{"pageNumber":"206","pageRowStart":"5125","pageSize":"25","recordCount":68807,"records":[{"id":70218744,"text":"70218744 - 2021 - Avoidance of cold-, cool-, and warm-water fishes to Zequanox® exposure","interactions":[],"lastModifiedDate":"2021-06-01T17:47:28.644424","indexId":"70218744","displayToPublicDate":"2021-03-01T08:25:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Avoidance of cold-, cool-, and warm-water fishes to Zequanox® exposure","docAbstract":"<p><span>Zequanox® is a biopesticide registered by the U.S. Environmental Protection Agency (USEPA) and the Canadian Pest Management Regulatory Agency for controlling dreissenid mussels with demonstrated selective toxicity. However, some research has indicated that Zequanox may impact the body condition and survival of some non-target species. We assessed avoidance behaviors of two species of cold-, cool-, and warm-water fishes to Zequanox at the maximum concentration allowed by the USEPA label (100 mg/L as active ingredient). Naïve, juvenile fish (n = 30 per species) were individually observed in a two-flume choice tank through which Zequanox-treated and untreated water simultaneously flowed in an unobstructed arena. Individual fish were observed during an untreated control period (20 min) and two Zequanox-exposure periods (20 min each). Treatment was alternated between arena sides to account for potential side bias in the test subjects. Positional data were collected and tabulated in real time with EthoVision® XT software. Zequanox concentrations and water quality properties (pH, dissolved oxygen, temperature, and specific conductance) were monitored during each trial. Analysis of treatment response was performed using a contrast within linear mixed-effects models. Our results indicate that Brook Trout, Lake Trout, and Bluegill avoided Zequanox-treated water, Yellow Perch were indifferent to Zequanox-treated water, and Lake Sturgeon and Fathead Minnow were attracted to Zequanox-treated water. These results combined with existing species sensitivity literature may help inform resource managers of potential treatment-related risks.</span></p>","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre - REABIC","doi":"10.3391/mbi.2021.12.1.07","usgsCitation":"Barbour, M., Luoma, J.A., Severson, T.J., Wise, J.K., and Bennie, B., 2021, Avoidance of cold-, cool-, and warm-water fishes to Zequanox® exposure: Management of Biological Invasions, v. 12, no. 1, p. 96-107, https://doi.org/10.3391/mbi.2021.12.1.07.","productDescription":"12 p.","startPage":"96","endPage":"107","ipdsId":"IP-111883","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":453273,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.3391/mbi.2021.12.1.07","text":"Publisher Index Page"},{"id":436481,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BWGW8F","text":"USGS data release","linkHelpText":"Avoidance behavior of cold-, cool-, and warmwater fish exposed to Zequanox in a two-choice preference chamber, data release"},{"id":385080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barbour, Matthew 0000-0002-0095-9188 mbarbour@usgs.gov","orcid":"https://orcid.org/0000-0002-0095-9188","contributorId":195580,"corporation":false,"usgs":true,"family":"Barbour","given":"Matthew","email":"mbarbour@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811580,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luoma, James A. 0000-0003-3556-0190 jluoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":4449,"corporation":false,"usgs":true,"family":"Luoma","given":"James","email":"jluoma@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811581,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Severson, Todd J. 0000-0001-5282-3779 tseverson@usgs.gov","orcid":"https://orcid.org/0000-0001-5282-3779","contributorId":4749,"corporation":false,"usgs":true,"family":"Severson","given":"Todd","email":"tseverson@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811582,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wise, Jeremy K. 0000-0003-0184-6959 jwise@usgs.gov","orcid":"https://orcid.org/0000-0003-0184-6959","contributorId":5009,"corporation":false,"usgs":true,"family":"Wise","given":"Jeremy","email":"jwise@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811583,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bennie, Barbara","contributorId":257430,"corporation":false,"usgs":false,"family":"Bennie","given":"Barbara","email":"","affiliations":[],"preferred":false,"id":814234,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70218685,"text":"70218685 - 2021 - Comparing tree-ring based reconstructions of snowpack variability at different scales for the Navajo Nation","interactions":[],"lastModifiedDate":"2021-03-05T13:57:13.433738","indexId":"70218685","displayToPublicDate":"2021-03-01T07:53:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5567,"text":"Climate Services","active":true,"publicationSubtype":{"id":10}},"title":"Comparing tree-ring based reconstructions of snowpack variability at different scales for the Navajo Nation","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab005\" class=\"abstract author\" lang=\"en\"><div id=\"as005\"><p id=\"sp005\">Snowpack<span>&nbsp;</span>in the western U.S. is on the decline, largely attributed to increasing temperatures in the region. This is a critical issue for many Native American communities who disproportionately rely on local snow-fed water supplies. In light of a combined ongoing drought and limited climate information for the Navajo Nation, Navajo water managers face decision-making challenges complicated by past and future climate uncertainty. Developed in partnership with the Navajo Nation Water Management Branch, this study documents two snowpack reconstruction options to address Navajo concerns about the amount and variability of snowpack in the Chuska Mountains. We used two separate snowpack datasets with tree rings collected in northern Arizona to develop and evaluate reconstructions of Chuska snowpack and their potential relevance and usefulness to Navajo water managers’ decision-making. We found that both reconstructions skillfully estimated snowpack, though there were differences that may have meaningful implications for water managers. Major snow droughts occurred roughly once per century over the last 300&nbsp;years, with droughts in 1728–1744, 1818–1834, 1950–1977, and 1999–2006. Extremely dry individual years in each reconstruction punctuate multi-year drought periods in a way that has not been recognized from instrumental data alone and that can have a large influence on the overall intensity of a given drought. The reconstruction that is most representative of Chuska snowpack has less explanatory power than the regionally representative reconstruction, but the Chuska reconstruction effectively captures snowpack extremes and snow drought timing unique to the Chuska Mountains, and may hold greater relevance to Navajo water management.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.cliser.2021.100213","usgsCitation":"Brice, R.L., Guiterman, C.H., Woodhouse, C.A., McClellan, C., and Sheppard, P., 2021, Comparing tree-ring based reconstructions of snowpack variability at different scales for the Navajo Nation: Climate Services, v. 22, 100213, 12 p., https://doi.org/10.1016/j.cliser.2021.100213.","productDescription":"100213, 12 p.","ipdsId":"IP-121757","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":453276,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cliser.2021.100213","text":"Publisher Index Page"},{"id":384066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, New Mexico","otherGeospatial":"Navajo Nation","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.697998046875,\n              35.15584570226544\n            ],\n            [\n              -108.017578125,\n              35.15584570226544\n            ],\n            [\n              -108.017578125,\n              37.03763967977139\n            ],\n            [\n              -111.697998046875,\n              37.03763967977139\n            ],\n            [\n              -111.697998046875,\n              35.15584570226544\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"22","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brice, Rebecca Lynn 0000-0003-0023-5988","orcid":"https://orcid.org/0000-0003-0023-5988","contributorId":247868,"corporation":false,"usgs":true,"family":"Brice","given":"Rebecca","email":"","middleInitial":"Lynn","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":811350,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guiterman, Christopher H.","contributorId":190553,"corporation":false,"usgs":false,"family":"Guiterman","given":"Christopher","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":811351,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodhouse, Connie A.","contributorId":187601,"corporation":false,"usgs":false,"family":"Woodhouse","given":"Connie","email":"","middleInitial":"A.","affiliations":[{"id":32413,"text":"University of Arizona, Tucson, AZ, USA, 85721","active":true,"usgs":false}],"preferred":false,"id":811352,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McClellan, Carlee","contributorId":254322,"corporation":false,"usgs":false,"family":"McClellan","given":"Carlee","email":"","affiliations":[{"id":51082,"text":"Navajo Nation Department of Water Resources, Water Management Branch","active":true,"usgs":false}],"preferred":false,"id":811353,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sheppard, Paul","contributorId":254323,"corporation":false,"usgs":false,"family":"Sheppard","given":"Paul","email":"","affiliations":[{"id":51083,"text":"Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":811354,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70218831,"text":"70218831 - 2021 - The role of the U.S. Geological Survey in monitoring North Dakota’s environmental conditions","interactions":[],"lastModifiedDate":"2021-03-16T12:11:22.92428","indexId":"70218831","displayToPublicDate":"2021-03-01T07:09:29","publicationYear":"2021","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"title":"The role of the U.S. Geological Survey in monitoring North Dakota’s environmental conditions","docAbstract":"<p>No abstract available.&nbsp;</p>","largerWorkType":{"id":25,"text":"Newsletter"},"largerWorkTitle":"North Dakota Climate Bulletin","language":"English","publisher":"North Dakota State Climate Office","usgsCitation":"Ryberg, K.R., 2021, The role of the U.S. Geological Survey in monitoring North Dakota’s environmental conditions, v. 15, no. 1, p. 11-14.","productDescription":"4 p.","startPage":"11","endPage":"14","ipdsId":"IP-127214","costCenters":[{"id":34685,"text":"Dakota Water Science 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,{"id":70224331,"text":"70224331 - 2021 - The influence of land cover and storm magnitude on hydrologic flowpath activation and runoff generation in steep tropical catchments of central Panama","interactions":[],"lastModifiedDate":"2021-09-23T12:45:37.785547","indexId":"70224331","displayToPublicDate":"2021-02-27T07:43:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"The influence of land cover and storm magnitude on hydrologic flowpath activation and runoff generation in steep tropical catchments of central Panama","docAbstract":"<div id=\"ab015\" class=\"abstract author\" lang=\"en\"><div id=\"as015\"><p id=\"sp0015\">Despite abundant research documenting that land use/land cover (LULC) have substantial impacts on the hydrology of humid tropical systems, field-based evidence for the physical mechanisms behind these impacts are still lacking. In particular, our understanding of the hydrologic flowpaths that generate runoff in these systems, and how they vary with respect to LULC is insufficient to inform both physically-based hydrologic modeling and land-use decision-making. In this study, we use end-member mixing analysis (EMMA) of stream chemistry, and hydrometric characterizations of hillslope soil moisture to identify hydrologic flowpaths in humid tropical steep-land catchments of varying LULC: mature tropical forest, young secondary tropical forest, cattle pasture. EMMA was applied to data from 14 storm events (six at the mature forest, five at the young secondary forest, and three at the cattle pasture) that were intensively sampled during the 2017 wet season representing a wide range of rainfall magnitudes and intensities. Additionally, volumetric-soil-moisture responses at multiple depths were characterized during and after 74 storm events occurring from 2015 to 2017. EMMA results indicated that lateral preferential flow within the top 30&nbsp;cm of the soil profile was a dominant source of runoff generation at the two forested catchments, with the contribution of this flow path increasing with rainfall magnitude and intensity. This was corroborated by volumetric-soil-moisture data, that showed that a perched zone of saturation developed at 30&nbsp;cm at the time of peak storm runoff during the largest events and lasted for the remaining duration of the event. EMMA indicated that runoff was a combination of infiltration-excess overland flow and lateral subsurface flow in the actively grazed pastoral catchment. There, overland flow contributed 62 % of runoff during the highest runoff rate sampled (35.3&nbsp;mm/hr) and this contribution increased substantially with storm magnitude. This flowpath identification was also supported by volumetric-soil-moisture data at the pasture, with peak saturation at all depths during the largest storm events occurring up to 30&nbsp;min after peak runoff. These results provide a mechanistic explanation for previously observed hydrologic differences among tropical LULCs. Additionally, the wide range of hydrologic conditions during these storm events provide a basis for understanding how future changes to this, and similar humid tropical regions will impact hydrological processes and water availability.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2021.126138","usgsCitation":"Birch, A.L., Stallard, R., Bush, S.A., and Barnard, H.R., 2021, The influence of land cover and storm magnitude on hydrologic flowpath activation and runoff generation in steep tropical catchments of central Panama: Journal of Hydrology, v. 596, 126138, 15 p., https://doi.org/10.1016/j.jhydrol.2021.126138.","productDescription":"126138, 15 p.","ipdsId":"IP-121672","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453291,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2021.126138","text":"Publisher Index Page"},{"id":389644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Panama","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.22216796875,\n              8.743936220084125\n            ],\n            [\n              -79.29931640625,\n              8.743936220084125\n            ],\n            [\n              -79.29931640625,\n              9.432805545760889\n            ],\n            [\n              -80.22216796875,\n              9.432805545760889\n            ],\n            [\n              -80.22216796875,\n              8.743936220084125\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"596","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Birch, Andrew L.","contributorId":257522,"corporation":false,"usgs":false,"family":"Birch","given":"Andrew","email":"","middleInitial":"L.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":823777,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stallard, Robert 0000-0001-8209-7608","orcid":"https://orcid.org/0000-0001-8209-7608","contributorId":215272,"corporation":false,"usgs":true,"family":"Stallard","given":"Robert","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":823778,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bush, Sidney A. 0000-0002-8359-7927","orcid":"https://orcid.org/0000-0002-8359-7927","contributorId":265930,"corporation":false,"usgs":false,"family":"Bush","given":"Sidney","email":"","middleInitial":"A.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":823779,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnard, Holly R.","contributorId":257523,"corporation":false,"usgs":false,"family":"Barnard","given":"Holly","email":"","middleInitial":"R.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":823780,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70218470,"text":"70218470 - 2021 - Composition and distribution of fish environmental DNA in an Adirondack watershed","interactions":[],"lastModifiedDate":"2021-03-01T16:30:56.694236","indexId":"70218470","displayToPublicDate":"2021-02-26T10:24:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Composition and distribution of fish environmental DNA in an Adirondack watershed","docAbstract":"<h2 class=\"heading\">Background</h2><p>Environmental DNA (eDNA) surveys are appealing options for monitoring aquatic biodiversity. While factors affecting eDNA persistence, capture and amplification have been heavily studied, watershed-scale surveys of fish communities and our confidence in such need further exploration.</p><h2 class=\"heading\">Methods</h2><p>We characterized fish eDNA compositions using rapid, low-volume filtering with replicate and control samples scaled for a single Illumina MiSeq flow cell, using the mitochondrial 12S ribosomal RNA locus for taxonomic profiling. Our goals were to determine: (1) spatiotemporal variation in eDNA abundance, (2) the filtrate needed to achieve strong sequencing libraries, (3) the taxonomic resolution of 12S ribosomal sequences in the study environment, (4) the portion of the expected fish community detectable by 12S sequencing, (5) biases in species recovery, (6) correlations between eDNA compositions and catch per unit effort (CPUE) and (7) the extent that eDNA profiles reflect major watershed features. Our bioinformatic approach included (1) estimation of sequencing error from unambiguous mappings and simulation of taxonomic assignment error under various mapping criteria; (2) binning of species based on inferred assignment error rather than by taxonomic rank; and (3) visualization of mismatch distributions to facilitate discovery of distinct haplotypes attributed to the same reference. Our approach was implemented within the St. Regis River, NY, USA, which supports tribal and recreational fisheries and has been a target of restoration activities. We used a large record of St. Regis-specific observations to validate our assignments.</p><h2 class=\"heading\">Results</h2><p>We found that 300 mL drawn through 25-mm cellulose nitrate filters yielded greater than 5 ng/µL DNA at most sites in summer, which was an approximate threshold for generating strong sequencing libraries in our hands. Using inferred sequence error rates, we binned 12S references for 110 species on a state checklist into 85 single-species bins and seven multispecies bins. Of 48 bins observed by capture survey in the St. Regis, we detected eDNA consistent with 40, with an additional four detections flagged as potential contaminants. Sixteen unobserved species detected by eDNA ranged from plausible to implausible based on distributional data, whereas six observed species had no 12S reference sequence. Summed log-ratio compositions of eDNA-detected taxa correlated with log(CPUE) (Pearson’s<span>&nbsp;</span><i>R</i><span>&nbsp;</span>= 0.655,<span>&nbsp;</span><i>P</i><span>&nbsp;</span>&lt; 0.001). Shifts in eDNA composition of several taxa and a genotypic shift in channel catfish (<i>Ictalurus punctatus</i>) coincided with the Hogansburg Dam, NY, USA. In summary, a simple filtering apparatus operated by field crews without prior expertise gave useful summaries of eDNA composition with minimal evidence of field contamination. 12S sequencing achieved useful taxonomic resolution despite the short marker length, and data exploration with standard bioinformatic tools clarified taxonomic uncertainty and sources of error.</p>","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.10539","usgsCitation":"Cornman, R.S., McKenna, J.E., and Fike, J., 2021, Composition and distribution of fish environmental DNA in an Adirondack watershed: PeerJ, v. 9, e10539, 34 p., https://doi.org/10.7717/peerj.10539.","productDescription":"e10539, 34 p.","ipdsId":"IP-121794","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":453296,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.10539","text":"Publisher Index Page"},{"id":436485,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EEOAZK","text":"USGS data release","linkHelpText":"Taxonomic composition of environmental DNA acquired by filtration from the St. Regis River, New York"},{"id":383691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"St. Regis River watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.135498046875,\n              44.23732831822538\n            ],\n            [\n              -74.4873046875,\n              44.23732831822538\n            ],\n            [\n              -74.4873046875,\n              44.98034238084973\n            ],\n            [\n              -75.135498046875,\n              44.98034238084973\n            ],\n            [\n              -75.135498046875,\n              44.23732831822538\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKenna, James E. Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":195894,"corporation":false,"usgs":true,"family":"McKenna","given":"James","suffix":"Jr.","email":"jemckenna@usgs.gov","middleInitial":"E.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":811097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811098,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70220257,"text":"70220257 - 2021 - Status of Cisco in Lake Superior in 2017","interactions":[],"lastModifiedDate":"2021-04-29T13:25:58.497396","indexId":"70220257","displayToPublicDate":"2021-02-26T08:22:45","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Status of Cisco in Lake Superior in 2017","docAbstract":"This report describes the status of fish species and their habitat in Lake Superior during the reporting period of 2012-2016 in response to achievement of fish community objectives (FCOs) established by fishery managers for the lake. The overarching goal for the FCOs continued to be met as the fish community remained diverse, self-regulating, dominated by indigenous species, and able to support sustainable fisheries, although further rehabilitation of certain fish is required. The Lake Superior Lakewide Action and Management Plan classified all habitat indicators for Lake Superior as good. Primary production and zooplankton abundance were stable during the reporting period and unchanged from the two previous reporting periods, indicating the lower food web is healthy. Abundance of the invertebrates Mysis diluviana and Diporeia spp. were stable during the reporting period, and Diporeia spp. density exceeded target levels defined in the Great Lakes Water Quality Agreement. Lake Whitefish (Coregonus clupeaformis) abundance was lower than during the previous reporting period but was within the FCO target. Abundance of lean, siscowet, and humper forms of Lake Trout (Salvelinus namaycush) remained stable at levels seen in previous reporting periods. The FCO for non-indigenous salmonids was met as Chinook Salmon (Oncorhynchus tshawytscha), Coho Salmon (O. kisutch), and steelhead/Rainbow Trout (O. mykiss) were being sustained by natural reproduction, and their abundance remained stable or increased from previous reporting periods. The FCO for Walleye (Sander vitreus) was not met, although populations showed signs of improvement since the previous reporting period. The fish community in littoral areas and embayments continued to be diverse and composed mostly of indigenous species. No new invasive species were found in Lake Superior during the reporting period. Degraded embayment and tributary habitats continued to prevent achievement of the FCOs for Brook Trout (S. fontinalis) and Lake Sturgeon (Acipenser fulvescens). Barriers created for hydroelectric generation either blocked Lake Sturgeon from historically important spawning grounds or reduced stream flows necessary for its reproduction. In tributaries without man-made barriers, Lake Sturgeon was relatively abundant and appeared healthy. Attainment of the FCOs for Brook Trout and Lake Sturgeon will be challenging and can only be attained through development of large-scale management actions like those implemented for Lake Trout rehabilitation and Sea Lamprey (Petromyzon marinus) control. Sea Lamprey-control expenditures doubled in 2016 from past levels and should result in suppression of the population closer to its FCO after 2016. The prey-fish FCO appears to have been met, but biomass of nearly all prey-fish species declined from that reported for the previous reporting period and has been on a downward trajectory since 2000. Predation by Lake Trout is probably to blame for the declining biomass of prey fish. Poor recruitment by Cisco (C. artedi) over the last 15 years is exacerbating the declines in prey-fish biomass because Lake Trout must compensate for the loss of Cisco by consuming other, less-abundant prey fish.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"The state of Lake Superior ecosystem in 2017","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Goldsworthy, C., and Yule, D.L., 2021, Status of Cisco in Lake Superior in 2017, 4 p.","productDescription":"4 p.","startPage":"29","endPage":"32","ipdsId":"IP-122168","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":385390,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385372,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/pubs/SpecialPubs/Sp21_02.pdf"}],"country":"Canada, United States","otherGeospatial":"Lake 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]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goldsworthy, Cory","contributorId":257723,"corporation":false,"usgs":false,"family":"Goldsworthy","given":"Cory","email":"","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":814914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yule, Daniel L. 0000-0002-0117-5115","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":248693,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814915,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70218480,"text":"70218480 - 2021 - The contribution of water radiolysis to marine sedimentary life","interactions":[],"lastModifiedDate":"2021-03-01T14:24:40.886787","indexId":"70218480","displayToPublicDate":"2021-02-26T08:22:43","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":"The contribution of water radiolysis to marine sedimentary life","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Water radiolysis continuously produces H<sub>2</sub><span>&nbsp;</span>and oxidized chemicals in wet sediment and rock. Radiolytic H<sub>2</sub><span>&nbsp;</span>has been identified as the primary electron donor (food) for microorganisms in continental aquifers kilometers below Earth’s surface. Radiolytic products may also be significant for sustaining life in subseafloor sediment and subsurface environments of other planets. However, the extent to which most subsurface ecosystems rely on radiolytic products has been poorly constrained, due to incomplete understanding of radiolytic chemical yields in natural environments. Here we show that all common marine sediment types catalyse radiolytic H<sub>2</sub><span>&nbsp;</span>production, amplifying yields by up to 27X relative to pure water. In electron equivalents, the global rate of radiolytic H<sub>2</sub><span>&nbsp;</span>production in marine sediment appears to be 1-2% of the global organic flux to the seafloor. However, most organic matter is consumed at or near the seafloor, whereas radiolytic H<sub>2</sub><span>&nbsp;</span>is produced at all sediment depths. Comparison of radiolytic H<sub>2</sub><span>&nbsp;</span>consumption rates to organic oxidation rates suggests that water radiolysis is the principal source of biologically accessible energy for microbial communities in marine sediment older than a few million years. Where water permeates similarly catalytic material on other worlds, life may also be sustained by water radiolysis.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1038/s41467-021-21218-z","usgsCitation":"Sauvage, J.F., Flinders, A.F., Spivack, A.J., Pockalny, R., Dunlea, A.G., Anderson, C.H., Smith, D., Murray, R.W., and D’Hondt, S., 2021, The contribution of water radiolysis to marine sedimentary life: Nature Communications, v. 12, 1297, 9 p., https://doi.org/10.1038/s41467-021-21218-z.","productDescription":"1297, 9 p.","ipdsId":"IP-107348","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":453299,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-021-21218-z","text":"Publisher Index Page"},{"id":383679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2021-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Sauvage, Justine F. 0000-0003-4079-3167","orcid":"https://orcid.org/0000-0003-4079-3167","contributorId":252939,"corporation":false,"usgs":false,"family":"Sauvage","given":"Justine","email":"","middleInitial":"F.","affiliations":[{"id":50477,"text":"Graduate School of Oceanography, University of Rhode Island, Narragansett, RI 02882, USA","active":true,"usgs":false}],"preferred":false,"id":811173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flinders, Ashton F. 0000-0003-2483-4635 aflinders@usgs.gov","orcid":"https://orcid.org/0000-0003-2483-4635","contributorId":196960,"corporation":false,"usgs":true,"family":"Flinders","given":"Ashton","email":"aflinders@usgs.gov","middleInitial":"F.","affiliations":[{"id":153,"text":"California Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":811174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spivack, Arthur J.","contributorId":252940,"corporation":false,"usgs":false,"family":"Spivack","given":"Arthur","email":"","middleInitial":"J.","affiliations":[{"id":50477,"text":"Graduate School of Oceanography, University of Rhode Island, Narragansett, RI 02882, USA","active":true,"usgs":false}],"preferred":false,"id":811175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pockalny, Robert","contributorId":252941,"corporation":false,"usgs":false,"family":"Pockalny","given":"Robert","email":"","affiliations":[{"id":50477,"text":"Graduate School of Oceanography, University of Rhode Island, Narragansett, RI 02882, USA","active":true,"usgs":false}],"preferred":false,"id":811176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunlea, Ann G.","contributorId":252942,"corporation":false,"usgs":false,"family":"Dunlea","given":"Ann","email":"","middleInitial":"G.","affiliations":[{"id":50478,"text":"Department of Earth and Environment, Boston University, Boston, MA 02215, USA","active":true,"usgs":false}],"preferred":false,"id":811177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Chloe H.","contributorId":252943,"corporation":false,"usgs":false,"family":"Anderson","given":"Chloe","email":"","middleInitial":"H.","affiliations":[{"id":50478,"text":"Department of Earth and Environment, Boston University, Boston, MA 02215, USA","active":true,"usgs":false}],"preferred":false,"id":811178,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, David 0000-0001-6074-9257","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":1989,"corporation":false,"usgs":false,"family":"Smith","given":"David","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":811179,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Murray, Richard W.","contributorId":252944,"corporation":false,"usgs":false,"family":"Murray","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":50478,"text":"Department of Earth and Environment, Boston University, Boston, MA 02215, USA","active":true,"usgs":false}],"preferred":false,"id":811180,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"D’Hondt, Steven","contributorId":252945,"corporation":false,"usgs":false,"family":"D’Hondt","given":"Steven","affiliations":[{"id":37391,"text":"University of Rhode Island, Graduate School of Oceanography","active":true,"usgs":false}],"preferred":false,"id":811181,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70220263,"text":"70220263 - 2021 - State of Lake Superior ecosystem in 2017","interactions":[],"lastModifiedDate":"2021-04-29T13:25:14.781881","indexId":"70220263","displayToPublicDate":"2021-02-26T08:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"State of Lake Superior ecosystem in 2017","docAbstract":"This report describes the status of fish species and their habitat in Lake Superior during the reporting period of 2012-2016 in response to achievement of fish community objectives (FCOs) established by fishery managers for the lake. The overarching goal for the FCOs continued to be met as the fish community remained diverse, self-regulating, dominated by indigenous species, and able to support sustainable fisheries, although further rehabilitation of certain fish is required. The Lake Superior Lakewide Action and Management Plan classified all habitat indicators for Lake Superior as good. Primary production and zooplankton abundance were stable during the reporting period and unchanged from the two previous reporting periods, indicating the lower food web is healthy. Abundance of the invertebrates Mysis diluviana and Diporeia spp. were stable during the reporting period, and Diporeia spp. density exceeded target levels defined in the Great Lakes Water Quality Agreement. Lake Whitefish (Coregonus clupeaformis) abundance was lower than during the previous reporting period but was within the FCO target. Abundance of lean, siscowet, and humper forms of Lake Trout (Salvelinus namaycush) remained stable at levels seen in previous reporting periods. The FCO for non-indigenous salmonids was met as Chinook Salmon (Oncorhynchus tshawytscha), Coho Salmon (O. kisutch), and steelhead/Rainbow Trout (O. mykiss) were being sustained by natural reproduction, and their abundance remained stable or increased from previous reporting periods. The FCO for Walleye (Sander vitreus) was not met, although populations showed signs of improvement since the previous reporting period. The fish community in littoral areas and embayments continued to be diverse and composed mostly of indigenous species. No new invasive species were found in Lake Superior during the reporting period. Degraded embayment and tributary habitats continued to prevent achievement of the FCOs for Brook Trout (S. fontinalis) and Lake Sturgeon (Acipenser fulvescens). Barriers created for hydroelectric generation either blocked Lake Sturgeon from historically important spawning grounds or reduced stream flows necessary for its reproduction. In tributaries without man-made barriers, Lake Sturgeon was relatively abundant and appeared healthy. Attainment of the FCOs for Brook Trout and Lake Sturgeon will be challenging and can only be attained through development of large-scale management actions like those implemented for Lake Trout rehabilitation and Sea Lamprey (Petromyzon marinus) control. Sea Lamprey-control expenditures doubled in 2016 from past levels and should result in suppression of the population closer to its FCO after 2016. The prey-fish FCO appears to have been met, but biomass of nearly all prey-fish species declined from that reported for the previous reporting period and has been on a downward trajectory since 2000. Predation by Lake Trout is probably to blame for the declining biomass of prey fish. Poor recruitment by Cisco (C. artedi) over the last 15 years is exacerbating the declines in prey-fish biomass because Lake Trout must compensate for the loss of Cisco by consuming other, less-abundant prey fish.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"The State of Lake Superior Ecosystem in 2017","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Matthias, B.G., Hrabik, T.R., Hoffman, J.C., Gorman, O., Seider, M.J., Sierszen, M.E., Vinson, M., Yule, D.L., and Yurista, P.M., 2021, State of Lake Superior ecosystem in 2017, 6 p.","productDescription":"6 p.","startPage":"55","endPage":"60","ipdsId":"IP-122172","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":385389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385376,"type":{"id":15,"text":"Index 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,{"id":70218678,"text":"70218678 - 2021 - Nitrogen and phosphorus sources and delivery from the Mississippi/Atchafalaya River Basin: An update using 2012 SPARROW models","interactions":[],"lastModifiedDate":"2021-06-30T17:50:46.651868","indexId":"70218678","displayToPublicDate":"2021-02-26T07:37:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Nitrogen and phosphorus sources and delivery from the Mississippi/Atchafalaya River Basin: An update using 2012 SPARROW models","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Nitrogen (N) and phosphorus (P) inputs throughout the Mississippi/Atchafalaya River Basin (MARB) have been linked to the Gulf of Mexico hypoxia and water‐quality problems throughout the MARB. To describe N and P loading throughout the MARB, SPAtially Referenced Regression On Watershed attributes (SPARROW) models were previously developed based on nutrient inputs and management similar to 1992 and 2002. In this study, refined SPARROW models were developed with higher resolution basin delineation, updated (2012) source inputs, improved calibration (load) targets, and additional statistical techniques than used in the previous SPARROW models. Based on the refined models, consistent with past models, N and P loads/yields were the highest from the central part of the MARB (Corn Belt) and along the Mississippi River. Agricultural activities remained the most important N and P source, but more so for N because its input, which could now be distinguished from atmospheric deposition, could be estimated. Natural loss of P from geologic material throughout the MARB was an important source, contributing about 23% of the total P from the MARB, and resulted in specific areas, such as Kentucky and Tennessee, being larger sources of P than previously estimated. This information can help managers decide where efforts will have the largest effects (highest ranked areas) on reducing nutrient loading to the Gulf hypoxia and what are the most important sources of N and P in these areas.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12905","usgsCitation":"Robertson, D.M., and Saad, D., 2021, Nitrogen and phosphorus sources and delivery from the Mississippi/Atchafalaya River Basin: An update using 2012 SPARROW models: Journal of the American Water Resources Association, v. 57, no. 3, p. 406-429, https://doi.org/10.1111/1752-1688.12905.","productDescription":"24 p.","startPage":"406","endPage":"429","ipdsId":"IP-118531","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":453302,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12905","text":"Publisher Index Page"},{"id":436486,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W8PYTO","text":"USGS data release","linkHelpText":"Long-term mean annual total nitrogen and total phosphorus loads estimated using Fluxmaster 5-parameter models and detrended to 2012, Midwest Region of the United States, 1999-2014"},{"id":384063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi/Atchafalaya River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.296875,\n              29.53522956294847\n            ],\n            [\n              -91.62597656249999,\n              30.637912028341123\n            ],\n            [\n              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0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":204668,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"","middleInitial":"M.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saad, David A. 0000-0001-6559-6181","orcid":"https://orcid.org/0000-0001-6559-6181","contributorId":217251,"corporation":false,"usgs":true,"family":"Saad","given":"David A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811338,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70218300,"text":"sir20205126 - 2021 - Volcanic Aquifers of Hawai‘i—Construction and calibration of numerical models for assessing groundwater availability on Kaua‘i, O‘ahu, and Maui","interactions":[],"lastModifiedDate":"2023-06-08T16:44:08.092879","indexId":"sir20205126","displayToPublicDate":"2021-02-24T14:18:53","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5126","displayTitle":"Volcanic Aquifers of Hawai‘i—Construction and Calibration of Numerical Models for Assessing Groundwater Availability on Kaua‘i, O‘ahu, and Maui","title":"Volcanic Aquifers of Hawai‘i—Construction and calibration of numerical models for assessing groundwater availability on Kaua‘i, O‘ahu, and Maui","docAbstract":"<p>Steady-state numerical groundwater-flow models were constructed for the islands of Kaua‘i, O‘ahu, and Maui to enable quantification of the hydrologic consequences of withdrawals and other stresses that can place limits on groundwater availability. The volcanic aquifers of Hawai‘i supply nearly all drinking water for the islands’ residents, freshwater for diverse industries, and natural discharge to springs, streams, and nearshore areas that support ecosystems, cultural practices, aesthetics, and recreation. Increases in groundwater withdrawal and changes in climate can cause water-table depression, saltwater rise, and reduction of natural groundwater discharge—all of which can limit fresh groundwater availability. The numerical models described in this report are designed to quantify these consequences. Separate models were created for each island using MODFLOW-2005 with the Seawater Intrusion package, which allows simulation of freshwater and saltwater in ocean-island aquifers. Calibration resulted in models that generally replicate observed water-level, stream base-flow, and spring-flow data, and simulate groundwater-flow directions and fresh groundwater thicknesses that are consistent with conceptual models. The calibrated models use hydraulic properties that are consistent with the ranges reported in previous studies. The models show that the relative distribution of fresh groundwater discharge to the ocean, streams, and springs and withdrawals for human use differ substantially among the three islands studied here. These differences indicate that consequences that limit the availability of fresh groundwater for human use are likely to differ among the three islands.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205126","usgsCitation":"Izuka, S.K., Rotzoll, K., and Nishikawa, T., 2021, Volcanic Aquifers of Hawai‘i—Construction and calibration of numerical models for assessing groundwater availability on Kaua‘i, O‘ahu, and Maui: U.S. Geological Survey Scientific Investigations Report 2020-5126, 63 p., https://doi.org/10.3133/sir20205126.","productDescription":"Report: viii, 63 p.; Data Release","numberOfPages":"63","ipdsId":"IP-071367","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":383611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5126/covrthb.jpg"},{"id":383612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5126/sir20205126.pdf","text":"Report","size":"53 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383613,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K4DK2P","linkHelpText":"MODFLOW-2005 and SWI2 models for assessing groundwater availability in volcanic aquifers on Kaua‘i, O‘ahu, and Maui, Hawai‘i"},{"id":416444,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20155164","text":"Scientific Investigations Report 2015-5164","description":"Izuka, S.K., Engott, J.A., Rotzoll, Kolja, Bassiouni, Maoya, Johnson, A.G., Miller, L.D., and Mair, Alan, 2018, Volcanic aquifers of Hawai‘i—Hydrogeology, water budgets, and conceptual models (ver. 2.0, March 2018): U.S. Geological Survey Scientific Investigations Report 2015-5164, 158 p., https://doi.org/10.3133/sir20155164.","linkHelpText":"- Volcanic Aquifers of Hawai‘i—Hydrogeology, Water budgets, and Conceptual Models"},{"id":416445,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1876","text":"Professional Paper 1876","description":"Izuka, S.K., and Rotzoll, K., 2023, Volcanic aquifers of Hawaiʻi—Contributions to assessing groundwater availability on Kauaʻi, Oʻahu, and Maui: U.S. Geological Survey Professional Paper 1876, 100 p., https://doi.org/10.3133/pp1876.","linkHelpText":"- Volcanic Aquifers of Hawai‘i—Contributions to Assessing Groundwater Availability on Kaua‘i, O‘ahu, and Maui"},{"id":417944,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233010","text":"Fact Sheet 2023-3010","description":"Izuka, S.K., and Rotzoll, K., 2023, Availability of groundwater from the volcanic aquifers of the Hawaiian Islands: U.S. Geological Survey Fact Sheet 2023-3010, 4 p., https://doi.org/10.3133/fs20233010.","linkHelpText":"- Availability of Groundwater from the Volcanic Aquifers of the Hawaiian Islands"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaua'i, Maui, O'ahu","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -156.73095703125,\n              20.57365332356332\n            ],\n            [\n              -155.90423583984375,\n              20.57365332356332\n            ],\n            [\n              -155.90423583984375,\n              21.04861794324536\n            ],\n            [\n              -156.73095703125,\n              21.04861794324536\n            ],\n            [\n              -156.73095703125,\n              20.57365332356332\n            ]\n          ]\n        ]\n     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Names</li><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Overview of the Regional Setting</li><li>Numerical Groundwater Models</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-02-24","noUsgsAuthors":false,"publicationDate":"2021-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Izuka, Scot K. 0000-0002-8758-9414 skizuka@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-9414","contributorId":2645,"corporation":false,"usgs":true,"family":"Izuka","given":"Scot","email":"skizuka@usgs.gov","middleInitial":"K.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rotzoll, Kolja 0000-0002-5910-888X kolja@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-888X","contributorId":3325,"corporation":false,"usgs":true,"family":"Rotzoll","given":"Kolja","email":"kolja@usgs.gov","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":false,"id":810916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nishikawa, Tracy 0000-0002-7348-3838 tnish@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-3838","contributorId":1515,"corporation":false,"usgs":true,"family":"Nishikawa","given":"Tracy","email":"tnish@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810917,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70225728,"text":"70225728 - 2021 - Long-term ecosystem and biogeochemical research in Loch Vale watershed, Rocky Mountain National Park, Colorado","interactions":[],"lastModifiedDate":"2021-11-05T11:44:16.72004","indexId":"70225728","displayToPublicDate":"2021-02-24T06:36:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Long-term ecosystem and biogeochemical research in Loch Vale watershed, Rocky Mountain National Park, Colorado","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Loch Vale watershed was instrumented in 1983 with initial support from the National Acid Precipitation Assessment Program to ask whether ecosystems of Rocky Mountain National Park (RMNP) were affected by acidic atmospheric deposition. Research and monitoring activities were expanded in 1991 by the U.S. Geological Survey Water, Energy, and Biogeochemical Budgets program to understand the processes, and their interactions, controlling water, energy, and biogeochemical fluxes. With help from many collaborators we have characterized trends and patterns in atmospheric deposition, climate, and hydrology, including glaciers and other ice features. Instead of acidic deposition, we documented high atmospheric inputs of reactive nitrogen (Nr), and have studied the ecological consequences in soils, surface water, and vegetation. Using paleolimnology, we documented the onset of human-caused change to lake primary producers ca. 1950 in response to increased Nr deposition and warming. Our results provided the basis for the Colorado Nitrogen Deposition Reduction Plan, a state policy that aims to reduce Nr emissions to protect resources in RMNP by 2032. Carbon cycle research revealed mountain wetlands now release more carbon than they store, and respiration and methane flux occurs even during winter through deep snow packs. Trend analyses found export of Nr to be closely tied to atmospheric inputs, but can lag in response to drought. Current research explores consequences of the combination of warming, changes in precipitation dynamics, and atmospheric deposition of Nr and dust on stream and lake CO<sub>2</sub><span>&nbsp;</span>dynamics, lake biology and trophic state, and soil carbon composition. Dramatic increases in park visitors have prompted studies on the effects of recreational use on water quality. New tools such as remote sensing and high frequency instream water quality sensors are being applied to lake and stream studies. Monitoring, combined with experiments, models, and spatial comparisons is an essential foundation for science-based resource management.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14107","usgsCitation":"Baron, J., Clow, D.W., Oleksy, I., Weinmann, T., Charlton, C., and Jayo, A., 2021, Long-term ecosystem and biogeochemical research in Loch Vale watershed, Rocky Mountain National Park, Colorado: Hydrological Processes, v. 35, no. 3, e14107, 5 p., https://doi.org/10.1002/hyp.14107.","productDescription":"e14107, 5 p.","ipdsId":"IP-123087","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":391419,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountain National Park, Loch Vale","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -106.35314941406249,\n              39.47860556892209\n            ],\n            [\n              -105.14465332031249,\n              39.47860556892209\n            ],\n            [\n              -105.14465332031249,\n              40.40931350359072\n            ],\n            [\n              -106.35314941406249,\n              40.40931350359072\n            ],\n            [\n              -106.35314941406249,\n              39.47860556892209\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"35","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Baron, Jill S. 0000-0002-5902-6251","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":215101,"corporation":false,"usgs":true,"family":"Baron","given":"Jill S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826423,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826424,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oleksy, Isabella A.","contributorId":268330,"corporation":false,"usgs":false,"family":"Oleksy","given":"Isabella A.","affiliations":[{"id":33412,"text":"Cary Institute for Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":826425,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weinmann, Timothy 0000-0003-1502-5254","orcid":"https://orcid.org/0000-0003-1502-5254","contributorId":268331,"corporation":false,"usgs":true,"family":"Weinmann","given":"Timothy","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826426,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Charlton, Caitlin","contributorId":268332,"corporation":false,"usgs":false,"family":"Charlton","given":"Caitlin","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":826427,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jayo, Amanda","contributorId":268333,"corporation":false,"usgs":false,"family":"Jayo","given":"Amanda","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":826428,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70218199,"text":"ofr20201149 - 2021 - Hydrographic and benthic mapping—St. Croix National Scenic Riverway—Osceola landing","interactions":[],"lastModifiedDate":"2021-02-24T12:54:58.879829","indexId":"ofr20201149","displayToPublicDate":"2021-02-23T14:19:37","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":"2020-1149","displayTitle":"Hydrographic and Benthic Mapping—St. Croix National Scenic Riverway—Osceola Landing","title":"Hydrographic and benthic mapping—St. Croix National Scenic Riverway—Osceola landing","docAbstract":"<p>High-resolution topographic and bathymetric mapping can assist in the analysis of river habitat. The National Park Service has been planning to relocate a boat ramp along the St. Croix River in Minnesota, across the river from the town of Osceola, Wisconsin, to improve visitor safety, improve operations for commercial use, enhance the overall visitor experience, and eliminate deferred maintenance at the landing. This landing grants access to the St. Croix River, which is a part of the National Park Service St. Croix National Scenic Riverway. Hydrographic and topographic surveys were needed to determine where the new location should be. The objective for these surveys was to provide baseline information in order to assess the direct effects of the landing relocation on physical habitat in areas adjacent to Osceola, Wisconsin. The study area for these surveys was about 18.5 hectares and located directly off the existing landing. Although the existing boat launch is referred to as the Osceola landing, it is located on the Minnesota side of the river and is the busiest National Park Service landing on the St. Croix River (National Park Service St. Croix National Scenic Riverway, 2020). This report documents methods and results of aquatic benthic mapping in a small area of the St. Croix River.</p><p>The hydroacoustic and topographic surveys were collected from October 16–17, 2019. The hydrographic surveys consisted of multibeam and sidescan sound navigation and ranging (sonars). The topographic shoreline survey consisted of light detection and ranging (lidar) captured by boat adjacent to riverbanks. Additionally, an acoustic Doppler current profiler was used to measure flow velocities. The water level was higher than normal, and therefore had faster flow during the hydroacoustic surveys. Multibeam, lidar, and sidescan surveys occurred the first day, and the velocity mapping and ground truthing was conducted the second day. Multibeam and lidar provided derivative datasets that included bathymetry and a topobathy with a spatial resolution of 1 foot. From these data, additional data could be measured including slope and terrain ruggedness. Sidescan (acoustic reflectance measures) provided imagery that was used to help with interpretation of the river bottom.</p><p>Outcomes from these combined datasets were substrate and bedform maps. Much of the area was covered in sand ripples or small dunes. A small area running adjacent to the deeper valley or cut down the river consisted of harder substrates, such as cobble and gravel. Large woody debris piles were found throughout the study area. Multiple stationary moving-bed tests were completed, and no corrections were recommended for the conditions occurring during survey. Mussel presence was noted in some of the underwater videos. The physical parameters of depth, flow, bedforms, and substrate derived from the datasets provided baseline measures for a benthic habitat map. Further analysis of benthic habitat might be possible with additional biological and chemical data.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201149","collaboration":"Prepared in cooperation with the National Park Service, the St. Croix National Scenic Riverway, and the Denver Service Center","usgsCitation":"Hanson, J.L., and Strange, J.M., 2021, Hydrographic and benthic mapping—St. Croix National Scenic Riverway—Osceola landing: U.S. Geological Survey Open-File Report 2020–1149, 26 p., https://doi.org/10.3133/ofr20201149.","productDescription":"Report: vi, 26 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-118301","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":383330,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1149/coverthb3.jpg"},{"id":383331,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1149/ofr20201149.pdf","text":"Report","size":"37.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1149"},{"id":383332,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O0QH8B","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Saint Croix National Scenic Riverway (SACN)—Osceola boat landing 2019 benthic and bathymetry data"}],"country":"United States","state":"Wisconsin","county":"Osceola","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -92.72460937499999,\n              45.3111146177239\n            ],\n            [\n              -92.69577026367185,\n              45.3111146177239\n            ],\n            [\n              -92.69577026367185,\n              45.33187500352944\n            ],\n            [\n              -92.72460937499999,\n              45.33187500352944\n            ],\n            [\n              -92.72460937499999,\n              45.3111146177239\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/umesc\" href=\"https://www.usgs.gov/centers/umesc\">Upper Midwest Environmental Sciences Center</a><br>U.S. Geological Survey<br>2630 Fanta Reed Road<br>La Crosse, WI 54603</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Purpose and Scope</li><li>Methods</li><li>Derived Datasets and Benthic Analysis from Sonar Data</li><li>Conclusions</li><li>References Cited</li><li>Appendix 1. Attributes from the Bed Observations Shapefile</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2021-02-23","noUsgsAuthors":false,"publicationDate":"2021-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Hanson, Jenny L. 0000-0001-8353-6908 jhanson@usgs.gov","orcid":"https://orcid.org/0000-0001-8353-6908","contributorId":461,"corporation":false,"usgs":true,"family":"Hanson","given":"Jenny","email":"jhanson@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810404,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stone, Jayme 0000-0002-0512-3072","orcid":"https://orcid.org/0000-0002-0512-3072","contributorId":251712,"corporation":false,"usgs":false,"family":"Stone","given":"Jayme","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":810405,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70220225,"text":"70220225 - 2021 - Machine learning predicted redox conditions in the glacial aquifer system, northern continental United States","interactions":[],"lastModifiedDate":"2021-04-28T13:03:48.833583","indexId":"70220225","displayToPublicDate":"2021-02-23T08:00:14","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":"Machine learning predicted redox conditions in the glacial aquifer system, northern continental United States","docAbstract":"<div class=\"article-section__content en main\"><p>Groundwater supplies 50% of drinking water worldwide and 30% in the United States. Geogenic and anthropogenic contaminants can, however, compromise water quality, thus limiting groundwater availability. Reduction/oxidation (redox) processes and redox conditions affect groundwater quality by influencing the mobility and transport of common geogenic and anthropogenic contaminants. In the glacial aquifer system, northern United States (GLAC, 1.87 million km<sup>2</sup>), groundwater with high arsenic or manganese concentration is associated with reducing conditions and high nitrate with oxidizing conditions. This study uses machine learning to identify the relative influence of drivers of redox conditions (e.g., residence time vs. reactivity) across the glacial landscape. We developed three‐dimensional boosted regression tree models to predict redox conditions using the likelihood of low dissolved oxygen or high iron as indicators of anoxic conditions. Results indicate that variation in redox condition is controlled primarily by residence time (e.g., groundwater age and relative depth) and to a lesser extent by geochemical reactivity (e.g., subsurface contact time, soil carbon). Older water and deeper wells, along with more water storage or slower water movement was associated with higher probability of anoxic conditions. Mapped model results illustrate regions where anoxic redox conditions may mobilize geogenic contaminants or oxic conditions may limit denitrification potential. Results may also provide simplified redox input for process or predictive models of, for example, arsenic, manganese, or nitrate. Machine learning modeling methods can lead to improved understanding of contaminant occurrence and what drives redox conditions, and the methods may be transferable to other settings.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028207","usgsCitation":"Erickson, M., Elliott, S.M., Brown, C., Stackelberg, P.E., Ransom, K.M., and Reddy, J.E., 2021, Machine learning predicted redox conditions in the glacial aquifer system, northern continental United States: Water Resources Research, v. 57, no. 4, e2020WR028207, 19 p., https://doi.org/10.1029/2020WR028207.","productDescription":"e2020WR028207, 19 p.","ipdsId":"IP-117737","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":488015,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr028207","text":"Publisher Index Page"},{"id":436492,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96KKPMD","text":"USGS data release","linkHelpText":"Groundwater data, predictor variables, and rasters used for predicting redox conditions in the glacial aquifer, northern continental United States"},{"id":385348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northern Continental United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.6953125,\n              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Center","active":true,"usgs":true}],"preferred":true,"id":814859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Craig J. 0000-0002-3858-3964","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":210450,"corporation":false,"usgs":true,"family":"Brown","given":"Craig J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":814862,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814863,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reddy, James E. 0000-0002-6998-7267","orcid":"https://orcid.org/0000-0002-6998-7267","contributorId":202976,"corporation":false,"usgs":true,"family":"Reddy","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814864,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70218500,"text":"70218500 - 2021 - Atmospheric nitrogen deposition in the Chesapeake Bay watershed: A history of change","interactions":[],"lastModifiedDate":"2021-07-02T13:34:45.078734","indexId":"70218500","displayToPublicDate":"2021-02-23T06:47:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":924,"text":"Atmospheric Environment","active":true,"publicationSubtype":{"id":10}},"title":"Atmospheric nitrogen deposition in the Chesapeake Bay watershed: A history of change","docAbstract":"<p><span>The Chesapeake Bay watershed has been the focus of pioneering studies of the role of atmospheric nitrogen (N) deposition as a nutrient source and driver of estuarine trophic status. Here, we review the history and evolution of scientific investigations of the role of atmospheric N deposition, examine trends from wet and&nbsp;<a title=\"Learn more about dry deposition from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/dry-deposition\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/dry-deposition\">dry deposition</a>&nbsp;networks, and present century-long (1950–2050) atmospheric N deposition estimates. Early investigations demonstrated the importance of&nbsp;<a title=\"Learn more about atmospheric deposition from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/atmospheric-deposition\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/atmospheric-deposition\">atmospheric deposition</a>&nbsp;as an N source to the Bay, providing 25%–40% among all major N sources. These early studies led to the unprecedented inclusion of targeted decreases in atmospheric N deposition as part of the multi-stakeholder effort to reduce N loads to the Bay. Emissions of nitrogen oxides (NO</span><sub>x</sub><span>) and deposition of wet nitrate, oxidized dry N, and dry ammonium (NH</span><sub>4</sub><sup>+</sup><span>) sharply and synchronously declined by 60%–73% during 1995–2019. These decreases largely resulted from implementation of Title IV of the 1990 Clean Air Act Amendments, which began in 1995. Wet NH</span><sub>4</sub><sup>+</sup><span>&nbsp;deposition shows no significant trend during this period. The century-long atmospheric N deposition estimates indicate an increase in total atmospheric N deposition in the Chesapeake watershed from 1950 to a peak of ~15&nbsp;kg&nbsp;N/ha/yr in 1979, trailed by a slight decline of &lt;10% through the mid-1990s, and followed by a sharp decline of about 40% thereafter through 2019. An additional 21% decline in atmospheric N deposition is projected from 2015 to 2050. A comparison of the Potomac River and James River watersheds indicates higher atmospheric N deposition in the Potomac, likely resulting from greater emissions from higher proportions of agricultural and urban land in this basin. Atmospheric N deposition rose from 30% among all N sources to the Chesapeake Bay watershed in 1950 to a peak of 40% in 1973, and a decline to 28% by 2015. These data highlight the important role of atmospheric N deposition in the Chesapeake Bay watershed and present a potential opportunity for decreases in deposition to contribute to further reducing N loads and improving the trophic status of tidal waters.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.atmosenv.2021.118277","usgsCitation":"Burns, D., Bhatt, G., Linker, L.C., Bash, J., Capel, P., and Shenk, G.W., 2021, Atmospheric nitrogen deposition in the Chesapeake Bay watershed: A history of change: Atmospheric Environment, v. 251, 118277, 12 p., https://doi.org/10.1016/j.atmosenv.2021.118277.","productDescription":"118277, 12 p.","ipdsId":"IP-116699","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":453341,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.atmosenv.2021.118277","text":"Publisher Index Page"},{"id":436494,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P953SO6P","text":"USGS data release","linkHelpText":"Nitrogen sources to and export from the Chesapeake Bay watershed, 1950 to 2050"},{"id":383705,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"New York, New Jersey, Delaware, Maryland, Pennsylvania, North Carolina, Tennessee, Kentucky, West Virginia, Virginia, Ohio, Indiana, Michigan","otherGeospatial":"Chesapeake Bay watershed, Chesapeake airshed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.1904296875,\n              38.41916639395372\n            ],\n            [\n              -75.223388671875,\n              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University","active":true,"usgs":false}],"preferred":false,"id":811212,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Linker, Lewis C. 0000-0002-3456-3659","orcid":"https://orcid.org/0000-0002-3456-3659","contributorId":252964,"corporation":false,"usgs":false,"family":"Linker","given":"Lewis","email":"","middleInitial":"C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":811213,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bash, Jesse 0000-0001-8736-0102","orcid":"https://orcid.org/0000-0001-8736-0102","contributorId":252965,"corporation":false,"usgs":false,"family":"Bash","given":"Jesse","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":811214,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Capel, Paul 0000-0003-1620-5185","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":252966,"corporation":false,"usgs":false,"family":"Capel","given":"Paul","affiliations":[{"id":27811,"text":"Univ. of Minnesota","active":true,"usgs":false}],"preferred":false,"id":811215,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shenk, Gary W. 0000-0001-6451-2513","orcid":"https://orcid.org/0000-0001-6451-2513","contributorId":225440,"corporation":false,"usgs":true,"family":"Shenk","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811216,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70223867,"text":"70223867 - 2021 - Temporal influences on selenium partitioning, trophic transfer, and exposure in a major U.S. river","interactions":[],"lastModifiedDate":"2021-09-10T16:56:34.608508","indexId":"70223867","displayToPublicDate":"2021-02-22T11:48:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Temporal influences on selenium partitioning, trophic transfer, and exposure in a major U.S. river","docAbstract":"<p><span>Hydrologic and irrigation regimes mediate the timing of selenium (Se) mobilization to rivers, but the extent to which patterns in Se uptake and trophic transfer through recipient food webs reflect the temporal variation in Se delivery is unknown. We investigated Se mobilization, partitioning, and trophic transfer along approximately 60 river miles of the selenium-impaired segment of the Lower Gunnison River (Colorado, USA) during six sampling trips between June 2015 and October 2016. We found temporal patterns in Se partitioning and trophic transfer to be independent of those in dissolved Se concentrations and that the recipient food web sustained elevated Se concentrations from earlier periods of high Se mobilization. Using an ecosystem-scale Se accumulation model tailored to the Lower Gunnison River, we predicted that the endangered Razorback Sucker (</span><i>Xyrauchen texanus</i><span>) and Colorado Pikeminnow (</span><i>Ptychocheilus lucius</i><span>) achieve whole-body Se concentrations exceeding aquatic life protection criteria during periods of high runoff and irrigation activity (April–August) that coincide with susceptible phases of reproduction and early-life development. The results of this study challenge assumptions about Se trophodynamics in fast-flowing waters and introduce important considerations for the management of Se risks for biota in river ecosystems.</span></p>","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c06582","usgsCitation":"Brandt, J., Roberts, J., Stricker, C.A., Rogers, H., Nease, P., and Schmidt, T., 2021, Temporal influences on selenium partitioning, trophic transfer, and exposure in a major U.S. river: Environmental Science and Technology, v. 55, no. 6, p. 3645-3656, https://doi.org/10.1021/acs.est.0c06582.","productDescription":"12 p.","startPage":"3645","endPage":"3656","ipdsId":"IP-122278","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":453343,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.0c06582","text":"Publisher Index Page"},{"id":436495,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TD4THX","text":"USGS data release","linkHelpText":"Dataset for temporal influences on selenium partitioning, trophic transfer, and exposure in a major U.S. river"},{"id":389070,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -108.666,\n              38.0\n            ],\n            [\n              -107.25,\n              38.0\n            ],\n            [\n              -107.25,\n              39.16666667\n            ],\n            [\n              -108.666,\n              39.16666667\n            ],\n            [\n              -108.666,\n              38.0\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"55","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Brandt, Jessica E","contributorId":257351,"corporation":false,"usgs":false,"family":"Brandt","given":"Jessica E","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":823037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":823039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogers, Holly hrogers@usgs.gov","contributorId":174358,"corporation":false,"usgs":true,"family":"Rogers","given":"Holly","email":"hrogers@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":823040,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nease, Patricia","contributorId":265586,"corporation":false,"usgs":false,"family":"Nease","given":"Patricia","email":"","affiliations":[],"preferred":false,"id":823041,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":823042,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70220237,"text":"70220237 - 2021 - Discovery of a large subsoil nitrate reservoir in an arroyo floodplain and associated aquifer contamination","interactions":[],"lastModifiedDate":"2021-06-30T18:46:44.051993","indexId":"70220237","displayToPublicDate":"2021-02-22T07:57:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Discovery of a large subsoil nitrate reservoir in an arroyo floodplain and associated aquifer contamination","docAbstract":"<div class=\"article-section-wrapper js-article-section js-content-section  \"><p>In an area of elevated nitrate (NO<sub>3</sub>) groundwater concentrations in the northern Chihuahuan Desert in central New Mexico (United States), a large reservoir of nitrate was found in the subsoil of an arroyo floodplain. Nitrate inventories in the floodplain subsoils ranged from 10,000 to 38,000 kg NO<sub>3</sub>-N/ha—over twice as high as any previously measured arid region. The floodplain subsoil NO<sub>3</sub><span>&nbsp;</span>reservoir was over 100 times higher than the adjacent desert (59–95 kg NO<sub>3</sub>-N/ha). Chloride mass balance calculations of subsoils indicate arroyo floodplain subsoils have undergone negative recharge since 2600–8600 yr ago, while the surrounding desert has had negative recharge since 13,000–17,000 yr ago. Compared to the adjacent desert, plant communities are larger and more abundant in the floodplain, though subsoil NO<sub>3</sub><span>&nbsp;</span>is apparently not utilized. We demonstrate that NO<sub>3</sub><span>&nbsp;</span>accumulates in the subsoil of the floodplain through evaporation of monsoon season precipitation funneled into the arroyo. Through a one-dimensional vadose zone model, we show that the NO<sub>3</sub><span>&nbsp;</span>inventories in the arroyo floodplain could be acquired 8 to 75 times faster than through atmospheric deposition through the lateral movement</p></div>","language":"English","publisher":"Geological Society of America","doi":"10.1130/G47916.1","usgsCitation":"Linhoff, B., and Lunzer, J.J., 2021, Discovery of a large subsoil nitrate reservoir in an arroyo floodplain and associated aquifer contamination: Geology, v. 49, no. 6, p. 667-671, https://doi.org/10.1130/G47916.1.","productDescription":"5 p.","startPage":"667","endPage":"671","ipdsId":"IP-119573","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":453348,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/g47916.1","text":"Publisher Index Page"},{"id":385347,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Chihuahuan Desert","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.70996093749999,\n              34.30714385628804\n            ],\n            [\n              -105.6005859375,\n              34.30714385628804\n            ],\n            [\n              -105.6005859375,\n              35.871246850027966\n            ],\n            [\n              -107.70996093749999,\n              35.871246850027966\n            ],\n            [\n              -107.70996093749999,\n              34.30714385628804\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"49","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Linhoff, Benjamin Shawn 0000-0002-9478-7558","orcid":"https://orcid.org/0000-0002-9478-7558","contributorId":257665,"corporation":false,"usgs":true,"family":"Linhoff","given":"Benjamin Shawn","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814873,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lunzer, John Joseph 0000-0002-5159-7826","orcid":"https://orcid.org/0000-0002-5159-7826","contributorId":257666,"corporation":false,"usgs":true,"family":"Lunzer","given":"John","email":"","middleInitial":"Joseph","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814874,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70218643,"text":"70218643 - 2021 - Gulf of Mexico blue hole harbors high levels of novel microbial lineages","interactions":[],"lastModifiedDate":"2025-05-13T16:08:29.968527","indexId":"70218643","displayToPublicDate":"2021-02-21T06:58:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7746,"text":"Interational Society of Microbial Ecology (ISME) Journal","active":true,"publicationSubtype":{"id":10}},"title":"Gulf of Mexico blue hole harbors high levels of novel microbial lineages","docAbstract":"<p><span>Exploration of oxygen-depleted marine environments has consistently revealed novel microbial taxa and metabolic capabilities that expand our understanding of microbial evolution and ecology. Marine blue holes are shallow karst formations characterized by low oxygen and high organic matter content. They are logistically challenging to sample, and thus our understanding of their biogeochemistry and microbial ecology is limited. We present a metagenomic and geochemical characterization of Amberjack Hole on the Florida continental shelf (Gulf of Mexico). Dissolved oxygen became depleted at the hole’s rim (32 m water depth), remained low but detectable in an intermediate hypoxic zone (40–75 m), and then increased to a secondary peak before falling below detection in the bottom layer (80–110 m), concomitant with increases in nutrients, dissolved iron, and a series of sequentially more reduced sulfur species. Microbial communities in the bottom layer contained heretofore undocumented levels of the recently discovered phylum Woesearchaeota (up to 58% of the community), along with lineages in the bacterial Candidate Phyla Radiation (CPR). Thirty-one high-quality metagenome-assembled genomes (MAGs) showed extensive biochemical capabilities for sulfur and nitrogen cycling, as well as for resisting and respiring arsenic. One uncharacterized gene associated with a CPR lineage differentiated hypoxic from anoxic zone communities. Overall, microbial communities and geochemical profiles were stable across two sampling dates in the spring and fall of 2019. The blue hole habitat is a natural marine laboratory that provides opportunities for sampling taxa with under-characterized but potentially important roles in redox-stratified microbial processes.</span></p>","language":"English","publisher":"Nature","doi":"10.1038/s41396-021-00917-x","usgsCitation":"Patin, N., Dietrich, Z., Stancil, A., Quinan, M., Beckler, J., Hall, E.R., Culter, J., Smith, C., Taillefert, M., and Stewart, F., 2021, Gulf of Mexico blue hole harbors high levels of novel microbial lineages: Interational Society of Microbial Ecology (ISME) Journal, v. 15, p. 2206-2232, https://doi.org/10.1038/s41396-021-00917-x.","productDescription":"17 p.","startPage":"2206","endPage":"2232","ipdsId":"IP-121475","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":383739,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":453364,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41396-021-00917-x","text":"Publisher Index Page"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.68359375,\n              25.20494115356912\n            ],\n            [\n              -83.3203125,\n              29.458731185355344\n            ],\n            [\n              -84.24316406249999,\n              30.031055426540206\n            ],\n            [\n              -85.20996093749999,\n              29.649868677972304\n            ],\n            [\n              -86.7919921875,\n              30.486550842588485\n            ],\n            [\n              -89.384765625,\n              30.06909396443887\n            ],\n            [\n              -90.2197265625,\n              29.22889003019423\n            ],\n            [\n              -93.9990234375,\n              29.649868677972304\n            ],\n            [\n              -97.119140625,\n              27.994401411046148\n            ],\n            [\n              -97.6025390625,\n              25.284437746983055\n            ],\n            [\n              -97.7783203125,\n              21.983801417384697\n            ],\n            [\n              -80.68359375,\n              25.20494115356912\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2021-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Patin, N.V. 0000-0001-8522-7682","orcid":"https://orcid.org/0000-0001-8522-7682","contributorId":253112,"corporation":false,"usgs":false,"family":"Patin","given":"N.V.","email":"","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":811229,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dietrich, Z.A.","contributorId":253113,"corporation":false,"usgs":false,"family":"Dietrich","given":"Z.A.","email":"","affiliations":[{"id":33315,"text":"Bowdoin College","active":true,"usgs":false}],"preferred":false,"id":811230,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stancil, A.","contributorId":253114,"corporation":false,"usgs":false,"family":"Stancil","given":"A.","email":"","affiliations":[{"id":26984,"text":"Harbor Branch Oceanographic Institute, Florida Atlantic University","active":true,"usgs":false}],"preferred":false,"id":811231,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quinan, M.","contributorId":253115,"corporation":false,"usgs":false,"family":"Quinan","given":"M.","email":"","affiliations":[{"id":13147,"text":"Mote Marine Laboratory","active":true,"usgs":false}],"preferred":false,"id":811232,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beckler, J.S.","contributorId":253116,"corporation":false,"usgs":false,"family":"Beckler","given":"J.S.","email":"","affiliations":[{"id":26984,"text":"Harbor Branch Oceanographic Institute, Florida Atlantic University","active":true,"usgs":false}],"preferred":false,"id":811233,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hall, E. R. 0000-0002-9218-6097","orcid":"https://orcid.org/0000-0002-9218-6097","contributorId":253129,"corporation":false,"usgs":false,"family":"Hall","given":"E.","email":"","middleInitial":"R.","affiliations":[{"id":37075,"text":"Mote Marine Laboratory, Tropical Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":811268,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Culter, J","contributorId":253117,"corporation":false,"usgs":false,"family":"Culter","given":"J","email":"","affiliations":[{"id":13147,"text":"Mote Marine Laboratory","active":true,"usgs":false}],"preferred":false,"id":811235,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":811236,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Taillefert, Martial","contributorId":214794,"corporation":false,"usgs":false,"family":"Taillefert","given":"Martial","email":"","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":811237,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stewart, F.J.","contributorId":253118,"corporation":false,"usgs":false,"family":"Stewart","given":"F.J.","email":"","affiliations":[{"id":50483,"text":"Georgia Institute of Technology; Montana State University","active":true,"usgs":false}],"preferred":false,"id":811238,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70218257,"text":"70218257 - 2021 - Long-term trends in regional wet mercury deposition and lacustrine mercury concentrations in four lakes in Voyageurs National Park","interactions":[],"lastModifiedDate":"2021-02-22T14:43:03.094759","indexId":"70218257","displayToPublicDate":"2021-02-20T08:38:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5841,"text":"Applied Sciences","onlineIssn":"2076-3417","active":true,"publicationSubtype":{"id":10}},"title":"Long-term trends in regional wet mercury deposition and lacustrine mercury concentrations in four lakes in Voyageurs National Park","docAbstract":"<p><span>Although anthropogenic mercury (Hg) releases to the environment have been substantially lowered in the United States and Canada since 1990, concerns remain for contamination in fish from remote lakes and rivers where atmospheric deposition is the predominant source of mercury. How have aquatic ecosystems responded? We report on one of the longest known multimedia data sets for mercury in atmospheric deposition: aqueous total mercury (THg</span><sub>aq</sub><span>), methylmercury (MeHg</span><sub>aq</sub><span>), and sulfate from epilimnetic lake-water samples from four lakes in Voyageurs National Park (VNP) in northern Minnesota; and total mercury (THg) in aquatic biota from the same lakes from 2001–2018. Wet Hg deposition at two regional Mercury Deposition Network sites (Fernberg and Marcell, Minnesota) decreased by an average of 22 percent from 1998–2018; much of the decreases occurred prior to 2009, with relatively flat trends since 2009. In the four VNP lakes, epilimnetic MeHg</span><sub>aq</sub><span>&nbsp;concentrations declined by an average of 44 percent and THg</span><sub>aq</sub><span>&nbsp;by an average of 27 percent. For the three lakes with long-term biomonitoring, temporal patterns in biotic THg concentrations were similar to patterns in MeHg</span><sub>aq</sub><span>&nbsp;concentrations; however, biotic THg concentrations declined significantly in only one lake. Epilimnetic MeHg</span><sub>aq</sub><span>&nbsp;may be responding both to a decline in atmospheric Hg deposition as well as a decline in sulfate deposition, which is an important driver of mercury methylation in the environment. Results from this case study suggest that regional- to continental-scale decreases in both mercury and sulfate emissions have benefitted aquatic resources, even in the face of global increases in mercury emissions.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/app11041879","usgsCitation":"Brigham, M.E., VanderMeulen, D.D., Eagles-Smith, C., Krabbenhoft, D.P., Maki, R., and DeWild, J.F., 2021, Long-term trends in regional wet mercury deposition and lacustrine mercury concentrations in four lakes in Voyageurs National Park: Applied Sciences, v. 11, no. 4, 1879, 21 p., https://doi.org/10.3390/app11041879.","productDescription":"1879, 21 p.","ipdsId":"IP-125080","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":453370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/app11041879","text":"Publisher Index Page"},{"id":383419,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Voyageurs National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -93.16131591796875,\n              48.29781249243716\n            ],\n            [\n              -92.449951171875,\n              48.29781249243716\n            ],\n            [\n              -92.449951171875,\n              48.64470577018957\n            ],\n            [\n              -93.16131591796875,\n              48.64470577018957\n            ],\n            [\n              -93.16131591796875,\n              48.29781249243716\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Brigham, Mark E. 0000-0001-7412-6800 mbrigham@usgs.gov","orcid":"https://orcid.org/0000-0001-7412-6800","contributorId":1840,"corporation":false,"usgs":true,"family":"Brigham","given":"Mark","email":"mbrigham@usgs.gov","middleInitial":"E.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"VanderMeulen, David D.","contributorId":196965,"corporation":false,"usgs":false,"family":"VanderMeulen","given":"David","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":810745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":810746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810747,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maki, Ryan P.","contributorId":190131,"corporation":false,"usgs":false,"family":"Maki","given":"Ryan P.","affiliations":[],"preferred":false,"id":810748,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeWild, John F. 0000-0003-4097-2798 jfdewild@usgs.gov","orcid":"https://orcid.org/0000-0003-4097-2798","contributorId":2525,"corporation":false,"usgs":true,"family":"DeWild","given":"John","email":"jfdewild@usgs.gov","middleInitial":"F.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810749,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70240743,"text":"70240743 - 2021 - NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms","interactions":[],"lastModifiedDate":"2023-02-17T14:27:52.452025","indexId":"70240743","displayToPublicDate":"2021-02-20T07:25:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-gulliver text-s\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0085\">The 2017–2027 National Academies' Decadal Survey,<span>&nbsp;</span><i>Thriving on Our Changing Planet</i><span>, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global,&nbsp;high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500&nbsp;nm; ~30&nbsp;m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5&nbsp;μm; TIR: 8–12&nbsp;μm; ~60&nbsp;m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and&nbsp;aquatic ecology,&nbsp;atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal&nbsp;aquatic ecosystems&nbsp;physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and&nbsp;albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on&nbsp;surface energy, water, momentum, and&nbsp;carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice,&nbsp;aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.</span></p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112349","usgsCitation":"Cawse-Nicholson, K., Townsend, P.A., Schimel, D., Assiri, A.M., Blake, P.L., Buongiorno, M.F., Campbell, P., Carmon, N., Casey, K.A., Correa-Pabon, R.E., Dahlin, K., Dashti, H., Dennison, P., Dierrsen, H., Erickson, A., Fisher, J.B., Frouin, R., Gatebe, C.K., Gholizadeh, H., Gierach, M.M., Glenn, N.F., Goodman, J., Griffith, D.M., Guild, L., Hakkenberg, C.R., Hochberg, E.J., Holmes, T.R., Hu, C., Hulley, G., Huemmrich, K.F., Kudela, R.M., Kokaly, R.F., Lee, C.M., Martin, R.E., Miller, C.E., Moses, W.J., Muller-Karger, F.E., Ortiz, J.P., Otis, D.B., Pahlevan, N., Painter, T.H., Pavlick, R., Poulter, B., Qi, Y., Realmuto, V.J., Roberts, D.A., Schaepman, M., Schneider, F.D., Schwandner, F.M., Serbin, S., Shiklomanov, A.N., Stavros, E.N., Thompson, D.R., Torres-Perez, J.L., Turpie, K.R., Tzortziou, M., Ustin, S.L., Yu, Q., Yusup, Y., Zhang, Q., and The SBG Algorithms Working Group Community, 2021, NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms: Remote Sensing of Environment, v. 257, 112349, 25 p., https://doi.org/10.1016/j.rse.2021.112349.","productDescription":"112349, 25 p.","ipdsId":"IP-123132","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":453373,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112349","text":"Publisher Index Page"},{"id":413171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"257","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cawse-Nicholson, 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M","contributorId":302529,"corporation":false,"usgs":false,"family":"Assiri","given":"Ali","email":"","middleInitial":"M","affiliations":[{"id":65485,"text":"KACST, Space and Aeronautics Research Institute, Riyadh, KSA","active":true,"usgs":false}],"preferred":false,"id":864610,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blake, Pamela L.","contributorId":302530,"corporation":false,"usgs":false,"family":"Blake","given":"Pamela","email":"","middleInitial":"L.","affiliations":[{"id":65486,"text":"The Boeing Company, Seal Beach CA","active":true,"usgs":false}],"preferred":false,"id":864611,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Buongiorno, Maria Fabrizia 0000-0002-6095-6974","orcid":"https://orcid.org/0000-0002-6095-6974","contributorId":221726,"corporation":false,"usgs":false,"family":"Buongiorno","given":"Maria","email":"","middleInitial":"Fabrizia","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, 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,{"id":70218251,"text":"70218251 - 2021 - Salinity changes the dynamics of pyrethroid toxicity in terms of behavioral effects on newly hatched delta smelt larvae","interactions":[],"lastModifiedDate":"2021-02-22T13:41:06.189067","indexId":"70218251","displayToPublicDate":"2021-02-20T06:37:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7597,"text":"Toxics","active":true,"publicationSubtype":{"id":10}},"title":"Salinity changes the dynamics of pyrethroid toxicity in terms of behavioral effects on newly hatched delta smelt larvae","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Salinity can interact with organic compounds and modulate their toxicity. Studies have shown that the fraction of pyrethroid insecticides in the aqueous phase increases with increasing salinity, potentially increasing the risk of exposure for aquatic organisms at higher salinities. In the San Francisco Bay Delta (SFBD) estuary, pyrethroid concentrations increase during the rainy season, coinciding with the spawning season of Delta Smelt (<span class=\"html-italic\">Hypomesus transpacificus</span>), an endangered, endemic fish. Furthermore, salinity intrusion in the SFBD is exacerbated by global climate change, which may change the dynamics of pyrethroid toxicity on aquatic animals. Therefore, examining the effect of salinity on the sublethal toxicity of pyrethroids is essential for risk assessments, especially during the early life stages of estuarine fishes. To address this, we investigated behavioral effects of permethrin and bifenthrin at three environmentally relevant concentrations across a salinity gradient (0.5, 2 and 6 PSU) on Delta Smelt yolk-sac larvae. Our results suggest that environmentally relevant concentrations of pyrethroids can perturb Delta Smelt larvae behavior even at the lowest concentrations (&lt;1 ng/L) and that salinity can change the dynamic of pyrethroid toxicity in terms of behavioral effects, especially for bifenthrin, where salinity was positively correlated with anti-thigmotaxis at each concentration.</div>","language":"English","publisher":"MDPI","doi":"10.3390/toxics9020040","usgsCitation":"Segarra, A., Mauduit, F., Amer, N., Biefel, F.K., Hladik, M.L., Connon, R., and Brander, S.M., 2021, Salinity changes the dynamics of pyrethroid toxicity in terms of behavioral effects on newly hatched delta smelt larvae: Toxics, v. 9, no. 2, 40, 20 p., https://doi.org/10.3390/toxics9020040.","productDescription":"40, 20 p.","ipdsId":"IP-125612","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":453375,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/toxics9020040","text":"Publisher Index Page"},{"id":383407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"California","otherGeospatial":"San Francisco Bay Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.6953125,\n              37.33522435930639\n            ],\n            [\n              -121.40991210937499,\n              37.33522435930639\n            ],\n            [\n              -121.40991210937499,\n              38.30718056188316\n            ],\n            [\n              -122.6953125,\n              38.30718056188316\n            ],\n            [\n              -122.6953125,\n              37.33522435930639\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"9","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Segarra, Amelie 0000-0002-0551-0013","orcid":"https://orcid.org/0000-0002-0551-0013","contributorId":251846,"corporation":false,"usgs":false,"family":"Segarra","given":"Amelie","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":810696,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mauduit, Florian","contributorId":251847,"corporation":false,"usgs":false,"family":"Mauduit","given":"Florian","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":810697,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Amer, Nermeen","contributorId":251848,"corporation":false,"usgs":false,"family":"Amer","given":"Nermeen","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":810698,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Biefel, Felix KJ","contributorId":251849,"corporation":false,"usgs":false,"family":"Biefel","given":"Felix","email":"","middleInitial":"KJ","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":810699,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221087,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810700,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Connon, Richard E","contributorId":152478,"corporation":false,"usgs":false,"family":"Connon","given":"Richard E","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":810701,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brander, Susanne M.","contributorId":187546,"corporation":false,"usgs":false,"family":"Brander","given":"Susanne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":810702,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70248971,"text":"70248971 - 2021 - Glacier-related outburst floods","interactions":[],"lastModifiedDate":"2023-09-27T16:48:03.123722","indexId":"70248971","displayToPublicDate":"2021-02-19T11:45:38","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Glacier-related outburst floods","docAbstract":"<p id=\"sp0090\">Water bodies impounded by glaciers, moraines, and ice jams on rivers can drain suddenly, with disastrous downstream consequences. Lakes can form at the margins of an alpine glacier or ice cap, on its surface, or at its base. Smaller pockets of water may also be present within some glaciers. In all cases, these water bodies might drain by enlarging subglacial tunnels or by mechanical collapse of the glacier dam. Many formerly stable glacier lakes have failed over the past century, in some cases repeatedly, as Earth’s atmosphere has warmed and glaciers thinned and receded. The<span>&nbsp;</span>peak discharge, duration, and volume of a subglacial outburst flood depend mainly on (1) the geometry and rate of development of the tunnel at the base of the glacier and (2) the size and geometry of the impounded water body. Discharge commonly increases exponentially during the outburst, but ends quickly when the lake empties or when the drainage tunnel is plugged by collapse of the tunnel roof or closes due to plastic ice flow. Some glacier outburst floods result from the mechanical collapse of the ice dam. In such cases, the peak flow is achieved rapidly during the collapse. Outburst floods from glacier lakes attenuate due to temporary storage of floodwaters in channels and on valley floors.</p><p id=\"sp0095\">Many hazardous lakes are dammed by lateral and<span>&nbsp;</span>end moraines<span>&nbsp;that formed in the past two centuries when valley and&nbsp;cirque&nbsp;glaciers retreated from advanced positions reached during the Little Ice Age. Moraine dams are susceptible to failure because they are steep and relatively narrow, because they comprise loose poorly sorted sediment, and because they may contain ice cores or interstitial ice. These dams generally fail by overtopping and incision. The triggering event may be a heavy&nbsp;rainstorm, strong winds, or an ice avalanche or landslide into the lake that generates waves that overtop the dam. Melting of moraine ice cores and piping are other possible failure mechanisms. Outflow from a moraine-dammed lake increases as the breach enlarges and then decreases as the level of the lake falls. The moraine breach may become armored, preventing further incision, or the hydraulic gradient at the breach may decrease to a point that erosion ceases.</span></p><p id=\"sp0100\">Outburst floods from glacier- and moraine-dammed lakes typically entrain, transport, and deposit large amounts of sediment. If the channel is steeper than about 0.10-0.15 and contains abundant loose sediment, the flood likely will transform into a debris flow. Such flows may be larger and more destructive than the flood from which they formed. A period of protracted warming is required to trap lakes behind moraines and create conditions that lead to dam failure. The warming also forces glaciers to retreat, prompting ice avalanches, and landslides that have destroyed many moraine dams.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Snow and ice-related hazards, risks, and disasters","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-817129-5.00019-6","usgsCitation":"Clague, J.J., and O'Connor, J., 2021, Glacier-related outburst floods, chap. <i>of</i> Snow and ice-related hazards, risks, and disasters, p. 467-499, https://doi.org/10.1016/B978-0-12-817129-5.00019-6.","productDescription":"33 p.","startPage":"467","endPage":"499","ipdsId":"IP-053640","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":421268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Second Edition","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Clague, John J.","contributorId":270213,"corporation":false,"usgs":false,"family":"Clague","given":"John","email":"","middleInitial":"J.","affiliations":[{"id":56114,"text":"Department of Earth Sciences, Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":884393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":884394,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70218245,"text":"ofr20211002 - 2021 - Mangrove species’ response to sea-level rise across Pohnpei, Federated States of Micronesia","interactions":[],"lastModifiedDate":"2021-02-19T21:35:50.775863","indexId":"ofr20211002","displayToPublicDate":"2021-02-19T10:56:11","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":"2021-1002","displayTitle":"Mangrove Species’ Response to Sea-Level Rise Across Pohnpei, Federated States of Micronesia","title":"Mangrove species’ response to sea-level rise across Pohnpei, Federated States of Micronesia","docAbstract":"<p>Mangrove forests are likely vulnerable to accelerating sea-level rise; however, we lack the tools necessary to understand their future resilience. On the Pacific island of Pohnpei, Federated States of Micronesia, mangroves are habitat to endangered species and provide critical ecosystem services that support local communities. We developed a generalizable modeling framework for mangroves that accounts for species interactions and the belowground processes that dictate soil elevation. The modeling framework was calibrated with extensive field datasets, including accretion rates derived from thirty 1-meter-deep soil cores dated with lead-210, more than 300 forest inventory plots, water-level monitoring, and differential leveling elevation surveys. We applied the model using a community of five mangrove species and across seven regions around Pohnpei to identify which regions are most vulnerable to sea-level rise. The responses of mean elevation and the mangrove community&nbsp; composition were analyzed under four global sea-level rise scenarios: an increase of 37, 52, 67, or 117 centimeters by 2100. The model was validated against a 20-year surface elevation table record (1999–2019) and showed good agreement when driven by observed water levels.</p><p>The model projected that mangroves around Pohnpei can build their elevations relative to moderate rates of sea-level rise to prevent submergence, with limited changes in mangrove community composition through 2060. By 2100, however, the model projected a decreasing abundance of high-elevation mangrove species and an increasing abundance of lower elevation species adapted to more persistent flooding. Under higher sea-level rise scenarios, forest elevation decreased substantially relative to mean sea level and there were more drastic changes in the tree community composition and loss of suitable mangrove habitat by 2100. Variation in accretion rates, water levels, and initial forest elevation led to differential&nbsp; vulnerability around the island, such that mangroves on the leeward side of the island generally were the most at-risk to higher rates of sea-level rise. Our findings indicate that the relatively undisturbed state of the mangrove forests and the surrounding landscape is an important factor in their ability to keep pace with sea-level rise.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211002","collaboration":"Prepared in cooperation with the U.S. Forest Service","usgsCitation":"Buffington, K.J., MacKenzie, R.A., Carr, J.A., Apwong, M., Krauss, K.W., and Thorne, K.M., 2021, Mangrove species’ response to sea-level rise across Pohnpei, Federated States of Micronesia: U.S. Geological Survey Open-File Report 2021–1002, 44 p., https://doi.org/10.3133/ofr20211002.","productDescription":"Report: vii, 44 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-121673","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436498,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R8MZQ","text":"USGS data release","linkHelpText":"Mangrove Elevation and Species' Responses to Sea-level Rise Across Pohnpei, Federated States of Micronesia (ver. 1.1, December 2021)"},{"id":383370,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1002/covrthb.jpg"},{"id":383371,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1002/ofr20211002.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383372,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1002/ofr20211002.xml"},{"id":383373,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1002/images"},{"id":383374,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DDZX32","linkHelpText":"Pohnpei, Federated States of Micronesia Mangrove Elevation Survey Data"}],"country":"Federated States of Micronesia","state":"Pohnpei","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              158.06442260742188,\n              6.7723525317661215\n            ],\n            [\n              158.3740997314453,\n              6.7723525317661215\n            ],\n            [\n              158.3740997314453,\n              7.013667927566642\n            ],\n            [\n              158.06442260742188,\n              7.013667927566642\n            ],\n            [\n              158.06442260742188,\n              6.7723525317661215\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director,<br><a href=\"https://www.usgs.gov/%20centers/%20werc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/ centers/ werc\">Western Ecological Research Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>3020 State University Drive East<br>Sacramento, California 95819</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>References Cited</li><li>Appendix</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-02-19","noUsgsAuthors":false,"publicationDate":"2021-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":810637,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"MacKenzie, Richard A.","contributorId":169073,"corporation":false,"usgs":false,"family":"MacKenzie","given":"Richard","email":"","middleInitial":"A.","affiliations":[{"id":25408,"text":"Institute of Pacific Islands Forestry, Pacific Southwest Research Station, Hilo, HI, USA","active":true,"usgs":false}],"preferred":false,"id":810638,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":810639,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Apwong, Maybeleen","contributorId":251804,"corporation":false,"usgs":false,"family":"Apwong","given":"Maybeleen","email":"","affiliations":[{"id":25408,"text":"Institute of Pacific Islands Forestry, Pacific Southwest Research Station, Hilo, HI, USA","active":true,"usgs":false}],"preferred":true,"id":810640,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krauss, Ken W. 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":221923,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":810641,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thorne, Karen M. 0000-0002-1381-0657 kthorne@usgs.gov","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":4191,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen","email":"kthorne@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":810642,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70218754,"text":"70218754 - 2021 - Re‐purposing groundwater flow models for age assessments: Important characteristics","interactions":[],"lastModifiedDate":"2021-09-14T16:00:16.897383","indexId":"70218754","displayToPublicDate":"2021-02-19T08:37:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Re‐purposing groundwater flow models for age assessments: Important characteristics","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Groundwater flow model construction is often time‐consuming and costly, with development ideally focused on a specific purpose, such as quantifying well capture from water bodies or providing flow fields for simulating advective transport. As environmental challenges evolve, the incentive to re‐purpose existing groundwater flow models may increase. However, few studies have evaluated which characteristics of groundwater flow models deserve greatest consideration when re‐purposing models for groundwater age and advective transport simulations. In this paper, we compare simulated age metrics produced by three MODFLOW‐MODPATH models of the same area but with differing levels of complexity (layering and heterogeneity). Comparisons are made at three watershed scales (HUC 8 to HUC 12). Groundwater age metrics, specifically the young fraction and median age of the young and old fractions, are used for evaluation because they relate to intrinsic susceptibility of aquifers and are simpler to interpret than full age distributions used for advective transport. Results indicate that: 1. the young fraction is less sensitive to model layering than the median age of young and old fractions, suggesting that simple models may suffice for basic intrinsic susceptibility assessments; 2. water table mounding and associated discharge into partially penetrating boundaries, such as head‐water streams, is important for simulating both the young fraction and the median age of the young fraction; and 3. the influence of partially penetrating head‐water streams is maintained regardless of the porosity distribution. Results of this work should aid modelers with evaluating the appropriateness of re‐purposing existing groundwater flow models for age simulations.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13088","usgsCitation":"Juckem, P.F., and Starn, J., 2021, Re‐purposing groundwater flow models for age assessments: Important characteristics: Groundwater, v. 59, no. 5, p. 710-727, https://doi.org/10.1111/gwat.13088.","productDescription":"18 p.","startPage":"710","endPage":"727","ipdsId":"IP-109098","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436501,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99YKM02","text":"USGS data release","linkHelpText":"MODPATH6 models used to evaluate effects of complexity on groundwater age metrics in the Fox-Wolf-Peshtigo watersheds, Wisconsin"},{"id":384353,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811687,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Starn, J. Jeffrey 0000-0001-5909-0010 jjstarn@usgs.gov","orcid":"https://orcid.org/0000-0001-5909-0010","contributorId":1916,"corporation":false,"usgs":true,"family":"Starn","given":"J. Jeffrey","email":"jjstarn@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":false,"id":811688,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70218255,"text":"70218255 - 2021 - Determination of vadose zone and saturated zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning","interactions":[],"lastModifiedDate":"2021-02-22T14:29:43.76892","indexId":"70218255","displayToPublicDate":"2021-02-19T08:20:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Determination of vadose zone and saturated zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning","docAbstract":"<p><span>In this study, we explored the use of statistical machine learning and long-term groundwater nitrate monitoring data to estimate vadose zone and saturated zone lag times in an irrigated alluvial agricultural setting. Unlike most previous statistical machine learning studies that sought to predict groundwater nitrate concentrations within aquifers, the focus of this study was to leverage available groundwater nitrate concentrations and other environmental variables to determine mean regional vertical velocities (transport rates) of water and solutes in the vadose zone and saturated zone (3.50 and 3.75 </span><span class=\"inline-formula\">m yr<sup>−1</sup></span><span>, respectively). The statistical machine learning results are consistent with two primary recharge processes in this western Nebraska aquifer, namely (</span><a class=\"scrollto\" href=\"https://hess.copernicus.org/articles/25/811/2021/#Ch1.E1\" data-fixed-element=\".auto-fixed-top-forced.article-title\" data-mce-href=\"https://hess.copernicus.org/articles/25/811/2021/#Ch1.E1\">1</a><span>) diffuse recharge from irrigation and precipitation across the landscape and (</span><a class=\"scrollto\" href=\"https://hess.copernicus.org/articles/25/811/2021/#Ch1.E2\" data-fixed-element=\".auto-fixed-top-forced.article-title\" data-mce-href=\"https://hess.copernicus.org/articles/25/811/2021/#Ch1.E2\">2</a><span>) focused recharge from leaking irrigation conveyance canals. The vadose zone mean velocity yielded a mean recharge rate (0.46 </span><span class=\"inline-formula\">m yr<sup>−1</sup></span><span>) consistent with previous estimates from groundwater age dating in shallow wells (0.38 </span><span class=\"inline-formula\">m yr<sup>−1</sup></span><span>). The saturated zone mean velocity yielded a recharge rate (1.31 </span><span class=\"inline-formula\">m yr<sup>−1</sup></span><span>) that was more consistent with focused recharge from leaky irrigation canals, as indicated by previous results of groundwater age dating in intermediate-depth wells (1.22 </span><span class=\"inline-formula\">m yr<sup>−1</sup></span><span>). Collectively, the statistical machine learning model results are consistent with previous observations of relatively high water fluxes and short transit times for water and nitrate in the primarily oxic aquifer. Partial dependence plots from the model indicate a sharp threshold in which high groundwater nitrate concentrations are mostly associated with total travel times of 7&nbsp;years or less, possibly reflecting some combination of recent management practices and a tendency for nitrate concentrations to be higher in diffuse infiltration recharge than in canal leakage water. Limitations to the machine learning approach include the non-uniqueness of different transport rate combinations when comparing model performance and highlight the need to corroborate statistical model results with a robust conceptual model and complementary information such as groundwater age.</span></p>","language":"English","publisher":"Copernicus Publications","doi":"10.5194/hess-25-811-2021","usgsCitation":"Wells, M.J., Gilmore, T., Nelson, N., Mittelstet, A., and Bohlke, J., 2021, Determination of vadose zone and saturated zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning: Hydrology and Earth System Sciences, v. 25, p. 811-829, https://doi.org/10.5194/hess-25-811-2021.","productDescription":"19 p.","startPage":"811","endPage":"829","ipdsId":"IP-118404","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453386,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-25-811-2021","text":"Publisher Index Page"},{"id":383417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","county":"Scotts Bluff County, Sioux County","otherGeospatial":"Dutch Flats","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -104.03228759765625,\n              41.27367811566259\n            ],\n            [\n              -102.39257812499999,\n              41.27367811566259\n            ],\n            [\n              -102.39257812499999,\n              42.407234661551875\n            ],\n            [\n              -104.03228759765625,\n              42.407234661551875\n            ],\n            [\n              -104.03228759765625,\n              41.27367811566259\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2021-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Wells, Martin J.","contributorId":251868,"corporation":false,"usgs":false,"family":"Wells","given":"Martin","email":"","middleInitial":"J.","affiliations":[{"id":50406,"text":"U Nebraska","active":true,"usgs":false}],"preferred":false,"id":810735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilmore, Troy E.","contributorId":251869,"corporation":false,"usgs":false,"family":"Gilmore","given":"Troy E.","affiliations":[{"id":50406,"text":"U Nebraska","active":true,"usgs":false}],"preferred":false,"id":810736,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, Natalie","contributorId":251870,"corporation":false,"usgs":false,"family":"Nelson","given":"Natalie","affiliations":[{"id":50407,"text":"North Carolina State U","active":true,"usgs":false}],"preferred":false,"id":810737,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mittelstet, Aaron","contributorId":251871,"corporation":false,"usgs":false,"family":"Mittelstet","given":"Aaron","affiliations":[{"id":50406,"text":"U Nebraska","active":true,"usgs":false}],"preferred":false,"id":810738,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":810739,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70218809,"text":"70218809 - 2021 - National-scale reservoir thermal energy storage pre-assessment for the United States","interactions":[],"lastModifiedDate":"2021-03-15T13:24:49.326555","indexId":"70218809","displayToPublicDate":"2021-02-19T08:19:31","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"National-scale reservoir thermal energy storage pre-assessment for the United States","docAbstract":"The U.S. Geological Survey is performing a pre-assessment of the cooling potential for reservoir thermal energy storage (RTES) in five generalized geologic regions (Basin and Range, Coastal Plains, Illinois Basin, Michigan Basin, Pacific Northwest) across the United States. Reservoir models are developed for the metropolitan areas of eight cities (Albuquerque, New Mexico; Charleston, South Carolina; Chicago and Decatur, Illinois; Lansing, Michigan; Memphis, Tennessee; Phoenix, Arizona; and Portland, Oregon) so that computed metrics can be compared to evaluate RTES potential across diverse climates, geologic settings, and physiography. Permeable, semi-confined/confined units that underlie more-utilized aquifers and contain low-quality groundwater are selected for each city. Energy storage metrics are computed for the anticipated total thickness of stratigraphy for which RTES might be feasible, including estimated required well spacing, thermal storage capacity, and thermal recovery efficiency over time. Falta et al. (2016) showed that for a modern 25,000 square-foot (2,323 square-meter), two-story office building, cooling needs exceed heating demand for almost every region of the country. We therefore use Falta et al.’s cooling demand for each city as the representative RTES stress condition for metric computation, allowing comparisons across regions. Results indicate that favorable RTES conditions exist in each region, particularly in the Illinois Basin, Coastal Plains, and Basin and Range. Thermal recovery efficiencies are very high in all regions and increase over time. The thermal storage capacity metric is most informative in the pre-assessment and underscores the importance of mapping reservoir thicknesses and porosities to permit detailed mapping of thermal storage capacity per unit area as a key RTES resource classification standard. This assessment provides a basic understanding of the RTES potential in several metropolitan areas and geologic regions throughout the United States and will aid further evaluation of national RTES efficacy.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, 46th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceDate":"February 16-18, 2021","conferenceLocation":"Sanford, California","language":"English","publisher":"Stanford Geothermal Workshop","usgsCitation":"Pepin, J.D., Burns, E., Dickinson, J.E., Duncan, L.L., Kuniansky, E.L., and Reeves, H.W., 2021, National-scale reservoir thermal energy storage pre-assessment for the United States, <i>in</i> Proceedings, 46th workshop on geothermal reservoir engineering, Sanford, California, February 16-18, 2021, 10 p.","productDescription":"10 p.","ipdsId":"IP-125276","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":384378,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384368,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2021/Pepin.pdf"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n     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