Shallow shear‐wave velocities (Vs) sometimes are estimated from joint inversions of horizontal‐to‐vertical (H/V) spectral ratios and surface‐wave dispersion curves derived from ambient noise or small active sources. Here, we evaluate carrying out these inversions using Rayleigh‐wave dispersion curves computed from crustal‐scale P‐wave seismic refraction data. We use data from the 2014–2015 Eastern North American Margin (ENAM) experiment in Virginia and North Carolina, but similar seismic refraction data sets have been acquired over sedimentary basins of interest for seismic hazard studies, including in major urban areas. The ENAM project deployed a pair of ∼215 km long, northwest–southeast linear arrays with ∼300 m receiver spacing to record 11 dynamite shots, and 80 continuously recording seismometers with 5–6 km spacing along the same arrays to record offshore airguns. The arrays crossed the onland portion of the Atlantic Coastal Plain sediments, which are a seaward‐thickening wedge of Cretaceous and younger sediments deposited mostly on crystalline bedrock. We compute Rayleigh‐wave dispersion curves from 3 to 9 km long portions of the receiver arrays on each side of the dynamite shots, and we compute ambient‐noise H/V ratios from the continuously recording seismometers. We use a genetic inversion algorithm in which forward velocity models in each “generation” are evaluated for misfits compared to the observed data, with subsequent generations constructed from the models with the smallest misfits. Velocities to depths of 500 m are defined well, as shown by a narrow range of velocities in the best‐fit models, by the consistency between multiple inversion runs at a site, and by forward modeling of site responses. The resulting velocity cross‐section of the Coastal Plain strata has seaward‐dipping contours in the thinner portions of the Coastal Plain but smaller dips in the deeper portions. We interpret these results as showing that velocity contours in the ACP strata are influenced by a combination of lithology and overburden pressure. Results demonstrate that existing seismic refraction data have the potential for determining detailed shallow shear‐wave velocity profiles.
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Authorized under the Earthquake Hazards Reduction Authorization Act, the U.S. Geological Survey (USGS) Earthquake Hazards Program (EHP) provides the scientific information, situational awareness, and knowledge necessary to reduce deaths, injuries, and economic losses from earthquakes and earthquake-induced tsunamis, landslides, and soil liquefaction. The EHP supports activities in three focused topical areas: (1) earthquake monitoring, (2) hazard assessment, and (3) applied research, using the results of each—and the coordination among them—to further support risk translation and communication in regions at risk nationwide.
For earthquake monitoring, the Advanced National Seismic System (ANSS), a cooperative effort of USGS networks, university partner regional seismic networks, and real-time geodetic networks, collects and analyzes data on earthquakes; issues timely, reliable notifications of their occurrence and impacts; and provides data for earthquake research, hazard, and risk assessment as a foundation for building an earthquake-resilient Nation. The USGS-operated ShakeAlert Earthquake Early Warning system is a recent addition to EHP’s ANSS infrastructure.
In the realm of earthquake hazard assessment, the EHP contributes to earthquake risk mitigation strategies by developing the National Seismic Hazard Model and maps, and other related products, that describe the likelihood and potential effects of earthquakes nationwide, especially in the urban areas of highest risk. The EHP also conducts research on the causes, characteristics, and effects of earthquakes and prioritizes work that directly increases the accuracy and precision of earthquake hazards assessments, earthquake forecasts, and earthquake monitoring and situational-awareness products and that supports the Nation’s earthquake mitigation practices.
Bridging the EHP’s efforts across research, hazard assessments, and earthquake monitoring is a broad and comprehensive collection of earthquake information products, including the National Seismic Hazard Model, ShakeAlert, and other products describing impact, such as ShakeMap and PAGER (Prompt Assessment of Global Earthquakes for Response), which have been developed and integrated into EHP’s real-time monitoring systems.
EHP funds external partners to carry out many important collaborative activities through an active external grants program—one of the largest in the USGS—and through cooperative agreements with other partners such as the university-operated regional seismic networks, funded as part of the ANSS.
To continue its support of earthquake hazard characterization and risk reduction, the EHP aims to strengthen its foundational products and practices while positioning itself to respond to the evolving needs of the Nation and follow best practices of the scientific community. This document describes a strategy for the program to ensure it can meet these demands. The foundational priorities outlined in this strategy represent those activities that remain critical to the core functionality of the program and those that can be supported under current fiscal year 2024-level appropriations. Priorities described as aspirational are important for future growth, and to maintain the program’s position as a leading global resource in earthquake science, but would require increases in appropriated funding to be fully realized.
Across the program’s portfolio of activities, several major themes have been identified as the most critical activities to advance EHP science over the coming decade. Together, these activities provide the framework necessary to integrate critical hazard characterization and risk reduction activities across the program. They provide the structure for research to advance the understanding of where, when, and why earthquakes occur and how we can use improved knowledge to drive short-term and actionable forecasts of seismic activity. They expand the usefulness of critical earthquake products and advance the sophistication of those products to keep pace with the rapidly evolving needs of an ever-expanding user base while maintaining the position of the USGS as a global leader in earthquake science.
This science strategy is organized into three primary sections. The first section provides an overview of the EHP and its budget, governance, and program council. Readers familiar with the program may wish to focus on the second section, which describes the core of the science strategy, including priorities across each of the EHP’s major program activities in monitoring, hazard assessment, and targeted research. The third section outlines science priorities that cut across program activities, including those involving collaborations external to the EHP.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1544","usgsCitation":"Hayes, G.P., Baltay Sundstrom, A.S., Barnhart, W.D., Blanpied, M.L., Davis, L.A., Earle, P.S., Field, N., Franks, J.M., Given, D.D., Gold, R.D., Goulet, C.A., Guy, M.M., Hardebeck, J.L., Luco, N., Pollitz, F., Ringler, A.T., Scharer, K.M., Sobieszczyk, S., Thomas, V.I., and Wolfe, C.J., 2024, U.S. Geological Survey Earthquake Hazards Program decadal science strategy, 2024–33: U.S. Geological Survey Circular 1544, 55 p., https://doi.org/10.3133/cir1544.","productDescription":"ix, 55 p.","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160564","costCenters":[{"id":234,"text":"Earthquake Hazards 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U.S. Geological Survey
Mail Stop 905
12201 Sunrise Valley Drive
Reston, VA 20192
Anadromous fishes play important roles in nutrient dynamics for freshwater ecosystems; however, the trophic pathways have been less documented for iteroparous species like river herring (Alosa pseudoharengus and A. aestivalis) compared to semelparous species like Pacific salmon (Oncorhynchus spp.). Given recent increases in restoration activities to improve connectivity, an understanding of how anadromous river herring influence the morphology, physiology, and life history of predatory fishes can help predict restoration responses. We aimed to quantify the trophic influence of juvenile anadromous river herring on predatory white perch (Morone americana) and yellow perch (Perca flavescens) using a combination of stable isotopes, growth rates, and condition indices. We sampled six lakes in coastal Massachusetts—three lakes with anadromous river herring and three similar lakes without river herring. Bayesian mixing models of δ13C and δ15N indicated white perch consumed juvenile river herring in higher proportions (69–75%) compared to co-occurring prey fishes (11–16%). Lakes with juvenile river herring contained perch with significantly higher condition values, higher immature growth rates (age 1 and 2), lower mature growth rates (> age 3), significantly smaller mature lengths, and lower mortality rates compared to perch in lakes without river herring. These divergent life history traits of perch in response to consumption of juvenile river herring are consistent with observations in other predatory fishes. Direct links between river herring and predator condition, growth, and life history trajectories suggest broad influences on ecosystem structure across trophic levels through physiological, morphometric, and life history modifications.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10641-024-01595-2","usgsCitation":"Mattocks, S., Bittner, S., Luzanau, V., Mohammadi, H., Roy, A.H., Staudinger, M., and Jordaan, A., 2024, River herring influence perch morphology, physiology, and life history: Environmental Biology of Fishes, v. 107, p. 1179-1201, https://doi.org/10.1007/s10641-024-01595-2.","productDescription":"23 p.","startPage":"1179","endPage":"1201","ipdsId":"IP-103045","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.03687326915752,\n 42.873848597989394\n ],\n [\n -71.03687326915752,\n 42.047149016365864\n ],\n [\n -70.5974201441574,\n 42.047149016365864\n ],\n [\n -70.5974201441574,\n 42.873848597989394\n ],\n [\n -71.03687326915752,\n 42.873848597989394\n ]\n ]\n ],\n \"type\": \"Polygon\"\n },\n \"id\": 0\n }\n ]\n}","volume":"107","noUsgsAuthors":false,"publicationDate":"2024-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Mattocks, Steven","contributorId":349651,"corporation":false,"usgs":false,"family":"Mattocks","given":"Steven","affiliations":[{"id":83496,"text":"Massachusetts Division of Fisheries and Widlife","active":true,"usgs":false}],"preferred":false,"id":924535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bittner, Steven","contributorId":349652,"corporation":false,"usgs":false,"family":"Bittner","given":"Steven","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":924536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luzanau, Vasili","contributorId":349653,"corporation":false,"usgs":false,"family":"Luzanau","given":"Vasili","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":924537,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mohammadi, Habibollah","contributorId":349654,"corporation":false,"usgs":false,"family":"Mohammadi","given":"Habibollah","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":924538,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924534,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Staudinger, Michelle D.","contributorId":349655,"corporation":false,"usgs":false,"family":"Staudinger","given":"Michelle D.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":924539,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jordaan, Adrian","contributorId":349656,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":924540,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70261527,"text":"70261527 - 2024 - Model sensitivity analysis for coastal morphodynamics: Investigating sediment parameters and bed composition in Delft3D","interactions":[],"lastModifiedDate":"2025-05-13T15:59:07.445123","indexId":"70261527","displayToPublicDate":"2024-11-20T09:00:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Model sensitivity analysis for coastal morphodynamics: Investigating sediment parameters and bed composition in Delft3D","docAbstract":"Numerical simulation of sediment transport and subsequent morphological evolution rely on accurate parameterizations of sediment characteristics. However, these data are often not available or are spatially and/or temporally limited. This study approaches the problem of limited sediment grain-size data with a series of simulations assessing model sensitivity to sediment parameters and initial bed composition configurations in Delft3D, leading to improved modeling practices. A previously validated Delft3D sediment transport and morphology model for Dauphin Island, Alabama, USA, is used as the benchmark case. A method for the generation of representative sediment grain sizes and their spatially varying distributions is presented via end-member analysis of in situ surficial sediment samples. Derived sediment classes and their spatial distributions are applied to two sensitivity case simulations with increasing bed composition complexity. First, multiple sediment classes are applied in a single fully mixed layer, regardless of sediment type. Second, multiple sediment classes are applied in a thin, fully mixed transport layer with underlayers containing only the non-cohesive sediment classes below. Simulations were carried out in a probabilistic, Delft3D MorMerge configuration to capture long-term morphology change for 10 years. We found there is sensitivity to the inclusion of additional sediment classes and sediment distribution made evident in bed level and morphology change. Inclusion of highly mobile fine sediments altered model results in each sensitivity case. The model was also found to be sensitive to initial bed composition in terms of bed level and morphology change, with notable differences between sensitivity cases on decadal timescales, indicating an armoring effect in the second sensitivity case, which used the transport and underlayer bed configuration. The results of this study offer guidance for numerical modelers concerned with sediment behavior in coastal and estuarine environments.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse12112108","usgsCitation":"Jenkins, R., Smith, C., Passeri, D., and Ellis, A.M., 2024, Model sensitivity analysis for coastal morphodynamics: Investigating sediment parameters and bed composition in Delft3D: Journal of Marine Science and Engineering, v. 12, no. 11, 2108, 29 p., https://doi.org/10.3390/jmse12112108.","productDescription":"2108, 29 p.","ipdsId":"IP-170386","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466755,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse12112108","text":"Publisher Index Page"},{"id":465109,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.39043742176105,\n 30.854668400719234\n ],\n [\n -88.39043742176105,\n 30.17648303472457\n ],\n [\n -87.49364396321667,\n 30.17648303472457\n ],\n [\n -87.49364396321667,\n 30.854668400719234\n ],\n [\n -88.39043742176105,\n 30.854668400719234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Robert L. III 0000-0003-2078-4618","orcid":"https://orcid.org/0000-0003-2078-4618","contributorId":202181,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert L.","suffix":"III","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":920895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":920896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":920897,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellis, Alisha M. 0000-0002-1785-020X aellis@usgs.gov","orcid":"https://orcid.org/0000-0002-1785-020X","contributorId":192957,"corporation":false,"usgs":true,"family":"Ellis","given":"Alisha","email":"aellis@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":920898,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273866,"text":"70273866 - 2024 - Fine-scale surficial soil moisture mapping using UAS-based L-band remote sensing in a mixed oak-grassland landscape","interactions":[],"lastModifiedDate":"2026-02-10T15:13:38.843978","indexId":"70273866","displayToPublicDate":"2024-11-19T08:03:14","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17157,"text":"Frontiers in Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Fine-scale surficial soil moisture mapping using UAS-based L-band remote sensing in a mixed oak-grassland landscape","docAbstract":"Soil moisture maps provide quantitative information that, along with climate and energy balance, is critical to integrate with hydrologic processes for characterizing landscape conditions. However, soil moisture maps are difficult to produce for natural landscapes because of vegetation cover and complex topography. Satellite-based L-band microwave sensors are commonly used to develop spatial soil moisture data products, but most existing L-band satellites provide only coarse scale (one to tens of kilometers grid size), information that is unsuitable for measuring soil moisture variation at hillslope or watershed-scales. L-band sensors are typically deployed on satellite platforms and aircraft but have been too large to deploy on small uncrewed aircraft systems (UAS). There is a need for greater spatial resolution and development of effective measures of soil moisture across a variety of natural vegetation types. To address these challenges, a novel UAS-based L-band radiometer system was evaluated that has recently been tested in agricultural settings. In this study, L-band UAS was used to map soil moisture at 3–50-m (m) resolution in a 13 square kilometer (km2) mixed grassland-forested landscape in Sonoma County, California. The results represent the first application of this technology in a natural landscape with complex topography and vegetation. The L-band inversion of the radiative transfer model produced soil moisture maps with an average unbiased root mean squared error (ubRMSE) of 0.07 m3/m3 and a bias of 0.02 m3/m3. Improved fine-scale soil moisture maps developed using UAS-based systems may be used to help inform wildfire risk, improve hydrologic models, streamflow forecasting, and early detection of landslides.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frsen.2024.1337953","usgsCitation":"Stern, M.A., Ferrell, R., Flint, L.E., Kozanitas, M., Ackerly, D., Elston, J., Stachura, M., Dai, E., and Thorne, J.H., 2024, Fine-scale surficial soil moisture mapping using UAS-based L-band remote sensing in a mixed oak-grassland landscape: Frontiers in Remote Sensing, v. 5, 1337953, 12 p., https://doi.org/10.3389/frsen.2024.1337953.","productDescription":"1337953, 12 p.","ipdsId":"IP-159618","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":499941,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frsen.2024.1337953","text":"Publisher Index Page"},{"id":499713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Sonoma County","city":"Santa Rosa","otherGeospatial":"Mayacamas Mountains, Pepperwood Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.71354880264356,\n 38.57616137564548\n ],\n [\n -122.71354880264356,\n 38.565372954642044\n ],\n [\n -122.68982536456959,\n 38.565372954642044\n ],\n [\n -122.68982536456959,\n 38.57616137564548\n ],\n [\n -122.71354880264356,\n 38.57616137564548\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955319,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferrell, Ryan","contributorId":366124,"corporation":false,"usgs":false,"family":"Ferrell","given":"Ryan","affiliations":[{"id":37798,"text":"Pepperwood Preserve","active":true,"usgs":false}],"preferred":false,"id":955320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Lorraine E. 0000-0002-7868-441X","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":306090,"corporation":false,"usgs":false,"family":"Flint","given":"Lorraine","email":"","middleInitial":"E.","affiliations":[{"id":66369,"text":"Earth Knowledge, Inc.","active":true,"usgs":false}],"preferred":false,"id":955321,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kozanitas, Melina","contributorId":366125,"corporation":false,"usgs":false,"family":"Kozanitas","given":"Melina","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":955322,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ackerly, David","contributorId":139541,"corporation":false,"usgs":false,"family":"Ackerly","given":"David","affiliations":[{"id":7102,"text":"University of California, Berkeley, Dept. of Civil & Envir. 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The purpose of DPM experimental program was to make measurements before, during, and after seasonal high-flow releases that could help guide the Congressionally authorized Everglades restoration project known as the Decompartmentalization and Sheet Flow Enhancement Project.
The DPM facility was operated by the South Florida Water Management District, with the U.S. Geological Survey (USGS) and several universities participating in experimental design and leading aspects of the research. The USGS research at DPM focused on measuring high-flow hydraulics and its sedimentary and ecological responses in downstream wetlands. USGS investigated interactions between flow and vegetation and microtopography that influenced flow velocity and water depth, bed shear stress, sediment entrainment, and the resulting downstream transport of suspended sediment and fate of particle-associated phosphorus. USGS also investigated high-flow changes in water-column mixing and gas exchange and resulting effects on metabolism of the aquatic ecosystem (primary productivity and respiration). USGS also investigated effects of built structures such as levee gaps that were constructed to reconnect levee-enclosed basins. This report describes the methods and results of the USGS-led data collection at DPM.
The USGS studies at DPM have identified factors that influence effectiveness of restoration, specifically how high-flow releases maximize sheet flow and affect sediment and nutrient dynamics while minimizing undesirable outcomes caused by past management that bypassed wetlands by conveying polluted water through canals to ecologically sensitive downstream areas. The DPM high-flow experiments reconnected the Water Conservation Area 3A and Water Conservation Area 3B basins, and it therefore has become a central feature of the restoration’s Decompartmentalization and Sheet Flow Enhancement Project. DPM’s scientific findings have already influenced the adaptive management of Everglades restoration in guiding elements of the final design and implementation of the Central Everglades Planning Project-South. In addition to serving Everglades restoration, the DPM has the potential to influence similar adaptive management programs throughout the nation’s network of federal and state-managed river corridors, floodplains, and riparian ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20241063","usgsCitation":"Harvey, J., Choi, J., Larsen, L., Skalak, K., Maglio, M., Quion, K., Swartz, A., Lin, J.T.Y., Gomez-Velez, J., and Schmadel, N., 2024, High-flow experimental outcomes to inform Everglades restoration, 2010–22: U.S. Geological Survey Open-File Report 2024–1063, 72 p., https://doi.org/10.3133/ofr20241063.","productDescription":"Report: xi, 72 p.; 3 Data Releases","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-148372","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":464267,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1063/coverthb.jpg"},{"id":464268,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1063/ofr20241063.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":464271,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241063/full"},{"id":464270,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1063/images"},{"id":464269,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1063/ofr20241063.XML"},{"id":464274,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A9SQ85","text":"USGS Data Release","description":"Harvey, J.W., Choi, J., Quion, K., Lin, J.T., Swartz, A., Larsen, L.G., Haase, K., and Schmadel, N., 2024, High-flow Experimental Outcomes for Everglades Hydraulics and Aquatic Metabolism: U.S. Geological Survey, data release, https://doi.org/10.5066/P9A9SQ85.","linkHelpText":"- High-flow Experimental Outcomes for Everglades Hydraulics and Aquatic Metabolism"},{"id":464272,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DQYB1O","text":"USGS Data Release","description":"Harvey, J.W., and Choi, J., 2022, Biophysical Data for Simulating Overland Flow in the Everglades: U.S. Geological Survey data release, https://doi.org/10.5066/P9DQYB1O.","linkHelpText":"- Biophysical Data for Simulating Overland Flow in the Everglades"},{"id":464273,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SP0HM1","text":"USGS Data Release","description":"Harvey, J.W., Choi, J., Larsen, L., Skalak, K., Maglio, M.M., Quion, K.M., Lin, T., Psaltakis, J.W., Buskirk, B.A., Swartz, A.G., Lewis, J.M., Gomez-Velez, J.D., and Schmadel, N.M., 2022, High-Flow Field Experiments to Inform Everglades Restoration: Experimental Data 2010 to 2022 (ver. 2.0, October 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9SP0HM1.","linkHelpText":"- High-Flow Field Experiments to Inform Everglades Restoration: Experimental Data 2010 to 2022 (ver. 2.0, October 2023)"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.1076224101966,\n 26.691819233104567\n ],\n [\n -82.1076224101966,\n 24.751056659514802\n ],\n [\n -79.55347920896048,\n 24.751056659514802\n ],\n [\n -79.55347920896048,\n 26.691819233104567\n ],\n [\n -82.1076224101966,\n 26.691819233104567\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Water Resources Mission Area
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The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980–2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2402028121","usgsCitation":"Zhi, W., Baniecki, H., Liu, J., Boyer, E.W., Shen, C., Shenk, G.W., Liu, X., and Li, L., 2024, Increasing phosphorus loss despite widespread concentration decline in US rivers: PNAS, v. 121, no. 48, e2402028121, 9 p., https://doi.org/10.1073/pnas.2402028121.","productDescription":"e2402028121, 9 p.","ipdsId":"IP-167332","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":489078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2402028121","text":"Publisher Index Page"},{"id":464807,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": 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USA","active":true,"usgs":false}],"preferred":false,"id":920317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baniecki, Hubert 0000-0001-6661-5364","orcid":"https://orcid.org/0000-0001-6661-5364","contributorId":346942,"corporation":false,"usgs":false,"family":"Baniecki","given":"Hubert","email":"","affiliations":[{"id":83024,"text":"University of Warsaw, Warsaw, Poland","active":true,"usgs":false}],"preferred":false,"id":920318,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liu, Jiangtao","contributorId":346943,"corporation":false,"usgs":false,"family":"Liu","given":"Jiangtao","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":920319,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyer, Elizabeth W.","contributorId":44659,"corporation":false,"usgs":false,"family":"Boyer","given":"Elizabeth","email":"","middleInitial":"W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":920320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shen, Chaopeng","contributorId":152465,"corporation":false,"usgs":false,"family":"Shen","given":"Chaopeng","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":920321,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shenk, Gary W. 0000-0001-6451-2513","orcid":"https://orcid.org/0000-0001-6451-2513","contributorId":225440,"corporation":false,"usgs":true,"family":"Shenk","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":920322,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Liu, Xiaofeng 0000-0002-8296-7076","orcid":"https://orcid.org/0000-0002-8296-7076","contributorId":317075,"corporation":false,"usgs":false,"family":"Liu","given":"Xiaofeng","email":"","affiliations":[{"id":68932,"text":"Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA","active":true,"usgs":false}],"preferred":false,"id":920323,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Li, Li","contributorId":223548,"corporation":false,"usgs":false,"family":"Li","given":"Li","affiliations":[],"preferred":false,"id":920324,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70261271,"text":"70261271 - 2024 - Awakening of Maunaloa linked to melt shared from Kilauea’s mantle source","interactions":[],"lastModifiedDate":"2024-12-04T15:22:55.307538","indexId":"70261271","displayToPublicDate":"2024-11-16T08:12:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2420,"text":"Journal of Petrology","active":true,"publicationSubtype":{"id":10}},"title":"Awakening of Maunaloa linked to melt shared from Kilauea’s mantle source","docAbstract":"Maunaloa—the largest active volcano on Earth—erupted in 2022 after its longest known repose period (~38 years) and two decades of volcanic unrest. This eruptive hiatus at Maunaloa encompasses most of the ~35-year-long Puʻuʻōʻō eruption of neighboring Kīlauea, which ended in 2018 with a collapse of the summit caldera and an unusually voluminous (~1 km3) rift eruption. A long-term pattern of such anticorrelated eruptive behavior suggests that a magmatic connection exists between these volcanoes within the asthenospheric mantle source and melting region, the lithospheric mantle, and/or the volcanic edifice. The exact nature of this connection is enigmatic. In the past, the distinct compositions of lavas from Kīlauea and Maunaloa were thought to require completely separate magma pathways from the mantle source of each volcano to the surface. Here, we use a nearly 200-yr record of lava chemistry from both volcanoes to demonstrate that melt from a shared mantle source within the Hawaiian plume may be transported alternately to Kīlauea or Maunaloa on a timescale of decades. This process led to a correlated temporal variation in 206Pb/204Pb and 87Sr/86Sr at these volcanoes since the early 19th century with each becoming more active when it received melt from the shared source. Ratios of highly over moderately incompatible trace elements (e.g., Nb/Y) at Kīlauea reached a minimum from ~2000 to 2010, which coincides with an increase in seismicity and inflation at the summit of Maunaloa. Thereafter, a reversal in Nb/Y at Kīlauea signals a decline in the degree of mantle partial melting at this volcano and suggests that melt from the shared source is now being diverted from Kīlauea to Maunaloa for the first time since the early to mid-20th century. These observations link a mantle-related shift in melt generation and transport at Kīlauea to the awakening of Maunaloa in 2002 and its eruption in 2022. Monitoring of lava chemistry is a potential tool that may be used to forecast the behavior (e.g., eruption rate and frequency) of these adjacent volcanoes on a timescale of decades. A future increase in eruptive activity at Maunaloa is likely if the temporal increase in Nb/Y continues at Kīlauea.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/petrology/egae121","usgsCitation":"Pietruszka, A., Heaton, D.E., Marske, J.P., Norman, M.D., Robbins, M.G., Mershon, R.B., Lynn, K.J., Downs, D.T., Steiner, A.R., Rhodes, J.M., and Garcia, M.O., 2024, Awakening of Maunaloa linked to melt shared from Kilauea’s mantle source: Journal of Petrology, v. 65, no. 12, egae121, 9 p., https://doi.org/10.1093/petrology/egae121.","productDescription":"egae121, 9 p.","ipdsId":"IP-169683","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":466762,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/petrology/egae121","text":"Publisher Index Page"},{"id":464749,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea volcano, Maunaloa volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.62647011347312,\n 19.636068332660543\n ],\n [\n -155.62647011347312,\n 19.326511618337022\n ],\n [\n -155.16252314077784,\n 19.326511618337022\n ],\n [\n -155.16252314077784,\n 19.636068332660543\n ],\n [\n -155.62647011347312,\n 19.636068332660543\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"65","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Pietruszka, Aaron J.","contributorId":346909,"corporation":false,"usgs":false,"family":"Pietruszka","given":"Aaron J.","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":920179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heaton, Daniel E.","contributorId":172800,"corporation":false,"usgs":false,"family":"Heaton","given":"Daniel","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":920180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marske, Jared P.","contributorId":172801,"corporation":false,"usgs":false,"family":"Marske","given":"Jared","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":920181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norman, Marc D.","contributorId":344700,"corporation":false,"usgs":false,"family":"Norman","given":"Marc","email":"","middleInitial":"D.","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":920182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robbins, Mahinaokalani G.","contributorId":346912,"corporation":false,"usgs":false,"family":"Robbins","given":"Mahinaokalani","email":"","middleInitial":"G.","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":920183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mershon, Reed B.","contributorId":346915,"corporation":false,"usgs":false,"family":"Mershon","given":"Reed","email":"","middleInitial":"B.","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":920184,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lynn, Kendra J. 0000-0001-7886-4376","orcid":"https://orcid.org/0000-0001-7886-4376","contributorId":290327,"corporation":false,"usgs":true,"family":"Lynn","given":"Kendra","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":920185,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Downs, Drew T. 0000-0002-9056-1404 ddowns@usgs.gov","orcid":"https://orcid.org/0000-0002-9056-1404","contributorId":173516,"corporation":false,"usgs":true,"family":"Downs","given":"Drew","email":"ddowns@usgs.gov","middleInitial":"T.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":920186,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Steiner, Arron R.","contributorId":346918,"corporation":false,"usgs":false,"family":"Steiner","given":"Arron","email":"","middleInitial":"R.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":920187,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rhodes, J. Michael","contributorId":215130,"corporation":false,"usgs":false,"family":"Rhodes","given":"J.","email":"","middleInitial":"Michael","affiliations":[],"preferred":false,"id":920188,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Garcia, Michael O.","contributorId":225524,"corporation":false,"usgs":false,"family":"Garcia","given":"Michael","email":"","middleInitial":"O.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":920189,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70260970,"text":"70260970 - 2024 - Three-dimensional temperature maps of the Williston Basin, USA: Implications for deep hot sedimentary and enhanced geothermal resources","interactions":[],"lastModifiedDate":"2024-11-27T16:09:53.482598","indexId":"70260970","displayToPublicDate":"2024-11-15T11:29:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"title":"Three-dimensional temperature maps of the Williston Basin, USA: Implications for deep hot sedimentary and enhanced geothermal resources","docAbstract":"As part of U.S. Geological Survey's (USGS) efforts to identify and assess geothermal energy resources of the US, a three-dimensional (3D) geologic and thermal model has been constructed for the Williston Basin, USA. The geologic model consists of all sedimentary units above the Proterozoic and Archean crystalline rock (called basement herein), with a total sedimentary thickness of up to 5 km near the basin center. Twenty-nine geologic units were mapped from interpreted formation tops from 16,465 wells. A 3D temperature model was constructed to a depth of 7 km by constructing a 3D heat flow model for the sedimentary units, followed by estimating underlying temperature using a one-dimensional (1D) analytic solution for heat flow within the underlying crystalline basement. Using the sedimentary basin model, heat flow was simulated in 3D and was calibrated using three temperature datasets: 1) 24 high-confidence static temperature logs (equilibrium thermal profiles), 2) more than15,000 drill stem test (DST) measurements from >7,000 wells, and 3) more than 45,000 bottomhole temperature (BHT) measurements from >14,000 wells. The DST and BHT datasets provide broad spatial coverage, but are lower confidence, primarily because measurements were made prior to attaining thermal equilibrium. DST and BHT measurements were binned regionally to develop representative thermal profiles that generally agree with these lower quality data (hereafter called pseudowell temperature profiles). Layer properties (primarily thermal conductivity and compaction curves) were set to best estimate values, then the heat flow model was calibrated to fit pseudowell and static temperature logs primarily by adjusting basal heat flow to approximate the overall temperature profile. Minor adjustments to thermal conductivity allowed adjusting changes in slope at lithologic contacts. Resulting maps include 3D temperature and basal (bottom of sedimentary units) heat flow estimates, which are used as input for the temperature model of the basement. The crystalline basement temperature model uses an analytic 1D solution to the heat flow equation that requires estimates of heat flow and temperature at the upper boundary (i.e., the sediment/basement contact), radiogenic heat production within the crystalline basement, and reference thermal conductivity (i.e., uncorrected for temperature). Two regions of high heat flow are identified: 1) in western North Dakota along the North American Central Plains Conductivity Anomaly and 2) in eastern Montana near the Poplar dome. Within the sedimentary column in the center of the basin of the basin, an area of approximately 100,000 km2 is predicted to have moderate- to high-temperature geothermal resources (>90 °C) under the thickest sequences of sediments. Where thick insulation and high heat flow coincide, electric-grade resources can be less than 4 km deep. Assuming a maximum feasible drilling depth of 7 km, temperatures are predicted to be as high as 175 °C. The geologic model may be used to identify strata at sufficient temperatures that may have natural permeability or that may have conditions that favor development of enhanced/engineered geothermal systems resources.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geothermics.2024.103196","usgsCitation":"Gelman, S.E., and Burns, E.R., 2024, Three-dimensional temperature maps of the Williston Basin, USA: Implications for deep hot sedimentary and enhanced geothermal resources: Geothermics, v. 125, 103196, 9 p., https://doi.org/10.1016/j.geothermics.2024.103196.","productDescription":"103196, 9 p.","ipdsId":"IP-165645","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":466763,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geothermics.2024.103196","text":"Publisher Index Page"},{"id":464292,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.05487036081263,\n 49.09401622161886\n ],\n [\n -107.05487036081263,\n 45.9204646960259\n ],\n [\n -100.81731222757732,\n 45.9204646960259\n ],\n [\n -100.81731222757732,\n 49.09401622161886\n ],\n [\n -107.05487036081263,\n 49.09401622161886\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"125","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gelman, Sarah E. 0000-0003-2549-9509","orcid":"https://orcid.org/0000-0003-2549-9509","contributorId":270004,"corporation":false,"usgs":true,"family":"Gelman","given":"Sarah","email":"","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":918757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":192154,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":918758,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70270062,"text":"70270062 - 2024 - Differentiating cheatgrass and medusahead phenological characteristics in western United States rangelands","interactions":[],"lastModifiedDate":"2025-08-08T15:24:31.376369","indexId":"70270062","displayToPublicDate":"2024-11-15T10:19:32","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Differentiating cheatgrass and medusahead phenological characteristics in western United States rangelands","docAbstract":"Expansions in the extent and infestation levels of exotic annual grass (EAG) within the rangelands of the western United States are well documented. Land managers are tasked with developing plans to limit EAG spread and prevent irreversible ecosystem deterioration. The most common EAG species and the subject of extensive study is Bromus tectorum (cheatgrass). Cheatgrass has spread rapidly in western rangelands since its initial invasion more than 100 years ago. Another concerning aggressive EAG, Taeniatherum caput-medusae (medusahead), is also commonly found in some of these areas. To control the spread of EAGs, researchers have investigated applying several control methods during different developmental stages of cheatgrass and medusahead. These control strategies require accurate maps of the timing and spatial patterns of the developmental stages to apply mitigation strategies in the correct areas at the right time. In this study, we developed annual phenological datasets for cheatgrass and medusahead with two objectives. The first objective was to determine if cheatgrass and medusahead can be differentiated at 30 m resolution using their phenological differences. The second objective was to establish an annual phenology metric regression tree model used to map the growing seasons of cheatgrass and medusahead. Harmonized Landsat and Sentinel-2 (HLS)-derived predicted weekly cloud-free 30 m normalized difference vegetation index (NDVI) images were used to develop these metric maps. The result of this effort was maps that identify the start and end of sustained growing season time for cheatgrass and medusahead at 30 m for the Snake River Plain and Northern Basin and Range ecoregions. These phenological datasets also identify the start and end-of-season NDVI values, along with maximum NDVI throughout the study period. These metrics may be utilized to characterize annual growth patterns for cheatgrass and medusahead. This approach can be utilized to plan time-sensitive control measures such as herbicide applications or cattle grazing.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16224258","usgsCitation":"Benedict, T.D., Boyte, S., and Dahal, D., 2024, Differentiating cheatgrass and medusahead phenological characteristics in western United States rangelands: Remote Sensing, v. 16, no. 22, 4258, 21 p., https://doi.org/10.3390/rs16224258.","productDescription":"4258, 21 p.","ipdsId":"IP-171996","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":494184,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16224258","text":"Publisher Index Page"},{"id":493848,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100.28187929406397,\n 48.82060636906533\n ],\n [\n -124.99361768794509,\n 48.88697493321598\n ],\n [\n -124.99361768794509,\n 30.85327470627726\n ],\n [\n -99.69664006714606,\n 30.849197853937767\n ],\n [\n -100.28187929406397,\n 48.82060636906533\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"22","noUsgsAuthors":false,"publicationDate":"2024-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Benedict, Trenton David 0000-0001-8672-2204","orcid":"https://orcid.org/0000-0001-8672-2204","contributorId":346111,"corporation":false,"usgs":true,"family":"Benedict","given":"Trenton","middleInitial":"David","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":945268,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyte, Stephen P. 0000-0002-5462-3225","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":205374,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":945269,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahal, Devendra 0000-0001-9594-1249","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":192023,"corporation":false,"usgs":false,"family":"Dahal","given":"Devendra","affiliations":[],"preferred":false,"id":945270,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70260977,"text":"70260977 - 2024 - Advancing sustainable groundwater management with a hydro-economic system model: Investigations in the Harney Basin, Oregon","interactions":[],"lastModifiedDate":"2024-11-19T19:40:18.336461","indexId":"70260977","displayToPublicDate":"2024-11-14T13:32:20","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":"Advancing sustainable groundwater management with a hydro-economic system model: Investigations in the Harney Basin, Oregon","docAbstract":"Groundwater resources frequently trend toward unsustainable levels because, absent effective institutions, individual water users generally act independently without considering the impacts on other users. Hydro-economic models (HEMs) of human-natural systems can play a positive role toward successful groundwater management by yielding valuable knowledge and insight. The current study explores how an HEM that captures essential physical and economic characteristics of a system can shed light on the system's processes and dynamics to benefit stakeholders, managers, and also researchers. These propositions are illustrated using the Harney Basin, Oregon, which has seen large groundwater declines in the past 20 years. The HEM shows that: (a) although current groundwater pumping rates will gradually raise costs and reduce well yields, irrigators gain the highest aggregate economic return by continuing current pumping; (b) lowland areas of the basin are hydrologically connected, which limits the efficacy of remedies focused on regulations only in some portions of the basin; (c) community expectations regarding the efficacy of several proposed solutions are overly optimistic; and (d) the study's scenarios identify interventions that would stabilize the groundwater system and prevent additional adverse impacts on residential and livestock wells and groundwater-dependent ecosystems. These interventions would require limiting groundwater pumping by nearly half and reducing annual profits by $7.5–$9.0M. The HEM also demonstrated its value to researchers: its insights shifted attention toward questions about Oregon's existing groundwater institutions and their inability to adaptively manage the transition from abundant groundwater to scarce groundwater in a timely manner.","language":"English","publisher":"Wiley","doi":"10.1029/2023WR036972","usgsCitation":"Jaeger, W.K., Antle, J.M., Gingerich, S.B., and Bigelow, D., 2024, Advancing sustainable groundwater management with a hydro-economic system model: Investigations in the Harney Basin, Oregon: Water Resources Research, v. 60, e2023WR036972, 26 p., https://doi.org/10.1029/2023WR036972.","productDescription":"e2023WR036972, 26 p.","ipdsId":"IP-159409","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":466765,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr036972","text":"Publisher Index Page"},{"id":464300,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","county":"Harney","otherGeospatial":"Harney Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.44430319880412,\n 44.965776942074626\n ],\n [\n -121.44430319880412,\n 42.262073209475204\n ],\n [\n -117.31344382380401,\n 42.262073209475204\n ],\n [\n -117.31344382380401,\n 44.965776942074626\n ],\n [\n -121.44430319880412,\n 44.965776942074626\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","noUsgsAuthors":false,"publicationDate":"2024-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Jaeger, William K.","contributorId":338398,"corporation":false,"usgs":false,"family":"Jaeger","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":918781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Antle, John M.","contributorId":197804,"corporation":false,"usgs":false,"family":"Antle","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":918782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gingerich, Stephen B. 0000-0002-4381-0746 sbginger@usgs.gov","orcid":"https://orcid.org/0000-0002-4381-0746","contributorId":1426,"corporation":false,"usgs":true,"family":"Gingerich","given":"Stephen","email":"sbginger@usgs.gov","middleInitial":"B.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918783,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bigelow, Daniel 0000-0002-1154-2302","orcid":"https://orcid.org/0000-0002-1154-2302","contributorId":346353,"corporation":false,"usgs":false,"family":"Bigelow","given":"Daniel","email":"","affiliations":[{"id":82838,"text":"Oregon State University Applied Economics Department","active":true,"usgs":false}],"preferred":false,"id":918784,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267760,"text":"70267760 - 2024 - Evaluating spatially explicit management alternatives for an invasive species in a riverine network","interactions":[],"lastModifiedDate":"2025-05-30T15:47:18.945176","indexId":"70267760","displayToPublicDate":"2024-11-14T10:37:09","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5071,"text":"NeoBiota","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating spatially explicit management alternatives for an invasive species in a riverine network","docAbstract":"Invasive species have substantial ecological and economic costs and removing them can require large investments by management agencies. Optimal spatial allocation of removal effort is critical for efficient and effective management of invasive species. Using a series of ecologically informed model simulations, we evaluated and compared different spatially explicit removal strategies for invasive rusty crayfish (Faxonius rusticus) in the John Day River, USA. We assessed strategies in terms of their performance on three likely management objectives: suppression (minimise overall population abundance), containment (minimise the spatial extent of invasion) and prevention (minimise spread into a specific area). We developed five spatial removal strategies to achieve those objectives, denoted as: Target Abundance (removal at locations with the highest population abundance), Target Growth (removal at locations with the highest population growth), Target Edges (removal at the most distant locations in the river), Target Downstream (removal at the most downstream invaded segments on the Mainstem), and Target Random (removal at randomly selected locations). Each strategy was assessed at various effort levels, referring to the number of spatial segments in the river in which removals were conducted, after seven years of management. We identified the alternative that best achieved each objective, based on decision criteria for risk-neutral and risk-averse decision-makers and further evaluated strategies based on Pareto efficiency, which identifies the set of alternatives for which an improvement on one objective cannot be had without a decline in performance on another. We found that Target Abundance and Target Growth strategies best achieved the suppression objective, for risk neutral and risk averse decision-makers, respectively and Target Downstream was always best in achieving the prevention objective across both types of decision-makers. No single strategy consistently performed best in terms of the containment objective. In terms of all three objectives, Target Downstream was consistently Pareto efficient across all levels of management effort and both decision criteria. The modelling framework we provided is adaptable to a variety of riverine invasive species to help assess and compare spatial management strategies.
","language":"English","publisher":"Pensoft","doi":"10.3897/neobiota.96.132363","usgsCitation":"Thompson, B., Olden, J., and Converse, S.J., 2024, Evaluating spatially explicit management alternatives for an invasive species in a riverine network: NeoBiota, v. 96, p. 151-172, https://doi.org/10.3897/neobiota.96.132363.","productDescription":"22 p.","startPage":"151","endPage":"172","ipdsId":"IP-171897","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490646,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/neobiota.96.132363","text":"Publisher Index Page"},{"id":489267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"John Day River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.88075847388461,\n 45.69676723476786\n ],\n [\n -120.88724466132285,\n 44.11199343230538\n ],\n [\n -118.7532889941624,\n 44.11661864634385\n ],\n [\n -118.77274755647663,\n 45.69676723476786\n ],\n [\n -120.88075847388461,\n 45.69676723476786\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"96","noUsgsAuthors":false,"publicationDate":"2024-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Brielle K.","contributorId":355570,"corporation":false,"usgs":false,"family":"Thompson","given":"Brielle K.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":938754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olden, Julian D.","contributorId":338326,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[],"preferred":false,"id":938755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":938756,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70262249,"text":"70262249 - 2024 - A synthesis of the characteristics and drivers of introduced fishes in prairie streams: Can we manage introduced harmful fishes in these dynamic environments?","interactions":[],"lastModifiedDate":"2025-01-17T16:45:52.438011","indexId":"70262249","displayToPublicDate":"2024-11-14T09:36:50","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"A synthesis of the characteristics and drivers of introduced fishes in prairie streams: Can we manage introduced harmful fishes in these dynamic environments?","docAbstract":"Prairie streams of North America support native fishes that are adapted to the dynamic environment that characterizes these ecologically and economically important ecosystems. However, prairie streams have been altered by landscape changes that may affect the proportions of native and introduced species in fish communities. Herein, we investigate drivers of introduced fish in prairie streams, detail common introduced species and their traits and effects, investigate how climate change may alter the balance between native and introduced species, and summarize management options. Commonly introduced fishes are those with the ability to tolerate extreme variations in temperature, hydrology, and salinity and, as a result, most of the introduced fishes were native to other prairie streams within the Great Plains ecoregion. This suggests environmental extremes may act as a filter for establishment or that short-distance translocations are more common than introductions from other ecoregions. The mechanisms or extent to which introduced species affect native fishes is often assumed or understudied. Climate change may amplify environmental disturbances in ways that may favor native or introduced fishes depending on species traits and biotic interactions. Actions such as habitat modifications or disturbances may favor introduced fishes over native fishes. Research to understand the relative roles of trait preadaptation and spatial proximity of source populations in introduced species establishment could benefit future management. Moreover, patterns observed in other ecosystems may not be transferrable to prairie streams, highlighting the need to understand the context dependency of effects of introduced species.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10530-024-03450-y","usgsCitation":"Coulter, A., Moore, M.J., Golcher-Benavides, J., Rahel, F.J., Walters, A.W., Brewer, S., and Wildhaber, M.L., 2024, A synthesis of the characteristics and drivers of introduced fishes in prairie streams: Can we manage introduced harmful fishes in these dynamic environments?: Biological Invasions, v. 26, p. 4011-4033, https://doi.org/10.1007/s10530-024-03450-y.","productDescription":"23 p.","startPage":"4011","endPage":"4033","ipdsId":"IP-160187","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481049,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10530-024-03450-y","text":"Publisher Index Page"},{"id":480749,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"North American Great Plains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.30865918818435,\n 51.00019043175968\n ],\n [\n -108.8244372572745,\n 47.17125929409586\n ],\n [\n -106.13072410712726,\n 42.33953855703288\n ],\n [\n -104.22086600055933,\n 40.10613156257109\n ],\n [\n -104.39837770581909,\n 36.40878493463546\n ],\n [\n -104.26570852411,\n 33.836825487233355\n ],\n [\n -102.59989428051855,\n 31.991484684655916\n ],\n [\n -98.85669537584786,\n 31.98987602981056\n ],\n [\n -94.06006847133565,\n 38.705570253107695\n ],\n [\n -94.4212804260574,\n 43.37128007692609\n ],\n [\n -96.14739688752542,\n 48.2616932328075\n ],\n [\n -96.41979434976253,\n 50.75373398643449\n ],\n [\n -96.92898991711421,\n 51.5227386971378\n ],\n [\n -99.57727818526257,\n 52.66077258131\n ],\n [\n -107.3372532002433,\n 51.958499229615185\n ],\n [\n -111.30865918818435,\n 51.00019043175968\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","noUsgsAuthors":false,"publicationDate":"2024-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Coulter, A. A.","contributorId":348595,"corporation":false,"usgs":false,"family":"Coulter","given":"A. A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":923644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Michael J. 0000-0002-5495-7049","orcid":"https://orcid.org/0000-0002-5495-7049","contributorId":304258,"corporation":false,"usgs":true,"family":"Moore","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Golcher-Benavides, Jimena","contributorId":348598,"corporation":false,"usgs":false,"family":"Golcher-Benavides","given":"Jimena","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":923646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rahel, Frank J.","contributorId":171824,"corporation":false,"usgs":false,"family":"Rahel","given":"Frank","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":923647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":923648,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":923649,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wildhaber, Mark L. 0000-0002-6538-9083 mwildhaber@usgs.gov","orcid":"https://orcid.org/0000-0002-6538-9083","contributorId":1386,"corporation":false,"usgs":true,"family":"Wildhaber","given":"Mark","email":"mwildhaber@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":923650,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70261775,"text":"70261775 - 2024 - Simple stated preference questions can enhance transdisciplinary projects: Linking perceived risks with willingness to spray and pay","interactions":[],"lastModifiedDate":"2024-12-23T16:37:01.405686","indexId":"70261775","displayToPublicDate":"2024-11-14T09:26:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5848,"text":"Environmental and Resource Economics","onlineIssn":"1573-1502","printIssn":"0924-6460","active":true,"publicationSubtype":{"id":10}},"title":"Simple stated preference questions can enhance transdisciplinary projects: Linking perceived risks with willingness to spray and pay","docAbstract":"Transdisciplinary projects can uncover crucial insights on people’s past and future risk-mitigation behavior. We focus on a novel risk context: increasing health threats from ticks on Staten Island, a New York City borough where the combination of high population density and extensive park systems and green spaces has resulted in a rise in locally-acquired tick-transmitted disease cases. We administered a knowledge, attitudes, and practices survey that additionally included simple economic stated preference questions about people’s willingness to spray tick pesticides in the future. We first analyze factors that are correlated with people’s perceptions of two types of risks: exposure to ticks and infection with Lyme disease. Next, we use the nonmarket valuation questions to test people’s willingness to spray and pay as a function of attributes of the hypothetical pesticides, including cost, effectiveness, and type. Across all model specifications, overall willingness to pay (WTP) to spray increases with increases in pesticide effectiveness, as well as with favorable pesticide type (organic). We uncover threshold pesticide effectiveness levels at which WTP to spray turns positive, and we find that lower pesticide effectiveness is required for organic pesticide. Finally, we test how perceived risks and various individual-specific characteristics correlate with WTP to spray. Combinations of higher perceived risks are linked both with higher WTP and lower breakeven pesticide effectiveness. Our work shows that a broad range of variables influence demand for self-protection actions, both directly and indirectly (through their effects on perceived risks). Such insights on people’s tradeoffs carry important policy implications, but they can be missed if economic information is either not elicited or elicited alone.","language":"English","publisher":"Springer Nature","doi":"10.1007/s10640-024-00923-5","usgsCitation":"Enriquez, A.J., Berry, K., Fernandez, M., Gregory, N., Ernst, K.C., Hayden, M.H., and Diuk-Wasser, M.A., 2024, Simple stated preference questions can enhance transdisciplinary projects: Linking perceived risks with willingness to spray and pay: Environmental and Resource Economics, v. 88, p. 81-124, https://doi.org/10.1007/s10640-024-00923-5.","productDescription":"44 p.","startPage":"81","endPage":"124","ipdsId":"IP-155058","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":466766,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10640-024-00923-5","text":"Publisher Index Page"},{"id":465439,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Staten Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.20533995856798,\n 40.64942836406519\n ],\n [\n -74.21801184111787,\n 40.577295563468994\n ],\n [\n -74.24899218747878,\n 40.548387271105895\n ],\n [\n -74.26692137394798,\n 40.507364607877975\n ],\n [\n -74.2473514200188,\n 40.4985817976507\n ],\n [\n -74.14951843995719,\n 40.519525822175325\n ],\n [\n -74.07777920044828,\n 40.56028515839262\n ],\n [\n -74.05165602307073,\n 40.600174493513464\n ],\n [\n -74.06691131937252,\n 40.64774134809241\n ],\n [\n -74.20533995856798,\n 40.64942836406519\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"88","noUsgsAuthors":false,"publicationDate":"2024-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Enriquez, Aaron Joey 0000-0002-0305-4333","orcid":"https://orcid.org/0000-0002-0305-4333","contributorId":346485,"corporation":false,"usgs":true,"family":"Enriquez","given":"Aaron","email":"","middleInitial":"Joey","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":921764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berry, Kevin","contributorId":346487,"corporation":false,"usgs":false,"family":"Berry","given":"Kevin","email":"","affiliations":[{"id":82879,"text":"Department of Economics, University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":921765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fernandez, Maria del Pilar","contributorId":347473,"corporation":false,"usgs":false,"family":"Fernandez","given":"Maria del Pilar","affiliations":[{"id":83167,"text":"Paul G. Allen School for Global Health, College of Veterinary Medicine, Washington State University and Department of Ecology, Evolution and Environmental Biology, Columbia University","active":true,"usgs":false}],"preferred":false,"id":921766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gregory, Nichar","contributorId":347474,"corporation":false,"usgs":false,"family":"Gregory","given":"Nichar","email":"","affiliations":[{"id":83169,"text":"Department of Ecology, Evolution and Environmental Biology, Columbia University and EcoHealth Alliance","active":true,"usgs":false}],"preferred":false,"id":921767,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ernst, Kacey C.","contributorId":346484,"corporation":false,"usgs":false,"family":"Ernst","given":"Kacey","email":"","middleInitial":"C.","affiliations":[{"id":82875,"text":"Department of Epidemiology and Biostatistics, College of Public Health, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":921768,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hayden, Mary H.","contributorId":148034,"corporation":false,"usgs":false,"family":"Hayden","given":"Mary","email":"","middleInitial":"H.","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":921769,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Diuk-Wasser, Maria A.","contributorId":148025,"corporation":false,"usgs":false,"family":"Diuk-Wasser","given":"Maria","email":"","middleInitial":"A.","affiliations":[{"id":7254,"text":"Columbia University - Lamont Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":921770,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70261407,"text":"70261407 - 2024 - Reduced injection rates and shallower depths mitigated induced seismicity in Oklahoma","interactions":[],"lastModifiedDate":"2024-12-09T15:16:44.012708","indexId":"70261407","displayToPublicDate":"2024-11-13T08:11:21","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"Reduced injection rates and shallower depths mitigated induced seismicity in Oklahoma","docAbstract":"The proximity of wastewater disposal to the Precambrian basement is a critical factor influencing induced earthquake rates in the Central United States, but the impact of reducing injection depths has not been widely demonstrated. Beginning in 2015, state regulatory efforts in Oklahoma and Kansas mandated that wells injecting into the lower Arbuckle Group, a basal sedimentary unit, be backfilled with cement (i.e. “plugged back”) so that they inject into shallower formations. This plug back activity gives us a unique opportunity to investigate the relationship between injection depth and induced seismicity rate. To evaluate the impact that decreased injection rates and plug backs had on the seismicity rates, we create a suite of rate-state earthquake models. Observed seismicity rates are best fit when only lower Arbuckle volumes are considered, suggesting the lower Arbuckle injectors were primarily responsible for the seismicity and that plug backs were effective at isolating the injected volumes to shallower formations. Our models demonstrate that if these wells had not been plugged back, seismicity rates would be multiple times larger than they are today. We find that the combination of well plug backs and injection volume decreases can be effective strategies for reducing induced seismicity rates.","language":"English","publisher":"GeoScienceWorld","doi":"10.1785/0320240030","usgsCitation":"Skoumal, R.J., Barbour, A.J., Rubenstein, J.L., and Glasgow, M.E., 2024, Reduced injection rates and shallower depths mitigated induced seismicity in Oklahoma: The Seismic Record, v. 4, no. 4, p. 279-287, https://doi.org/10.1785/0320240030.","productDescription":"9 p.","startPage":"279","endPage":"287","ipdsId":"IP-169748","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":466770,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320240030","text":"Publisher Index Page"},{"id":464919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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conservation populations","interactions":[],"lastModifiedDate":"2025-01-22T14:51:26.518694","indexId":"70262575","displayToPublicDate":"2024-11-12T10:34:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Before the fire: Predicting burn severity and potential post-fire debris-flow hazards to Colorado River Cutthroat Trout (Oncorhynchus clarkii pleuriticus) conservation populations","title":"Before the fire: Predicting burn severity and potential post-fire debris-flow hazards to Colorado River Cutthroat Trout (Oncorhynchus clarkii pleuriticus) conservation populations","docAbstract":"Background
Colorado River Cutthroat Trout (CRCT; Oncorhynchus clarkii pleuriticus) conservation populations may be at risk from wildfire and post-fire debris flows hazards.
Aim
To predict burn severity and potential post-fire debris flow hazard classifications to CRCT conservation populations before wildfires occur.
Methods
We used remote sensing, spatial analyses, and machine learning to model 28 wildfire incidents (2016–2020) and spatially predict burn severity from pre-wildfire environmental factors to evaluate the likelihood (%) and volume (m3) hazard classification of post-fire debris flow.
Key results
Burn severity was best predicted by fuels, followed by topography, physical ecosystem conditions, and weather (mean adjusted R2 = 0.54). Predictions of high or moderate burn severity covered 1.1 (15% of study area) and 1.5 (19% of study area) million ha, respectively, and varied by watershed. Combined high or moderate debris flow hazard classification included 80% of stream reaches with conservation populations and 97% of conservation population point nodes.
Conclusions
Predicted burn severity and potential post-fire debris flow indicated moderate to high hazard for CRCT conservation populations native to the Green and Yampa rivers of the Upper Colorado River Basin.
Implications
Future management actions can incorporate predicted burn severity and potential post-fire debris flow to mitigate impacts to CRCT and other at-risk resource values before a wildfire occurs.
","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/WF23199","usgsCitation":"Wells, A.G., Yackulic, C., Kostelnik, J., Bock, A.R., Zuellig, R.E., Carlisle, D.M., Roberts, J., Rogers, K., and Munson, S.M., 2024, Before the fire: Predicting burn severity and potential post-fire debris-flow hazards to Colorado River Cutthroat Trout (Oncorhynchus clarkii pleuriticus) conservation populations: International Journal of Wildland Fire, v. 33, WF23199, 19 p., https://doi.org/10.1071/WF23199.","productDescription":"WF23199, 19 p.","ipdsId":"IP-160628","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":481050,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wf23199","text":"Publisher Index Page"},{"id":480839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.0498046875,\n 44.731125592643274\n ],\n [\n -111.1376953125,\n 42.114523952464246\n ],\n [\n -112.78564453124999,\n 41.902277040963696\n ],\n [\n -112.6318359375,\n 38.03078569382294\n ],\n [\n -111.9287109375,\n 36.756490329505176\n ],\n [\n -110.1708984375,\n 35.8356283888737\n ],\n [\n -106.63330078125,\n 35.55010533588552\n ],\n [\n -106.787109375,\n 35.88905007936091\n ],\n [\n -104.83154296875,\n 38.77121637244273\n ],\n [\n -104.96337890625,\n 40.93011520598305\n ],\n [\n -111.0498046875,\n 44.731125592643274\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"33","noUsgsAuthors":false,"publicationDate":"2024-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Wells, Adam Gerhard 0000-0001-9675-4963","orcid":"https://orcid.org/0000-0001-9675-4963","contributorId":270137,"corporation":false,"usgs":true,"family":"Wells","given":"Adam","email":"","middleInitial":"Gerhard","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":924578,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":924579,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kostelnik, Jaime 0000-0002-1817-5461","orcid":"https://orcid.org/0000-0002-1817-5461","contributorId":300717,"corporation":false,"usgs":true,"family":"Kostelnik","given":"Jaime","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":924580,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924581,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zuellig, Robert E. 0000-0002-4784-2905 rzuellig@usgs.gov","orcid":"https://orcid.org/0000-0002-4784-2905","contributorId":1620,"corporation":false,"usgs":true,"family":"Zuellig","given":"Robert","email":"rzuellig@usgs.gov","middleInitial":"E.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924582,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":924583,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924584,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rogers, Kevin B.","contributorId":220104,"corporation":false,"usgs":false,"family":"Rogers","given":"Kevin B.","affiliations":[],"preferred":false,"id":924585,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":924586,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70260892,"text":"70260892 - 2024 - Triggering the 2022 eruption of Mauna Loa","interactions":[],"lastModifiedDate":"2024-11-14T15:40:19.077739","indexId":"70260892","displayToPublicDate":"2024-11-12T09:35:20","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Triggering the 2022 eruption of Mauna Loa","docAbstract":"Distinguishing periods of intermittent unrest from the run-up to eruption is a major challenge at volcanoes around the globe. Comparing multidisciplinary monitoring data with mineral chemistry that records the physical and spatio-temporal evolution of magmas fundamentally advances our ability to forecast eruptions. The recent eruption of Mauna Loa, Earth’s largest active volcano, provides a unique opportunity to differentiate unrest from run-up and improve forecasting of future eruptions. After decades of intermittent seismic and geodetic activity over 38 years of repose, Mauna Loa began erupting on 27 November 2022. Here we present a multidisciplinary synthesis that tracks the spatio-temporal evolution of precursory activity by integrating mineral and melt chemistry, fluid inclusion barometry, numerical modeling of mineral zoning, syn-eruptive gas plume measurements, the distribution and frequency of earthquake hypocenters, seismic velocity changes, and ground deformation. These diverse data indicate that the eruption occurred following a 2-month period of sustained magma intrusion from depths of 3–5 km up to 1–2 km beneath the summit caldera, providing a new model of the plumbing system at this very high threat volcano. Careful correlation of both the geochemistry and instrumental monitoring data improves our ability to distinguish unrest from the run-up to eruption by providing deeper understanding of the both the monitoring data and the magmatic system—an approach that could be applied at other volcanic systems worldwide.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-024-52881-7","usgsCitation":"Lynn, K.J., Downs, D.T., Trusdell, F., Wieser, P.E., Rangel, B., McDade, B.R., Hotovec-Ellis, A.J., Bennington, N.L., Anderson, K.R., Ruth, D.C., DeVitre, C., Ellis, A.P., Nadeau, P.A., Clor, L., Kelly, P.J., Dotray, P., and Chang, J., 2024, Triggering the 2022 eruption of Mauna Loa: Nature Communications, v. 15, 9451, 12 p., https://doi.org/10.1038/s41467-024-52881-7.","productDescription":"9451, 12 p.","ipdsId":"IP-166194","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":466771,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-024-52881-7","text":"Publisher Index Page"},{"id":464028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Loa","geographicExtents":"{\n \"type\": 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reservoirs","interactions":[],"lastModifiedDate":"2024-12-18T22:20:30.736077","indexId":"70260865","displayToPublicDate":"2024-11-11T16:12:21","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"displayTitle":"A methodology to estimate CO2 and energy gas storage resources in depleted conventional gas reservoirs","title":"A methodology to estimate CO2 and energy gas storage resources in depleted conventional gas reservoirs","docAbstract":"Depleted hydrocarbon reservoirs are subsurface geological structures capable of sequestering vast quantities of carbon dioxide (CO2) as well as storing other energy gases for later usage, such as natural gas, and potentially hydrogen (H2). Here we outline a methodology to quantify multi-gas storage resources in depleted conventional gas reservoirs for usage in assessments by the United States Geological Survey (USGS) at the scale of sedimentary basins. The methodology consists first of quantifying accessible pore volume in a depleted reservoir for natural gas storage using up to three equations. Input data are derived from commonly reported or estimated reservoir parameters and natural gas production volumes, and equations may be combined in linear models to improve pore volume estimates. Storage estimates from these equations are tested and validated for 31 reservoirs in the Michigan Basin Province, USA that were previously converted to underground gas storage facilities and have known (federally reported) natural gas storage capacities. Secondly, natural gas storage capacities can be transformed via fluid substitution calculations to estimate the storage resources for non-native fluids, applied here for, CO2, H2, and methane-H2 blends, accounting for molecule-specific deviations from ideal gas behavior at reservoir pressures and temperatures as well as differing storage efficiencies. Importantly, the storage of non-native fluids may not be appropriate in all depleted gas reservoir settings due to potential risks like leakage, in particular in the case of H2 storage, requiring additional knowledge of caprock sealing capacity. Given this caveat, we demonstrate the fluid substitution method for natural gas reservoirs of the Northern Niagaran Reef and Southern Niagaran Reef USGS plays in the Michigan Basin Province, as these trends of Silurian pinnacle reefs are capped with tight-sealing evaporite facies. The deterministic equations outlined from this methodology can be incorporated into future probabilistic USGS gas storage assessments for CO2, H2, and natural gas resources in the United States.
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Assessment of impoundment and environmental conditions that affect detection probability may aid in reducing sample variance and benefit inferences regarding changes to Flathead Catfish populations. We sampled eight small impoundments in Kansas (37–114 surface ha) using low-frequency electrofishing in summer, 2021. We revisited sites nine times over three months using an occupancy modeling framework to estimate the influence of impoundment and environmental conditions on detection probability of Flathead Catfish. We employed an information theoretic approach and ranked models built with impoundment as a random effect and three environmental variables predicted to influence detection of Flathead Catfish in small impoundments. Detection probability across all populations was 0.526 (SE = 0.020) and was influenced by water temperature, mean depth of the impoundment, and proportion of impoundment sampled. Generally, detection probability increased with all measured variables. The inclusion of detection probability in assessments of Flathead Catfish in small impoundments can inform interpretation of catch-related metrics. Further, variable detection suggests collection of multiple samples during a defined sampling period might be more suitable for characterizing populations than a single sample.
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\"}}]}","volume":"15","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Brett T.","contributorId":351286,"corporation":false,"usgs":false,"family":"Miller","given":"Brett T.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neely, Ben C.","contributorId":351287,"corporation":false,"usgs":false,"family":"Neely","given":"Ben C.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chance-Ossowski, Connor J.","contributorId":351288,"corporation":false,"usgs":false,"family":"Chance-Ossowski","given":"Connor J.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waters, Micah J.","contributorId":351289,"corporation":false,"usgs":false,"family":"Waters","given":"Micah J.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Salazar, Vanessa","contributorId":351290,"corporation":false,"usgs":false,"family":"Salazar","given":"Vanessa","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928336,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lucas K. Kowalewski","contributorId":351291,"corporation":false,"usgs":false,"family":"Lucas K. Kowalewski","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928337,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kramer, Nicholas W.","contributorId":351292,"corporation":false,"usgs":false,"family":"Kramer","given":"Nicholas W.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928338,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lundgren, Seth A.","contributorId":351293,"corporation":false,"usgs":false,"family":"Lundgren","given":"Seth A.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928339,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Spurgeon, Jonathan J. 0000-0002-6888-5867","orcid":"https://orcid.org/0000-0002-6888-5867","contributorId":304259,"corporation":false,"usgs":true,"family":"Spurgeon","given":"Jonathan","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":928340,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70260886,"text":"70260886 - 2024 - Trimming the UCERF3-TD logic tree: Model order reduction for an earthquake rupture forecast considering loss exceedance","interactions":[],"lastModifiedDate":"2025-03-11T14:49:17.072232","indexId":"70260886","displayToPublicDate":"2024-11-11T08:52:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Trimming the UCERF3-TD logic tree: Model order reduction for an earthquake rupture forecast considering loss exceedance","docAbstract":"The Uniform California Earthquake Rupture Forecast version 3-Time Dependent depicts California’s seismic faults and their activity. Its logic tree has 5760 leaves. Considering 30 more model combinations related to ground motion produces 172,800 distinct models representing so-called epistemic uncertainties. To calculate risk to a portfolio of buildings, one also considers millions of earthquakes and spatially correlated ground-motion variability. We offer a tree-trimming technique that retains the probability distribution of portfolio loss and identifies the leading sources of uncertainty for further study. We applied it to a California statewide building portfolio and various levels of nonexceedance probability between one in 100 and one in 2500. We trimmed the logic tree from 172,800 leaves to as few as 15. The result: a supercomputer that would otherwise run 24 h to estimate the distribution of one-in-250-year loss can calculate it in moments with the reduced-order model. Others can use the reduced-order model to calculate risk to different California portfolios, and scientists can prioritize study to reduce the remaining epistemic uncertainty.
","language":"English","publisher":"Sage","doi":"10.1177/87552930241280401","usgsCitation":"Porter, K., Milner, K.R., and Field, E.H., 2024, Trimming the UCERF3-TD logic tree: Model order reduction for an earthquake rupture forecast considering loss exceedance: Earthquake Spectra, v. 41, no. 1, p. 636-653, https://doi.org/10.1177/87552930241280401.","productDescription":"19 p.","startPage":"636","endPage":"653","ipdsId":"IP-161498","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":464026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-11-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Porter, Keith","contributorId":191074,"corporation":false,"usgs":false,"family":"Porter","given":"Keith","affiliations":[],"preferred":false,"id":918429,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milner, Kevin R.","contributorId":194141,"corporation":false,"usgs":false,"family":"Milner","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":918430,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":52242,"corporation":false,"usgs":true,"family":"Field","given":"Edward","email":"field@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":918431,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263253,"text":"70263253 - 2024 - A decade of shaking in the Garden City: The dynamics of preparedness, perceptions, and beliefs in Canterbury, New Zealand, and implications for earthquake information","interactions":[],"lastModifiedDate":"2025-02-03T16:41:31.554672","indexId":"70263253","displayToPublicDate":"2024-11-10T09:24:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20065,"text":"Frontiers Communication - Disaster Communications","active":true,"publicationSubtype":{"id":10}},"title":"A decade of shaking in the Garden City: The dynamics of preparedness, perceptions, and beliefs in Canterbury, New Zealand, and implications for earthquake information","docAbstract":"This study explored earthquake preparedness over time - before, during, and 10 years after the Canterbury Earthquake Sequence (CES) in Aotearoa New Zealand (NZ). Surveys of Canterbury residents were conducted in 2009, 2013 and again in 2021, using variables derived from Community Engagement Theory (CET). The surveys measured earthquake perceptions and beliefs, participation and engagement, and preparedness actions. Results were compared across the three samples. Findings indicate that perceptions and beliefs (e.g. risk perception, outcome expectancy beliefs), and types of preparedness actions taken (e.g. collection of survival items, structural preparedness, community and agency relationships), differed over time, depending on people’s experiences before, during, and after the CES. For example, during and after the CES people were more likely to believe that preparing provided a benefit to daily life, but less likely to think it could reduce property damage, perhaps due to people’s experiences of disruption and damage during the earthquakes. An understanding of such dynamics can assist with the provision and timing of risk and preparedness information. This study highlights the importance of providing applicable and actionable preparedness information, that is relevant to people’s experiences, throughout an earthquake sequence. Such information might evolve and change in focus over time depending on risks and needs. Focus could also be given to information that builds peoples beliefs and capacities to undertake preparedness in evolving situations. Understanding preparedness in the context of different experiences and timeframes is useful in helping update models such as the CET, where the dynamics of time might be better incorporated.","language":"English","publisher":"Frontiers in Communication","doi":"10.17605/OSF.IO/4T9U6","usgsCitation":"Becker, J., Hudson-Doyle, E.E., Vinnell, L., McBride, S., Paton, D., and Johnston, D.A., 2024, A decade of shaking in the Garden City: The dynamics of preparedness, perceptions, and beliefs in Canterbury, New Zealand, and implications for earthquake information: Frontiers Communication - Disaster Communications, v. 9, 1410333, 17 p., https://doi.org/10.17605/OSF.IO/4T9U6.","productDescription":"1410333, 17 p.","ipdsId":"IP-166034","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":481617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","otherGeospatial":"Canterbury","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 173.90311410586924,\n -41.90491335110837\n ],\n [\n 173.0671112994046,\n -42.34762205459946\n ],\n [\n 172.65824372717864,\n -42.12977595965312\n ],\n [\n 171.5759622163327,\n -42.76805561899313\n ],\n [\n 169.58271546684648,\n -43.891078936023206\n ],\n [\n 169.8484364471409,\n -44.994495013131015\n ],\n [\n 171.2960770344631,\n -44.972998012300614\n ],\n [\n 171.40906022229615,\n -44.44185763613985\n ],\n [\n 171.86277407338306,\n -44.13592354193222\n ],\n [\n 173.19397839209154,\n -43.90197178118605\n ],\n [\n 172.9675920785376,\n -43.298534792782036\n ],\n [\n 173.37940278385906,\n -42.951952400210985\n ],\n [\n 174.11640295427313,\n -42.004263620426265\n ],\n [\n 173.90311410586924,\n -41.90491335110837\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Becker, Julia S.","contributorId":217541,"corporation":false,"usgs":false,"family":"Becker","given":"Julia S.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":926027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hudson-Doyle, Emma E. 0000-0002-2878-0972","orcid":"https://orcid.org/0000-0002-2878-0972","contributorId":240959,"corporation":false,"usgs":false,"family":"Hudson-Doyle","given":"Emma","email":"","middleInitial":"E.","affiliations":[{"id":13571,"text":"Massey University","active":true,"usgs":false}],"preferred":false,"id":926028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vinnell, Lauren","contributorId":292282,"corporation":false,"usgs":false,"family":"Vinnell","given":"Lauren","email":"","affiliations":[{"id":13571,"text":"Massey University","active":true,"usgs":false}],"preferred":false,"id":926029,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":926030,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paton, Douglas","contributorId":350440,"corporation":false,"usgs":false,"family":"Paton","given":"Douglas","affiliations":[{"id":12877,"text":"Charles Darwin University","active":true,"usgs":false}],"preferred":false,"id":926031,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnston, David A.","contributorId":64637,"corporation":false,"usgs":false,"family":"Johnston","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":6956,"text":"GNS Science/Massey University","active":true,"usgs":false}],"preferred":false,"id":926032,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70260228,"text":"sir20245088 - 2024 - Inset groundwater-flow models for the Cache and Grand Prairie Critical Groundwater Areas, northeastern Arkansas","interactions":[],"lastModifiedDate":"2025-12-22T21:29:22.991335","indexId":"sir20245088","displayToPublicDate":"2024-11-08T12:11:54","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":"2024-5088","displayTitle":"Inset Groundwater-Flow Models for the Cache and Grand Prairie Critical Groundwater Areas, Northeastern Arkansas","title":"Inset groundwater-flow models for the Cache and Grand Prairie Critical Groundwater Areas, northeastern Arkansas","docAbstract":"The water resources in the Mississippi alluvial plain, located in parts of Missouri, Kentucky, Tennessee, Mississippi, Louisiana, and Arkansas, supports a multibillion-dollar agricultural industry that relies heavily on pumping of groundwater for irrigation of crops and aquaculture. The primary source of groundwater for agricultural-related pumping is the Mississippi River Valley alluvial aquifer, which has declined in storage for decades; secondary groundwater sources include the middle Claiborne aquifer and Wilcox aquifer system. Two areas in northeastern Arkansas that lie within the Mississippi alluvial plain, part of the Cache and Grand Prairie regions, have been designated as Critical Groundwater Areas owing to decades of groundwater declines that resulted from past and current water use. The multidisciplinary Mississippi Alluvial Plain project, led by the U.S. Geological Survey, and funded by their Water Availability and Use Science Program, included objectives to develop numerical groundwater models in focus regions, including the part of the Cache and Grand Prairie regions of northeastern Arkansas. Two inset models were developed using the child model capabilities of MODFLOW 6, the U.S. Geological Survey’s Modular Hydrologic Model simulation software. Both models, called the Cache model and Grand Prairie model, simulated the groundwater system and surface-water/groundwater interactions for the Mississippi River Valley alluvial aquifer and underlying Tertiary-age aquifers and confining units to the Midway confining unit. Each model was spatially discretized into 500-meter x 500-meter orthogonal cells on a grid with 5-meter constant-thickness vertical layers that represented the Mississippi River Valley alluvial aquifer and increasing thickness layers for the aquifers and confining units below the alluvial aquifer. The Cache and Grand Prairie models were calibrated with the PEST++ iterative ensemble smoother Version 5 and employed high dimensional parameterization schemes of 13,740 and 30,436 parameters, respectively. The Cache mean absolute residual for groundwater-level observations within each model domain for the priority well was 1.58 meters. Grand Prairie mean absolute residuals for the alluvial aquifer and middle Claiborne aquifer groundwater-level observations were 2.71 and 10.78 meters, respectively. The groundwater budgets for the Cache and Grand Prairie models were characterized by substantial outflows to irrigation wells, which constituted about 52 and 54 percent of all outflows, with the primary source of water to those wells being releases from unconfined aquifer storage.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245088","programNote":"Water Availability and Use Science Program","usgsCitation":"Traylor, J.P., Duncan, L.L., Leaf, A.T., Weisser, A.R., Dietsch, B.J., and Guira, M., 2024, Inset groundwater-flow models for the Cache and Grand Prairie Critical Groundwater Areas, northeastern Arkansas: U.S. Geological Survey Scientific Investigations Report 2024–5088, 152 p., https://doi.org/10.3133/sir20245088.","productDescription":"Report: xi, 152 p.; 13 Figures: 8.50 x 11.00 inches; Data Release; Dataset","numberOfPages":"168","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-155030","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497914,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117774.htm","linkFileType":{"id":5,"text":"html"}},{"id":463425,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HZWI8S","text":"USGS data release","linkHelpText":"Simulations of the groundwater-flow system in the Cache and Grand Prairie Critical Groundwater Areas, northeastern Arkansas"},{"id":463426,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245088/full"},{"id":463424,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":463419,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5088/coverthb.jpg"},{"id":463422,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5088/images/"},{"id":463420,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5088/sir20245088.pdf","text":"Report","size":"22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5088"},{"id":463421,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5088/sir20245088.XML"},{"id":463423,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2024/5088/downloads/","text":"Layered figures","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arkansas","otherGeospatial":"Cache and Grand Prairie Critical Groundwater Areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.4216070279204,\n 35.763468234275166\n ],\n [\n -92.62077527136867,\n 35.763468234275166\n ],\n [\n -92.62077527136867,\n 33.579387250010626\n ],\n [\n -90.4216070279204,\n 33.579387250010626\n ],\n [\n -90.4216070279204,\n 35.763468234275166\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Peak-flow (flood) frequency analysis is essential to water-resources management applications, including the design of critical infrastructure such as bridges and culverts, and floodplain mapping. Federal guidelines for performing peak-flow flood frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C, which documents the guidelines for determining annual peak streamflow frequency, is that, for basins without major hydrologic alterations (for example, regulation, diversion, and urbanization), statistical properties of the distribution of annual peak streamflows are stationary; that is, the mean, variance, and skew are constant through time. Nonstationarity is a statistical property of a peak-flow series such that the long-term (on the order of decades) distributional properties change one or more times either gradually or abruptly through time. Individual nonstationarities may be attributed to one source such as flow regulation, land-use change, or climate but are often the result of a combination of sources, making detection and attribution of nonstationarities challenging.
In response to a growing concern regarding nonstationarity in peak streamflows in the region, the U.S. Geological Survey, in cooperation with the Departments of Transportation of Illinois, Iowa, Michigan, Minnesota, Missouri, South Dakota, and Wisconsin; the Montana Department of Natural Resources and Conservation; and the North Dakota Department of Water Resources, assessed the potential nonstationarity in peak streamflows in the north-central United States. This chapter characterizes the effects of natural hydroclimatic shifts and potential climate change on annual peak streamflows in the State of South Dakota. Annual peak and daily streamflow as well as model-simulated gridded climatic data were examined for temporal monotonic trends, change points, and other statistical properties indicative of changing climatic and environmental conditions.
Changes in annual peak and daily flows were evaluated among 13, 35, and 81 qualifying U.S. Geological Survey streamgages for the 75-, 50-, and 30-year trend periods through water year 2020 (the period from October 1, 2019, to September 30, 2020) in South Dakota, respectively. No qualifying streamgages were in the 100-year trend period in the State. Statistical tests for autocorrelation (independent and identically distributed assumption), monotonic trends, and change points in the median and scale are analyzed to evaluate potential stationarity violations (nonstationarity) for performing at-site peak-flow flood-frequency analysis. The trends are reported using a likelihood approach as an alternative to simply reporting significant trends with an arbitrary p-value cutoff point.
A distinct east-west spatial pattern of likely upward and downward monotonic trends and change points, respectively, was detected in 75- and 50-year trend periods, but an inconsistent spatial pattern was detected in the 30-year trend period. Additionally, change points in the median annual peak streamflows were detected in the late 1970s and early 1980s in the western part of the State, but in the east, the change point was more commonly detected in 1992–93. A similar east-west spatial pattern of likely upward and downward trends was detected in the annual peak-flow timing, the day of the year of the annal peak streamflow. In the western part of the State, the annual peak streamflows are arriving earlier, but in the east, the annual peak streamflows are arriving later. A peaks-over-threshold (POT) analysis where, on average, there are two events per year (POT2) and four events per year (POT4) was also used to evaluate changes in the frequency (count) of daily streamflows exceeding the threshold. Similar to detected changes in the annual peak streamflow, an east-west likely upward or downward change corresponding to an increase or decrease, respectively, in the frequency of daily streamflow greater than a POT2 and POT4 threshold was detected.
A monthly water-balance model was used to evaluate hydroclimatic variation in annual and seasonal precipitation, snowfall, potential evapotranspiration, and soil moisture storage for all qualifying streamgages in the 75-, 50-, and 30-year trend periods. Detected trends in the annual hydroclimatic metrics for the 75- and 50-year trend periods indicate a spatially consistent statewide increase in precipitation, decrease in snowfall, increase in potential evapotranspiration, and increase in soil moisture storage. Furthermore, detected trends in seasonal precipitation in the 75- and 50-year trend periods highlight a pronounced change in precipitation in winter and later into the summer season, especially in the 50-year trend period in the eastern part of the State. Statewide increases in seasonal soil moisture storage were also detected, highlighting year-round increasing flood magnitudes, particularly in the eastern part of the State.
Based on the results of these stationarity tests for the qualifying streamgages in South Dakota among the 75-, 50-, and 30-year trend periods, consistent temporal and spatial patterns of nonstationarity were detected among the 75- and 50-year trend periods. Furthermore, when nonstationarity is detected in daily streamflow, increased streamflow and volume (increasing frequency in POT), as well as potentially bridge scour, may have implications on culvert and highway design in the eastern part of South Dakota. Thus, when performing at-site peak-flow flood-frequency analyses in South Dakota, potential nonstationarities and alternative approaches are important considerations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064I","collaboration":"Prepared in cooperation with the South Dakota Department of Transportation","usgsCitation":"Barth, N.A., and Sando, S.K., 2024, Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020, chap. I of Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 70 p., https://doi.org/10.3133/sir20235064I.","productDescription":"Report: x, 70 p.; Data Release; Dataset","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146340","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":497916,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117775.htm","linkFileType":{"id":5,"text":"html"}},{"id":463794,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235064I/full"},{"id":463793,"rank":6,"type":{"id":30,"text":"Data 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U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Fluid inclusion microthermometry and Raman spectroscopy of fluid inclusions in quartz veins from the Pennsylvanian rocks of the Anthracite belt, eastern Pennsylvania support a deep burial model of coalification in favor of focused orogenic hot fluid flow. High-temperature (250 to 255 °C) trapping of CH4 ± CO2 saturated aqueous fluids and CH4 ± CO2 inclusions indicate fluid trapping at depths of 11.5 to 13.4 km under a cover of Pennsylvanian to Permian(?) syntectonic load. In the folded rocks to the south of the Anthracite belt, CH4 ± CO2 fluid inclusions indicate a sediment load that was up to 16.3 km thick. Re-equilibrated aqueous fluid inclusions from veins in Silurian through Devonian rocks give the same range of trapping conditions but a wide range of fluid salinities suggesting that folding, fracturing, and meteoric recharge resulted in the intermixing of fluids from throughout the stratigraphic succession.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2024.104646","usgsCitation":"Evans, M.A., and Jubb, A., 2024, Deep syntectonic burial of the Anthracite belt, Eastern Pennsylvania: International Journal of Coal Geology, v. 295, 104646, 27 p., https://doi.org/10.1016/j.coal.2024.104646.","productDescription":"104646, 27 p.","ipdsId":"IP-164265","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":463907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75,\n 41.75\n ],\n [\n -80,\n 41.75\n ],\n [\n -80,\n 39.5\n ],\n [\n -75,\n 39.5\n ],\n [\n -75,\n 41.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"295","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, Mark A.","contributorId":197411,"corporation":false,"usgs":false,"family":"Evans","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":918370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":918369,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70270845,"text":"70270845 - 2024 - Biological feasibility of introducing bighorn sheep to the Jicarilla Apache Nation","interactions":[],"lastModifiedDate":"2025-08-28T15:11:50.278531","indexId":"70270845","displayToPublicDate":"2024-11-08T09:59:02","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CSS-159-2024","title":"Biological feasibility of introducing bighorn sheep to the Jicarilla Apache Nation","docAbstract":"The biological feasibility of introducing Rocky Mountain bighorn sheep (Ovis canadensis canadensis) to the Dulce area of the Jicarilla Apache Nation (JAN) depends on availability and condition of potential habitat and the potential for disease risk, as pneumonia is the largest current threat to wild sheep populations. We modeled quality and quantity of potential bighorn sheep habitat incorporating the three most recent fire scars around Dulce, determined potential winter range within preferred habitat, assessed on the ground vegetation characteristics, and examined potential for disease transmission via risk of contact with domestic sheep and goats. Most of the area of interest for this study has a suitability value ≥ 50%, with approximately 23-29% of the study area considered preferred, or high-quality habitat for bighorn sheep. High-quality habitat for Rocky Mountain bighorn sheep is defined as being within 300 m of escape terrain, within 1.6 km of water, and containing ≤ 30% shrub and tree cover. Of this, approximately 43-56% of potential preferred habitat qualifies as winter range, which is mostly concentrated in the narrow valley bottoms where roads are commonly located and the south-facing slopes surrounding valleys. Analysis of field-collected vegetation data indicate most of the existing forage within the surveyed area to be of moderate or high forage value to bighorn sheep, but horizontal visibility, predominantly in the form of shrubs, is more obscured than what bighorn sheep prefer for most of the area of interest. Maximum shrub and tree cover is also the most limiting factor in the suitability model, primarily due to the prevalence of dense shrub regeneration, particularly Gambel oak, which occurs after high severity burns. The largest quantities of high-quality potential bighorn sheep habitat within the study area occur in unburned areas or those burnt by the predominantly low-moderate severity Amargo fire in 2021. Relative to risk of contact with domestic sheep and goats that can transmit lethal pneumonia-causing pathogens, potentially causing an introduction effort to fail, there is high risk because of the proximity to two hobby-subsistence herds (< 3 km away).
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/10.3996/css73616801","usgsCitation":"Thompson, C.J., and Cain, J.W., 2024, Biological feasibility of introducing bighorn sheep to the Jicarilla Apache Nation: Cooperator Science Series CSS-159-2024, 80 p., https://doi.org/10.3996/10.3996/css73616801.","productDescription":"80 p.","ipdsId":"IP-167003","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":494875,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/biological-feasibility-introducing-bighorn-sheep-jicarilla-apache-nation","linkFileType":{"id":5,"text":"html"}},{"id":495006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jicarilla Apache Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.01771249033892,\n 36.99249959403626\n ],\n [\n -107.5487950989812,\n 36.99249959403626\n ],\n [\n -107.5487950989812,\n 36.05621273485714\n ],\n [\n -106.01771249033892,\n 36.05621273485714\n ],\n [\n -106.01771249033892,\n 36.99249959403626\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Cara J.","contributorId":360559,"corporation":false,"usgs":false,"family":"Thompson","given":"Cara","middleInitial":"J.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":947210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":947211,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}