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Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Effects of recent wildfires on giant sequoia groves were anomalous at millennial timescales: a response to Hanson et al.","docAbstract":"The giant sequoia (Sequoiadendron giganteum [Lindley] Buchholz) of California’s Sierra Nevada recently suffered historically unprecedented wildfires that killed an estimated 13–19% of seed-bearing sequoias across their native range. Hanson et al. recently sought to characterize post-fire reproduction in two severely burned sequoia groves, but their two papers (1) inaccurately portrayed sequoia fire ecology, (2) had methodological flaws, and (3) without supporting evidence, questioned efforts to prevent large, stand-replacing wildfires and to plant sequoia seedlings in areas of low post-fire regeneration.
Our analyses and literature review contradict many of Hanson et al.’s claims and implications. First, evidence indicates that preceding the recent wildfires, large, contiguous areas (>10 to >100 ha) of fire severe enough to kill most sequoias had been absent for at least a millennium, and probably much longer. The ancient sequoia fire regime was instead overwhelmingly dominated by surface fires in which most forest area burned at low or moderate severity interspersed with small forest gaps (hundredths of a hectare to a few hectares) created by local patches of higher-severity fire, within which most mature sequoias survived and most successful reproduction occurred. Prescribed fires have typically mimicked ancient fires and induced adequate sequoia regeneration. In contrast, in some extensive areas where recent wildfires killed most (or all) mature sequoias, regeneration has been well below historical levels, threatening a net loss of sequoia grove area. Methodologically, Hanson et al. reported sixfold greater post-fire sequoia seedling densities than others who sampled the same area; our assessments suggest their higher densities may have largely resulted from plot-placement bias. Finally, Hanson et al.’s comparisons of median seedling densities were inappropriate.
Hanson et al. questioned efforts to prevent large, high-severity wildfires in sequoia groves but did not acknowledge (1) that past fires sustained sequoia reproduction without the deaths of large fractions of mature sequoias, (2) the anomalous effects of recent wildfires, and (3) the acute conservation threat of losing large fractions of seed-bearing sequoias. Hanson et al.’s further implication, made without supporting evidence, that decisions to plant sequoia seedlings may be unwarranted ignores research showing that recent post-wildfire regeneration has often been well below historical levels.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-024-00316-5","usgsCitation":"Stephenson, N.L., Soderberg, D.N., Flickinger, J.A., Caprio, A., and Das, A., 2024, Effects of recent wildfires on giant sequoia groves were anomalous at millennial timescales: a response to Hanson et al.: Fire Ecology, v. 20, 89, 24 p., https://doi.org/10.1186/s42408-024-00316-5.","productDescription":"89, 24 p.","ipdsId":"IP-168419","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":466888,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-024-00316-5","text":"Publisher Index Page"},{"id":463193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Kings Canyon National Park, Sequoia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.83705439798453,\n 37.25363007777847\n ],\n [\n -118.91001077861513,\n 36.88691192471105\n ],\n [\n -119.31285688035645,\n 36.91044907614862\n ],\n [\n -118.83750177290662,\n 36.304901464389204\n ],\n [\n -118.50716467687376,\n 36.25484105374991\n ],\n [\n -118.20265108815596,\n 36.40564230741613\n ],\n [\n -118.32953175012142,\n 37.09692155858474\n ],\n [\n -118.83705439798453,\n 37.25363007777847\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","noUsgsAuthors":false,"publicationDate":"2024-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Stephenson, Nathan L. 0000-0003-0208-7229 nstephenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0208-7229","contributorId":2836,"corporation":false,"usgs":true,"family":"Stephenson","given":"Nathan","email":"nstephenson@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":916759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soderberg, David Nicolas Bertil 0000-0002-8517-4143","orcid":"https://orcid.org/0000-0002-8517-4143","contributorId":316729,"corporation":false,"usgs":true,"family":"Soderberg","given":"David","email":"","middleInitial":"Nicolas Bertil","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":916760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flickinger, Joshua A.","contributorId":345490,"corporation":false,"usgs":false,"family":"Flickinger","given":"Joshua","email":"","middleInitial":"A.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":916761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caprio, Anthony C.","contributorId":35863,"corporation":false,"usgs":false,"family":"Caprio","given":"Anthony C.","affiliations":[],"preferred":false,"id":916762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Das, Adrian 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":201236,"corporation":false,"usgs":true,"family":"Das","given":"Adrian","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":916763,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259343,"text":"70259343 - 2024 - Brodifacoum isomer formulations with potentially lower risk to non-target wildlife","interactions":[],"lastModifiedDate":"2024-10-04T14:54:04.241083","indexId":"70259343","displayToPublicDate":"2024-09-30T09:51:11","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Brodifacoum isomer formulations with potentially lower risk to non-target wildlife","docAbstract":"Anticoagulant rodenticides (ARs) have a long history of successful use in controlling vertebrate pest and invasive species. Despite regulatory efforts to mitigate risk, non-target wildlife may be unintentionally exposed to ARs through various trophic pathways, and depending on dose, exposure can result in adverse effects and mortality. Second-generation ARs (SGARs) are mixtures of cis- and trans-diastereoisomers (each including two stereoisomers) that exhibit similar in vitro inhibitory potency for vitamin K epoxide reductase in rodent microsomal assay systems. Some diastereoisomers and hence some individual stereoisomers are preferentially metabolized in vivo, resulting in residue patterns in exposed target rodents that differ from the bait formulations. Use of less persistent but equally potent SGAR stereoisomers in baits results in lower tissue residues in target rodents, which in turn constitutes lower risk when consumed by non-target wildlife. The toxicity of two brodifacoum formulations with stereoisomers having markedly different elimination half-lives in rats (Formulation A containing the two least persistent stereoisomers, and Formulation B containing the most persistent stereoisomer) were tested in a 7-day dietary feeding trial with American kestrels. Based on previous kestrel studies using commercially available brodifacoum, Formulations A and B were each provided at three dietary concentrations (0.05, 0.1 and 0.5 µg/g diet, 4 kestrels/dose level) predicted to cause a range of toxicity. Compared to unexposed controls, all kestrels that ingested 0.5 µg/g diet of the longer-lived Formulation B exhibited extreme coagulopathy. In contrast, the 0.5 µg/g diet of the shorter-lived Formulation A yielded only a modest lengthening of clotting time in just 1 of the 4 exposed kestrels. These findings support the notion that SGAR baits enriched with less persistent stereoisomers may pose lower hazard and ultimately risk to non-target wildlife.
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The U.S. Geological Survey visited 28 of these lakes and impoundments at 14 single and multiple waterbody systems to evaluate their appropriateness for characterizing drought. The data collection and computation methods at each system were then reviewed and checked for consistency. The types of historical monthly data available varied by system and included water surface elevation, depth of water below the spillway, volume, or reservoir capacity (percent full). For this analysis, water surface elevations and reservoir capacities were converted to volumes to assess the interannual variability in lake volumes. As a second level of assessment, analysis was also done on water surface elevation variability. Systems that did not have enough differentiation in monthly values between lake volume or water surface elevations to clearly demarcate drought levels were identified as unsuitable for use in the drought index for that month. This report discusses the limitations of using the reviewed lakes and impoundments as a drought index, as well as a list of best practices for data collection techniques to improve the confidence and reliability of the data collected.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Secchi depth measurements in Lake Tahoe have been collected at a Long-Term Profile (LTP) monitoring site since 1968. Periodic updates in Secchi trend analysis are needed to understand changes in the long-term record, changes in seasonal pattern, and to provide insight into the progress of restoration efforts in improving lake clarity. As such, this analysis is intended to evaluate the long-term and seasonal clarity conditions by updating the analysis by Jassby and others, (1999) and to demonstrate the use of statistical measures as metrics for comparing ongoing monitoring data (Watanabe, 2024).
","language":"English","publisher":"Tahoe Science Advisory Council","usgsCitation":"Naranjo, R.C., 2024, Statistical analysis of Lake Tahoe secchi depth data, 16 p.","productDescription":"16 p.","ipdsId":"IP-168331","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":462818,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.tahoesciencecouncil.org/project-list","linkFileType":{"id":5,"text":"html"}},{"id":462826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Lake Tahoe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.11446470824197,\n 39.21366571450457\n ],\n [\n -120.16965242081064,\n 39.102249370483676\n ],\n [\n -120.08208791686843,\n 38.932606385440636\n ],\n [\n -119.94227904502787,\n 38.933750744029425\n ],\n [\n -119.93050566634669,\n 39.25014399290501\n ],\n [\n -120.0666353573491,\n 39.24444253042536\n ],\n [\n -120.11446470824197,\n 39.21366571450457\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Naranjo, Ramon C. 0000-0003-4469-6831 rnaranjo@usgs.gov","orcid":"https://orcid.org/0000-0003-4469-6831","contributorId":3391,"corporation":false,"usgs":true,"family":"Naranjo","given":"Ramon","email":"rnaranjo@usgs.gov","middleInitial":"C.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915688,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70259407,"text":"70259407 - 2024 - Microplastic and associated black particles from road-tire wear: Implications for radiative effects across the cryosphere and in the atmosphere","interactions":[],"lastModifiedDate":"2024-10-07T11:57:36.115271","indexId":"70259407","displayToPublicDate":"2024-09-30T06:55:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18737,"text":"JGR - Atmospheres","active":true,"publicationSubtype":{"id":10}},"title":"Microplastic and associated black particles from road-tire wear: Implications for radiative effects across the cryosphere and in the atmosphere","docAbstract":"The environmental effects of airborne micro- and nano-size plastic particles are poorly understood. Microscopy and chemical analyses of atmospherically deposited particles on snow surfaces at high elevation (2,865–3,690 m) in the Upper Colorado River basin (UCRB; Colorado Rocky Mountains) revealed the presence of black substances intimately associated with microplastic fibers, particles interpreted to have originated as tire matter. Identical and similar particles occur in shredded tires and road-surface samples. The substance responsible for the black color of all tires is carbon black, a graphitic light-absorbing tire additive produced by hydrocarbon combustion that homogeneously permeates the mixture of tire polymers and other additives. Such black tire matter may thus exert radiative effects closely similar to those of black carbon. The presence in snow of many organic compound types common to tires, measured by two-dimensional gas chromatography, suggests that atmospherically deposited black road-tire-wear matter is among the light-absorbing particulates that advance the onset and rate of snow melt in the UCRB. The mass of road-tire-wear particles shed from vehicles may be estimated by multiplying measured amounts of eroded tire-per-distance traveled by vehicular distances. Under a combination of measurements and assumptions about the amounts and radiative properties of atmospheric tire-wear particles, the radiative effects of these particles might add about 10%–30% to those effects from black carbon, an estimate ripe for revision. On regional and global scales, the amounts and effects of emitted and deposited tire-wear matter likely vary by factors of geographic source, transport pathway, and depositional setting.
Land use influences surface water quality, often alleviating stoichiometric constraints on primary production and altering biogeochemical cycling. However, land use effects on nutrient content and potential trace metal accumulation in aquatic plants remain unclear, and high concentrations of metals and altered nutrient ratios could impact the health of herbivores and detritivores. We tested for land use effects on nutrient and trace metal accumulation in a widespread riverine macrophyte, Podostemum ceratophyllum, collected from 91 locations from Georgia to Maine, USA in 2014–2016. We quantified carbon (C), nitrogen (N), phosphorus (P), their molar and mass ratios, N and C stable isotopes, and 17 additional elements in dried plants collected from each location to estimate relationships between plant tissue content and watershed land use, which we quantified as agriculture, forest, and development. Decreasing forest cover was correlated with increasing δ15N, Mg, Mn, and P in Podostemum tissue. Increasing urban development was correlated with increasing δ15N, Mg and P, while increasing agriculture was correlated with a decrease in C: P and the concentrations of multiple metals, along with increases in P, Mg and δ15N. Decreases in ratios of N: P and C:P with increasing agriculture and urban development in the watershed indicate more rapid P storage relative to C and N in plant tissue, and increased resource quality of the plant to consumers in these watersheds. We also observed potentially toxic dietary concentrations of some trace metals (B, Cd, Tl, Zn) in plant tissue which could be related to the plant's natural herbivory defense system or to land use. We conclude that land use influences the elemental composition of P. ceratophyllum, and potentially the quality and toxicity of the plant to herbivores and detritivores in eastern North American rivers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.176118","usgsCitation":"Wood, J., Dietterich, L.H., Leasure, D.R., Jantzi, S., Maddox, T., Wenger, S., Skaggs, J., Rosemond, A.D., and Freeman, M., 2024, Elemental composition and potential toxicity of the riverine macrophyte Podostemum ceratophyllum Michx. reflects land use in eastern North America: Science of the Total Environment, v. 954, 176118, 14 p., https://doi.org/10.1016/j.scitotenv.2024.176118.","productDescription":"176118, 14 p.","ipdsId":"IP-144142","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":490988,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2024.176118","text":"Publisher Index Page"},{"id":465144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -69.54256609727352,\n 43.88143574292508\n ],\n [\n -70.647563027507,\n 44.26997672061174\n ],\n [\n -73.8976845593064,\n 43.177908703325954\n ],\n [\n -74.07002050845693,\n 40.99340531964512\n ],\n [\n -72.05924526688266,\n 41.33951624483788\n ],\n [\n -70.23920091350011,\n 41.51880370179515\n ],\n [\n -69.54256609727352,\n 43.88143574292508\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.53957439072548,\n 37.96935046600281\n ],\n [\n -80.27133242619323,\n 38.11626273373102\n ],\n [\n -85.94943603496614,\n 34.90130349851556\n ],\n [\n -85.30759808373625,\n 33.510279887546815\n ],\n [\n -82.4448300394111,\n 34.00184838830782\n ],\n [\n -77.9718785239481,\n 36.58735830390653\n ],\n [\n -77.87536535646905,\n 37.23153468843074\n ],\n [\n -79.53957439072548,\n 37.96935046600281\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"954","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, James","contributorId":174400,"corporation":false,"usgs":false,"family":"Wood","given":"James","affiliations":[],"preferred":false,"id":921060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dietterich, Lee H.","contributorId":333170,"corporation":false,"usgs":false,"family":"Dietterich","given":"Lee","email":"","middleInitial":"H.","affiliations":[{"id":79766,"text":"Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523; US Army Engineer Research and Development Center, Environmental Laboratory, Vicksburg, MS 39180","active":true,"usgs":false}],"preferred":false,"id":921061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leasure, Douglas R.","contributorId":145643,"corporation":false,"usgs":false,"family":"Leasure","given":"Douglas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":921062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jantzi, Sarah","contributorId":347214,"corporation":false,"usgs":false,"family":"Jantzi","given":"Sarah","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":921063,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maddox, Thomas","contributorId":347217,"corporation":false,"usgs":false,"family":"Maddox","given":"Thomas","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":921064,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":921065,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Skaggs, Jonathan","contributorId":260315,"corporation":false,"usgs":false,"family":"Skaggs","given":"Jonathan","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":921066,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rosemond, Amy D.","contributorId":279630,"corporation":false,"usgs":false,"family":"Rosemond","given":"Amy","email":"","middleInitial":"D.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":921067,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":921068,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70259510,"text":"70259510 - 2024 - Fish health altered by contaminants and low water temperatures compounded by prolonged regional drought in the Lower Colorado River Basin, USA","interactions":[],"lastModifiedDate":"2024-10-10T13:46:25.073316","indexId":"70259510","displayToPublicDate":"2024-09-28T08:40:14","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7597,"text":"Toxics","active":true,"publicationSubtype":{"id":10}},"title":"Fish health altered by contaminants and low water temperatures compounded by prolonged regional drought in the Lower Colorado River Basin, USA","docAbstract":"The goal of this study was to assess health of male Common Carp (carp, Cyprinus carpio) at four sites with a wide range in environmental organic contaminant (EOC) concentrations and water temperatures in Lake Mead National Recreation Area NV/AZ, US, and the potential influence of regional drought. Histological and reproductive biomarkers were measured in 17–30 carp at four sites and 130 EOCs in water per site were analyzed using passive samplers in 2010. Wide ranges among sites were noted in total EOC concentrations (>10Xs) and water temperature/degree days (10Xs). In 2007/08, total polychlorinated biphenyls (tPCBs) in fish whole bodies from Willow Beach (WB) in the free-flowing Colorado River below Hoover Dam were clearly higher than at the other sites. This was most likely due to longer exposures in colder water (12–14 °C) and fish there having the longest lifespan (up to 54 years) for carp reported in the Colorado River Basin. Calculated estrogenicity in water exceeded long-term, environmentally safe criteria of 0.1–0.4 ng/L by one to three orders of magnitude at all sites except the reference site. Low ecological screening values for four contaminants of emerging concern (CEC) in water were exceeded for one CEC in the reference site, two in WB and Las Vegas Bay and three in the most contaminated site LVW. Fish health biomarkers in WB carp had 25% lower liver glycogen, 10Xs higher testicular pigmented cell aggregates and higher sperm abnormalities than the reference site. Sperm from LVW fish also had significantly higher fragmentation of DNA, lower motility and testis had lower percent of spermatozoa, all of which can impair reproduction. Projections from a 3D water quality model performed for WB showed that EOC concentrations due to prolonged regional drought and reduced water levels could increase as high as 135%. Water temperatures by late 21st century are predicted to rise between 0.7 and 2.1 °C that could increase eutrophication, algal blooms, spread disease and decrease dissolved oxygen over 5%.
","language":"English","publisher":"MDPI","doi":"10.3390/toxics12100708","usgsCitation":"Goodbred, S.L., Patino, R., Alvarez, D.A., Johnson, D., Hannoun, D., Echols, K.R., and Jenkins, J., 2024, Fish health altered by contaminants and low water temperatures compounded by prolonged regional drought in the Lower Colorado River Basin, USA: Toxics, v. 12, no. 10, 708, 29 p., https://doi.org/10.3390/toxics12100708.","productDescription":"708, 29 p.","ipdsId":"IP-157522","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":466891,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/toxics12100708","text":"Publisher Index Page"},{"id":462787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Nevada","otherGeospatial":"Lake Mead National Recreation Area, Lower Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.31285412120533,\n 36.6\n ],\n [\n -115.31285412120533,\n 35.75\n ],\n [\n -113.64554275683693,\n 35.75\n ],\n [\n -113.64554275683693,\n 36.6\n ],\n [\n -115.31285412120533,\n 36.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Goodbred, Steven L 0009-0006-1165-3295 sgoodbred@usgs.gov","orcid":"https://orcid.org/0009-0006-1165-3295","contributorId":345073,"corporation":false,"usgs":true,"family":"Goodbred","given":"Steven","email":"sgoodbred@usgs.gov","middleInitial":"L","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":915546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alvarez, David A. 0000-0002-6918-2709","orcid":"https://orcid.org/0000-0002-6918-2709","contributorId":220763,"corporation":false,"usgs":true,"family":"Alvarez","given":"David","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":915547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Darren 0000-0002-0502-6045","orcid":"https://orcid.org/0000-0002-0502-6045","contributorId":205688,"corporation":false,"usgs":false,"family":"Johnson","given":"Darren","affiliations":[{"id":37106,"text":"Cherokee Nation","active":true,"usgs":false}],"preferred":false,"id":915548,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hannoun, Deena 0000-0003-3928-4023","orcid":"https://orcid.org/0000-0003-3928-4023","contributorId":345075,"corporation":false,"usgs":false,"family":"Hannoun","given":"Deena","email":"","affiliations":[{"id":35387,"text":"Southern Nevada Water Authority","active":true,"usgs":false}],"preferred":false,"id":915549,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Echols, Kathy R. 0000-0003-2631-9143 kechols@usgs.gov","orcid":"https://orcid.org/0000-0003-2631-9143","contributorId":2799,"corporation":false,"usgs":true,"family":"Echols","given":"Kathy","email":"kechols@usgs.gov","middleInitial":"R.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":915550,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jenkins, Jill 0000-0002-5087-0894","orcid":"https://orcid.org/0000-0002-5087-0894","contributorId":222865,"corporation":false,"usgs":true,"family":"Jenkins","given":"Jill","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":915551,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70259783,"text":"70259783 - 2024 - The Khonkho tephra: A large-magnitude volcanic eruption coincided with the rise of Tiwanaku in the Andes","interactions":[],"lastModifiedDate":"2024-11-25T14:22:03.142574","indexId":"70259783","displayToPublicDate":"2024-09-28T07:15:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3562,"text":"The Holocene","active":true,"publicationSubtype":{"id":10}},"title":"The Khonkho tephra: A large-magnitude volcanic eruption coincided with the rise of Tiwanaku in the Andes","docAbstract":"We report a tephra deposit in the southern Lake Titicaca Basin, Bolivia, which was deposited by a major, previously unrecognized eruption sometime between AD 400 and 720. Archaeological data suggest these centuries were characterized by a substantial community migration to Tiwanaku, where social interaction networks gave birth to one of the Andes’ first large complex societies. Here we provide an initial characterization of this tephra, based on samples from the archaeological site Khonkho Wankane. The same tephra is present at two other archaeological sites in the region. Given the great distance to the nearest active volcano, this tephra layer likely derives from a large-magnitude, Late-Holocene explosive eruption of a Central Andean volcano. We suggest that this major event be included in the human history of the region, given its inferred magnitude and wide dispersal area. Future research could confirm the Khonkho tephra at other sites, identify the source volcano, estimate its volume, and more precisely date the eruption.
","language":"English","publisher":"Sage Journals","doi":"10.1177/09596836241275000","usgsCitation":"Marsh, E.J., Harpel, C., and Damby, D., 2024, The Khonkho tephra: A large-magnitude volcanic eruption coincided with the rise of Tiwanaku in the Andes: The Holocene, v. 34, no. 12, p. 1865-1874, https://doi.org/10.1177/09596836241275000.","productDescription":"10 p.","startPage":"1865","endPage":"1874","ipdsId":"IP-154637","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":489003,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/09596836241275000","text":"Publisher Index Page"},{"id":463145,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bolivia, Peru","otherGeospatial":"southern Lake Titicaca Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.1162116388946,\n -15.80639805549444\n ],\n [\n -70.1162116388946,\n -17.442518219155275\n ],\n [\n -68.4533885170531,\n -17.442518219155275\n ],\n [\n -68.4533885170531,\n -15.80639805549444\n ],\n [\n -70.1162116388946,\n -15.80639805549444\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Marsh, Erik J. 0000-0003-2355-5415","orcid":"https://orcid.org/0000-0003-2355-5415","contributorId":333619,"corporation":false,"usgs":false,"family":"Marsh","given":"Erik","email":"","middleInitial":"J.","affiliations":[{"id":79949,"text":"Consejo Nacional de Investigaciones Científicas y Técnicas","active":true,"usgs":false}],"preferred":false,"id":916662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harpel, Christopher 0000-0001-8587-7845","orcid":"https://orcid.org/0000-0001-8587-7845","contributorId":204746,"corporation":false,"usgs":true,"family":"Harpel","given":"Christopher","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":916663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Damby, David 0000-0002-3238-3961","orcid":"https://orcid.org/0000-0002-3238-3961","contributorId":206614,"corporation":false,"usgs":true,"family":"Damby","given":"David","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":916664,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259093,"text":"sir20245080 - 2024 - Estimating groundwater level records using MOVE.1 and computing monthly percentiles from estimated groundwater records in Massachusetts","interactions":[],"lastModifiedDate":"2025-12-23T22:00:34.67459","indexId":"sir20245080","displayToPublicDate":"2024-09-27T16:00:00","publicationYear":"2024","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":"2024-5080","displayTitle":"Estimating Groundwater Level Records Using MOVE.1 and Computing Monthly Percentiles From Estimated Groundwater Records in Massachusetts","title":"Estimating groundwater level records using MOVE.1 and computing monthly percentiles from estimated groundwater records in Massachusetts","docAbstract":"The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, performed record extensions on groundwater levels at select wells using the Maintenance of Variance Extension type 1 (MOVE.1) method. The groundwater levels estimated from these record extensions were used to compute monthly percentiles to improve future determinations of a groundwater index. In Massachusetts, 27 of 29 short-record study wells with continuous groundwater levels between 0.8 and 8.1 years were suitable for record extensions; 37 long-record index wells were used to extend the groundwater level records at the study wells. The index well selected to pair with a study well was chosen based on Pearson correlation coefficient values; cross-correlation between the two wells; geologic and topographic similarity; and smallest distance spanning the wells. Each study well and its corresponding index well have 1 or more years of concurrent, overlapping data; a Pearson correlation coefficient that exceeded a threshold value of 0.8; and a similar aquifer type and hydrologic characteristics. Of the 29 study wells, 2 showed poor correlations with all index wells and were not considered for record extensions.
Performance metrics used to assess the accuracy of the MOVE.1 models indicated that most models provided reasonable estimates of groundwater levels. Root mean square error values ranged from 0.097 to 2.292 feet, with a median of 0.536 foot. Nash-Sutcliffe efficiency coefficient values ranged from 0.623 to 0.996, with a median value of 0.759. Generally, study wells in close geographical proximity to their index well resulted in stronger model performance.
The average length of groundwater level records was extended by 14.1 years to a new average of 18.1 years. The estimated groundwater level records from the MOVE.1 models resulted in an increase in the range of highest and lowest groundwater levels at 23 of 27 wells. The increase in range of groundwater levels was between 0.08 to 7.95 feet. Monthly percentiles for State drought indices were computed from the estimated MOVE.1 records and observed records through December 31, 2021. Percentiles computed from estimated records show an average groundwater level about 1.0 foot lower than observed data at the 2d percentile and 0.1 foot lower at the 30th percentile.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Coal fly ash is a potential resource of valuable elements, such as rare earth elements (REEs), which are retained and concentrated upon combustion. Understanding REE occurrence within fly ash is vital to developing recovery methods. Some of the highest REE contents occur in fly ash derived from U.S. Appalachian Basin coals, and coals influenced by input volcanic ash are especially enriched. Leaching studies of bulk fly ash show that, as a proportion of the total REEs present, samples from eastern U.S. coals are generally less extractable than fly ash derived from lower REEs western U.S. coals. Aluminosilicate glasses formed during combustion comprise the largest mass fraction of coal fly ash. REE-enriched domains are present locally in fly ash at the nanometer scale. Annual fly ash production, combined with coal ash already in storage, makes up a large resource for potential recovery of rare earths and associated critical elements. Further extraction technology developments are needed to overcome difficulties in REE concentration and purification to produce coal-ash-derived REE materials of saleable purity.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Rare Earth Elements: Sustainable recovery, processing, and purification","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Geophysical Union","doi":"10.1002/9781119515005.ch2","usgsCitation":"Hower, J., Kolker, A., Hsu-Kim, H., and Platta, D., 2024, Rare Earth Elements in coal fly ash and their potential recovery, chap. 2 of Rare Earth Elements: Sustainable recovery, processing, and purification, p. 27-73, https://doi.org/10.1002/9781119515005.ch2.","productDescription":"47 p.","startPage":"27","endPage":"73","ipdsId":"IP-104795","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":466892,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/9781119515005.ch2","text":"Publisher Index Page"},{"id":463157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2024-09-27","publicationStatus":"PW","contributors":{"editors":[{"text":"Karamalidis, Athanasios","contributorId":100622,"corporation":false,"usgs":false,"family":"Karamalidis","given":"Athanasios","email":"","affiliations":[{"id":417,"text":"National Energy Technology Laboratory","active":false,"usgs":true}],"preferred":false,"id":916687,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Eggert, Roderick","contributorId":172613,"corporation":false,"usgs":false,"family":"Eggert","given":"Roderick","email":"","affiliations":[],"preferred":false,"id":916688,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Hower, James C. 0000-0003-4694-2776","orcid":"https://orcid.org/0000-0003-4694-2776","contributorId":215373,"corporation":false,"usgs":false,"family":"Hower","given":"James","middleInitial":"C.","affiliations":[{"id":39231,"text":"University of Kentucky Center for Applied Energy Research","active":true,"usgs":false}],"preferred":false,"id":762195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolker, Allan 0000-0002-5768-4533 akolker@usgs.gov","orcid":"https://orcid.org/0000-0002-5768-4533","contributorId":643,"corporation":false,"usgs":true,"family":"Kolker","given":"Allan","email":"akolker@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":762194,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hsu-Kim, Heileen","contributorId":178880,"corporation":false,"usgs":false,"family":"Hsu-Kim","given":"Heileen","email":"","affiliations":[],"preferred":false,"id":762196,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Platta, Desiree 0000-0003-0657-7735","orcid":"https://orcid.org/0000-0003-0657-7735","contributorId":215374,"corporation":false,"usgs":false,"family":"Platta","given":"Desiree","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":762197,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266205,"text":"70266205 - 2024 - An evaluation of fin ray microchemistry to describe movement of White Sturgeon in the Kootenai River basin: Insights and limitations","interactions":[],"lastModifiedDate":"2025-04-30T16:01:00.53097","indexId":"70266205","displayToPublicDate":"2024-09-27T10:50:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18328,"text":"Frontiers in Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of fin ray microchemistry to describe movement of White Sturgeon in the Kootenai River basin: Insights and limitations","docAbstract":"Introduction: White Sturgeon Acipenser transmontanus in the Kootenai River basin is listed as endangered in the United States and Canada. Declines have been mainly attributed to poor recruitment exacerbated by the environmental effects of Libby Dam in Montana. Reduced primary production downstream of Libby Dam has been identified as one factor limiting growth of White Sturgeon, thereby limiting natural reproduction through delayed sexual maturity. However, estimating changes in growth over time without knowledge of fish location (i.e., Kootenai River vs. Kootenay Lake) is difficult. The objective of this project was to use microchemistry analysis to describe the movement of White Sturgeon within the Kootenai River basin.
Methods: Water samples (n = 27) and White Sturgeon fin rays (n = 162) collected in the Kootenai River basin were measured for strontium isotope (87Sr:86Sr) ratios using laser ablation multicollector inductively coupled plasma mass spectrometry. All water samples and a subset of fin rays (n = 8) were also analyzed for trace elements (e.g., Sr, Ba, Mg). Fin ray annuli were measured and tested using knowledge of known age; known locations from physical capture events were assigned to age at capture.
Results: Strontium isotope analysis was unable to detect differences in the Kootenai River and Kootenay Lake using water or fin ray samples. The Kootenai River and Kootenay Lake were distinguishable using trace element data from water samples, but not fin rays. The discrepancy with trace elements appears to be associated with the physiology of fin ray growth.
Discussion: Although this study did not meet its original objective of describing the movement of White Sturgeon in the Kootenai River basin from fin ray microchemistry, our results provide insight into the potential influence of physiology on microchemistry analysis. In particular, fin ray microchemistry of slow-growing fishes may be possible in freshwater systems with further investigation into the physiological processes associated with growth and the incorporation of elements into calcified structures.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffwsc.2024.1475115","usgsCitation":"Ghere, C., Quist, M.C., Hardy, R.S., Willmes, M., Lewis, L., Wilson, S., and Smith, T., 2024, An evaluation of fin ray microchemistry to describe movement of White Sturgeon in the Kootenai River basin: Insights and limitations: Frontiers in Freshwater Science, v. 2, 1475115, 16 p., https://doi.org/10.3389/ffwsc.2024.1475115.","productDescription":"1475115, 16 p.","ipdsId":"IP-167840","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487891,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffwsc.2024.1475115","text":"Publisher Index Page"},{"id":485211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, Idaho, Montana","otherGeospatial":"Kootenai River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.58401652080497,\n 50.69930521150616\n ],\n [\n -117.49821629131017,\n 49.509131619753205\n ],\n [\n -116.48726648061708,\n 48.128537941603184\n ],\n [\n -115.02431190642167,\n 48.23747385319575\n ],\n [\n -115.07423825028928,\n 48.982857626149\n ],\n [\n -115.7490124116938,\n 50.94774297789954\n ],\n [\n -117.58401652080497,\n 50.69930521150616\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationDate":"2024-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Ghere, Courtnie L.","contributorId":352032,"corporation":false,"usgs":false,"family":"Ghere","given":"Courtnie","middleInitial":"L.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":934915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":934916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hardy, Ryan S.","contributorId":167032,"corporation":false,"usgs":false,"family":"Hardy","given":"Ryan","email":"","middleInitial":"S.","affiliations":[{"id":6764,"text":"Idaho Department of Fish and Game, Nampa, Idaho","active":true,"usgs":false}],"preferred":false,"id":934917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Willmes, Malte","contributorId":198500,"corporation":false,"usgs":false,"family":"Willmes","given":"Malte","email":"","affiliations":[],"preferred":false,"id":934918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lewis, Levi","contributorId":313579,"corporation":false,"usgs":false,"family":"Lewis","given":"Levi","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":934919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, Sean","contributorId":352033,"corporation":false,"usgs":false,"family":"Wilson","given":"Sean","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":934920,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Troy","contributorId":353368,"corporation":false,"usgs":false,"family":"Smith","given":"Troy","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":934921,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70263402,"text":"70263402 - 2024 - Extending the Boore and Abrahamson (2023) modified square-root-impedance method for the development of site amplifications consistent with the full-resonance approach to a range of VS30 values","interactions":[],"lastModifiedDate":"2025-02-10T16:50:36.940557","indexId":"70263402","displayToPublicDate":"2024-09-27T09:38:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Extending the Boore and Abrahamson (2023) modified square-root-impedance method for the development of site amplifications consistent with the full-resonance approach to a range of VS30 values","docAbstract":"The square-root-impedance (SRI) method is commonly used to approximate the seismic site amplifications computed using the full-resonance (FR) method for gradient shear-wave velocity (VS) profiles that are smoothly varying with depth. The SRI site amplifications have been observed to systematically underpredict the FR site amplifications by a ratio of FR/SRI amplifications around 1.05 to 1.3 across a wide frequency range (Boore, 2013). Recently, Boore and Abrahamson (2023; hereafter, BA23) related this difference in the SRI and FR methods to differences in the exponent η of the ratio of seismic impedances between the two methods. They proposed the implementation of a modified frequency-dependent η in the SRI method to improve its match to the FR site amplifications. This modified η was derived using only five VS profiles. We investigate the performance of the BA23 η for a wide range of realistic gradient VS profiles with VS30 ranging from 180 to 1500 m/s. These gradient VS profiles are constructed using two power-law functions of depth and are constrained by the assigned VS30 value, the depth and velocity of the half-space, and depths to shear-wave velocity horizons of 1.0 and 2.5 km/s (Z1.0 and Z2.5) based on western United States sites. Despite observing a VS30 dependence of η, we find that the BA23 η generally works reasonably well for the range of VS profiles analyzed. Using the VS30-dependent η derived in this study results in improvements in matching the FR site amplification compared to using the BA23 η. These improvements are more pronounced for the soft-site conditions and become modest to negligible for the stiff site conditions
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120240112","usgsCitation":"Al Atik, L., and Boore, D., 2024, Extending the Boore and Abrahamson (2023) modified square-root-impedance method for the development of site amplifications consistent with the full-resonance approach to a range of VS30 values: Bulletin of the Seismological Society of America, v. 114, no. 6, p. 3093-3102, https://doi.org/10.1785/0120240112.","productDescription":"10 p.","startPage":"3093","endPage":"3102","ipdsId":"IP-167085","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481879,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"114","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Al Atik, Linda","contributorId":140526,"corporation":false,"usgs":false,"family":"Al Atik","given":"Linda","email":"","affiliations":[],"preferred":false,"id":926857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boore, David 0000-0002-8605-9673 boore@usgs.gov","orcid":"https://orcid.org/0000-0002-8605-9673","contributorId":140502,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926858,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259385,"text":"70259385 - 2024 - Evaluation of the lithium resource in the Smackover Formation brines of southern Arkansas using machine learning","interactions":[],"lastModifiedDate":"2024-10-07T14:39:04.576768","indexId":"70259385","displayToPublicDate":"2024-09-27T09:34:41","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of the lithium resource in the Smackover Formation brines of southern Arkansas using machine learning","docAbstract":"Global demand for lithium, the primary component of lithium-ion batteries, greatly exceeds known supplies, and this imbalance is expected to increase as the world transitions away from fossil fuel energy sources. High concentrations of lithium in brines have been observed in the Smackover Formation in southern Arkansas (>400 milligrams per liter). We used published and newly collected brine lithium concentration data to train a random forest machine-learning model using geologic, geochemical, and temperature explanatory variables and create a map of predicted lithium concentrations in Smackover Formation brines across southern Arkansas. Using these predicted lithium maps with reservoir parameters and geologic information, we calculated that there are 5.1 to 19 million tons of lithium in Smackover Formation brines in southern Arkansas, which represents 35 to 136% of the current US lithium resource estimate. Based on these calculations, in 2022, 5000 tons of dissolved lithium were brought to the surface within brines as waste streams of the oil, gas, and bromine industries.
","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.adp8149","usgsCitation":"Knierim, K.J., Blondes, M., Masterson, A., Freeman, P., McDevitt, B., Herzberg, A., Li, P., Mills, C., Doolan, C.A., Jubb, A., Ausbrooks, S., and Chenault, J., 2024, Evaluation of the lithium resource in the Smackover Formation brines of southern Arkansas using machine learning: Science Advances, v. 10, no. 39, eadp8149, 11 p., https://doi.org/10.1126/sciadv.adp8149.","productDescription":"eadp8149, 11 p.","ipdsId":"IP-153803","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":466893,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adp8149","text":"Publisher Index Page"},{"id":462662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Smackover Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.97434190011148,\n 33.77226311729616\n ],\n [\n -93.97434190011148,\n 32.98074805942153\n ],\n [\n -92.06801891555762,\n 32.98074805942153\n ],\n [\n -92.06801891555762,\n 33.77226311729616\n ],\n [\n -93.97434190011148,\n 33.77226311729616\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"39","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":915107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Masterson, Andrew Laurence 0000-0002-3422-2985","orcid":"https://orcid.org/0000-0002-3422-2985","contributorId":343951,"corporation":false,"usgs":true,"family":"Masterson","given":"Andrew Laurence","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":915108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":224150,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":915109,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McDevitt, Bonnie 0000-0001-8390-0028","orcid":"https://orcid.org/0000-0001-8390-0028","contributorId":291246,"corporation":false,"usgs":true,"family":"McDevitt","given":"Bonnie","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":915110,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herzberg, Amanda Sha 0000-0003-0343-9425","orcid":"https://orcid.org/0000-0003-0343-9425","contributorId":333089,"corporation":false,"usgs":true,"family":"Herzberg","given":"Amanda Sha","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":915111,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Peng","contributorId":344957,"corporation":false,"usgs":false,"family":"Li","given":"Peng","affiliations":[{"id":82440,"text":"Arkansas Department of Energy and Environment, Office of the State Geologist","active":true,"usgs":false}],"preferred":false,"id":915112,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mills, Ciara","contributorId":344958,"corporation":false,"usgs":false,"family":"Mills","given":"Ciara","email":"","affiliations":[{"id":82440,"text":"Arkansas Department of Energy and Environment, Office of the State Geologist","active":true,"usgs":false}],"preferred":false,"id":915113,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Doolan, Colin A. 0000-0002-7595-7566 cdoolan@usgs.gov","orcid":"https://orcid.org/0000-0002-7595-7566","contributorId":3046,"corporation":false,"usgs":true,"family":"Doolan","given":"Colin","email":"cdoolan@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":915114,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":915115,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ausbrooks, Scott","contributorId":344959,"corporation":false,"usgs":false,"family":"Ausbrooks","given":"Scott","affiliations":[{"id":82440,"text":"Arkansas Department of Energy and Environment, Office of the State Geologist","active":true,"usgs":false}],"preferred":false,"id":915116,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Chenault, Jessica 0000-0002-5974-0762","orcid":"https://orcid.org/0000-0002-5974-0762","contributorId":222078,"corporation":false,"usgs":true,"family":"Chenault","given":"Jessica","email":"","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":915117,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70263979,"text":"70263979 - 2024 - Length-weight relationships of native and non-native fishes in the lower Red River catchment, USA","interactions":[],"lastModifiedDate":"2025-03-04T15:28:51.917668","indexId":"70263979","displayToPublicDate":"2024-09-27T08:21:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2166,"text":"Journal of Applied Ichthyology","active":true,"publicationSubtype":{"id":10}},"title":"Length-weight relationships of native and non-native fishes in the lower Red River catchment, USA","docAbstract":"Length-weight relationships are useful for stock assessments and modeling alternative conservation and management strategies for both native and non-native fishes. We developed length-weight relationships for 18 native and non-native riverine fishes in the lower Red River catchment. Fishes were sampled in the summer and autumn seasons between May 2021 and March 2024 via electrofishing and gill nets. Measurements for each specimen consisted of total length (mm) and weight (g). We provide L-W relationships for 14 native fishes consisting of 5 families (Lepisosteidae, Catostomidae, Ictaluridae, Sciaenidae, and Polyodontidae) and 4 non-native species belonging to the family Cyprinidae. We collected 6,845 individuals ranging from 67 alligator gar to 1,848 smallmouth buffalo. All the L-W relationships were significant (p < 0.05), and the majority (72% of species) of relationships between length and weight had r2 values > 0.70. Our findings provide insight into the L-W relationships of riverine fishes and can be useful for modeling alternatives targeted at native fishes of recreational value and the removal efforts of non-native fishes.
","language":"English","publisher":"Wiley","doi":"10.1155/2024/5578825","usgsCitation":"Vilchez, M., Dattilo, J., and Brewer, S., 2024, Length-weight relationships of native and non-native fishes in the lower Red River catchment, USA: Journal of Applied Ichthyology, v. 2024, no. 1, 5578825, 5 p., https://doi.org/10.1155/2024/5578825.","productDescription":"5578825, 5 p.","ipdsId":"IP-165911","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487737,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1155/2024/5578825","text":"Publisher Index Page"},{"id":482798,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma, Texas","otherGeospatial":"Red River catchment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.93228187371122,\n 34.22047917116373\n ],\n [\n -94.93228187371122,\n 33.04015559284352\n ],\n [\n -93.76736032095783,\n 33.04015559284352\n ],\n [\n -93.76736032095783,\n 34.22047917116373\n ],\n [\n -94.93228187371122,\n 34.22047917116373\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2024","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Vilchez, Mariaguadalupe","contributorId":351761,"corporation":false,"usgs":false,"family":"Vilchez","given":"Mariaguadalupe","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":929405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dattilo, John","contributorId":341000,"corporation":false,"usgs":false,"family":"Dattilo","given":"John","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":929406,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":340552,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":929407,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70260405,"text":"70260405 - 2024 - Framework for mapping liquefaction hazard–Targeted design ground motions","interactions":[],"lastModifiedDate":"2024-11-01T13:20:58.073239","indexId":"70260405","displayToPublicDate":"2024-09-27T07:12:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2327,"text":"Journal of Geotechnical and Geoenvironmental Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Framework for mapping liquefaction hazard–Targeted design ground motions","docAbstract":"The management of developed barrier islands is often piece-meal and reactionary despite the complex, dynamic nature of these systems, and sustainable practices will become increasingly difficult due to heightened pressures of climate change. Adaptation actions, including nature-based solutions, need to be thoroughly evaluated prior to implementation to understand system-wide impacts and avoid maladaptation. Anarde et al. (2024a), (https://doi.org/10.1029/2023ef003672), Anarde et al. (2024b), (https://doi.org/10.1029/2023ef004200) is the latest important contribution in a growing body of transdisciplinary research that more robustly evaluates the complex physical process-and-response relationship of barrier systems via sophisticated numerical modeling approaches that also interface with socioeconomic models to inform coastal management actions in response to mitigating coastal risk. This new research indicates the importance of coordinated system-scale barrier island management, as strategies to reduce coastal hazard risk in one location will directly affect adjacent communities. Further, this work demonstrates that reducing barrier management interventions may actually promote barrier recovery and sustainability in the face of sea level rise. In addition, recent advances in the analysis and application of remotely sensed data from satellites and oblique aerial photography provide scientists an unprecedented opportunity to track coastal evolution over a wide range of spatial and temporal scales at minimal cost. As sea level rise and changing storm patterns challenge the sustainable management of barrier island systems, integrating these advanced, transdisciplinary tools will enable scientists and coastal practitioners to more thoroughly evaluate coastal adaptation options, efficiently invest limited resources to mitigate coastal hazard risk for communities, support healthy ecosystems, and reduce system-wide impacts.
Vegetation communities in restored bottomland hardwood forests in northeast Indiana were studied 6–21 years after restoration to assess progress toward restoration objectives. The study focused on four sites that were restored to compensate for resource injuries after contaminant releases. The restored sites were compared with four reference-site conditions, including crops (prerestoration condition), old field communities representing a no-management alternative, locally sampled second-growth mature forests, and forest community types described by the US National Vegetation Classification (USNVC), which represent ideal or defining conditions of recognized vegetation communities. Fixed-area plots provided data on field-sampled environmental variables, vegetation, soil, and hydrological conditions for crops, old fields, restored areas, and mature forests. The USNVC database provided quantitative data for three historically and geographically relevant reference forest community types for comparison with the sampled communities. Results of nonmetric multidimensional scaling based on species cover revealed clear gradients relating to site age and canopy development. Along those gradients, restored areas demonstrated increasing similarity to mature forest reference communities in terms of floristic composition. Specifically, the floristic quality of restored areas was significantly greater than that of crops and old fields. Furthermore, soil health measurements of physical, chemical, and hydrological conditions indicated significant improvements in restored site soils compared with prerestoration conditions represented by cropland soils. Descriptions and data from the USNVC provided ecological context for restoration target conditions and facilitated the assessment of restoration recovery along a trajectory from starting conditions to those target conditions. Descriptions by USNVC also helped identify deviations from the intended restoration objectives (e.g., invasive species recruitment) and potential adaptive management actions to return sites to their intended trajectories. Integr Environ Assess Manag 2024;00:1–22. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
Saltwater intrusion (SWI) is a well-studied phenomenon that threatens the freshwater supplies of coastal communities around the world. The development and advancement of numerical models has led to improved assessment of the risk of salinization. However, these studies often fail to include the impact of surface waters as potential sources of aquifer salinity and how they may impact SWI. Based on field-collected data, we developed a regional, variable-density groundwater model using SEAWAT for east Dover, Delaware. In this location, major users of groundwater from the surficial aquifer are the City of Dover and irrigation for agriculture. Our model includes salinized marshland and tidal streams, along with irrigation and municipal pumping wells. Model scenarios were run for 100 years and included changes in pumping rates and sea-level rise (SLR). We examined how these drivers of SWI affect the extent and location of salinization in the surficial aquifer by evaluating differences in chloride concentration near surface waters and the subsurface freshwater-saltwater interface. We found the presence of the marsh inverts the typical freshwater-saltwater wedge interface and that the edge of the interface did not migrate farther inland. Additionally, we found that tidal streams are the dominant pathways of SWI at our site with salinization from streams being exacerbated by SLR. Our results also show that spatial distribution of pumping affects both the magnitude and extent of salinization, with an increase in concentrated pumping leading to more intensive salinization than a more widely distributed increase of the same total pumping volume.
The energy derived from available foods is an important factor used in conservation planning for migratory species. Estimating true metabolizable energy (TME) of available foods has become a common method for resource managers to increase reliability in energetic carrying-capacity estimates. Lesser scaup (Aythya affinis; hereafter scaup), have experienced a population decline concurrent with suspected decreases in foraging habitat quality and quantity at spring stopover sites in the upper Midwest, USA. Unfortunately, few TME estimates are available for common diet items of scaup. We estimated nitrogen-adjusted TME (TMEN) of 5 common foods of scaup by conducting feeding trials on wild females and males. True metabolizable energy varied by food taxa, but not by pretrial body mass or sex. Mean TMEN (kcal/g[dry] ± SE) was greatest for wild millet (Echinochloa crus-galli; 2.20 ± 0.14), followed by chironomids (Chironomus spp.; 1.41 ± 0.49), amphipods (Gammarus spp.; 1.33 ± 0.23), planorbid snails (Planorbidae; 0.17 ± 0.07), and fingernail clams (Sphaeriidae; −0.79 ± 0.27). Our results, combined with scaup diet literature indicated that the management of spring staging areas for high-energy invertebrates (i.e., chironomids and amphipods) would provide improved opportunity for energy acquisition during migration. Further study could help determine if the acclimation of scaup to particular diets, especially bivalves, increases their TMEN values.
Elevated nitrogen loads are pervasive in the Long Island Sound, an estuary that receives freshwater and nutrients from both surface-water and groundwater discharge. Surface-water nitrogen loads to the Long Island Sound are relatively well characterized, but less is known about groundwater-transported nitrogen loads. Prior work on the northern shore of Long Island Sound (Connecticut and areas of New York and Rhode Island) suggested that groundwater travel times are relatively short (median less than 2 years) and that decade-long nutrient legacies are not widespread. Because the travel times are short, groundwater flow and nutrient loads likely vary substantially between months. In the current study, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection, developed a set of models to better characterize spatial and temporal patterns of groundwater-transported nitrogen loading from atmospheric deposition, septic systems, and fertilizers within the study area. The models provide an estimate, with uncertainty, of groundwater-transported nitrogen loads in the study area, filling a key gap in the nitrogen budget for Long Island Sound. The models also highlight the spatial and temporal variation in nitrogen loading throughout the study area.
The modeling workflow involved four models. (1) A soil-water-balance model was developed by using the Soil-Water-Balance software to simulate groundwater recharge across the study area for water years 2005 through 2022. The simulated mean monthly recharge from the soil-water-balance model was used as input into a groundwater-flow model. (2) The groundwater-flow model was developed by using the MODFLOW 6 software and data for water years 1993 through 2022 and simulates average monthly hydrologic conditions. The groundwater-flow model was calibrated by using the Iterative Ensemble Smoother method within the PEST++ software. The Iterative Ensemble Smoother method generates an ensemble of sets of parameter values, with each set producing reasonable simulated hydrologic parameter values. (3) An ensemble of MODPATH particle-tracking simulations were run to generate particle flow paths and travel times, with each simulation using a different set of the flow model parameters. (4) A nitrogen load model uses the MODPATH simulation outputs to track nitrogen from the land surface through multiple attenuation zones until it discharges into fresh or saline surface water. As with the groundwater-flow model, the nitrogen model simulated average monthly groundwater-transported nitrogen loads for water years 1993 through 2022. One novel aspect of the nitrogen load model is that the nitrogen attenuation parameters were calibrated to observed nitrogen loads.
Across the ensemble of simulated nitrogen loads, the median study-area-wide monthly simulated nitrogen loads from the aquifer to Long Island Sound throughout the year ranged from 900 to 18,600 kilograms of nitrogen per day, with a median load of 5,100 kilograms of nitrogen per day. The simulated loads were based on average monthly conditions for water years 1993 through 2022. Loads were highest during the winter and early spring and lowest during the late summer. However, simulated travel times for groundwater and nitrogen loads discharged to Long Island Sound during summer were longer than travel times for groundwater and loads discharged during the winter, indicating that, on average, groundwater discharged during summer traveled along different, and longer, flow paths, than groundwater discharged during winter. This indicates that summer loads would respond more slowly to changes in nitrogen inputs at the water table than winter loads. Over the entire study area, approximately 15 percent of the simulated load is from atmospheric deposition sources, 30 to 40 percent is from fertilizer, and 50 to 60 percent is from septic systems.
The final analysis of the study involved simulating the change in groundwater-transported nitrogen load in response to upgrading septic systems or reducing fertilizing inputs to areas of turf grass. Both management interventions reduced the groundwater-transported nitrogen load, and reductions were greater in areas with greater loads from septic systems or turf-grass fertilizers. The delay between management actions and substantial reductions in groundwater-transported nitrogen loads varied seasonally; loads during the late summer months remained elevated longer than the winter loads.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245090","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Barclay, J.R., Holland, M.J., and Mullaney, J.R., 2024, Simulated mean monthly groundwater-transported nitrogen loads in watersheds on the north shore of Long Island Sound, 1993–2022: U.S. Geological Survey Scientific Investigations Report 2024–5090, 63 p., https://doi.org/10.3133/sir20245090.","productDescription":"Report: xi, 63; 3 Data Releases","numberOfPages":"63","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150246","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":462294,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1HKENGV","text":"USGS data release","linkHelpText":"MODFLOW6 groundwater flow model, MODPATH particle-tracking simulation, and groundwater-transported nitrogen load model of average monthly conditions in coastal Connecticut and adjacent areas of New York and Rhode Island, 1993–2022"},{"id":497952,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117502.htm","linkFileType":{"id":5,"text":"html"}},{"id":462296,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215116","text":"Scientific Investigations Report 2021–5116","linkHelpText":"- Simulation of Groundwater Budgets and Travel Times for Watersheds on the North Shore of Long Island Sound, With Implications for Nitrogen-Transport Studies"},{"id":462295,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1GUC7FE","text":"USGS data release","linkHelpText":"Soil-Water-Balance model developed to simulate net infiltration in watersheds on the north shore of the Long Island Sound"},{"id":462293,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1XEN74S","text":"USGS data release","linkHelpText":"Summary simulated groundwater-transported nitrogen loads on the north shore of Long Island Sound and associated data"},{"id":462292,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5090/sir20245090.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5090 XML"},{"id":462291,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5090/images/"},{"id":462290,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245090/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5090 HTML"},{"id":462289,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5090/sir20245090.pdf","text":"Report","size":"24.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5090 PDF"},{"id":462288,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5090/coverthb.jpg"}],"country":"United States","state":"Connecticut, Rhode Island","otherGeospatial":"Long Island Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.60521599677895,\n 40.99974278286342\n ],\n [\n -71.33663482988541,\n 40.99974278286342\n ],\n [\n -71.33663482988541,\n 41.7908892811372\n ],\n [\n -73.60521599677895,\n 41.7908892811372\n ],\n [\n -73.60521599677895,\n 40.99974278286342\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The effectiveness of using neural networks for picking seismic phase arrival times has been demonstrated through several case studies, and seismic monitoring programs are starting to adopt the technology into their workflows. However, published models were designed and trained using rather arbitrary choices of hyperparameters, limiting their performance. In this study, we use phase picks from both routine and template-matching analyses from multiple regions (Ridgecrest, California; Kilauea, Hawaii; Yellowstone, Wyoming-Montana-Idaho) to test a hyperparameter optimization scheme for phase-picking neural networks and to evaluate their performance. We show that a published model, namely PhaseNet (Zhu and Beroza, 2019), can be simplified and improved with reasonable effort and there are preferred choices of hyperparameters that increase the performance. We also show that models optimized based on the arrival times reported in routine event catalogs consistently perform well when picking arrival times of smaller events, which is crucial for certain tasks from microseismicity to explosion monitoring.
","language":"English","publisher":"GeoScienceWorld","doi":"10.1785/0320240025","usgsCitation":"Park, Y., and Shelly, D.R., 2024, The value of hyperparameter optimization in phase-picking neural networks: The Seismic Record, v. 4, no. 3, p. 231-239, https://doi.org/10.1785/0320240025.","productDescription":"9 p.","startPage":"231","endPage":"239","ipdsId":"IP-167611","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":487286,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320240025","text":"Publisher Index Page"},{"id":481863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Hawaii, Idaho, Montana, 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\"}}]}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Park, Yongsoo","contributorId":350716,"corporation":false,"usgs":false,"family":"Park","given":"Yongsoo","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":926908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":926909,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70263332,"text":"70263332 - 2024 - Stream nitrate dynamics driven primarily by discharge and watershed physical and soil characteristics at intensively monitored sites: Insights from deep learning","interactions":[],"lastModifiedDate":"2025-02-06T15:50:05.857714","indexId":"70263332","displayToPublicDate":"2024-09-26T08:45:09","publicationYear":"2024","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":"Stream nitrate dynamics driven primarily by discharge and watershed physical and soil characteristics at intensively monitored sites: Insights from deep learning","docAbstract":"We developed a suite of models using deep learning to make hindcast predictions of the 7‐day\naverage backward‐looking nitrate concentration at 46 predominantly agricultural sites across the midwestern and eastern United States. The models used daily observations of discharge and meteorological variables and watershed attributes describing anthropogenic modification to hydrology, nitrogen application, climate, groundwater, land use, watershed physiographic attributes, and soils. Across all sites, discharge and watershed soil and physiographic attributes showed a strong influence on model performance. Analysis of drivers across sites revealed considerable regional differences related to controlling processes such as groundwater contributions. We tested several ways to pool data across sites to develop accurate models and make the most effective use of available data. Single‐site models, in which models are trained and tested at a single location, showed generally strong predictive performance (median Kling‐Gupta Efficiency = 0.66), and accuracy at poorly performing sites could be improved by grouping sites with similar characteristics. Developing a single model for all sites reduced performance at several locations with distinct characteristics, suggesting that there is a threshold of dissimilarity beyond which more data does not improve the model. While many deep learning studies have shown that national or even global models can outperform local models, it is not clear that this is true for water quality constituents. This study demonstrates how data can be combined effectively, using deep learning to develop accurate and interpretable models of instream nitrate at sites where varying processes are responsible for changes in nitrate concentration.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR036591","usgsCitation":"Gorski, G., Larsen, L., Wingenroth, J., Zhang, L., Bellugi, D., and Appling, A.P., 2024, Stream nitrate dynamics driven primarily by discharge and watershed physical and soil characteristics at intensively monitored sites: Insights from deep learning: Water Resources Research, v. 60, no. 9, e2023WR036591, 20 p., https://doi.org/10.1029/2023WR036591.","productDescription":"e2023WR036591, 20 p.","ipdsId":"IP-159507","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":487626,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr036591","text":"Publisher Index Page"},{"id":481745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.40564338832684,\n 45.91506689989822\n ],\n [\n -96.40564338832684,\n 37.394013908611555\n ],\n [\n -74.76198078442837,\n 37.394013908611555\n ],\n [\n -74.76198078442837,\n 45.91506689989822\n ],\n [\n -96.40564338832684,\n 45.91506689989822\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"9","noUsgsAuthors":false,"publicationDate":"2024-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Gorski, Galen 0000-0003-0083-4251","orcid":"https://orcid.org/0000-0003-0083-4251","contributorId":329714,"corporation":false,"usgs":true,"family":"Gorski","given":"Galen","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":926442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larsen, Laurel 0000-0001-7057-5377","orcid":"https://orcid.org/0000-0001-7057-5377","contributorId":298678,"corporation":false,"usgs":false,"family":"Larsen","given":"Laurel","affiliations":[{"id":64654,"text":"University of California, Berkeley, Berkeley, CA, USA","active":true,"usgs":false}],"preferred":false,"id":926443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wingenroth, Jordan 0000-0002-7970-841X","orcid":"https://orcid.org/0000-0002-7970-841X","contributorId":350622,"corporation":false,"usgs":false,"family":"Wingenroth","given":"Jordan","affiliations":[{"id":36572,"text":"Resources for the Future","active":true,"usgs":false}],"preferred":false,"id":926444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhang, Liang","contributorId":288484,"corporation":false,"usgs":false,"family":"Zhang","given":"Liang","email":"","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":926445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bellugi, Dino","contributorId":148040,"corporation":false,"usgs":false,"family":"Bellugi","given":"Dino","email":"","affiliations":[],"preferred":false,"id":926446,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":926447,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}