, 77 p., https://doi.org/10.36967/nrr-2284409.","productDescription":"viii, 77 p.","ipdsId":"IP-120277","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":384274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"American Samoa","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -170.87310791015625,\n              -14.386797246182454\n            ],\n            [\n              -170.53253173828125,\n              -14.386797246182454\n            ],\n            [\n              -170.53253173828125,\n              -14.20914185212544\n            ],\n            [\n              -170.87310791015625,\n              -14.20914185212544\n            ],\n            [\n              -170.87310791015625,\n              -14.386797246182454\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Judge, Seth 0000-0003-3832-3246","orcid":"https://orcid.org/0000-0003-3832-3246","contributorId":189965,"corporation":false,"usgs":false,"family":"Judge","given":"Seth","email":"","affiliations":[],"preferred":false,"id":811588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":811589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vaivai, Visa","contributorId":254982,"corporation":false,"usgs":false,"family":"Vaivai","given":"Visa","affiliations":[{"id":51382,"text":"National Park Service, I&M","active":true,"usgs":false}],"preferred":false,"id":811590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":811591,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70218813,"text":"70218813 - 2021 - The making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)","interactions":[],"lastModifiedDate":"2021-03-15T13:59:10.529639","indexId":"70218813","displayToPublicDate":"2021-03-05T07:53:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"The making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)","docAbstract":"<div class=\"JournalAbstract\"><p class=\"mb15\">The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-eastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a three-phase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decision-making process based on a multiple-expert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step-1) probabilistic earthquake model; Step-2) tsunami generation and modeling in deep water; Step-3) shoaling and inundation; Step-4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level-0 always describes the input data; the other Levels describe the intermediate results needed to proceed from one Step to another. Alternative datasets and models were considered in the implementation. The epistemic hazard uncertainty was quantified through an ensemble modeling technique accounting for alternative models’ weights and yielding a distribution of hazard curves represented by the mean and various percentiles. Hazard curves were calculated at 2,343 Points of Interest (POI) distributed at an average spacing of ∼20&nbsp;km. Precalculated probability maps for five maximum inundation heights (MIH) and hazard intensity maps for five average return periods (ARP) were produced from hazard curves. In the entire NEAM Region, MIHs of several meters are rare but not impossible. Considering a 2% probability of exceedance in 50&nbsp;years (ARP≈2,475&nbsp;years), the POIs with MIH &gt;5&nbsp;m are fewer than 1% and are all in the Mediterranean on Libya, Egypt, Cyprus, and Greece coasts. In the North-East Atlantic, POIs with MIH &gt;3&nbsp;m are on the coasts of Mauritania and Gulf of Cadiz. Overall, 30% of the POIs have MIH &gt;1&nbsp;m. NEAMTHM18 results and documentation are available through the TSUMAPS-NEAM project website (http://www.tsumaps-neam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute in-depth analyses at local scales, it represents the first action to start local and more detailed hazard and risk assessments and contributes to designing evacuation maps for tsunami early warning.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.616594","usgsCitation":"Basili, R., Brizuela, B., Herrero, A., Iqbal, S., Lorito, S., Maesano, F.E., Murphy, S., Perfetti, P., Romano, F., Scala, A., Selva, J., Taroni, M., Tiberti, M.M., Thio, H., Tonini, R., Volpe, M., Glimsdal, S., Harbitz, C.B., Lovholt, F., Baptista, M.A., Carrilho, F., Matias, L.M., Omira, R., Babeyko, A., Hoechner, A., Gurbuz, M., Pekcan, O., Yalciner, A., Canals, M., Lastras, G., Agalos, A., Papadapoulos, G., Triantafyllou, I., Benchekroun, S., Jaouadi, H.A., Abdallah, S.B., Bouallegue, A., Hamdi, H., Oueslati, F., Amato, A., Armigliato, A., Behrens, J., Davies, G., Di Bucci, D., Dolce, M., Geist, E.L., Gonzalez Vida, J.M., Gonzalez, M., 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To improve the understanding of the characteristics of velocity and suspended sediment concentration (SSC) surges and their contribution to sediment transport and local bed changes during periods of very shallow water, measurements of near-bed flow, and SSC were carried out at two cross-shore locations on an intertidal flat along the Jiangsu coast, China. Furthermore, the role of surges in local resuspension and morphological change was explored. Results indicate that flow and SSC surges occurred at both stations during very shallow water periods. On the lower intertidal flat, flood surges were erosive, while weaker surges on the middle intertidal flat were not. Surges on lower intertidal flats resulted in local resuspension and strong turbidity, contributing up to 25% of the onshore-suspended sediment flux during flood tides, even though they last only 10% of the flood duration. When surges travel across the flats, conditions change from erosional to depositional. Velocity surges on the middle intertidal flat were too weak to resuspend bed sediment, and the associated SSC surges were produced by advection.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2021.599799","usgsCitation":"Zhang, Q., Gong, Z., Zhang, C., Lacy, J.R., Jaffe, B.E., Xu, B., and Chen, X., 2021, The role of surges during periods of very shallow water on sediment transport over tidal flats: Frontiers in Marine Science, v. 8, 599799, 16 p., https://doi.org/10.3389/fmars.2021.599799.","productDescription":"599799, 16 p.","ipdsId":"IP-100858","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453181,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2021.599799","text":"Publisher Index Page"},{"id":384241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Jiangsu Coast","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              117.586669921875,\n              31.21280145833882\n            ],\n            [\n              123.00292968749999,\n              31.21280145833882\n            ],\n            [\n              123.00292968749999,\n              34.985003130171066\n            ],\n            [\n              117.586669921875,\n              34.985003130171066\n            ],\n            [\n              117.586669921875,\n              31.21280145833882\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2021-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Qian 0000-0003-0500-5655","orcid":"https://orcid.org/0000-0003-0500-5655","contributorId":174393,"corporation":false,"usgs":false,"family":"Zhang","given":"Qian","email":"","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":811517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gong, Zheng","contributorId":191939,"corporation":false,"usgs":false,"family":"Gong","given":"Zheng","email":"","affiliations":[],"preferred":false,"id":811518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Changkuan","contributorId":191947,"corporation":false,"usgs":false,"family":"Zhang","given":"Changkuan","email":"","affiliations":[],"preferred":false,"id":811519,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lacy, Jessica R. 0000-0002-2797-6172","orcid":"https://orcid.org/0000-0002-2797-6172","contributorId":201703,"corporation":false,"usgs":true,"family":"Lacy","given":"Jessica","email":"","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":811520,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jaffe, Bruce E. 0000-0002-8816-5920 bjaffe@usgs.gov","orcid":"https://orcid.org/0000-0002-8816-5920","contributorId":2049,"corporation":false,"usgs":true,"family":"Jaffe","given":"Bruce","email":"bjaffe@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":811521,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xu, Beibei","contributorId":210581,"corporation":false,"usgs":false,"family":"Xu","given":"Beibei","email":"","affiliations":[{"id":38117,"text":"Hohai University, Nanjing China","active":true,"usgs":false}],"preferred":false,"id":811522,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chen, Xindi","contributorId":254950,"corporation":false,"usgs":false,"family":"Chen","given":"Xindi","email":"","affiliations":[{"id":51365,"text":"Hohai University","active":true,"usgs":false}],"preferred":false,"id":811523,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70222590,"text":"70222590 - 2021 - Isolating the AFFF signature in coastal watersheds using oxidizable PFAS precursors and unexplained organofluorine","interactions":[],"lastModifiedDate":"2021-08-09T12:07:40.75407","indexId":"70222590","displayToPublicDate":"2021-03-05T07:05:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Isolating the AFFF signature in coastal watersheds using oxidizable PFAS precursors and unexplained organofluorine","docAbstract":"<div class=\"article_abstract\"><div class=\"container container_scaled-down\"><div class=\"row\"><div class=\"col-xs-12\"><div id=\"abstractBox\" class=\"article_abstract-content hlFld-Abstract\"><p class=\"articleBody_abstractText\">Water supplies for millions of U.S. individuals exceed maximum contaminant levels for per- and polyfluoroalkyl substances (PFAS). Contemporary and legacy use of aqueous film forming foams (AFFF) is a major contamination source. However, diverse PFAS sources are present within watersheds, making it difficult to isolate their predominant origins. Here we examine PFAS source signatures among six adjacent coastal watersheds on Cape Cod, MA, U.S.A. using multivariate clustering techniques. A distinct signature of AFFF contamination enriched in precursors with six perfluorinated carbons (C6) was identified in watersheds with an AFFF source, while others were enriched in C4 precursors. Principal component analysis of PFAS composition in impacted watersheds showed a decline in precursor composition relative to AFFF stocks and a corresponding increase in terminal perfluoroalkyl sulfonates with &lt; C6 but not those with ≥ C6. Prior work shows that in AFFF stocks, all extractable organofluorine (EOF) can be explained by targeted PFAS and precursors inferred using Bayesian inference on the total oxidizable precursor assay. Using the same techniques for the first time in impacted watersheds, we find that only 24%–63% of the EOF can be explained by targeted PFAS and oxidizable precursors. Our work thus indicates the presence of large non-AFFF organofluorine sources in these coastal watersheds.</p></div></div></div></div></div>","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c07296","usgsCitation":"Ruyle, B.J., Pickard, H.M., LeBlanc, D.R., Tokranov, A.K., Thackray, C.P., Hu, X.C., Vecitis, C.D., and Sunderland, E.M., 2021, Isolating the AFFF signature in coastal watersheds using oxidizable PFAS precursors and unexplained organofluorine: Environmental Science & Technology, v. 55, no. 6, p. 3686-3695, https://doi.org/10.1021/acs.est.0c07296.","productDescription":"11 p.","startPage":"3686","endPage":"3695","ipdsId":"IP-118643","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":453184,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11082878","text":"External Repository"},{"id":387758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70219036,"text":"70219036 - 2021 - Incorporating the effects of complex soil layering and thickness local variability into distributed landslide susceptibility assessments","interactions":[],"lastModifiedDate":"2021-03-19T11:44:31.211077","indexId":"70219036","displayToPublicDate":"2021-03-05T06:32:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating the effects of complex soil layering and thickness local variability into distributed landslide susceptibility assessments","docAbstract":"<p><span>Incorporating the influence of soil layering and local variability into the parameterizations of physics-based numerical models for distributed landslide susceptibility assessments remains a challenge. Typical applications employ substantial simplifications including homogeneous soil units and soil-hydraulic properties assigned based only on average textural classifications; the potential impact of these assumptions is usually disregarded. We present a multi-scale approach for parameterizing the distributed Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model that accounts for site-specific spatial variations in both soil thickness and complex layering properties by defining homogeneous soil properties that vary spatially for each model grid cell. These effective properties allow TRIGRS to accurately simulate the timing and distribution of slope failures without any modification of the model structure. We implemented this approach for the carbonate ridge of Sarno Mountains (southern Italy) whose slopes are mantled by complex layered soils of pyroclastic origin. The urbanized foot slopes enveloping these mountains are among the most landslide-prone areas of Italy and have been subjected to repeated occurrences of damaging and deadly rainfall-induced flow-type shallow landslides. At this scope, a primary local-scale application of TRIGRS was calibrated on physics-based rainfall thresholds, previously determined by a coupled VS2D (version 1.3) hydrological modeling and slope stability analysis. Subsequently, by taking into account the spatial distribution of soil thickness and vertical heterogeneity of soil hydrological and mechanical properties, a distributed assessment of landslide hazard was carried out by means of TRIGRS. The combination of these approaches led to the spatial assessment of landslide hazard under different hypothetical rainfall intensities and antecedent hydrological conditions. This approach to parameterizing TRIGRS can be adapted to other spatially variable soil layering and thickness to improve hazard assessments.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/w13050713","usgsCitation":"Fusco, F., Mirus, B.B., Baum, R.L., Calcaterra, D., and De Vita, P., 2021, Incorporating the effects of complex soil layering and thickness local variability into distributed landslide susceptibility assessments: Water, v. 13, no. 5, 27 p., https://doi.org/10.3390/w13050713.","productDescription":"27 p.","ipdsId":"IP-120315","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":453185,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13050713","text":"Publisher Index Page"},{"id":384490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Mount Vesuvius","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              14.327545166015625,\n              40.75974059207392\n            ],\n            [\n              14.53765869140625,\n              40.75974059207392\n            ],\n            [\n              14.53765869140625,\n              40.90832339902113\n            ],\n            [\n              14.327545166015625,\n              40.90832339902113\n            ],\n            [\n              14.327545166015625,\n              40.75974059207392\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Fusco, F. 0000-0002-6271-2228","orcid":"https://orcid.org/0000-0002-6271-2228","contributorId":219005,"corporation":false,"usgs":false,"family":"Fusco","given":"F.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":812516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":812517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calcaterra, D. 0000-0002-3480-3667","orcid":"https://orcid.org/0000-0002-3480-3667","contributorId":219008,"corporation":false,"usgs":false,"family":"Calcaterra","given":"D.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"De Vita, P.","contributorId":219006,"corporation":false,"usgs":false,"family":"De Vita","given":"P.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812519,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70218517,"text":"ofr20201145 - 2021 - Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018","interactions":[],"lastModifiedDate":"2021-03-05T12:53:46.034292","indexId":"ofr20201145","displayToPublicDate":"2021-03-04T15:39:22","publicationYear":"2021","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":"2020-1145","displayTitle":"Estimated Total Phosphorus Loads for Selected Sites on Great Lakes Tributaries, Water Years 2014–2018","title":"Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018","docAbstract":"<p>Monthly and annual total phosphorus loads were estimated for water years 2014 through 2018 for 23 streamgaged (gaged) sites on tributaries to the Great Lakes. Processing and regression methods described by Robertson and others (2018) were used with discrete and continuous data collected during water years 2011 and 2018 to update regression models for estimating instantaneous flux with the same form of equations as published by Robertson and others (2018). Monthly and water year average fluxes for all but two of the 23 gage sites were estimated using a weighted combination of results from surrogate models (which have streamflow, turbidity, and seasonal indicators as explanatory variables) and unit-value (UV)-flow models which have only UV streamflow and seasonal indicators as explanatory variables. Two of the gage sites had extensive periods of missing turbidity records, so average flux estimates for those stations were based solely on results from UV-flow models.</p><p>For most sites, estimated loads of total phosphorus were computed and summed for water years 2014–2018. The cumulative loads were used to compute yields and flow-weighted mean concentrations for water years 2014–2018. The estimated cumulative total phosphorus loads for water years 2014–2018 ranged from 112 to 11,500 metric tons. The Maumee River site (U.S. Geological Survey gage number 04193500) had the largest estimated cumulative load for water years 2014–2018 and the third largest estimated flow-weighted mean concentration. In fact, the estimated cumulative load at the Maumee River site was more than three times larger than the second largest estimated cumulative load.</p><p>Estimated average annual total phosphorus yields and flow-weighted mean concentrations for water years 2014–2018 ranged from 0.016 metric tons per square kilometer to 0.771 metric tons per square kilometer and 0.033 milligram per liter to 0.466 milligram per liter, respectively. The Cattaraugus Creek gage site (U.S. Geological Survey gage number 04213500) had the highest estimated average annual total phosphorus yield and flow-weighted mean concentration. The average annual total phosphorus yield at the Cattaraugus Creek gage site was almost twice as large as the second largest estimated yield.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201145","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Koltun, G.F., 2021, Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018: U.S. Geological Survey Open-File Report 2020–1145, 13 p., https://doi.org/10.3133/ofr20201145.","productDescription":"Report: v, 13 p.; 2 Appendixes; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122090","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":383717,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145//ofr20201145_appendix_2.csv","text":"Appendix 2","size":"64.8 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tributaries"},{"id":383715,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145_appendix_1.csv","text":"Appendix 1","size":"8.45 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2020–1145 Appendix 1","linkHelpText":"— Estimated annual total phosphorus loads and flow-weighted mean concentrations at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383716,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145_appendix_2.xlsx","text":"Appendix 2","size":"66.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1145 Appendix 2","linkHelpText":"— Estimated monthly total phosphorus loads at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383718,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEW32M","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model 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-76.57470703125,\n              43.28520334369384\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/oki-water\" href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a><br>U.S. Geological Survey<br>6460 Busch Boulevard Ste 100<br>Columbus, OH 43229-1737</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Study Methods</li><li>Regression Equations and Estimated Total Phosphorus Loads</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2021-03-04","noUsgsAuthors":false,"publicationDate":"2021-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Koltun, G. F. 0000-0003-0255-2960 gfkoltun@usgs.gov","orcid":"https://orcid.org/0000-0003-0255-2960","contributorId":140048,"corporation":false,"usgs":true,"family":"Koltun","given":"G.","email":"gfkoltun@usgs.gov","middleInitial":"F.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811224,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70224954,"text":"70224954 - 2021 - Life history and population dynamics","interactions":[],"lastModifiedDate":"2021-10-11T17:03:33.804355","indexId":"70224954","displayToPublicDate":"2021-03-04T11:59:35","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Life history and population dynamics","docAbstract":"<p><span>Lake charr&nbsp;</span><i class=\"EmphasisTypeItalic \">Salvelinus namaycush</i><span>&nbsp;life history and population dynamics metrics were reviewed to evaluate populations inside (</span><i class=\"EmphasisTypeItalic \">n</i><span>&nbsp;=&nbsp;462) and outside (</span><i class=\"EmphasisTypeItalic \">n</i><span>&nbsp;=&nbsp;24) the native range. Our goals were to create a database of metrics useful for evaluating population status and to test for large-scale patterns between metrics and latitude and lake size. An average lake charr grew from a 69-mm length at age-0 (</span><i class=\"EmphasisTypeItalic \">L</i><sub>0</sub><span>) at 89&nbsp;mm/year early growth rate (</span><i class=\"EmphasisTypeItalic \">ω</i><span>) to 50% maturity at 420&nbsp;mm (</span><i class=\"EmphasisTypeItalic \">L</i><sub>50</sub><span>) at age 8 (</span><i class=\"EmphasisTypeItalic \">t</i><sub>50</sub><span>), and then continued to grow toward a 717-mm asymptotic length (</span><i class=\"EmphasisTypeItalic \">L</i><sub>∞</sub><span>).&nbsp;</span><i class=\"EmphasisTypeItalic \">L</i><sub>50</sub><span>&nbsp;was positively correlated to&nbsp;</span><i class=\"EmphasisTypeItalic \">ω</i><span>, whereas&nbsp;</span><i class=\"EmphasisTypeItalic \">t</i><sub>50</sub><span>&nbsp;was inversely correlated to&nbsp;</span><i class=\"EmphasisTypeItalic \">ω</i><span>. Lake charr grew slower toward larger size and older age in northern latitudes and larger lakes than in southern latitudes and smaller lakes. Population density (number/ha) and yield density (kg/ha) decreased with lake size, and yield and total annual mortality (</span><i class=\"EmphasisTypeItalic \">A</i><span>) decreased with latitude. Native populations grew slower (</span><i class=\"EmphasisTypeItalic \">ω</i><span>), were heavier at 500&nbsp;mm (</span><i class=\"EmphasisTypeItalic \">W</i><sub>500</sub><span>), matured at shorter&nbsp;</span><i class=\"EmphasisTypeItalic \">L</i><sub>50</sub><span>, grew to a shorter&nbsp;</span><i class=\"EmphasisTypeItalic \">L</i><sub>∞</sub><span>, and suffered lower annual mortality&nbsp;</span><i class=\"EmphasisTypeItalic \">A</i><span>&nbsp;than non-native populations. Our review and database should be useful to managers and researchers for quantifying lake charr population status across the species range.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The lake charr Salvelinus namaycush: Biology, ecology, distribution, and management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer Link","doi":"10.1007/978-3-030-62259-6_8","usgsCitation":"Hansen, M.J., Guy, C.S., Bronte, C.R., and Nate, N.A., 2021, Life history and population dynamics, chap. <i>of</i> The lake charr Salvelinus namaycush: Biology, ecology, distribution, and management, p. 253-286, https://doi.org/10.1007/978-3-030-62259-6_8.","productDescription":"34 p.","startPage":"253","endPage":"286","ipdsId":"IP-105836","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":390397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Hansen, Michael J","contributorId":260100,"corporation":false,"usgs":false,"family":"Hansen","given":"Michael","email":"","middleInitial":"J","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":824840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true}],"preferred":true,"id":824839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bronte, Charles R.","contributorId":190727,"corporation":false,"usgs":false,"family":"Bronte","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":824841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nate, Nancy A.","contributorId":26626,"corporation":false,"usgs":true,"family":"Nate","given":"Nancy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":824842,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70217777,"text":"70217777 - 2021 - Trophic ecology","interactions":[],"lastModifiedDate":"2021-04-19T16:27:43.152161","indexId":"70217777","displayToPublicDate":"2021-03-04T10:57:58","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Trophic ecology","docAbstract":"<p><span>The trophic ecology of lake charr&nbsp;</span><i class=\"EmphasisTypeItalic \">Salvelinus namaycush</i><span>&nbsp;morphotypes from small and large lakes within their native and introduced ranges is reviewed over the past 50&nbsp;years. The lake charr is an apex predator in most habitats it occupies, where it plays a significant role in defining food webs. While often considered piscivores, lake charr feed on a range of aquatic prey throughout their life history, including zooplankton, benthic invertebrates, and fish, as well as terrestrial insects, mammals, birds, amphibians, and reptiles. Lake charr diets that vary within morphotypes among lakes and among sympatric morphotypes reflect differences in habitat use, prey availability, and individual preferences. Temporal variability in diet can result from seasonal prey pulses, thermal barriers, and long-term prey dynamics. Lake charr adapt quickly to consume invasive prey fishes, and often decimate native prey fishes and other piscivores in lakes into which they are introduced. Salient research topics in lake charr trophic ecology include: (1) how best to quantify spatial and temporal trophic niche space; and, (2) how changing environmental conditions, such as invasive species and lake warming, will influence lake charr feeding and broader lake food-web dynamics.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The lake charr Salvelinus namaycush: Biology, ecology, distribution, and management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-62259-6_9","usgsCitation":"Vinson, M., Chavarie, L., Rosinski, C.L., and Swanson, H.K., 2021, Trophic ecology, chap. <i>of</i> The lake charr Salvelinus namaycush: Biology, ecology, distribution, and management, p. 287-314, https://doi.org/10.1007/978-3-030-62259-6_9.","productDescription":"28 p.","startPage":"287","endPage":"314","ipdsId":"IP-101993","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":385200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Vinson, Mark R. 0000-0001-5256-9539 mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chavarie, Louise","contributorId":156227,"corporation":false,"usgs":false,"family":"Chavarie","given":"Louise","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":809629,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosinski, Caroline Lynn 0000-0003-3635-2748","orcid":"https://orcid.org/0000-0003-3635-2748","contributorId":248618,"corporation":false,"usgs":true,"family":"Rosinski","given":"Caroline","email":"","middleInitial":"Lynn","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814482,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swanson, Heidi K.","contributorId":203350,"corporation":false,"usgs":false,"family":"Swanson","given":"Heidi","email":"","middleInitial":"K.","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":809628,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70227736,"text":"70227736 - 2021 - Southwestern fish and aquatic systems: The climate challenge","interactions":[],"lastModifiedDate":"2022-01-28T16:29:16.917343","indexId":"70227736","displayToPublicDate":"2021-03-04T10:19:58","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"9","title":"Southwestern fish and aquatic systems: The climate challenge","docAbstract":"<p>No abstract available.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Standing between life and extinction: Ethics and ecology of conserving aquatic species in North American deserts","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University of Chicago Press","usgsCitation":"Overpeck, J.T., and Bonar, S.A., 2021, Southwestern fish and aquatic systems: The climate challenge, chap. 9 <i>of</i> Standing between life and extinction: Ethics and ecology of conserving aquatic species in North American deserts, p. 137-152.","productDescription":"16 p.","startPage":"137","endPage":"152","ipdsId":"IP-088747","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395070,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Overpeck, Jonathan T.","contributorId":146162,"corporation":false,"usgs":false,"family":"Overpeck","given":"Jonathan","email":"","middleInitial":"T.","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":832149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":831987,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70218708,"text":"70218708 - 2021 - Reproduction","interactions":[],"lastModifiedDate":"2021-03-08T15:50:36.638472","indexId":"70218708","displayToPublicDate":"2021-03-04T09:41:54","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Reproduction","docAbstract":"<p><span>Lake charr&nbsp;</span><i class=\"EmphasisTypeItalic \">Salvelinus namaycush</i><span>&nbsp;are typically fall spawners although one ecotype has populations that spawn during spring and fall (siscowets in Lake Superior). Lake charr are iteroparous (reproduce more than once in a lifetime) with group-synchronous ovarian development and typically spawn once per year. However, lake charr may not reproduce every year, a phenomenon known as skipped spawning. Free embryos are active on spawning reefs, make diurnal vertical movements from spawning substrate, and feed exogenously much earlier than previously assumed. The abundance of food and predators strongly affects the rate of development, yolk sac absorption, and duration of residence on spawning sites. The necessity for, and timing of, gas bladder inflation, and mechanisms for inflation without access to the surface, need further study. The low survival of free embryos due to thiamine deficiency has likely contributed to the lack of recruitment of lake charr in the Laurentian Great Lakes for decades. Thiaminase, a thiamine-degrading enzyme, appears to be the causal agent for thiamine deficiency in Great Lakes lake charr.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The lake charr Salvelinus namaycush: Biology, ecology, distribution, and management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-62259-6_10","usgsCitation":"W. 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Goetz, Frederick","contributorId":254936,"corporation":false,"usgs":false,"family":"W. Goetz","given":"Frederick","email":"","affiliations":[{"id":51363,"text":"University of Wisconsin-Milwaukee, School of Freshwater Sciences","active":true,"usgs":false}],"preferred":false,"id":811454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marsden, J. Ellen","contributorId":238544,"corporation":false,"usgs":false,"family":"Marsden","given":"J. Ellen","affiliations":[{"id":47733,"text":"Wildlife and Fisheries Biology Program, University of Vermont, Burlington, VT","active":true,"usgs":false}],"preferred":false,"id":811455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richter, Catherine A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":138994,"corporation":false,"usgs":true,"family":"Richter","given":"Catherine","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":811456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tillitt, Donald E. 0000-0002-8278-3955 dtillitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8278-3955","contributorId":1875,"corporation":false,"usgs":true,"family":"Tillitt","given":"Donald","email":"dtillitt@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":811457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sitar, Shawn P.","contributorId":181529,"corporation":false,"usgs":false,"family":"Sitar","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":811458,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Riley, Stephen 0000-0002-8968-8416 sriley@usgs.gov","orcid":"https://orcid.org/0000-0002-8968-8416","contributorId":169479,"corporation":false,"usgs":true,"family":"Riley","given":"Stephen","email":"sriley@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":811459,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krueger, Charles C.","contributorId":67821,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles C.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":811460,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70218710,"text":"70218710 - 2021 - Deepwater debrites and linked megaturbidites in confined basins: An example from the Onnuri Basin, East Sea of Korea","interactions":[],"lastModifiedDate":"2021-03-08T15:03:23.012626","indexId":"70218710","displayToPublicDate":"2021-03-04T08:51:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2451,"text":"Journal of Sedimentary Research","onlineIssn":"1938-3681","printIssn":"1527-1404","active":true,"publicationSubtype":{"id":10}},"title":"Deepwater debrites and linked megaturbidites in confined basins: An example from the Onnuri Basin, East Sea of Korea","docAbstract":"<p>We analyzed data from seven piston cores, multi-channel seismic-reflection (MCS) and chirp profiles, and multibeam echosounder (MBES) data to study the distribution, emplacement time, sedimentary facies, and depositional processes of sediment-gravity-flow deposits in the Onnuri Basin, a confined basin in the East Sea. These data reveal that debris flows have traveled ca. 30 km downslope, forming a seismic facies consisting of stacked, wedge-shaped, transparent units separated by high-amplitude continuous reflectors. Analysis of piston cores shows three distinct sedimentary units, throughout the basin. The lowest unit, I, is a debrite containing numerous mud clasts of varying size and color distributed in a mud-rich matrix; it is absent over elevated basinal highs or ridges, such as the Onnuri Ridge, suggesting that local topography controls its distribution. The debrite forms a recognizable acoustically transparent layer on subbottom chirp profiles (av. 7 m thick), covers approximately 500 km<sup>2</sup>, and has an estimated volume of ∼ 3.5 km<sup>3</sup>.</p><p>The overlying unit, II, contains normally graded beds composed of massive sand, laminated and cross-laminated sand and silt, and a thick cap of structureless mud. This unit is interpreted to be a megaturbidite deposited from turbidity currents that originated from the flow transformation of debris flows on the upper continental slope. The megaturbidite covers the entire basin (at least 650 km<sup>2</sup>), and has an average thickness of 2.8 m (maximum thickness of 4.35 m), and comprises a volume of 1.8 km<sup>3</sup>. Variations in grain size and sedimentary structures suggest that the megaturbidite was deposited by progressively waning flows that reflected off basin flanks and ridges. The thick (up to 3.65 m) structureless mud cap further indicates deposition in a confined basin. The sharp basal contact, together with the lack of hemipelagic sediments between debrite and overlying megaturbidite, suggest that both were deposited during the same flow event, likely to have originated from a single catastrophic slope failure. Collapsing slide material evolved into a debris flow, from which a turbidite formed by dilution of the debris flow. Radiocarbon dates suggest that the slope failure occurred about 13–11 ka, a time when sea level was ca. 50 m lower than at the present day. Hemipelagic sediments in the topmost unit, III-2, above the megaturbidite indicate that the basin has been stable since ca. 11 ka.</p><p>We provide robust evidence that submarine slope failures evolve downslope into slides, debris flows, and finally, thick megaturbidites. This contribution highlights the importance of seafloor morphology on the distribution and stratigraphy of submarine flows in confined basins.</p>","language":"English","publisher":"SEPM Society for Sedimentary Geology","doi":"10.2110/jsr.2020.115","usgsCitation":"Cukur, D., Um, I., Chun, J., Lee, G., Kong, G., Johnson, S., and Horozal, S., 2021, Deepwater debrites and linked megaturbidites in confined basins: An example from the Onnuri Basin, East Sea of Korea: Journal of Sedimentary Research, v. 91, no. 1, p. 1-20, https://doi.org/10.2110/jsr.2020.115.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-120291","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":384225,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"South Korea","otherGeospatial":"Onnuri Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              128.29833984375,\n              37.37888785004527\n            ],\n            [\n              131.3525390625,\n              37.37888785004527\n            ],\n            [\n              131.3525390625,\n              38.75408327579141\n            ],\n            [\n              128.29833984375,\n              38.75408327579141\n            ],\n            [\n              128.29833984375,\n              37.37888785004527\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"91","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Cukur, Deniz","contributorId":216636,"corporation":false,"usgs":false,"family":"Cukur","given":"Deniz","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":811463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Um, In-Kwon","contributorId":216640,"corporation":false,"usgs":false,"family":"Um","given":"In-Kwon","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":811464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chun, Jong-Hwa","contributorId":216638,"corporation":false,"usgs":false,"family":"Chun","given":"Jong-Hwa","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":811465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, Gwang-Soo","contributorId":254937,"corporation":false,"usgs":false,"family":"Lee","given":"Gwang-Soo","email":"","affiliations":[{"id":24820,"text":"Korea Institute of Geoscience and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":811466,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kong, Gee-Too","contributorId":254938,"corporation":false,"usgs":false,"family":"Kong","given":"Gee-Too","email":"","affiliations":[{"id":24820,"text":"Korea Institute of Geoscience and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":811467,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Samuel Y. 0000-0001-7972-9977","orcid":"https://orcid.org/0000-0001-7972-9977","contributorId":221270,"corporation":false,"usgs":true,"family":"Johnson","given":"Samuel Y.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":811468,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Horozal, Senay","contributorId":254939,"corporation":false,"usgs":false,"family":"Horozal","given":"Senay","email":"","affiliations":[{"id":24820,"text":"Korea Institute of Geoscience and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":811469,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70227101,"text":"70227101 - 2021 - Developing species-age cohorts from forest inventory and analysis data to parameterize a forest landscape model","interactions":[],"lastModifiedDate":"2021-12-29T14:14:01.03095","indexId":"70227101","displayToPublicDate":"2021-03-04T08:10:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2043,"text":"International Journal of Forestry Research","active":true,"publicationSubtype":{"id":10}},"title":"Developing species-age cohorts from forest inventory and analysis data to parameterize a forest landscape model","docAbstract":"<p>Simulating long-term, landscape level changes in forest composition requires estimates of stand age to initialize succession models. Detailed stand ages are rarely available, and even general information on stand history often is lacking. We used data from USDA Forest Service Forest Inventory and Analysis (FIA) database to estimate broad age classes for a forested landscape to simulate changes in landscape composition and structure relative to climate change at Fort Drum, a 43,000 ha U.S. Army installation in northwestern New York. Using simple linear regression, we developed relationships between tree diameter and age for FIA site trees from the host and adjacent ecoregions and applied those relationships to forest stands at Fort Drum. We observed that approximately half of the variation in age was explained by diameter breast height (DBH) across all species studied (<i>r</i><sup>2</sup> = 0.42 for sugar maple<span>&nbsp;</span><i>Acer saccharum</i><span>&nbsp;</span>to 0.63 for white ash<span>&nbsp;</span><i>Fraxinus americana</i>). We then used age-diameter relationships from published research on northern hardwood species to calibrate results from the FIA-based analysis. With predicted stand age, we used tree species life histories and environmental conditions represented by ecological site types to parameterize a stochastic forest landscape model (LANDIS-II) to spatially and temporally model successional changes in forest communities at Fort Drum. Forest stands modeled over 100 years without significant disturbance appeared to reflect expected patterns of increasing dominance by shade-tolerant mesophytic tree species such as sugar maple, red maple (<i>Acer rubrum</i>), and eastern hemlock (<i>Tsuga canadensis</i>) where soil moisture was sufficient. On drier sandy soils, eastern white pine (<i>Pinus strobus</i>), red pine (<i>P. resinosa</i>), northern red oak (<i>Quercus rubra</i>), and white oak (<i>Q. alba</i>) continued to be important components throughout the modeling period with no net loss at the landscape scale. Our results suggest that despite abundant precipitation and relatively low evapotranspiration rates for the region, low soil water holding capacity and fertility may be limiting factors for the spread of mesophytic species on excessively drained soils in the region. Increasing atmospheric temperatures projected for the region could alter moisture regimes for many coarse-textured soils providing a possible mechanism for expansion of xerophytic tree species.</p>","language":"English","publisher":"Hindawi","doi":"10.1155/2021/6650821","usgsCitation":"Odom, R.H., and Ford, W., 2021, Developing species-age cohorts from forest inventory and analysis data to parameterize a forest landscape model: International Journal of Forestry Research, v. 2021, 6650821, 16 p., https://doi.org/10.1155/2021/6650821.","productDescription":"6650821, 16 p.","ipdsId":"IP-111053","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":453196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.1155/2021/6650821","text":"Publisher Index Page"},{"id":393572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fort Drum","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -76.0089111328125,\n              43.95921358836687\n            ],\n            [\n              -75.509033203125,\n              43.95921358836687\n            ],\n            [\n              -75.509033203125,\n              44.209772586984485\n            ],\n            [\n              -76.0089111328125,\n              44.209772586984485\n            ],\n            [\n              -76.0089111328125,\n              43.95921358836687\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"2021","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Odom, Richard H.","contributorId":171659,"corporation":false,"usgs":false,"family":"Odom","given":"Richard","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":829633,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":829632,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70219538,"text":"70219538 - 2021 - Monitoring Tamarix changes using WorldView-2 satellite imagery in Grand Canyon National Park, Arizona","interactions":[],"lastModifiedDate":"2021-04-13T13:06:06.096863","indexId":"70219538","displayToPublicDate":"2021-03-04T08:04:32","publicationYear":"2021","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":"Monitoring Tamarix changes using WorldView-2 satellite imagery in Grand Canyon National Park, Arizona","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Remote sensing methods are commonly used to monitor the invasive riparian shrub tamarisk (<span class=\"html-italic\">Tamarix spp.</span>) and its response to the northern tamarisk beetle (<span class=\"html-italic\">D. carinulata</span>), a specialized herbivore introduced as a biocontrol agent to control tamarisk in the Southwest USA in 2001. We use a Spectral Angle Mapper (SAM) supervised classification method with WorldView-2 (2 m spatial resolution) multispectral images from May and August of 2019 to map healthy tamarisk, canopy dieback, and defoliated tamarisk over a 48 km segment of the Colorado River in the topographically complex Grand Canyon National Park, where coarse-resolution satellite images are of limited use. The classifications in May and August produced overall accuracies of 80.0% and 83.1%, respectively. Seasonal change detection between May and August 2019 indicated that 47.5% of the healthy tamarisk detected in May 2019 had been defoliated by August 2019 within the WorldView-2 image extent. When compared to a previously published tamarisk map from 2009, derived from multispectral aerial imagery, we found that 29.5% of healthy tamarisk canopy declined between 2009 and 2019. This implies that tamarisk beetle impacts are continuing to accumulate even though land managers have noted the presence of the beetles in this reach of the river for 7 years since 2012.<span>&nbsp;</span></div>","language":"English","publisher":"MDPI","doi":"10.3390/rs13050958","usgsCitation":"Bransky, N., Sankey, T.T., Sankey, J.B., Johnson, M.D., and Jamison, L.R., 2021, Monitoring Tamarix changes using WorldView-2 satellite imagery in Grand Canyon National Park, Arizona: Remote Sensing, v. 13, no. 5, 958, 16 p., https://doi.org/10.3390/rs13050958.","productDescription":"958, 16 p.","ipdsId":"IP-122127","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453199,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13050958","text":"Publisher Index Page"},{"id":436473,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WUMM1P","text":"USGS data release","linkHelpText":"Northern tamarisk beetle impact and classification maps, Grand Canyon National Park, Arizona"},{"id":385057,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.9559326171875,\n              35.54116627999815\n            ],\n            [\n              -111.3409423828125,\n              35.54116627999815\n            ],\n            [\n              -111.3409423828125,\n              36.94111143010769\n            ],\n            [\n              -112.9559326171875,\n              36.94111143010769\n            ],\n            [\n              -112.9559326171875,\n              35.54116627999815\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Bransky, Nathaniel D.","contributorId":219526,"corporation":false,"usgs":false,"family":"Bransky","given":"Nathaniel D.","affiliations":[],"preferred":false,"id":814091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":814092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":814093,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Matthew D. mjjohnson@usgs.gov","contributorId":193689,"corporation":false,"usgs":false,"family":"Johnson","given":"Matthew","email":"mjjohnson@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":false,"id":814094,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jamison, Levi R.","contributorId":204298,"corporation":false,"usgs":false,"family":"Jamison","given":"Levi","email":"","middleInitial":"R.","affiliations":[{"id":36908,"text":"SNRE University of Arizona","active":true,"usgs":false}],"preferred":false,"id":814095,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70218590,"text":"ofr20201146 - 2021 - Practical field survey operations for flood insurance rate maps","interactions":[],"lastModifiedDate":"2021-03-05T12:41:18.272992","indexId":"ofr20201146","displayToPublicDate":"2021-03-04T08:00:00","publicationYear":"2021","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":"2020-1146","displayTitle":"Practical Field Survey Operations for Flood Insurance Rate Maps","title":"Practical field survey operations for flood insurance rate maps","docAbstract":"<p>The U.S. Geological Survey assists the Federal Emergency Management Agency in its mission to identify flood hazards and zones for risk premiums for communities nationwide, by creating flood insurance rate maps through updating hydraulic models that use river geometry data. The data collected consist of elevations of river channels, banks, and structures, such as bridges, dams, and weirs that can affect flow. To account for the model complexity of river structure hydraulics and the fidelity between river channel and structure geometry, two distinct standards for collecting geometry data are presented, both using global navigation satellite system real-time network surveying. This method is adapted from U.S. Geological Survey manuals and is foundational in hydraulic surveying for flood insurance rate maps.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201146","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Taylor, N.J., and Simeone, C.E., 2021, Practical field survey operations for flood insurance rate maps: U.S. Geological Survey Open-File Report 2020–1146, 8 p., https://doi.org/10.3133/ofr20201146.","productDescription":"iv, 8 p.","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114316","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":383741,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/tm11D1","text":"Techniques and Methods 11-D1","linkHelpText":"- Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey"},{"id":383723,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1146/coverthb.jpg"},{"id":383724,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1146/ofr20201146.pdf","text":"Report","size":"662 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1146"},{"id":383725,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm11D3","text":"Techniques and Methods 11-D3","linkHelpText":"- Procedures and Best Practices for Trigonometric Leveling in the U.S. Geological Survey"}],"contact":"<p><a href=\"mailto:dc_ nweng@usgs.gov\" data-mce-href=\"mailto:dc_ nweng@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-england-water\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>10 Bearfoot Road<br>Northborough, MA 01532</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Procedures for Surveying Hydraulic Structures</li><li>Procedures for Surveying Cross Sections</li><li>Procedures for Metadata Quality Control</li><li>Limitations on Use</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li><li>Glossary</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-03-04","noUsgsAuthors":false,"publicationDate":"2021-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Nicholas J. 0000-0002-4266-0256","orcid":"https://orcid.org/0000-0002-4266-0256","contributorId":241051,"corporation":false,"usgs":true,"family":"Taylor","given":"Nicholas","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":811225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simeone, Caelan E. 0000-0003-3263-6452 csimeone@usgs.gov","orcid":"https://orcid.org/0000-0003-3263-6452","contributorId":221126,"corporation":false,"usgs":true,"family":"Simeone","given":"Caelan","email":"csimeone@usgs.gov","middleInitial":"E.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811226,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70218771,"text":"70218771 - 2021 - Continental-scale analysis of shallow and deep groundwater contributions to streams","interactions":[],"lastModifiedDate":"2021-03-12T13:58:45.144442","indexId":"70218771","displayToPublicDate":"2021-03-04T07:56:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Continental-scale analysis of shallow and deep groundwater contributions to streams","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Groundwater discharge generates streamflow and influences stream thermal regimes. However, the water quality and thermal buffering capacity of groundwater depends on the aquifer source-depth. Here, we pair multi-year air and stream temperature signals to categorize 1729 sites across the continental United States as having major dam influence, shallow or deep groundwater signatures, or lack of pronounced groundwater (atmospheric) signatures. Approximately 40% of non-dam stream sites have substantial groundwater contributions as indicated by characteristic paired air and stream temperature signal metrics. Streams with shallow groundwater signatures account for half of all groundwater signature sites and show reduced baseflow and a higher proportion of warming trends compared to sites with deep groundwater signatures. These findings align with theory that shallow groundwater is more vulnerable to temperature increase and depletion. Streams with atmospheric signatures tend to drain watersheds with low slope and greater human disturbance, indicating reduced stream-groundwater connectivity in populated valley settings.</p></div></div>","language":"English","publisher":"Nature","doi":"10.1038/s41467-021-21651-0","usgsCitation":"Hare, D., Helton, A.M., Johnson, Z.C., Lane, J., and Briggs, M.A., 2021, Continental-scale analysis of shallow and deep groundwater contributions to streams: Nature Communications, v. 12, 1450, 10 p., https://doi.org/10.1038/s41467-021-21651-0.","productDescription":"1450, 10 p.","ipdsId":"IP-118222","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453202,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-021-21651-0","text":"Publisher Index 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]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2021-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Hare, D.","contributorId":255088,"corporation":false,"usgs":false,"family":"Hare","given":"D.","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":811776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Helton, A. M.","contributorId":93289,"corporation":false,"usgs":false,"family":"Helton","given":"A.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":811777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Zachary C. 0000-0002-0149-5223","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":204647,"corporation":false,"usgs":false,"family":"Johnson","given":"Zachary","email":"","middleInitial":"C.","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":811778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lane, John W. Jr. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":210076,"corporation":false,"usgs":true,"family":"Lane","given":"John W.","suffix":"Jr.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811779,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":811780,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70218739,"text":"70218739 - 2021 - Response to ‘Stochastic and deterministic interpretation of pool models’","interactions":[],"lastModifiedDate":"2021-05-18T13:53:38.421627","indexId":"70218739","displayToPublicDate":"2021-03-04T07:48:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Response to ‘Stochastic and deterministic interpretation of pool models’","docAbstract":"<p><span>We concur with Azizi‐Rad et al. (2021) that it is vital to critically evaluate and compare different soil carbon models, and we welcome the opportunity to further describe the unique contribution of the PROMISE model (Waring et al. 2020) to this literature. The PROMISE framework does share many features with established biogeochemical models, as our original manuscript highlighted in Table 1, and our work builds upon model innovations developed by many different groups, including that of Azizi‐Rad and colleagues. Yet, the PROMISE framework is distinctive due to where it places mechanistic emphasis, and how these mechanisms are formalized in the mathematical model structure.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.15580","usgsCitation":"Waring, B.G., Sulman, B.N., Reed, S., Smith, A.P., Averill, C., Creamer, C., Cusack, D.F., Hall, S.J., Jastrow, J.D., Jilling, A., Kemner, K.M., Kleber, M., Allen Liu, X., Pett-Ridge, J., and Schulz, M., 2021, Response to ‘Stochastic and deterministic interpretation of pool models’: Global Change Biology, v. 27, no. 11, p. e11-e12, https://doi.org/10.1111/gcb.15580.","productDescription":"2 p.","startPage":"e11","endPage":"e12","ipdsId":"IP-127051","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453205,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.15580","text":"Publisher Index Page"},{"id":384273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Waring, Bonnie G. 0000-0002-8457-5164","orcid":"https://orcid.org/0000-0002-8457-5164","contributorId":245284,"corporation":false,"usgs":false,"family":"Waring","given":"Bonnie","email":"","middleInitial":"G.","affiliations":[{"id":49130,"text":"Utah State University, Department of Biology and Ecology Center, Logan UT 84322","active":true,"usgs":false}],"preferred":false,"id":811560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sulman, Benjamin N. 0000-0002-3265-6691","orcid":"https://orcid.org/0000-0002-3265-6691","contributorId":209890,"corporation":false,"usgs":false,"family":"Sulman","given":"Benjamin","email":"","middleInitial":"N.","affiliations":[{"id":7108,"text":"Princeton Univ.","active":true,"usgs":false}],"preferred":false,"id":811561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811562,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, A. Peyton","contributorId":245298,"corporation":false,"usgs":false,"family":"Smith","given":"A.","email":"","middleInitial":"Peyton","affiliations":[],"preferred":false,"id":811563,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Averill, Colin","contributorId":245299,"corporation":false,"usgs":false,"family":"Averill","given":"Colin","email":"","affiliations":[],"preferred":false,"id":811564,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Creamer, Courtney 0000-0001-8270-9387","orcid":"https://orcid.org/0000-0001-8270-9387","contributorId":201952,"corporation":false,"usgs":true,"family":"Creamer","given":"Courtney","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":811565,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cusack, Daniela F. 0000-0003-4681-7449","orcid":"https://orcid.org/0000-0003-4681-7449","contributorId":245300,"corporation":false,"usgs":false,"family":"Cusack","given":"Daniela","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":811566,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hall, Steven J. 0000-0002-7841-2019","orcid":"https://orcid.org/0000-0002-7841-2019","contributorId":244336,"corporation":false,"usgs":false,"family":"Hall","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":811567,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jastrow, Julie D.","contributorId":254970,"corporation":false,"usgs":false,"family":"Jastrow","given":"Julie","email":"","middleInitial":"D.","affiliations":[{"id":51371,"text":"Environmental Science Division, Argonne National Laboratory, Lemont IL 60439","active":true,"usgs":false}],"preferred":false,"id":811568,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jilling, Andrea","contributorId":254971,"corporation":false,"usgs":false,"family":"Jilling","given":"Andrea","email":"","affiliations":[{"id":51372,"text":"Department of Plant and Soil Sciences, Oklahoma State University, Stillwater OK 74078","active":true,"usgs":false}],"preferred":false,"id":811569,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kemner, Kenneth M.","contributorId":245301,"corporation":false,"usgs":false,"family":"Kemner","given":"Kenneth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":811570,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kleber, Markus","contributorId":254972,"corporation":false,"usgs":false,"family":"Kleber","given":"Markus","affiliations":[{"id":51374,"text":"Department of Crop and Soil Science, Oregon State University, Corvallis OR 97331","active":true,"usgs":false}],"preferred":false,"id":811571,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Allen Liu, Xiao-Jun","contributorId":254973,"corporation":false,"usgs":false,"family":"Allen Liu","given":"Xiao-Jun","affiliations":[{"id":51375,"text":"Department of Microbiology, University of Massachusetts, Amherst MA 01003","active":true,"usgs":false}],"preferred":false,"id":811572,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pett-Ridge, Jennifer","contributorId":254974,"corporation":false,"usgs":false,"family":"Pett-Ridge","given":"Jennifer","affiliations":[{"id":51376,"text":"Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore CA 94551","active":true,"usgs":false}],"preferred":false,"id":811573,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schulz, Marjorie S. 0000-0001-5597-6447 mschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-6447","contributorId":3720,"corporation":false,"usgs":true,"family":"Schulz","given":"Marjorie S.","email":"mschulz@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":811574,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}}
,{"id":70219458,"text":"70219458 - 2021 - Characterization of groundwater recharge and flow in California's San Joaquin Valley from InSAR-observed surface deformation","interactions":[],"lastModifiedDate":"2021-04-08T12:47:24.059845","indexId":"70219458","displayToPublicDate":"2021-03-04T07:44:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of groundwater recharge and flow in California's San Joaquin Valley from InSAR-observed surface deformation","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Surface deformation in California's Central Valley (CV) has long been linked to changes in groundwater storage. Recent advances in remote sensing have enabled the mapping of CV deformation and associated changes in groundwater resources at increasingly higher spatiotemporal resolution. Here, we use interferometric synthetic aperture radar (InSAR) from the Sentinel‐1 missions, augmented by continuous Global Positioning System (cGPS) positioning, to characterize the surface deformation of the San Joaquin Valley (SJV, southern two‐thirds of the CV) for consecutive dry (2016) and wet (2017) water years. We separate trends and seasonal oscillations in deformation time series and interpret them in the context of surface and groundwater hydrology. We find that subsidence rates in 2016 (mean −42.0&nbsp;mm/yr; peak −345&nbsp;mm/yr) are twice that in 2017 (mean −20.4&nbsp;mm/yr; peak −177&nbsp;mm/yr), consistent with increased groundwater pumping in 2016 to offset the loss of surface‐water deliveries. Locations of greatest subsidence migrated outwards from the valley axis in the wetter 2017 water year, possibly reflecting a surplus of surface‐water supplies in the lowest portions of the SJV. Patterns in the amplitude of seasonal deformation and the timing of peak seasonal uplift reveal entry points and potential pathways for groundwater recharge into the SJV and subsequent groundwater flow within the aquifer. This study provides novel insight into the SJV aquifer system that can be used to constrain groundwater flow and subsidence models, which has relevance to groundwater management in the context of California's 2014 Sustainable Groundwater Management Act (SGMA).</p></div></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028451","usgsCitation":"Neely, W., Borsa, A., Burney, J., Levy, M., Silverii, F., and Sneed, M., 2021, Characterization of groundwater recharge and flow in California's San Joaquin Valley from InSAR-observed surface deformation: Water Resources Research, v. 57, no. 4, e2020WR028451, 20 p., https://doi.org/10.1029/2020WR028451.","productDescription":"e2020WR028451, 20 p.","ipdsId":"IP-121027","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":453210,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr028451","text":"Publisher Index Page"},{"id":384924,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.28906250000001,\n              37.38761749978395\n            ],\n            [\n              -120.2783203125,\n              35.460669951495305\n            ],\n            [\n              -118.5205078125,\n              34.488447837809304\n            ],\n            [\n              -117.94921874999999,\n              35.44277092585766\n            ],\n            [\n              -119.5751953125,\n              37.50972584293751\n            ],\n            [\n              -121.28906250000001,\n              37.38761749978395\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"57","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Neely, W.R.","contributorId":256995,"corporation":false,"usgs":false,"family":"Neely","given":"W.R.","email":"","affiliations":[{"id":51948,"text":"Scripps Institute of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":813655,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Borsa, A.A.","contributorId":256996,"corporation":false,"usgs":false,"family":"Borsa","given":"A.A.","email":"","affiliations":[{"id":51948,"text":"Scripps Institute of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":813656,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burney, J.A.","contributorId":256997,"corporation":false,"usgs":false,"family":"Burney","given":"J.A.","email":"","affiliations":[{"id":51949,"text":"School of Global Policy and Strategy, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":813657,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Levy, M.C.","contributorId":256998,"corporation":false,"usgs":false,"family":"Levy","given":"M.C.","email":"","affiliations":[{"id":51949,"text":"School of Global Policy and Strategy, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":813658,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silverii, F.","contributorId":256999,"corporation":false,"usgs":false,"family":"Silverii","given":"F.","affiliations":[{"id":51952,"text":"Scripps Institute of Oceanography, University of California, San Diego; German Research Centre for Geoscience, Potsdam Germany","active":true,"usgs":false}],"preferred":false,"id":813659,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sneed, Michelle 0000-0002-8180-382X micsneed@usgs.gov","orcid":"https://orcid.org/0000-0002-8180-382X","contributorId":155,"corporation":false,"usgs":true,"family":"Sneed","given":"Michelle","email":"micsneed@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813660,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70218747,"text":"70218747 - 2021 - Greenhouse gas emissions from an arid-zone reservoir and their environmental policy significance: Results from existing global models and an exploratory dataset","interactions":[],"lastModifiedDate":"2021-03-10T13:48:59.529423","indexId":"70218747","displayToPublicDate":"2021-03-04T07:22:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1563,"text":"Environmental Science and Policy","active":true,"publicationSubtype":{"id":10}},"title":"Greenhouse gas emissions from an arid-zone reservoir and their environmental policy significance: Results from existing global models and an exploratory dataset","docAbstract":"<div id=\"abs0015\" class=\"abstract author\"><div id=\"abst0015\"><p id=\"spar0045\">Reservoirs in arid regions often provide critical water storage but little is known about their greenhouse gas (GHG) footprint. While there is growing appreciation of the role reservoirs play as GHG sources, there is a lack of understanding of GHG emission dynamics from reservoirs in arid regions and implications for environmental policy. Here we present initial GHG emission measurements from Lake Powell, a large water storage reservoir in the desert southwest United States. We report CO<sub>2</sub>-eq emissions from the shallow (&lt; 15 m) littoral regions of the reservoir that are higher than the global average areal emissions from reservoirs (9.4 vs. 5.8 g CO<sub>2</sub>-eq m<sup>−2</sup><span>&nbsp;</span>d<sup>−1</sup>) whereas fluxes from the main reservoir were two orders of magnitude lower (0.09 g CO<sub>2</sub>-eq m<sup>−2</sup><span>&nbsp;</span>d<sup>−1</sup>). We then compared our measurements to modeled CO<sub>2</sub><span>&nbsp;</span>+ CH<sub>4</sub><span>&nbsp;</span>emissions from the reservoir using four global scale models. Factoring these emissions into hydropower production at Lake Powell yielded low GHG emissions per MWh<sup>−1</sup><span>&nbsp;</span>as compared to fossil-fuel based energy sources. With the exception of one model, the estimated hydropower emissions for Lake Powell ranged from 10−32 kg CO<sub>2</sub>-eq MWh<sup>−1</sup>, compared to ∼400−1000 kg CO<sub>2</sub>-eq MWh<sup>−1</sup><span>&nbsp;</span>for natural gas, oil, and coal. We also estimate that reduced littoral habitat under low water levels leads to ∼50% reduction in the CO<sub>2</sub><span>&nbsp;</span>equivalent emissions per MWh. The sensitivity of GHG emissions to reservoir water levels suggests that the interaction will be an important policy consideration in the design and operation of arid region systems.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsci.2021.02.006","usgsCitation":"Waldo, S., Deemer, B., Bair, L.S., and Beaulieu, J.J., 2021, Greenhouse gas emissions from an arid-zone reservoir and their environmental policy significance: Results from existing global models and an exploratory dataset: Environmental Science and Policy, v. 120, p. 53-62, https://doi.org/10.1016/j.envsci.2021.02.006.","productDescription":"10 p.","startPage":"53","endPage":"62","ipdsId":"IP-120013","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453216,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11252906","text":"External Repository"},{"id":436474,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PRW8JX","text":"USGS data release","linkHelpText":"Modeled and measured greenhouse gas emissions from Lake Powell and bathymetric analysis of tributary littoral habitat at different water levels"},{"id":384272,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"Utah","otherGeospatial":"Lake Powell","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.76391601562499,\n              36.98500309285596\n            ],\n            [\n              -110.11596679687499,\n              36.98500309285596\n            ],\n            [\n              -110.11596679687499,\n              38.151837403006766\n            ],\n            [\n              -111.76391601562499,\n              38.151837403006766\n            ],\n            [\n              -111.76391601562499,\n              36.98500309285596\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"120","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Waldo, Sarah","contributorId":255013,"corporation":false,"usgs":false,"family":"Waldo","given":"Sarah","email":"","affiliations":[],"preferred":false,"id":811669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811585,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bair, Lucas S. 0000-0002-9911-3624 lbair@usgs.gov","orcid":"https://orcid.org/0000-0002-9911-3624","contributorId":5270,"corporation":false,"usgs":true,"family":"Bair","given":"Lucas","email":"lbair@usgs.gov","middleInitial":"S.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811586,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beaulieu, Jake J.","contributorId":191664,"corporation":false,"usgs":false,"family":"Beaulieu","given":"Jake","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":811670,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70218716,"text":"70218716 - 2021 - The tide turns: Episodic and localized cross-contamination of a California coastline with cyanotoxins","interactions":[],"lastModifiedDate":"2021-03-09T13:23:32.266991","indexId":"70218716","displayToPublicDate":"2021-03-04T07:18:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"The tide turns: Episodic and localized cross-contamination of a California coastline with cyanotoxins","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0002\" class=\"abstract author\"><div id=\"abss0002\"><p id=\"spara011\"><span>The contamination of coastal ecosystems from a variety of toxins of marine algal origin is a common and well-documented situation along the coasts of the United States and globally. The occurrence of toxins originating from cyanobacteria along marine coastlines is much less studied, and little information exists on whether toxins from marine and freshwater sources co-occur regularly. The current study focused on the discharge of cyanotoxins from a coastal lagoon (Santa Clara River Estuary) as a consequence of an extreme tide event (King Tides; December 3–5, 2017) resulting in a breach of the berm separating the lagoon from the ocean. Monthly monitoring in the lagoon throughout 2017 documented more than a dozen co-occurring cyanobacterial genera, as well as multiple algal and cyanobacterial toxins. Biotoxin monitoring before and following the King Tide event using Solid Phase Adsorption Toxin Tracking (SPATT) in the lagoon and along the coast revealed the co-occurrence of microcystins, anatoxin,&nbsp;domoic acid, and other toxins on multiple dates and locations. Domoic acid was ubiquitously present in SPATT deployed in the lagoon and along the coast.&nbsp;</span>Microcystins<span>&nbsp;were also commonly detected in both locations, although the beach berm retained the lagoonal water for much of the year.&nbsp;Mussels&nbsp;collected along the coast contained microcystins in approximately half the samples, particularly following the King Tide event. Anatoxin was observed in SPATT only in late December, following the breach of the berm. Our findings indicate both episodic and persistent occurrence of both cyanotoxins and marine toxins may commonly contaminate coastlines in proximity to cyanobacteria-laden creeks and lagoons.</span></p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2021.102003","usgsCitation":"Tatters, A.O., Smith, J., Kudela, R.M., Hayashi, K., Howard, M.D., Donovan, A., Loftin, K.A., and Caron, D.A., 2021, The tide turns: Episodic and localized cross-contamination of a California coastline with cyanotoxins: Harmful Algae, v. 103, 102003, 13 p., https://doi.org/10.1016/j.hal.2021.102003.","productDescription":"102003, 13 p.","ipdsId":"IP-121797","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":453218,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2021.102003","text":"Publisher Index Page"},{"id":436475,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TEYRNC","text":"USGS data release","linkHelpText":"Liquid Chromatography Triple Quadrupole Mass Spectrometry (LC/MS/MS) analysis of cyanotoxins and algal toxins in estuary samples collected from California, USA, in 2016-17"},{"id":384240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Santa Clara River Estuary","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.50927734374999,\n              34.03445260967645\n            ],\n            [\n              -118.86657714843749,\n              34.03445260967645\n            ],\n            [\n              -118.86657714843749,\n              34.58799745550482\n            ],\n            [\n              -119.50927734374999,\n              34.58799745550482\n            ],\n            [\n              -119.50927734374999,\n              34.03445260967645\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"103","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tatters, Avery O.","contributorId":247816,"corporation":false,"usgs":false,"family":"Tatters","given":"Avery","email":"","middleInitial":"O.","affiliations":[{"id":49660,"text":"California NanoSystems Institute, University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":811509,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Jayme 0000-0002-9669-4427","orcid":"https://orcid.org/0000-0002-9669-4427","contributorId":254947,"corporation":false,"usgs":false,"family":"Smith","given":"Jayme","email":"","affiliations":[{"id":12704,"text":"Southern California Coastal Water Research Project","active":true,"usgs":false}],"preferred":false,"id":811510,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kudela, Raphael M.","contributorId":205181,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":811511,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayashi, Kendra","contributorId":247815,"corporation":false,"usgs":false,"family":"Hayashi","given":"Kendra","email":"","affiliations":[{"id":49659,"text":"Department of Ocean Science, University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":811512,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howard, Meredith D. A. 0000-0002-1639-8143","orcid":"https://orcid.org/0000-0002-1639-8143","contributorId":247814,"corporation":false,"usgs":false,"family":"Howard","given":"Meredith","email":"","middleInitial":"D. A.","affiliations":[{"id":49658,"text":"Central Valley Regional Water Quality Control Board","active":true,"usgs":false}],"preferred":false,"id":811513,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Donovan, Ariel 0000-0002-8480-2793","orcid":"https://orcid.org/0000-0002-8480-2793","contributorId":222474,"corporation":false,"usgs":true,"family":"Donovan","given":"Ariel","email":"","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":811514,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221964,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":811515,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Caron, David A.","contributorId":247817,"corporation":false,"usgs":false,"family":"Caron","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":49661,"text":"Department of Biological Sciences, University of Southern California","active":true,"usgs":false}],"preferred":false,"id":811516,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
]}