When magma accumulates or migrates, it can cause pressurization and related ground deformation. Characterization of surface deformation provides important constraints on the potential for future volcanic activity, especially in combination with seismic activity, gas emissions, and other indicators. A wide variety of techniques and instrument types have been applied to the study of ground deformation at volcanoes (sidebar, p. 2; Dzurisin, 2000, 2003, 2007). Geodetic instruments include continuously recording Global Navigation Satellite System (GNSS; of which the United States’ Global Positioning System is one example) stations (fig. D1), borehole tiltmeters, and interferometric synthetic aperture radar (InSAR) measurements (from satellites, occupied and unoccupied aircraft systems, and ground-based sensors). Additional geodetic measurements like continuous- and survey-mode gravity (fig. D2) can contribute substantially to interpreting these data. Borehole strainmeters (see chapter K, this volume, by Hurwitz and Lowenstern, 2024) also have outstanding utility for monitoring deformation, although because of cost and permitting challenges, we do not include them as part of standard volcano monitoring networks for U.S. volcanoes. Still other techniques like light detection and ranging (lidar), structure from motion, and optical satellite data can be used to derive gross topographic changes, which can be used to map volcanic deposits, infer eruption rates, and gain insights into the source processes associated with eruptive activity (see chapter G, this volume, on tracking surface changes caused by volcanic activity; Orr and others, 2024).
Experience has shown that no single geodetic monitoring technique is adequate to detect and track the entire range of ground-motion patterns that occur at volcanoes, primarily because of the temporal and spatial diversity of volcano deformation (fig. D3). Similarly, the magnitude of surface deformation varies widely. Geodetic monitoring strategies should therefore include multiple techniques and instrument types to cover a wide range of spatial and temporal scales.
In identifying recommendations for geodetic instrumentation for volcano monitoring networks, we attempted to maximize the diversity of instrument types to measure the full range of deformation signals and minimize their expense and number; thus, we do not include several well-known deformation-monitoring techniques in our recommendations. Extensometers, for example, measure strains over distances of a few meters and have an excellent record of success in detecting changes in preeruptive localized ground motion across existing cracks, including at Mount St. Helens, Washington (Iwatsubo and others, 1992), and Piton de la Fournaise, Réunion Island (Peltier and others, 2006). Despite being relatively inexpensive, extensometers are best used primarily when localized ground displacements (for example, ground cracks) need to be tracked, and are not necessary at all volcanoes.
In considering volcano deformation monitoring strategies, two complicating factors are deserving of special attention. First, not all deformation is driven by subsurface magmatic activity—for example, at many large stratovolcanoes (for example, Mount Rainier), flank collapses and landslides are significant geologic hazards (Reid and others, 2001) that may occur even in the absence of magmatic activity. Monitoring the stability of volcanoes is thus another critical application of geodetic monitoring networks to inform hazard assessment. One of the most famous examples of edifice instability is the large flank collapse that initiated the May 18, 1980, eruption of Mount St. Helens. Deformation monitoring had detected a bulge on the north flank of the mountain in April 1980 that was expanding by several meters per day (Lipman and others, 1981). Given that flank collapses can happen at any time during a period of volcanic unrest (or even outside a period of unrest), the capability to assess edifice stability is critical.
Second, although volcanoes are commonly treated as idealized structures that erupt from single points, like centralvent stratovolcanoes, many are characterized by long rift zones from which eruptions may originate, and distributed volcanic fields are characterized by broadly spaced vents. For example, linear dikes are common at Kīlauea, Mauna Loa, and between Mount Shasta and Medicine Lake in California. At Kīlauea, one of these linear dikes emerged more than 40 kilometers (km) away from the summit of the volcano during the lower East Rift Zone eruption in 2018. Other volcanic fields, like Lassen volcanic center, California, or the San Francisco Volcanic Field, Arizona, have many small vents spread over a wide area. Although the instrumentation guidelines presented in this chapter remain phrased for central-vent volcanoes, they should be modified as needed in the context of the eruptive characteristics of each individual volcanic system.
Spatial analysis of geodetic network coverage could help to ensure adequate instrumentation in areas where volcanism can occur over a broad area as opposed to a central vent. As an example, consider the adjacent volcanoes Mount Shasta and Medicine Lake. If station locations are chosen based only on the distance from the centers of the volcanoes, then any geodetic anomalies between the two volcanoes—an area of potential volcanism as indicated by the presence of volcanic features—may remain undetected by ground-based instrumentation. The spatial analysis is accomplished via a grid of pressure point sources (Mogi, 1958) evenly distributed across the map area, at a depth of 5 km in this example (fig. D4). Each source is inflated until predicted deformations exceed the GNSS white noise uncertainty estimates at one site (Langbein, 2017; Murray and Svarc, 2017). This volume of detectable magma provides a measure of the quality of the coverage (fig. D4). The results indicate that, as of 2022, there is a large area between Mount Shasta and Medicine Lake volcano with existing mapped dikes in which a substantial amount of magma could intrude without being detected geodetically. Applying this style of analysis to individual volcanic systems can provide a guide for designing network geometry given the expected locations of future eruptions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062D","usgsCitation":"Montgomery-Brown, E.K., Anderson, K.R., Johanson, I.A., Poland, M.P., and Flinders, A.F., 2024, Ground deformation and gravity for volcano monitoring, chap. D of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–D, 11 p., https://doi.org/10.3133/sir20245062D.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"N","ipdsId":"IP-152739","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462454,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/d/sir20245062d.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":462453,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/d/covrthbd.jpg"}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Translocating amphibians to alternative, suitable habitat is a climate adaptation strategy aimed at minimizing the risk of extinction due to projected global warming and drying. Projected conditions could undermine their physiological performance, and thus survival and reproduction. Translocations minimize risks of extinction by increasing spatial redundancy across climate-resilient habitats, particularly for dispersal-limited species. However, outcomes of amphibian translocation attempts are poorly documented, and their effectiveness remains unclear. We released and tracked 34 Eleutherodactylus coqui to determine early postrelease survival of a control (nontranslocated) group (n = 14) and experimental (translocated) group (n = 20) moved 0.8 km from their capture location in west-central Puerto Rico in 2021. We defined “initial” as the first 17 d postrelease, a period during which we hypothesized that experimental individuals would have lower survival rates because they transitioned from known-familiar to novel-unfamiliar habitat. We found no evidence in the data to support our hypothesis. Daily survival rates were better explained by a model with no group effect but negatively influenced by in situ temperature. However, the effect of in situ temperature (proxy of operative temperature) was weak (95% confidence intervals overlapped 0). After 17 d, all but one of the recaptured frogs lost weight for a combined weight loss of 0.28 ± 0.13 g. However, weight loss was significantly higher in translocated frogs (0.81 ± 0.33 g). Average daily movements did not hinder survival even though experimental individuals traveled farther (～ eight times) than control ones. Our findings suggested that managed translocations have the potential to become a useful conservation tool, not an additive source of mortality. We outline challenges that remain before translocations of Eleutherodactylus species can be broadly applied.
","language":"English","publisher":"BioOne","doi":"10.1655/Herpetologica-D-24-00001.1","usgsCitation":"Chaparro, R., Rivera-Burgos, A., Eaton, M.J., Terando, A., Martinez, E., and Collazo, J.A., 2024, Postrelease survival of Eleutherodactylus coqui: Advancing managed translocations as an adaptive tool for climate-vulnerable anurans: Herpetologica, v. 80, no. 4, p. 314-320, https://doi.org/10.1655/Herpetologica-D-24-00001.1.","productDescription":"7 p.","startPage":"314","endPage":"320","ipdsId":"IP-157773","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487927,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1655/herpetologica-d-24-00001.1","text":"Publisher Index Page"},{"id":485331,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.02528621431577,\n 18.25\n ],\n [\n -67.02528621431577,\n 18.086320441291832\n ],\n [\n -66.90560779954839,\n 18.086320441291832\n ],\n [\n -66.90560779954839,\n 18.25\n ],\n [\n -67.02528621431577,\n 18.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"80","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Chaparro, Rafael","contributorId":354406,"corporation":false,"usgs":false,"family":"Chaparro","given":"Rafael","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":935586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rivera-Burgos, Ana C.","contributorId":354407,"corporation":false,"usgs":false,"family":"Rivera-Burgos","given":"Ana C.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":935587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eaton, Mitchell J. 0000-0001-7324-6333","orcid":"https://orcid.org/0000-0001-7324-6333","contributorId":213526,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":935588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":935589,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martinez, Eloy","contributorId":354408,"corporation":false,"usgs":false,"family":"Martinez","given":"Eloy","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":935590,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":935591,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259395,"text":"70259395 - 2024 - Arctic fishes reveal patterns in radiocarbon age across habitats and with recent climate change","interactions":[],"lastModifiedDate":"2024-11-22T16:13:19.150412","indexId":"70259395","displayToPublicDate":"2024-10-04T06:30:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5456,"text":"Limnology and Oceanography Letters","active":true,"publicationSubtype":{"id":10}},"title":"Arctic fishes reveal patterns in radiocarbon age across habitats and with recent climate change","docAbstract":"Climate change alters the sources and age of carbon in Arctic food webs by fostering the release of older carbon from degrading permafrost. Radiocarbon (14C) traces carbon sources and age, but data before rapid warming are rare and limit assessments over time. We capitalized on 14C data collected ~ 40 years ago that used fish as natural samplers by resampling the same species today. Among resampled fish, those using freshwater food webs had the oldest 14C ages (> 1000 yr BP), while those using marine food webs had the youngest 14C ages (near modern). One migratory species encompassed the entire range of 14C ages because juveniles fed in freshwater streams and adults fed in offshore marine habitats. Over ~ 40 yr, average 14C ages of freshwater and marine feeding fish shifted closer to atmospheric values, suggesting a potential influence from “greening of the Arctic.”
The dust cycle facilitates the exchange of particles among Earth's major systems, enabling dust to traverse ecosystems, cross geographic boundaries, and even move uphill against the natural flow of gravity. Dust in the atmosphere is composed of a complex and ever-changing mixture that reflects the evolving human footprint on the landscape. The emission, transport, and deposition of dust interacts with and connects Critical Zone processes at all spatial and temporal scales. Landscape properties, land use, and climatic factors influence the wind erosion of soil and nutrient loss, which alters the long-term ecological dynamics at erosional locations. Once in the atmosphere, dust particles influence the amount of solar radiation reaching Earth, and interact with longwave (terrestrial) radiation, with cascading effects on the climate system. Finally, the wet and dry deposition of particles influences ecosystem structure, composition, and function over both short and long-term scales. Tracking dust particles from source to sink relies on monitoring and measurement of the geochemical composition and size distribution of the particles, space-borne and ground-based remote sensing, and dust modeling. Dust is linked to human systems via land use and policies that contribute to dust emissions and the health-related consequences of particulate loads and composition. Despite the significant influence dust has in shaping coupled natural-human systems, it has not been considered a key component of the Critical Zone. Here, we demonstrate that dust particles should be included as a key component of the Critical Zone by outlining how dust interacts with and shapes Earth System processes from generation, through transport, to deposition. We synthesize current understanding from global research and identify critical data and knowledge gaps while showcasing case studies from North America.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2024.104942","usgsCitation":"Brahney, J., Heindel, R.C., Gill, T.E., Carling, G., Gonzalez-Olalla, J.M., Hand, J.L., Mallia, D.V., Munroe, J.S., Perry, K., Putman, A.L., Skiles, S.M., Adams, B.R., Aanderud, Z.T., Aarons, S.M., Aguirre, D., Ardon-Dryer, K., Blakowski, M.A., Creamean, J.M., Fernandez, D.P., Foroutan, H., Gaston, C.J., Hahnenberger, M., Hoch, S.W., Jones, D.K., Kelly, K.E., Lang, O.I., Lemonte, J., Reynolds, R.L., Singh, R.P., Sweeney, M., and Merrill, T.K., 2024, Dust in the Critical Zone: North American case studies: Earth-Science Reviews, v. 258, 104942, 34 p., https://doi.org/10.1016/j.earscirev.2024.104942.","productDescription":"104942, 34 p.","ipdsId":"IP-165083","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":486931,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index 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84112","active":true,"usgs":false}],"preferred":false,"id":917852,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Lang, Otto I. 0000-0002-3435-5032","orcid":"https://orcid.org/0000-0002-3435-5032","contributorId":345918,"corporation":false,"usgs":false,"family":"Lang","given":"Otto","email":"","middleInitial":"I.","affiliations":[{"id":82750,"text":"Department of Geography, University of Utah, Salt Lake City UT 84112","active":true,"usgs":false}],"preferred":false,"id":917853,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Lemonte, Josh 0000-0002-1100-028X","orcid":"https://orcid.org/0000-0002-1100-028X","contributorId":334716,"corporation":false,"usgs":false,"family":"Lemonte","given":"Josh","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":917854,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Reynolds, Richard L. 0000-0002-4572-2942 rreynolds@usgs.gov","orcid":"https://orcid.org/0000-0002-4572-2942","contributorId":139068,"corporation":false,"usgs":true,"family":"Reynolds","given":"Richard","email":"rreynolds@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":917855,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Singh, Ramesh P. 0000-0001-6649-7767","orcid":"https://orcid.org/0000-0001-6649-7767","contributorId":345919,"corporation":false,"usgs":false,"family":"Singh","given":"Ramesh","email":"","middleInitial":"P.","affiliations":[{"id":82751,"text":"School of Life and Environmental Science, Chapman University, Orange CA 92866","active":true,"usgs":false}],"preferred":false,"id":917856,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Sweeney, Mark 0000-0002-4396-4438","orcid":"https://orcid.org/0000-0002-4396-4438","contributorId":296383,"corporation":false,"usgs":false,"family":"Sweeney","given":"Mark","email":"","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":917857,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Merrill, Thorn K.","contributorId":345920,"corporation":false,"usgs":false,"family":"Merrill","given":"Thorn","email":"","middleInitial":"K.","affiliations":[{"id":82741,"text":"Department of Atmospheric Sciences, University of Utah, Salt Lake City UT 84112","active":true,"usgs":false}],"preferred":false,"id":917858,"contributorType":{"id":1,"text":"Authors"},"rank":31}]}} ,{"id":70267339,"text":"70267339 - 2024 - Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA","interactions":[],"lastModifiedDate":"2025-05-20T17:34:15.944311","indexId":"70267339","displayToPublicDate":"2024-10-01T10:27:10","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA","docAbstract":"Long‐term standardized monitoring programs are fundamental to assessing how fish populations respond to anthropogenic stressors. Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes that are associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark–recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the past two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually.
We investigated the feasibility of using two analytical methods, N‐mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River using the historical long‐term data set; these methods allow abundance to be estimated when a mark–recapture estimate cannot be obtained due to hydrologic conditions.
When compared with abundance estimates from mark–recapture methods, N‐mixture models most often resulted in negatively biased abundance estimates, whereas mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N‐mixture models produced negatively biased estimates when tested against true abundance values from simulated data sets. The bias in the N‐mixture model estimates was caused by poor model fit and variation in capture probability. The bias in the mean capture probability estimates was caused by heterogeneity in capture probability, likely caused by variable environmental conditions, which were not accounted for in the models.
N‐mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining the utility of long‐term data sets.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/nafm.11026","usgsCitation":"Briggs, M., Glassic, H.C., Guy, C.S., Opitz, S., Rotella, J., and Schmetterling, D., 2024, Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA: North American Journal of Fisheries Management, v. 44, no. 5, p. 947-961, https://doi.org/10.1002/nafm.11026.","productDescription":"15 p.","startPage":"947","endPage":"961","ipdsId":"IP-172896","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.11026","text":"Publisher Index Page"},{"id":486239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"upper Yellowstone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.27790695947738,\n 45.77750283140904\n ],\n [\n -111.27790695947738,\n 45.00210734009434\n ],\n [\n -110.23435146225052,\n 45.00210734009434\n ],\n [\n -110.23435146225052,\n 45.77750283140904\n ],\n [\n -111.27790695947738,\n 45.77750283140904\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Michelle A.","contributorId":355621,"corporation":false,"usgs":false,"family":"Briggs","given":"Michelle A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glassic, Hayley Corrine 0000-0001-6839-1026","orcid":"https://orcid.org/0000-0001-6839-1026","contributorId":305858,"corporation":false,"usgs":true,"family":"Glassic","given":"Hayley","email":"","middleInitial":"Corrine","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":937792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Opitz, Scott T.","contributorId":355622,"corporation":false,"usgs":false,"family":"Opitz","given":"Scott T.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":937794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rotella, Jay J.","contributorId":355623,"corporation":false,"usgs":false,"family":"Rotella","given":"Jay J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmetterling, David A.","contributorId":355624,"corporation":false,"usgs":false,"family":"Schmetterling","given":"David A.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":937796,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264248,"text":"70264248 - 2024 - Onset of aftershocks: Constraints on the Rate-and-State model","interactions":[],"lastModifiedDate":"2025-03-10T14:39:45.261636","indexId":"70264248","displayToPublicDate":"2024-10-01T09:39:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Onset of aftershocks: Constraints on the Rate-and-State model","docAbstract":"Aftershock rates typically decay with time t after the mainshock according to the Omori–Utsu law, R(t)=K(c+t)−p, with parameters K, c, and p. The rate‐and‐state (RS) model, which is currently the most popular physics‐based seismicity model, also predicts an Omori–Utsu decay with p = 1 and a c‐value that depends on the size of the coseismic stress change. Because the mainshock‐induced stresses strongly vary in space, the c‐value should vary accordingly. Short‐time aftershock incompleteness (STAI) in earthquake catalogs has prevented a detailed test of this prediction so far, but the newly developed a‐positive method for reconstructing the true earthquake rate now allows its testing. Using previously published slip models, we calculate the coseismic stress changes for the six largest mainshocks in Southern California in recent decades and estimate the maximum shear as a scalar proxy of the coseismic stress tensor. Aftershock rates reconstructed for events in different stress ranges show that the rates follow a power law with p = 1 independent of stress with no clear sign of a c‐value. The onset of the power‐law decay is abrupt and more delayed in areas with smaller stress changes. The observations do not necessarily contradict the RS model, as STAI limits the resolution for early aftershocks, and the RS model can reproduce the observations for specific Aσ values. However, the observations lead to strong constraints, namely Aσ<10 kPa and a power‐law decay of the background rate with distance to the fault, with exponent 2.7.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240176","usgsCitation":"Hainzl, S., Page, M.T., and van der Elst, N., 2024, Onset of aftershocks: Constraints on the Rate-and-State model: Seismological Research Letters, v. 95, no. 6, p. 3507-3516, https://doi.org/10.1785/0220240176.","productDescription":"10 p.","startPage":"3507","endPage":"3516","ipdsId":"IP-165790","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":483137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"95","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hainzl, Sebastian","contributorId":352178,"corporation":false,"usgs":false,"family":"Hainzl","given":"Sebastian","affiliations":[{"id":84127,"text":"Univ. of Potsdam","active":true,"usgs":false}],"preferred":false,"id":930194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Page, Morgan T. 0000-0001-9321-2990 mpage@usgs.gov","orcid":"https://orcid.org/0000-0001-9321-2990","contributorId":3762,"corporation":false,"usgs":true,"family":"Page","given":"Morgan","email":"mpage@usgs.gov","middleInitial":"T.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":930195,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van der Elst, Nicholas 0000-0002-3812-1153 nvanderelst@usgs.gov","orcid":"https://orcid.org/0000-0002-3812-1153","contributorId":147858,"corporation":false,"usgs":true,"family":"van der Elst","given":"Nicholas","email":"nvanderelst@usgs.gov","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":930196,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266769,"text":"70266769 - 2024 - Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA","interactions":[],"lastModifiedDate":"2025-05-13T15:43:28.159582","indexId":"70266769","displayToPublicDate":"2024-10-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA","docAbstract":"Objective
Long-term standardized monitoring programs are fundamental to assessing how fish populations respond to anthropogenic stressors. Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark-recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the last two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually.
Methods
We investigated the feasibility of using two analytical methods, N-mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River; these methods allow abundance to be estimated when a mark-recapture estimate cannot be obtained due to hydrologic conditions.
Result
When compared to abundance estimates from mark-recapture methods, N-mixture models most often resulted in negatively biased abundance estimates while mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N-mixture models produced negatively biased estimates compared to true abundance values from simulated datasets. Bias in N-mixture model estimates was caused by poor model fit and variation in capture probability. Bias in mean capture probability estimates was caused by heterogeneity in capture probability that was not accounted for in the models.
Conclusion
N-mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining long-term datasets.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/nafm.11026","usgsCitation":"Briggs, M., Glassic, H.C., Guy, C.S., Opitz, S., Rotella, J., and Schmetterling, D., 2024, Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA: North American Journal of Fisheries Management, v. 44, no. 5, p. 947-961, https://doi.org/10.1002/nafm.11026.","productDescription":"15 p.","startPage":"947","endPage":"961","ipdsId":"IP-160733","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488197,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.11026","text":"Publisher Index Page"},{"id":485823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"upper Yellowstone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.25391301846597,\n 45.79233815794254\n ],\n [\n -111.25391301846597,\n 45.00638613042193\n ],\n [\n -110.01308523101437,\n 45.00638613042193\n ],\n [\n -110.01308523101437,\n 45.79233815794254\n ],\n [\n -111.25391301846597,\n 45.79233815794254\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Michelle A.","contributorId":354954,"corporation":false,"usgs":false,"family":"Briggs","given":"Michelle A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":936730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glassic, Hayley Corrine 0000-0001-6839-1026","orcid":"https://orcid.org/0000-0001-6839-1026","contributorId":305858,"corporation":false,"usgs":true,"family":"Glassic","given":"Hayley","email":"","middleInitial":"Corrine","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":936731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true}],"preferred":true,"id":936732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Opitz, Scott T.","contributorId":354955,"corporation":false,"usgs":false,"family":"Opitz","given":"Scott T.","affiliations":[{"id":61825,"text":"Montana Fish","active":true,"usgs":false}],"preferred":false,"id":936733,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rotella, Jay J.","contributorId":354956,"corporation":false,"usgs":false,"family":"Rotella","given":"Jay J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":936734,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmetterling, David A.","contributorId":354957,"corporation":false,"usgs":false,"family":"Schmetterling","given":"David A.","affiliations":[{"id":61825,"text":"Montana Fish","active":true,"usgs":false}],"preferred":false,"id":936735,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259803,"text":"70259803 - 2024 - Effects of recent wildfires on giant sequoia groves were anomalous at millennial timescales: a response to Hanson et al.","interactions":[],"lastModifiedDate":"2024-10-25T15:30:37.48096","indexId":"70259803","displayToPublicDate":"2024-09-30T10:23:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire 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.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 31st vertebrate pest conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"University of California","usgsCitation":"Rattner, B., Erickson, R.A., Lankton, J.S., Benoit, E., and Lattard, V., 2024, Brodifacoum isomer formulations with potentially lower risk to non-target wildlife, in Proceedings of the 31st vertebrate pest conference, v. 31, 16, 7 p.","productDescription":"16, 7 p.","ipdsId":"IP-162909","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":462564,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://escholarship.org/uc/item/704044r7"},{"id":462603,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rattner, Barnett A. 0000-0003-3676-2843","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":316326,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett A.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":914987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":914988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lankton, Julia S. 0000-0002-6843-4388 jlankton@usgs.gov","orcid":"https://orcid.org/0000-0002-6843-4388","contributorId":5888,"corporation":false,"usgs":true,"family":"Lankton","given":"Julia","email":"jlankton@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":914989,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Benoit, Etienne","contributorId":228857,"corporation":false,"usgs":false,"family":"Benoit","given":"Etienne","email":"","affiliations":[{"id":41519,"text":"USC1233 RS2GP, INRA, VetAgro Sup, Univ Lyon, France","active":true,"usgs":false}],"preferred":false,"id":914990,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lattard, Virginie","contributorId":228858,"corporation":false,"usgs":false,"family":"Lattard","given":"Virginie","email":"","affiliations":[{"id":41519,"text":"USC1233 RS2GP, INRA, VetAgro Sup, Univ Lyon, France","active":true,"usgs":false}],"preferred":false,"id":914991,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"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":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
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.
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.
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":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards 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}]}} ,{"id":70258713,"text":"sir20245042 - 2024 - Groundwater quality near the Placerita Oil Field, California, 2018","interactions":[],"lastModifiedDate":"2025-12-23T22:04:59.870246","indexId":"sir20245042","displayToPublicDate":"2024-09-26T08:40:00","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":"2024-5042","displayTitle":"Groundwater Quality Near the Placerita Oil Field, California, 2018","title":"Groundwater quality near the Placerita Oil Field, California, 2018","docAbstract":"Groundwater-quality data and potential fluid-migration pathways near the Placerita Oil Field in Los Angeles County, California, were examined by the U.S. Geological Survey to determine if oil-field fluids (water and gas from oil-producing and non-producing zones) have mixed with groundwater resources. Six of the 13 new groundwater samples collected for this study contained petroleum hydrocarbons, thermogenic gas, inorganic chemical signatures, and (or) isotopic values consistent with potential mixing with fluids from hydrocarbon-bearing formations.
For historical groundwater samples, benzene was the most detected petroleum hydrocarbon. The historical groundwater samples with a benzene concentration greater than 0.5 micrograms per liter were from environmental monitoring wells at industrial or commercial facilities unrelated to oil and gas development that, in many cases, have identified soil or groundwater contamination and were not typically analyzed for other constituents that could provide additional lines of evidence for potential mixing with oil-field fluids. Methane was not detected in any of the 12 historical samples with a reported measurement.
Reviewing historical data revealed factors that could potentially adversely affect groundwater quality in the study area. These factors include modified hydraulic gradients caused by large volumes of water extracted from the main production area and reinjected downgradient into nonproducing zones, well-barrier failures in wells constructed in the northern part of the oil field before the 1970s, well-barrier failures in produced-water disposal wells downgradient from the main production area, and naturally occurring hydrocarbons at shallow intervals. The groundwater samples most geochemically similar to samples from hydrocarbon-bearing formations were in areas where hydrocarbons are naturally occurring at shallow intervals and where oil development is at shallow depths. Additional data for hydraulic heads, water quality, and formation temperatures at multiple depths in areas with large injection volumes and well-integrity issues are needed to evaluate whether those factors have contributed to mixing between fluids from oil-producing or injection formations and groundwater resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245042","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Stanton, J.S., Landon, M.K., Shimabukuro, D.H., Kulongoski, J.T., Hunt, A.G., McMahon, P.B., Cozzarelli, I.M., Anders, R., and Sowers, T.A., 2024, Groundwater quality near the Placerita Oil Field, California, 2018: U.S. Geological Survey Scientific Investigations Report 2024–5042, 65 p., https://doi.org/10.3133/sir20245042.","productDescription":"Report: ix, 65 p.; 2 Data Releases; 7 Tables","numberOfPages":"65","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-152098","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":462192,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5042/sir20245042_app2_csv.zip","text":"Appendix 2, Tables 2.1–2.7","size":"10.4 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- CSV files"},{"id":462187,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5042/images/"},{"id":462188,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5042/sir20245042.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5042 XML"},{"id":462186,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245042/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5042 HTML"},{"id":462191,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5042/sir20245042_app2.xlsx","text":"Appendix 2, Tables 2.1–2.7","size":"56.3 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Supplemental Tables for the Placerita Oil Field Study Area, California, 2018"},{"id":462190,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G5UD7J","text":"USGS data release","linkHelpText":"Water chemistry data for samples collected at groundwater sites in the Placerita Oil Field study area, June 2018—November 2018, Los Angeles County, California"},{"id":462189,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93KFFR8","text":"USGS data release","linkHelpText":"Produced water chemistry data collected from the Oxnard Oil Field, Ventura County, and the Placerita Oil Field, Los Angeles County, 2018, California"},{"id":462185,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5042/sir20245042.pdf","text":"Report","size":"7.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5042 PDF"},{"id":462184,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5042/coverthb.jpg"},{"id":497953,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117501.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"Placerita Oil Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.5667,\n 34.4333\n ],\n [\n -118.5667,\n 34.3333\n ],\n [\n -118.4,\n 34.3333\n ],\n [\n -118.4,\n 34.4333\n ],\n [\n -118.5667,\n 34.4333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey studied groundwater near the Placerita Oil Field in Los Angeles County to see if oil-field fluids have mixed with groundwater. Six out of 13 new samples showed signs of mixing with fluids from hydrocarbon-bearing formations. Historical data revealed factors that could affect groundwater quality, including modified hydraulic gradients, well-barrier failures, and naturally occurring hydrocarbons. More data are needed to evaluate the effects of these factors.
","publicationDate":"2024-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Stanton, Jennifer S. 0000-0002-2520-753X jstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-2520-753X","contributorId":830,"corporation":false,"usgs":true,"family":"Stanton","given":"Jennifer","email":"jstanton@usgs.gov","middleInitial":"S.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":913787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landon, Matthew K. 0000-0002-5766-0494 landon@usgs.gov","orcid":"https://orcid.org/0000-0002-5766-0494","contributorId":392,"corporation":false,"usgs":true,"family":"Landon","given":"Matthew","email":"landon@usgs.gov","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":913788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shimabukuro, David H. 0000-0002-6106-5284","orcid":"https://orcid.org/0000-0002-6106-5284","contributorId":208209,"corporation":false,"usgs":false,"family":"Shimabukuro","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":37762,"text":"California State University, Sacramento","active":true,"usgs":false}],"preferred":false,"id":913789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":913790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hunt, Andrew G. 0000-0002-3810-8610","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":206197,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":913791,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":913792,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":913793,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anders, Robert 0000-0002-2363-9072 randers@usgs.gov","orcid":"https://orcid.org/0000-0002-2363-9072","contributorId":1210,"corporation":false,"usgs":true,"family":"Anders","given":"Robert","email":"randers@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":913794,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sowers, Theron A. 0000-0002-3208-5411","orcid":"https://orcid.org/0000-0002-3208-5411","contributorId":211482,"corporation":false,"usgs":false,"family":"Sowers","given":"Theron","email":"","middleInitial":"A.","affiliations":[{"id":37762,"text":"California State University, Sacramento","active":true,"usgs":false}],"preferred":false,"id":913795,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70259106,"text":"70259106 - 2024 - A framework for estimating economic impacts of ecological restoration","interactions":[],"lastModifiedDate":"2024-11-22T16:07:40.961159","indexId":"70259106","displayToPublicDate":"2024-09-26T07:10:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"A framework for estimating economic impacts of ecological restoration","docAbstract":"Ecological restoration projects are designed to improve natural and cultural resources. Spending on restoration also stimulates economic impacts to the restoration economy through the creation or support of jobs and business activity. This paper presents accessible methods for quantifying the economic impacts supported by restoration spending and is written to be a guide and toolbox for an interdisciplinary audience of restoration practitioners and economists. Measuring the economic impacts of restoration can be challenging due to lacking or limited data. The complex, collaborative, and heterogeneous nature of restoration projects can make it difficult to clearly track costs, contributing to limited availability and inconsistency in restoration cost data. And business classification systems, such as the North American Industrial Classification System (NAICS), do not include restoration-sectors that consistently describe the patterns of restoration spending. The aims of this paper are to (1) provide restoration practitioners and program managers with a clear understanding of the application of economic impact analyses to restoration, (2) provide a framework for collecting project cost data for economic impact analyses, and (3) provide modeling best practices and an example application of the framework.
This paper introduces cross-fade sampling, a computationally efficient Markov Chain Monte Carlo simulation method that uses a semi-analytical approach to quickly solve Bayesian inverse problems that do not themselves have an analytical solution. Cross-fading is efficient in two ways. First, it requires fewer samples to obtain the same quality simulation of the target probability density function (PDF). Secondly, it is much faster to evaluate the posterior probability of each sample than conventional sampling methods for simulating Bayesian posterior PDFs. Conventional methods require evaluating the prior probability (which describes your a priori constraints) and data likelihood (which describes the fit between the observations and the predictions of the model) for each sample model. However, cross-fading does not require evaluating the data likelihood, meaning that ‘big data’ can be fit with zero additional computational cost. Further, the cross-fading approach can be used to calculate the marginal likelihood associated with a model design, facilitating model comparison and Bayesian model averaging. Topics covered in this paper include derivation of the cross-fade approach and how it can be used to simulate Bayesian posterior PDFs and compute the marginal likelihood, discussion of the class of problems to which cross-fading can be applied (with examples from earthquake statistics, earthquake ground motion modelling, volcanic eruption forecasting, and finite fault slip modelling), demonstration of efficiency relative to existing sampling methods and discussion of how cross-fading can be used to account for prediction errors (i.e. epistemic errors) as part of the geophysical inverse problem.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggae353","usgsCitation":"Minson, S.E., 2024, Cross-fade sampling: Extremely efficient Bayesian inversion for a variety of geophysical problems: Geophysical Journal International, v. 239, no. 3, p. 1629-1649, https://doi.org/10.1093/gji/ggae353.","productDescription":"21 p.","startPage":"1629","endPage":"1649","ipdsId":"IP-158117","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":466901,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggae353","text":"Publisher Index Page"},{"id":463412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"239","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":917419,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}