The U.S. Geological Survey (USGS) Minerals Yearbook discusses the performance of the worldwide minerals and materials industries and provides background information to assist in interpreting that performance. Content of the individual Minerals Yearbook volumes follows:
The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the Minerals Yearbook are welcome.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/mybvII","collaboration":"National Minerals Information CenterDirector, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
The U.S. Geological Survey (USGS) Minerals Yearbook discusses the performance of the worldwide minerals and materials industries and provides background information to assist in interpreting that performance. Content of the individual Minerals Yearbook volumes follows:
The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the Minerals Yearbook are welcome.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/mybvIII","collaboration":"National Minerals Information CenterDirector, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
The Federal Priority Streamgage (FPS) network of the U.S. Geological Survey (USGS), created in 1999 as the National Streamflow Information Program, receives Congressional appropriations to support the operation of a federally-funded “backbone” network of streamflow gages across the United States that are designated to meet the “Federal needs” or priorities of the country. Anticipating the evolution of Federal stakeholder water-data needs, the USGS launched a re-evaluation of the fundamental priorities for the FPS network in October 2020. In March 2022, the FPS Re-Prioritization Project used an online survey to solicit feedback from 767 stakeholders representing 22 Federal agencies who benefit from the FPS network. Additional feedback from survey respondents was obtained during online listening sessions to validate the USGS’s understanding of current Federal water-data needs. Results of the feedback show that the original five network priorities identified by the U.S. Geological Survey in 1999 are still valid but require modification to better incorporate additional needs, including Federal water operations, streamflow trends and extremes, water rights involving Federal lands, and streamflow data supporting ecosystem health. Federal stakeholder feedback also indicated that the inclusion of precipitation and water-temperature data collection, along with stream imagery, would enhance the value of the FPS network.
Results of the FPS Re-Prioritization Project and Open Season that ended in May 2024 revealed that the number of FPS locations meeting the updated eligibility criteria nearly tripled, which illustrates the value of the information provided by the FPS network. The Water Forecasting & Operations and the Water Quality network priorities contributed to the largest number of new eligible FPS sites, demonstrating the importance of the FPS network in supporting informed decisions related to the protection of life, property, the environment, and the economy of the United States.
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Birds face strong selective pressures to complete individual nesting attempts as quickly as possible to minimize exposure of nests to predators and weather, maximize renesting potential, and maximize hatching success. As a result, the duration of developmental periods and of overall nest periods are often relatively constant within species. However, birds may sometimes be subject to acute energetic constraints that may preclude them from initiating incubation at the optimal time. We report an extraordinary case of delayed incubation by a female Burrowing Owl (Athene cunicularia) in Oregon, USA, which contrasted sharply with a large sample of nests monitored by motion-activated video cameras in western North America from 2015 to 2018. The focal female delayed incubation by 11 d following clutch completion and subsequently experienced near total hatching failure of her clutch. This observation corroborates previous experimental studies regarding the limits to egg viability and trade-offs between the timing of incubation onset and hatching success. These findings illustrate the acute trade-offs faced by nesting birds, and the cost of delayed incubation that we observed in this owl could help explain the cause of asynchronous hatching.
","language":"English","publisher":"Taylor & Francis","doi":"10.1676/22-00089","usgsCitation":"Lundblad, C., Conway, C.J., 2023, Delayed incubation leads to hatching failure of a Burrowing Owl (Athene cunicularia) nest: Wilson Journal of Ornithology, v. 135, no. 3, p. 400-405, https://doi.org/10.1676/22-00089.","productDescription":"6 p.","startPage":"400","endPage":"405","ipdsId":"IP-144129","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500347,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Umatilla Army Depot","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.6494795743624,\n 46.43690242049303\n ],\n [\n -124.6494795743624,\n 41.96089422685421\n ],\n [\n -116.90302518029182,\n 41.96089422685421\n ],\n [\n -116.90302518029182,\n 46.43690242049303\n ],\n [\n -124.6494795743624,\n 46.43690242049303\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"135","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Lundblad, Carl G.","contributorId":287176,"corporation":false,"usgs":false,"family":"Lundblad","given":"Carl G.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":956320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956334,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259557,"text":"70259557 - 2023 - Getting Started with MODFLFOW","interactions":[],"lastModifiedDate":"2024-10-15T11:38:40.244852","indexId":"70259557","displayToPublicDate":"2024-10-01T06:37:38","publicationYear":"2023","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":14,"text":"Instruction"},"title":"Getting Started with MODFLFOW","docAbstract":"Numerical modeling of groundwater flow systems was once accessible only to modeling specialists in the hydrogeological community. Software such as MODFLOW—the most frequently used groundwater modeling program in the world—and associated graphical user interfaces (GUIs) have made modeling possible for most groundwater scientists. This book provides the bridge from understanding to implementing models by introducing the basics of MODFLOW version 6 and providing readers who have a working knowledge of groundwater flow with a guide through construction of their first groundwater model.
","language":"English","publisher":"Groundwater Project","doi":"10.21083/978-1-77470-030-3","usgsCitation":"Winston, R.B., 2023, Getting Started with MODFLFOW, 243 p., https://doi.org/10.21083/978-1-77470-030-3.","productDescription":"243 p.","ipdsId":"IP-159533","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":462868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Winston, Richard B. 0000-0002-6287-8834 rbwinst@usgs.gov","orcid":"https://orcid.org/0000-0002-6287-8834","contributorId":3567,"corporation":false,"usgs":true,"family":"Winston","given":"Richard","email":"rbwinst@usgs.gov","middleInitial":"B.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":915747,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70259931,"text":"70259931 - 2023 - The presence of silicate melt may enhance rates of cation diffusion in olivine","interactions":[],"lastModifiedDate":"2024-10-28T11:18:22.113863","indexId":"70259931","displayToPublicDate":"2024-09-27T06:17:16","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"The presence of silicate melt may enhance rates of cation diffusion in olivine","docAbstract":"The Arctic and its permafrost terrains are inherently dynamic, complex, and sensitive environments. Understanding the past and current changes occurring in these systems is key in predicting future variations, including the response of permafrost to climate change and to surface disturbances resulting from natural processes or anthropogenic activities. Here, we focus on advancing our understanding of the drivers controlling terrain vulnerability and resilience to thermal erosion gullying proximal to linear infrastructure in the lowlands of the Arctic Foothills (Alaska). This builds upon our previous work (Stephani et al. 2023) in the infrastructure corridor that includes the Dalton Highway and Trans Alaska Pipeline System (TAPS).
To identify locations affected by thermal erosion gullying and recognize changes in the ~80-km long corridor section that travels through the Arctic Foothills, we compared time series of high-resolution satellite imagery from 2001 to 2023. When we captured the timing of gullying onset and/or growth, we examined prevailing climatic conditions (Sagwon Station) prior to these terrain changes. We integrated our findings with our field-based data (e.g., drilling) described in Stephani et al. (2023).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"12th International conference on permafrost proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"International Permafrost Association","usgsCitation":"Stephani, E.A., Darrow, M., and Kanevskiy, M., 2023, Vulnerability and resilience of ice-rich permafrost to thermal erosion gullying in the Arctic Foothills infrastructure corridor, Alaska, in 12th International conference on permafrost proceedings, v. 2, p. 533-534.","productDescription":"2 p.","startPage":"533","endPage":"534","ipdsId":"IP-160342","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":501314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaska Foothills","volume":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stephani, Eva Anne 0000-0002-7006-4742","orcid":"https://orcid.org/0000-0002-7006-4742","contributorId":332297,"corporation":false,"usgs":true,"family":"Stephani","given":"Eva","email":"","middleInitial":"Anne","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":889940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Darrow, M.M.","contributorId":63286,"corporation":false,"usgs":true,"family":"Darrow","given":"M.M.","email":"","affiliations":[],"preferred":false,"id":957158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kanevskiy, Mikhail","contributorId":169366,"corporation":false,"usgs":false,"family":"Kanevskiy","given":"Mikhail","email":"","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":957159,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70265004,"text":"70265004 - 2023 - A review of the ecology and conservation biology of Sali (Micronesian Starling, Aplonis opaca guami) on Guam","interactions":[],"lastModifiedDate":"2025-03-27T15:10:52.983128","indexId":"70265004","displayToPublicDate":"2024-05-25T10:07:52","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2734,"text":"Micronesica","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A review of the ecology and conservation biology of Såli (Micronesian Starling, Aplonis opaca guami) on Guam","title":"A review of the ecology and conservation biology of Sali (Micronesian Starling, Aplonis opaca guami) on Guam","docAbstract":"The accidental introduction of the Brown Treesnake (BTS, Boiga irregularis) to Guam following World War II led to the extinction, extirpation, or severe decline of most of Guam’s native avifauna. One forest bird species that managed to persist is the cavity-nesting Såli (Micronesian Starling, Aplonis opaca guami), a once-ubiquitous native omnivore whose current Guam distribution is restricted primarily to urbanized areas in the northern half of the island, with a second much smaller population on Cocos Island off southern Guam. Såli on Guam breed year-round and are habitat generalists with large home ranges. Besides arthropods and small vertebrates, they consume fruit from a wide range of native and introduced plant species and are key seed dispersers within the local ecosystem. Såli on Guam suffer extremely high post-fledging mortality due to predation by BTS and cats, leading to a population age structure strongly skewed towards adult birds. Other threats include habitat loss and degradation, extreme weather events, and sea level rise. Key management activities aimed at restoring this species include predator control, while non-native ungulate removal and vegetation management may be undertaken to support broader ecological recovery. This species account on Såli provides a review of the literature on this species, synthesizing information on habitat use, life history, and demographics to provide a biological foundation for future recovery efforts.
","language":"English","publisher":"University of Guam","usgsCitation":"Nguyen, A., Kastner, M., Wiles, G., Wagner, C., Pollock, H., Rogers, H., Duenas, L., Fricke, E., Rehm, E., McElderry, R., Siers, S.R., and Paxton, E.H., 2023, A review of the ecology and conservation biology of Sali (Micronesian Starling, Aplonis opaca guami) on Guam: Micronesica, v. 2023, no. 04, p. 1-18.","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-138691","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":483929,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"http://micronesica.org/volumes/2023"},{"id":483945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 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S.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":932223,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rogers, Haldre S.","contributorId":352854,"corporation":false,"usgs":false,"family":"Rogers","given":"Haldre S.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":932224,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duenas, Laura","contributorId":352855,"corporation":false,"usgs":false,"family":"Duenas","given":"Laura","affiliations":[{"id":63321,"text":"Guam Division of Aquatic and Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":932225,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fricke, Evan C.","contributorId":352857,"corporation":false,"usgs":false,"family":"Fricke","given":"Evan C.","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":932226,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rehm, Evan M.","contributorId":352858,"corporation":false,"usgs":false,"family":"Rehm","given":"Evan M.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":932227,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McElderry, Robert M.","contributorId":352860,"corporation":false,"usgs":false,"family":"McElderry","given":"Robert M.","affiliations":[{"id":54632,"text":"Research Corporation of the University of Guam","active":true,"usgs":false}],"preferred":false,"id":932228,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Siers, Shane R.","contributorId":152305,"corporation":false,"usgs":false,"family":"Siers","given":"Shane","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":932229,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":932230,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70253912,"text":"70253912 - 2023 - Forest bird populations at the Big Island National Wildlife Refuge Complex, Hawai'i","interactions":[],"lastModifiedDate":"2024-06-03T15:05:06.821876","indexId":"70253912","displayToPublicDate":"2024-04-11T09:18:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Forest bird populations at the Big Island National Wildlife Refuge Complex, Hawai'i","docAbstract":"Endemic Hawaiian forest birds have experienced dramatic population declines. The Big Island National Wildlife Refuge Complex (Refuge Complex) was established for the conservation of endangered forest birds and their habitats. Surveys have been conducted at two units of the Refuge Complex to monitor forest bird populations and their response to management actions. We analyzed survey data from 1987 to 2019 at the Hakalau Forest Unit (HFU) and from 1995 to 2019 at the Kona Forest Unit (KFU). We analyzed three strata at HFU: open-forest, closed-forest, and afforested-pasture, and two strata at KFU: upper (>1,524 m elevation) and lower (<1,524 m). In all years, ‘i‘iwi Vestiaria coccinea, ‘apapane Himatione sanguinea, and Hawai'i ‘amakihi Chlorodrepanis virens virens were the most abundant species at HFU. Three endangered forest bird species, Hawai'i ‘ākepa Loxops coccineus, ‘alawī Loxops mana (also known as Hawai'i creeper) and ‘akiapōlā‘au Hemignathus wilsoni, had much lower densities. The most abundant species at KFU was ‘apapane, followed by Hawai'i ‘amakihi at much lower densities. We found a continuation of several trends observed in previous analyses at HFU up to 2012, with most species’ trends upward in afforested-pasture stratum, stable in the open-forest stratum, and downward in the closed-forest stratum. However, more species were showing downward trends in all three strata during the most recent decade. Results were mixed at KFU, with most species’ trends downward in the upper stratum and upward in the lower stratum. Populations of endangered species were either locally extirpated at KFU or in numbers too low to reliably estimate population abundance. The Refuge Complex is important for conservation of forest birds on Hawai'i Island. Our results show that HFU supports the majority of three endangered forest bird species. Threats to forest birds at the Refuge Complex appear to be having a negative impact. These threats include habitat loss, disease, feral ungulates, and nonnative predators. Continuing and enhancing management actions, such as forest restoration and removal of invasive species, could help mitigate these impacts and allow the Refuge Complex to remain a key site for forest bird conservation in Hawai'i.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-22-035","usgsCitation":"Kendall, S.J., Rounds, R.A., Camp, R.J., Genz, A., Cady, T., and Ball, D.L., 2023, Forest bird populations at the Big Island National Wildlife Refuge Complex, Hawai'i: Journal of Fish and Wildlife Management, v. 14, no. 2, p. 410-432, https://doi.org/10.3996/JFWM-22-035.","productDescription":"23 p.","startPage":"410","endPage":"432","ipdsId":"IP-145154","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":441323,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-22-035","text":"Publisher Index Page"},{"id":428355,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Big Island National Wildlife Refuge Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.21286438985624,\n 19.964079437115203\n ],\n [\n -155.35557208018355,\n 19.964079437115203\n ],\n [\n -155.35557208018355,\n 19.75471981087044\n ],\n [\n -155.21286438985624,\n 19.75471981087044\n ],\n [\n -155.21286438985624,\n 19.964079437115203\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.78367183276112,\n 19.326994002143053\n ],\n [\n -155.8768682174414,\n 19.326994002143053\n ],\n [\n -155.8768682174414,\n 19.257171984885417\n ],\n [\n -155.78367183276112,\n 19.257171984885417\n ],\n [\n -155.78367183276112,\n 19.326994002143053\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Kendall, Steven J.","contributorId":30911,"corporation":false,"usgs":false,"family":"Kendall","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":900081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rounds, Rachel A.","contributorId":290249,"corporation":false,"usgs":false,"family":"Rounds","given":"Rachel","email":"","middleInitial":"A.","affiliations":[{"id":62393,"text":"U.S. Fish and Wildlife Service, Pacific Islands Refuges and Monuments Office","active":true,"usgs":false}],"preferred":false,"id":900082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":900083,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Genz, Ayesha 0000-0002-2916-1436","orcid":"https://orcid.org/0000-0002-2916-1436","contributorId":196671,"corporation":false,"usgs":false,"family":"Genz","given":"Ayesha","email":"","affiliations":[],"preferred":false,"id":900084,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cady, Thomas","contributorId":336165,"corporation":false,"usgs":false,"family":"Cady","given":"Thomas","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":900085,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ball, Donna L.","contributorId":206227,"corporation":false,"usgs":false,"family":"Ball","given":"Donna","email":"","middleInitial":"L.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":900086,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252091,"text":"sir20235144 - 2023 - A comparison of water-quality and stormwater inflow and outflow during habitat restoration at the McEwen storm drainage pond, South Valley, Albuquerque, New Mexico, 2020–22","interactions":[],"lastModifiedDate":"2026-01-30T19:58:02.980208","indexId":"sir20235144","displayToPublicDate":"2024-03-15T07:34:31","publicationYear":"2023","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":"2023-5144","displayTitle":"A Comparison of Water-Quality and Stormwater Inflow and Outflow During Habitat Restoration at the McEwen Storm Drainage Pond, South Valley, Albuquerque, New Mexico, 2020–22","title":"A comparison of water-quality and stormwater inflow and outflow during habitat restoration at the McEwen storm drainage pond, South Valley, Albuquerque, New Mexico, 2020–22","docAbstract":"In 2020, the U.S. Geological Survey began targeted monitoring, in partnership with Bernalillo County, at three locations within the McEwen storm drainage pond to evaluate and compare the water quality of stormwater as it enters and exits the study area, which is channelized and routes urban stormwater runoff through a wetland area. Stage in McEwen pond and precipitation at a nearby precipitation gage were evaluated to observe relations between rainfall and stage, as well as how long the stage remained elevated at the site. Peak stage ranged from 0.73 to 2.4 feet, with the time to reach peak stage at McEwen pond ranging from 45 minutes to 10 hours and 45 minutes. The stage remained elevated for a median of 3 days. Monitored water-quality parameters included physical parameters, bacteria, sediment, and nutrients. Bacteria was the only parameter that frequently exceeded the New Mexico Water Quality standard. Significant differences (p less than 0.05) among sites were few, consisting of those for total nitrogen and dissolved ammonia concentrations, which decreased toward the middle of the pond and were lower in the outflow from the pond compared to concentrations at the east and west sites. The middle of McEwen pond showed an increase in the percentage of fine-grained sediment, which suggests that larger particles settled into the pond and were further filtered as water traveled through the swales. Concentrations of suspended sediment and dissolved nutrients were significantly lower in 2022 compared to previous years. Although the site is still undergoing restoration and plants are becoming established, observations over the last several years indicate that site restoration has resulted in changes to the study area through processes such as nutrient uptake and the filtering of larger sediment particles.
Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Contact Us- USGS Publications Warehouse
","tableOfContents":"In response to previous reports of per- and polyfluoroalkyl substances (PFAS) contamination in French Island’s (located in the Mississippi River within the town of Campbell, Wisconsin) primary source of drinking water, 11 locations were sampled by the U.S. Geological Survey (USGS) in October 2021 to assess the potential presence of contaminant mixtures, including PFAS, in tapwater. Three locations were assessed seven times each over the course of three days. These samples were chosen to evaluate the water quality of the deeper Mount Simon bedrock aquifer and the water quality of the shallower sand and gravel (alluvial) aquifer at two locations. The other eight sample locations were spatially distributed within Campbell and were sampled once each. For each of these 11 sites, tapwater samples were analyzed for disinfection byproducts (DBP), pesticides, PFAS, pharmaceuticals, semi-volatile organic compounds (SVOC), volatile organic compounds (VOC), cations, anions, trace elements, alkalinity, microbial indicators, as well as measurements of water temperature, specific conductance, and pH. Of the 506 organic compounds analyzed in each water-quality sample, 74 (14 percent) were detected at least one time in any of the samples collected. Of the 14 percent, detected analytes included 27 pesticides (5 percent), 14 PFAS (3 percent), 6 pharmaceuticals (1 percent), 7 SVOC (1 percent), and 20 VOC (4 percent). No DBP were detected. The total number of organic compounds detected per sample ranged from 0–20 (median of 10), with the sum of concentrations ranging from not detected (nd)–2.53 micrograms per liter (μg/L; median of 0.333 μg/L). Of the inorganic constituents measured, eight were not detected above their reporting limit in any of the samples. The inorganic constituents that were not detected were antimony, arsenic, beryllium, cadmium, cobalt, molybdenum, selenium, and vanadium.
Along with the 11 sites sampled throughout Campbell, Wisconsin, beginning in October 2021, four more wells were sampled on the Upper Midwest Environmental Sciences Center (UMESC) campus for PFAS. Three of these sites withdraw water from the shallow alluvial aquifer (the same source water for tapwater site 002) and one from the Mount Simon aquifer (the same source of water for tapwater site 001). This sampling is ongoing with results from samples through December 2022 summarized in this report. Of the 33 PFAS analyzed in samples from the four UMESC locations, 15 individual PFAS were detected at least one time in any of the samples analyzed with the sum of PFAS concentrations ranging from nd–1.49 μg/L (median of 0.309 μg/L).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231088","collaboration":"Prepared in cooperation with the Town of Campbell, Wisconsin","programNote":"Environmental Health Program","usgsCitation":"Romanok, K.M., Meppelink, S.M., Bradley, P.M., Breitmeyer, S.E., Donahue, L., Gaikowski, M.P., Hines, R.K., and Smalling, K.L., 2023, Occurrence of mixed organic and inorganic chemicals in groundwater and tapwater, town of Campbell, Wisconsin, 2021–22: U.S. Geological Survey Open-File Report 2023–1088, 29 p., https://doi.org/10.3133/ofr20231088.","productDescription":"Report: viii, 29 p.; 2 Data Releases","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150739","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499196,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115939.htm","linkFileType":{"id":5,"text":"html"}},{"id":423893,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J6XKVS","text":"USGS data release","linkHelpText":"Quarterly sample results for perand polyfluoroalkyl substances (PFAS) for locations in Campbell, Wisconsin, 2021–22"},{"id":423892,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EUBGUF","text":"USGS data release","linkHelpText":"Target-chemical concentrations for assessment of mixed-organic/inorganic chemical and biological exposures in private-well tapwater at Campbell, Wisconsin, 2021"},{"id":423887,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1088/coverthb.jpg"},{"id":423888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1088/ofr20231088.pdf","text":"Report","size":"1.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1088"},{"id":423889,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231088/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1088"},{"id":423890,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1088/ofr20231088.XML"},{"id":423891,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1088/images/"}],"country":"United States","state":"Wisconsin","county":"La Crosse County","city":"Campbell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.29371444024203,\n 43.904997377408506\n ],\n [\n -91.29371444024203,\n 43.84807720086516\n ],\n [\n -91.23878279961701,\n 43.84807720086516\n ],\n [\n -91.23878279961701,\n 43.904997377408506\n ],\n [\n -91.29371444024203,\n 43.904997377408506\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
3450 Princeton Pike, Suite 110
Lawrenceville, New Jersey 08648
From 2020 to 2023, a new spill program was implemented to aid the downstream passage of juvenile salmonids at mainstem dams on the Snake and Columbia rivers. Under this program, the total dissolved gas (TDG) cap was increased to 125% and monitoring of nonsalmonids for gas bubble trauma (GBT) became a requirement. The primary objective of this work and report was to measure the incidence and severity of GBT in nonsalmonids resulting from increased juvenile salmonid passage spill and associated levels of TDG during the spring spill period in 2023. Nonsalmonids were collected downstream from Bonneville, McNary, Ice Harbor, and Lower Granite dams and examined for the incidence and severity of GBT in 2023. Fish were collected at each location weekly (3 April to 20 June) during the spring spill period by backpack electrofishing and beach seining. Washington and Oregon State water quality agencies established minimum and target sample sizes for monitoring, but the minimum sample size of 50 fish and target sample size of 100 fish were not met in all weeks at individual projects due to high water flows and resulting low fish collections. Collected fish were examined for GBT according to the criteria and protocol established for the regional smolt monitoring program (SMP). TDG levels were often high relative to the 10-year average. GBT incidence rates and severity (according to SMP criteria) were low to moderate in most weeks. We found no apparent relationship between GBT incidence and TDG due to exposure history and interspecies susceptibility to elevated TDG that could not be quantified. GBT incidence rates exceeded the 15% threshold on two occasions below Ice Harbor Dam, triggering a reduction in spill under the State water quality standards. In the weeks immediately following the spill reductions, GBT incidence was zero or relatively low at this location. Sculpin (genus Cottus) was the main species collected at all locations. As in past years, we did find GBT in non-SMP protocol areas, particularly in sculpin. The variability in GBT incidence rates is likely due to variability in environmental conditions, fish exposure history to TDG, and species sensitivity to TDG. Many of the species encountered in shallow shoreline habitats rear for extended times and probably do not seek the water depths that would help them reduce the effects of exposure to elevated TDG through depth compensation. Limited systematic sampling of TDG in the tailraces of each project showed that TDG can vary spatially within the tailrace and with percentage of water spilled.
We investigated GBT progression and mortality in sculpin and threespine stickleback (Gasterosteus aculeatus) in laboratory experiments (Chapter Two of this report). Fish were tested at 120%, 125%, and 130% TDG. We found that sculpin are more sensitive to TDG than stickleback and that GBT and associated mortality progress faster in sculpin than in stickleback. GBT prevalence and severity increased through time at all TDG levels tested, but relationships between severity and exposure time were weak or nonexistent. GBT and mortality progressed more rapidly as TDG increased in both species. The SMP criteria used to rank GBT did not fully capture the incidence and severity of GBT in sculpin and stickleback compared to using criteria based on all areas of the fish. The lateral line, body, dorsal fin, and pectoral fins were common locations of GBT in sculpin at 120 and 125% TDG, but signs were more prevalent in all areas at 130% TDG. In stickleback, GBT was most common on the head and body at all TDG levels tested. Positive buoyancy of fish with severe GBT was observed in both species and may have consequences for similarly impaired fish in the wild. The proximate cause of GBT-related death in sculpin and stickleback was bubbles in the gills and heart, but unlike in other species, bubbles appeared rapidly just before the point of death. Our results help fill the information void GBT progression and mortality for sculpin and stickleback.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Tiffan, K., Liedtke, B.D., and Benson, S.L., 2023, Nonsalmonid gas bubble trauma investigations, iv, 72 p.","productDescription":"iv, 72 p.","ipdsId":"IP-161003","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":427256,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/PiscesPublication.mvc/SearchByTitleDescriptionAuthorOrDate","linkFileType":{"id":5,"text":"html"}},{"id":427266,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Columbia River, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.5048072586187,\n 46.75882851790945\n ],\n [\n -121.95470738831573,\n 46.75882851790945\n ],\n [\n -121.95470738831573,\n 44.57823653678298\n ],\n [\n -115.5048072586187,\n 44.57823653678298\n ],\n [\n -115.5048072586187,\n 46.75882851790945\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Tiffan, Kenneth 0000-0002-5831-2846","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":217812,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":897761,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Tiffan, Kenneth 0000-0002-5831-2846","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":217812,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":897723,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liedtke, Brad D. 0000-0002-0458-7377","orcid":"https://orcid.org/0000-0002-0458-7377","contributorId":303795,"corporation":false,"usgs":true,"family":"Liedtke","given":"Brad","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":897724,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benson, Scott Louis 0000-0003-0397-1200","orcid":"https://orcid.org/0000-0003-0397-1200","contributorId":303796,"corporation":false,"usgs":true,"family":"Benson","given":"Scott","email":"","middleInitial":"Louis","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":897725,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255085,"text":"70255085 - 2023 - Prioritizing imperiled native aquatic species for conservation propagation","interactions":[],"lastModifiedDate":"2024-06-12T22:54:24.628312","indexId":"70255085","displayToPublicDate":"2023-12-31T11:52:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing imperiled native aquatic species for conservation propagation","docAbstract":"Native aquatic species are in decline, and hatcheries can play an important role in stemming these losses until larger ecological issues are addressed. However, as more federal and state agencies face budget uncertainty and the number of imperiled species increases, it is necessary to develop a tool to prioritize species for conservation propagation. Our objective was to create prioritized lists of aquatic species that may benefit from conservation propagation for five states in the United States. Biologists developed an influence diagram and provided information for multiple attributes affecting prevalence of species. The influence diagram and information for each species was used in a Bayesian belief network to generate a score to prioritize propagation of a species and the feasibility of propagation. When all taxa were ranked together within a state, mussels, amphibians, and a crustacean were included among fishes in the top species that may benefit from propagation. We recognize that propagation is one tool for conservation of imperiled species and that additional factors will need to be addressed to ensure species persistence. Nevertheless, we contend our quantitative approach provides a useful framework for prioritizing conservation propagation.
","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-22-040","usgsCitation":"Webb, M., Guy, C.S., Treanor, H., Wilson, K.W., Mellon, C.D., Abate, P., Crockett, H.J., Hofmeier, J., Pasbrig, C., and Isakson, P., 2023, Prioritizing imperiled native aquatic species for conservation propagation: Journal of Fish and Wildlife Management, v. 14, no. 2, p. 337-353, https://doi.org/10.3996/JFWM-22-040.","productDescription":"17 p.","startPage":"337","endPage":"353","ipdsId":"IP-129358","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":441327,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-22-040","text":"Publisher Index Page"},{"id":430048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, North Dakota, South Dakota, 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We estimated upper thermal tolerances for the invasive Faxonius hylas and native F. peruncus and F. quadruncus in Missouri, USA, using critical thermal maxima (CTmax) methodology to determine if there were ecologically exploitable differences in estimates among species and if areas within their distributional ranges exceed their thermal maximums. Estimates of CTmax did not differ among species or sexes but differed among groups acclimated to different temperatures. Additionally, crayfish size had a small, yet significant effect on CTmax estimates with smaller crayfish having lower CTmax estimates than larger crayfish. The similarity among CTmax estimates indicates that for at least upper thermal tolerance, areas thermally available to the native species will also be thermally suitable for the invader. We did not observe water temperatures in the field that exceeded CTmax estimates for any species. However, areas within the mainstem St. Francis River did have warming tolerance estimates of less than 5°C, indicating that establishment of the invader in the mainstem could be limited by water temperature.
","language":"English","publisher":"International Association of Astacology","doi":"10.5869/fc.2023.v28-1.27","usgsCitation":"Westhoff, J.T., Abdelrahman, H.A., and Stoeckel, J.A., 2023, Upper thermal tolerances of two native and one invasive crayfish in Missouri, USA: Freshwater Crayfish, v. 28, no. 1, p. 27-36, https://doi.org/10.5869/fc.2023.v28-1.27.","productDescription":"10 p.","startPage":"27","endPage":"36","ipdsId":"IP-153042","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432488,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"28","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Westhoff, Jacob Thomas 0000-0002-2347-5098","orcid":"https://orcid.org/0000-0002-2347-5098","contributorId":288958,"corporation":false,"usgs":true,"family":"Westhoff","given":"Jacob","email":"","middleInitial":"Thomas","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907723,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abdelrahman, Hisham A.","contributorId":340951,"corporation":false,"usgs":false,"family":"Abdelrahman","given":"Hisham","email":"","middleInitial":"A.","affiliations":[{"id":81685,"text":"Cairo University","active":true,"usgs":false}],"preferred":false,"id":907724,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stoeckel, James A.","contributorId":330858,"corporation":false,"usgs":false,"family":"Stoeckel","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":907725,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70251207,"text":"70251207 - 2023 - Working together to advance subduction zone science within SZ4D and the USGS","interactions":[],"lastModifiedDate":"2026-03-23T15:08:51.238343","indexId":"70251207","displayToPublicDate":"2023-12-31T10:06:19","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Working together to advance subduction zone science within SZ4D and the USGS","docAbstract":"OnSunday, December 10, 2023, the U.S. Geological Survey (USGS) and Subduction Zones in Four Dimensions (SZ4D) co-hosted a workshop titled “Working Together to Advance Subduction Zone Science within SZ4D and the USGS”. The workshop attracted ~80 participants, with ~11 attendees from the USGS and ~17 participants from outside the United States Organizations represented included the Cascadia Region Earthquake Science Center (CRESCENT), the Cascadia Coastlines and Peoples (CoPes) Hub, and the Community Network for Volcanic Eruption Response (CONVERSE). Goals for this workshop included:
○ Discuss ideas and potential topics for SZ4D-USGS collaborations
○ Identify mechanisms and opportunities for potential collaboration
○ Identify barriers to collaboration (with possible solutions)
○ Compile topics addressed in the workshop and provide this information to SZ4D and USGS Leadership and the broader SZ4D and USGS Communities for their use.
","language":"English","publisher":"University of California Santa Cruz","usgsCitation":"Gomberg, J.S., Wirth, E.A., Watt, J., Grant, A.R., Brudzinski, M., and Billen, M., 2023, Working together to advance subduction zone science within SZ4D and the USGS, 5 p.","productDescription":"5 p.","ipdsId":"IP-161363","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":425075,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sz4d.org/events/working-together-to-advance-subduction-zone-science-within-sz4d-and-the-usgs"},{"id":501392,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":893472,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wirth, Erin A. 0000-0002-8592-4442","orcid":"https://orcid.org/0000-0002-8592-4442","contributorId":207853,"corporation":false,"usgs":true,"family":"Wirth","given":"Erin","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":893473,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watt, Janet 0000-0002-4759-3814 jwatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4759-3814","contributorId":146222,"corporation":false,"usgs":true,"family":"Watt","given":"Janet","email":"jwatt@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":893474,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":893475,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brudzinski, Micheal","contributorId":333642,"corporation":false,"usgs":false,"family":"Brudzinski","given":"Micheal","affiliations":[{"id":79951,"text":"Miami University of Ohio","active":true,"usgs":false}],"preferred":false,"id":893476,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Billen, Magali","contributorId":333643,"corporation":false,"usgs":false,"family":"Billen","given":"Magali","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":893477,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250934,"text":"70250934 - 2023 - GeoAI for spatial image processing","interactions":[],"lastModifiedDate":"2024-01-13T15:53:31.473536","indexId":"70250934","displayToPublicDate":"2023-12-31T09:52:20","publicationYear":"2023","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"GeoAI for spatial image processing","docAbstract":"The development of digital image processing, as a subset of digital signal processing, depended upon the maturity of photography and image science, introduction of computers, discovery and advancement of digital recording devices, and the capture of digital images. In addition, government and industry applications in the Earth and medical sciences were paramount to the growth of the technology. From the early days when photography was first introduced to science to today, artificial intelligence and deep learning technologies have been intensively used to analyze imagery. Spatial image processing has experienced breakthroughs and evolutions. This chapter presents an overview of the history of image processing, GeoAI-based image processing applications, and the role of GeoAI in advancing image processing methods and research. We also discussed the remaining challenges to using GeoAI for image processing regarding training data annotation, the issues of scale, resolution, and change in space over time.
No abstract available.
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Sediment thickness estimates from HVSR measurements were combined with boring data to make detailed maps of the bedrock surface altitude. The bulk electrical conductivity of the subsurface was indirectly measured with EMI methods and was used to identify lithologic variations, shallow bedrock, and conductive groundwater. Ground penetrating radar, which transmits pulses of electromagnetic energy into the subsurface and records the amplitude and timing of reflected signals, was used to identify bedding and changes in lithology or water content. 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0000-0001-6941-1578","orcid":"https://orcid.org/0000-0001-6941-1578","contributorId":245365,"corporation":false,"usgs":true,"family":"Johnson","given":"Carole D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":896954,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252082,"text":"70252082 - 2023 - A characterization of the deep-sea coral and sponge community along the Oregon Coast using a remotely operated vehicle on the EXPRESS 2022 expedition","interactions":[],"lastModifiedDate":"2024-03-13T12:00:53.285075","indexId":"70252082","displayToPublicDate":"2023-12-31T06:58:52","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"A characterization of the deep-sea coral and sponge community along the Oregon Coast using a remotely operated vehicle on the EXPRESS 2022 expedition","docAbstract":"Deep-sea coral and sponge (DSCS) communities serve as essential fish habitat (EFH) by providing shelter and nursery habitat, increasing diversity, and increasing prey availability (Freese and Wing, 2003; Bright, 2007; Baillon et al., 2012; Henderson et al., 2020). Off the U.S. West Coast, threats to these long-lived, fragile organisms from bottom contact fishing gear, potential offshore renewable energy development, and ocean warming and acidification have been the subject of recent research (Gomez et al., 2018; Salgado et al., 2018; Yoklavich, et al., 2018; Gugliotti et al., 2019). Other DSCS studies have reported new species (Yoklavich and Love, 2005), analyzed species distribution and abundance (Tissot et al., 2006, Watters et al., 2022), developed predictive distribution models (Huff et al., 2013; Rooper et al., 2017; Kreidler, 2020), and discovered medicinal uses for corals and sponges (Essack et al., 2011; Shrestha et al., 2018). Due to the vast area of unexplored seafloor within the territorial waters and the U.S. exclusive economic zone (EEZ; 12-200 nautical miles off the coast) and the technological requirements and expense of deep-sea research, there is still much to learn about the distributions and biology of DSCS. This information is critical to resource managers for effective conservation and management of DSCS habitats. In order to minimize the adverse impacts of fishing on EFH, the Pacific Fishery Management Council (PFMC) and National Marine Fisheries Service (NMFS) designated several seafloor habitat areas as EFH conservation areas (EFHCA), first in 2006 (as part of Amendment 19 to the Pacific coast groundfish fishery management plan) and then again in 2020 (as part of Amendment 28). These areas are closed to bottom trawl fishing at a minimum, and in some cases to all bottom contact fishing gears. In addition to protections afforded by EFH-related regulations, the National Marine Sanctuary Program prohibits certain non-fishing activities within areas designated as national marine sanctuaries, such as oil and gas exploration or extraction, cable laying, and other forms of seabed alteration or construction that disturb benthic communities. NOAA’s Deep-Sea Coral and Research Technology Program (DSCRTP) began a 4-yr funding initiative for the U.S. West Coast in 2017. The goals of the West Coast Deep-Sea Coral Initiative (WCDSCI) were to: 1) gather baseline information on areas subject to fishing regulation changes prior to the implementation of Amendment 28; 2) improve our understanding of known DSCS bycatch “hot spots”; and 3) explore and assess DSCS resources within NOAA National Marine Sanctuaries with emphasis on areas of sanctuary resource protection and management concerns. As part of the WCDSCU, an 11-day expedition (3 Sep – 13 Sep 2022) was launched from the NOAA Ship Bell M. Shimada, beginning and ending in Newport, OR. The science team assembled for this cruise were members of the EXpanding Pacific Research and Exploration of Submerged Systems (EXPRESS) campaign, which brings together researchers from federal and nonfederal institutions to collaborate on scientific expeditions targeting the deepwater areas off California, Oregon, and Washington. EXPRESS supports researchers leveraging funding, resources, personnel, and expertise to accomplish more science than would have been possible by a single entity alone. The 2022 expedition included research partners from National Marine Fisheries Service (NMFS) Southwest Fisheries Science Center (SWFSC) and Northwest Fisheries Science Center (NWFSC), Bureau of Ocean Energy Management (BOEM), U.S. Geological Survey (USGS), Pacific Fisheries Management Council Habitat Committee, and Woods Hole Oceanographic Institution.
","language":"English","publisher":"NOAA","doi":"10.25923/tmb0-ce70","usgsCitation":"Laidig, T., Watters, D., Everett, M., Prouty, N.G., and Clarke, E., 2023, A characterization of the deep-sea coral and sponge community along the Oregon Coast using a remotely operated vehicle on the EXPRESS 2022 expedition, 42 p., https://doi.org/10.25923/tmb0-ce70.","productDescription":"42 p.","ipdsId":"IP-161913","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":426580,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Laidig, Tom","contributorId":270131,"corporation":false,"usgs":false,"family":"Laidig","given":"Tom","email":"","affiliations":[{"id":56090,"text":"NOAA Fisheries, SWFSC, Fisheries Ecology Division, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":896545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watters, Diana","contributorId":270132,"corporation":false,"usgs":false,"family":"Watters","given":"Diana","email":"","affiliations":[{"id":56090,"text":"NOAA Fisheries, SWFSC, Fisheries Ecology Division, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":896546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Everett, Meredith","contributorId":270133,"corporation":false,"usgs":false,"family":"Everett","given":"Meredith","email":"","affiliations":[{"id":56092,"text":"NOAA Fisheries, NWFSC, Seattle WA","active":true,"usgs":false}],"preferred":false,"id":896547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":896548,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clarke, Elizabeth","contributorId":334799,"corporation":false,"usgs":false,"family":"Clarke","given":"Elizabeth","email":"","affiliations":[{"id":80252,"text":"NOAA Fisheries, Northwest Fisheries Science Center, Seattle, Washington","active":true,"usgs":false}],"preferred":false,"id":896549,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70251032,"text":"70251032 - 2023 - Predicting large hydrothermal systems","interactions":[],"lastModifiedDate":"2024-01-19T00:54:41.01672","indexId":"70251032","displayToPublicDate":"2023-12-29T18:53:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Predicting large hydrothermal systems","docAbstract":"We train five models using two machine learning (ML) regression algorithms (i.e., linear regression and XGBoost) to predict hydrothermal upflow in the Great Basin. Feature data are extracted from datasets supporting the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems project (INGENIOUS). The label data (the reported convective signals) are extracted from measured thermal gradients in wells by comparing the total estimated heat flow at the wells to the modeled background conductive heat flow. That is, the reported convective signal is the difference between the background conductive heat flow and the well heat flow. The reported convective signals contain outliers that may affect upflow prediction, so the influence of outliers is tested by constructing models for two cases: 1) using all the data (i.e., -91 to 11,105 mW/m2), and 2) truncating the range of labels to include only reported convective signals between -25 and 200 mW/m2. Because hydrothermal systems are sparse, models that predict high convective signal in smaller areas better match the natural frequency of hydrothermal systems. Early results demonstrate that XGBoost outperforms linear regression. For XGBoost using the truncated range of labels, half of the high reported signals are within < 3 % of the highest predictions. For XGBoost using the entire range of labels, half of the high reported signals are in < 13 % of the highest predictions. While this implies that the truncated regression is superior, the all-data model better predicts the locations of power-producing systems (i.e., the operating power plants are in a smaller fraction of the study area given by the highest predictions). Even though the models generally predict greater hydrothermal upflow for higher reported convective signals than for lower reported convective signals, both XGBoost models consistently underpredict the magnitude of higher signals. This behavior is attributed to low resolution/granularity of input features compared with the scale of a hydrothermal upflow zone (a few km or less across). Trouble estimating exact values while still reliably predicting high versus low convective signals suggests that a future strategy such as ranked ordinal regression (e.g., classifying into ordered bins for low, medium, high, and very high convective signal) might fit better models, since doing so reduces problems introduced by outliers while preserving the property of larger versus smaller signals.","language":"English","publisher":"Geothermal Rising","usgsCitation":"Mordensky, S.P., Burns, E.R., DeAngelo, J., and Lipor, J., 2023, Predicting large hydrothermal systems: Geothermal Resources Council Transactions, v. 47, p. 1763-1796.","productDescription":"34 p.","startPage":"1763","endPage":"1796","ipdsId":"IP-154718","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":424578,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034861"},{"id":424600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":892806,"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":892807,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":892808,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":892809,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251029,"text":"70251029 - 2023 - Cursed? Why one does not simply add new data sets to supervised geothermal machine learning models","interactions":[],"lastModifiedDate":"2024-01-19T00:50:22.910296","indexId":"70251029","displayToPublicDate":"2023-12-29T18:49:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Cursed? Why one does not simply add new data sets to supervised geothermal machine learning models","docAbstract":"Recent advances in machine learning (ML) identifying areas favorable to hydrothermal systems indicate that the resolution of feature data remains a subject of necessary improvement before ML can reliably produce better models. Herein, we consider the value of adding new features or replacing other, low-value features with new input features in existing ML pipelines. Our previous work identified stress and seismicity as having less value than the other feature types (i.e., heat flow, distance to faults, and distance to magmatic activity) for the 2008 USGS hydrothermal energy assessment; hence, a fundamental question regards if the addition of new but partially correlated features will improve resulting models for hydrothermal favorability. Therefore, we add new maps for shear strain rate and dilation strain rate to fit logistic regression and XGBoost models, resulting in new 7-feature models that are compared to the old 5-feature models. Because these new features share a degree of correlation with the original relatively uninformative stress and seismicity features, we also consider replacement of the two lower-value features with the two new features, creating new 5-feature models.\n\nAdding the new features improves the predictive skill of the new 7-feature model over that of the old 5-feature model; albeit, that improvement is not statistically significant because the new features are correlated with the old features and, consequently, the new features do not present considerable new information. However, the new 5-feature XGBoost model has a statistically significant increase in predictive skill for known positives over the old 5-feature model at p = 0.06. This improved performance is due to the lower-dimensional feature space of the former than that of the latter. In higher-dimensional feature space, relationships between features and the presence or absence of hydrothermal systems are harder to discern (i.e., the 7-feature model likely suffers from the “curse of dimensionality”).","language":"English","publisher":"Geothermal Rising","usgsCitation":"Mordensky, S.P., Burns, E.R., Lipor, J., and DeAngelo, J., 2023, Cursed? Why one does not simply add new data sets to supervised geothermal machine learning models: Geothermal Resources Council Transactions, v. 47, p. 1288-1313.","productDescription":"26 p.","startPage":"1288","endPage":"1313","ipdsId":"IP-153961","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":424577,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034860"},{"id":424599,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":892802,"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":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892803,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":892804,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":892805,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251035,"text":"70251035 - 2023 - Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions","interactions":[],"lastModifiedDate":"2024-01-19T00:46:05.573324","indexId":"70251035","displayToPublicDate":"2023-12-29T18:45:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions","docAbstract":"Selecting negative training sites is an important challenge to resolve when utilizing machine learning (ML) for predicting hydrothermal resource favorability because ideal models would discriminate between hydrothermal systems (positives) and all types of locations without hydrothermal systems (negatives). The Nevada Machine Learning project (NVML) fit an artificial neural network to identify areas favorable for hydrothermal systems by selecting 62 negative sites where the research team had confidence that no hydrothermal resource exists. Herein, we compare the implications of the expert selection of negatives (i.e., the NVML strategy) with a random sample strategy, where it is assumed that areas outside the favorable structural ellipses defined by NVML are negative. Because hydrothermal systems are sparse, it is highly probable that, in the absence of a favorable geological structure, hydrothermal favorability is low. We compare three training strategies: 1) the positive and negative labeled examples from NVML; 2) the positive examples from NVML with randomly selected negatives in equal frequency as NVML; and 3) the positive examples from NVML with randomly selected negatives reflecting the expected natural distribution of hydrothermal systems relative to the total area. We apply these training strategies to the NVML feature data (input data) using two ML algorithms (XGBoost and logistic regression) to create six favorability maps for hydrothermal resources. When accounting for the expected natural distribution of hydrothermal systems, we find that XGBoost performs better than the NVML neural network and its negatives. Model validation was less reliable using F1 scores, a common performance metric, than comparing probability estimates at known positives, likely because of the extreme natural class imbalance and the lack of negatively labeled sites. This work demonstrates that expert selection of negatives for training in NVML likely imparted modeling bias. Accounting for the sparsity of hydrothermal systems and all the types of locations without hydrothermal systems allows us to create better models for predicting hydrothermal resource favorability.","language":"English","publisher":"Geothermal Rising","usgsCitation":"Caraccioli, P.D., Mordensky, S.P., Lindsey, C.R., DeAngelo, J., Burns, E.R., and Lipor, J., 2023, Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions: Geothermal Resources Council Transactions, v. 47, p. 1672-1693.","productDescription":"22 p.","startPage":"1672","endPage":"1693","ipdsId":"IP-155549","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":424598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":424597,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034774"}],"volume":"47","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Caraccioli, Pascal D. 0009-0003-6711-8257","orcid":"https://orcid.org/0009-0003-6711-8257","contributorId":333435,"corporation":false,"usgs":false,"family":"Caraccioli","given":"Pascal","email":"","middleInitial":"D.","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":892810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":892811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindsey, Cary R. 0000-0001-5693-9664","orcid":"https://orcid.org/0000-0001-5693-9664","contributorId":333436,"corporation":false,"usgs":false,"family":"Lindsey","given":"Cary","email":"","middleInitial":"R.","affiliations":[{"id":79883,"text":"USGS for this work (just joined GBCGE at UNR)","active":true,"usgs":false}],"preferred":false,"id":892812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":892813,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892814,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":892815,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250715,"text":"pp1862R - 2023 - Overview of the Cenozoic geology of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia","interactions":[{"subject":{"id":70250715,"text":"pp1862R - 2023 - Overview of the Cenozoic geology of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia","indexId":"pp1862R","publicationYear":"2023","noYear":false,"chapter":"R","displayTitle":"Overview of the Cenozoic Geology of the Northern Harrat Rahat Volcanic Field, Kingdom of Saudi Arabia","title":"Overview of the Cenozoic geology of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia"},"predicate":"IS_PART_OF","object":{"id":70250730,"text":"pp1862 - 2023 - Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia","indexId":"pp1862","publicationYear":"2023","noYear":false,"title":"Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia"},"id":1}],"isPartOf":{"id":70250730,"text":"pp1862 - 2023 - Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia","indexId":"pp1862","publicationYear":"2023","noYear":false,"title":"Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia"},"lastModifiedDate":"2024-01-11T17:07:57.96341","indexId":"pp1862R","displayToPublicDate":"2023-12-29T14:31:26","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1862","chapter":"R","displayTitle":"Overview of the Cenozoic Geology of the Northern Harrat Rahat Volcanic Field, Kingdom of Saudi Arabia","title":"Overview of the Cenozoic geology of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia","docAbstract":"The Harrat Rahat volcanic field, located in the west-central part of the Kingdom of Saudi Arabia, is one of the larger Cenozoic harrats among the more than 17 harrats situated upon the Arabia Plate. The map plate contained herein shows, at a scale of 1:100,000, the mapped volcanic geology of northern Harrat Rahat, which consists of the northernmost one-fifth of Harrat Rahat. Northern Harrat Rahat has an area of about 3,340 square kilometers (km2), of which 2,567 km2 is covered by Harrat Rahat volcanic rocks and deposits, and it encompasses more than 900 exposed vents (that is, craters, cryptodomes, maars, and scoria cones), 289 of which are isolated by younger volcanic deposits and have not been correlated with the 234 volcanic rock units distinguished by geologic mapping.
Northern Harrat Rahat is of special interest owing to its proximity to the city of Al Madinah al Munawwarah, which sits within, and is continuing to expand southward over, the north end of the volcanic field. Al Madinah is home to an expanding population, currently at more than 2 million residents, together with the intermittent addition of approximately 3 million pilgrims during Hajj and Umrah (religious visitations). The center of Al Madinah is less than 8 km from lava flows of the only confirmed historically documented eruption, which occurred in 1256 C.E. (654 A.H.). Earlier prehistoric lava flows also encroached into the area of the present-day city limits, as demonstrated by volcanic rocks exposed widely throughout the city in roadcuts, parks, and excavations for new buildings, although no evidence has been found of any other than the 1256 C.E. lava having reached that area during times of human habitation.
Geologic mapping was undertaken by the U.S. Geological Survey in collaboration with the Saudi Geological Survey. The features of primary interest within the map area are scoria cones, lava flows, lava domes, craters, and pyroclastic deposits from the Quaternary, which have compositions of basalt, hawaiite, mugearite, benmoreite, and trachyte. The geologic mapping was published by Downs and others (2019) on a single sheet at 1:75,000 scale and two detailed sheets at 1:25,000 scale, accompanied by detailed explanations of the geology. This report presents the geology on a single sheet at 1:100,000 scale and provides condensed geologic explanations for the convenience of readers of this volume. Some minor errors of nomenclature and lava source regions that do not change fundamental interpretations have been corrected herein.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1862R","collaboration":"Jointly published with the Saudi Geological Survey [as Saudi Geological Survey Special Report SGS–SP–2021–1]","usgsCitation":"Robinson, J.E., and Downs, D.T., 2023, Overview of the Cenozoic geology of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia, chap. R of Sisson, T.W., Calvert, A.T., and Mooney, W.D., eds., Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia: U.S. Geological Survey Professional Paper 1862 [also released as Saudi Geological Survey Special Report SGS–SP–2021–1], 20 p., scale 1:100,000, https://doi.org/10.3133/pp1862R.","productDescription":"Report: v, 20 p.; 1 Plate: 58.72 x 39.51 inches; Data Release","numberOfPages":"20","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-128888","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":423999,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/pp/1862/r/pp1862r_plate.pdf","text":"Plate","size":"29.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1862-R Plate","linkHelpText":"- Map of the Cenozoic Geology of the Northern Harrat Rahat Volcanic Field, Kingdom of Saudi Arabia"},{"id":423998,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3WGTN","text":"USGS data release","linkHelpText":"Database for the geologic map of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia"},{"id":423997,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1862/r/pp1862r.pdf","text":"Report","size":"6.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1862-R"},{"id":423996,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1862/r/coverthbr.jpg"}],"country":"Kingdom of Saudi Arabia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 36.79510250167232,\n 26.52078234630855\n ],\n [\n 36.79510250167232,\n 21.77775441707078\n ],\n [\n 42.479119302334084,\n 21.77775441707078\n ],\n [\n 42.479119302334084,\n 26.52078234630855\n ],\n [\n 36.79510250167232,\n 26.52078234630855\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
We present a probabilistic seismic-hazard analysis (PSHA) for the west-central part of the Arabian Peninsula. Our study area includes the northern Harrat Rahat volcanic field and the nearby city of Al Madīnah, Kingdom of Saudi Arabia. This young, active volcanic field experienced one historical eruption in 1256 C.E. (654 in the year of the Hijra) that vented 20 to 22 kilometers (km) southeast of the center of Al Madīnah, which has a present population of about 1.4 million. The field also erupted numerous times in the late Pleistocene and possibly in the early Holocene. We used recently developed regional ground-motion prediction equations for Saudi Arabia to calculate the severity of ground shaking as a function of distance from the earthquake source. This information was combined with two key volcanic parameters for this area: (1) the return period of volcanic eruptions and (2) the spatial probability of the next vent opening. The calculated ground-motion levels of peak ground acceleration and peak ground velocity with a 2-percent probability of exceedance in 50 years are expected to be about 0.14 the acceleration due to gravity (g) and 10 centimeters per second (cm/s), respectively, at the most probable vent opening location, about 25 km southeast of Al Madīnah’s center, and about 0.07 g and 3 cm/s, respectively, in the city interior. These ground motions are higher than previous estimates that did not consider the nearby earthquakes associated with volcanic activity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1862Q","collaboration":"Jointly published with the Saudi Geological Survey [as Saudi Geological Survey Special Report SGS–SP–2021–1]","usgsCitation":"Kiuchi, R., Mooney, W.D., and Zahran, H.M., 2023, Probabilistic seismic-hazard analysis for the western Kingdom of Saudi Arabia, chap. Q of Sisson, T.W., Calvert, A.T., and Mooney, W.D., eds., Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia: U.S. Geological Survey Professional Paper 1862 [also released as Saudi Geological Survey Special Report SGS–SP–2021–1], 15 p., https://doi.org/10.3133/pp1862Q.","productDescription":"v, 15 p.","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123967","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":423995,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1862/q/pp1862q.pdf","text":"Report","size":"4.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1862-Q"},{"id":423994,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1862/q/coverthbq.jpg"}],"country":"Kingdom of Saudi Arabia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 36.79510250167232,\n 26.52078234630855\n ],\n [\n 36.79510250167232,\n 21.77775441707078\n ],\n [\n 42.479119302334084,\n 21.77775441707078\n ],\n [\n 42.479119302334084,\n 26.52078234630855\n ],\n [\n 36.79510250167232,\n 26.52078234630855\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
Earthquake swarms caused by volcanic activity, tectonic stresses, or industrial operations (oil and gas production) can pose considerable risk for nearby settlements. As a rule, a probabilistic seismic hazard assessment (PSHA) that is based on time-independent earthquakes does not take into account earthquake swarms because of their statistically time-dependent nature. We describe the technique and application of a scenario-based method for the estimation of seismic hazard from earthquake swarms that may occur in areas of volcanic activity in western Saudi Arabia. The method consists of the generation of a large number of scenario seismic swarms followed by the calculation of ground motion in a site of interest for all earthquakes in all of the swarms. The set of calculated ground-motion values permits the construction and analysis of probability distribution functions for possible ground-motion levels during the hypothetical earthquake swarms. The level of ground motion representing the hazard is selected considering an appropriate value of the probability of exceedance or non-exceedance, depending on the goal of the study. This swarm-scenario-based seismic hazard assessment may provide a valuable supplement to an ordinary PSHA and (or) deterministic seismic hazard assessment that are commonly used for emergency response and post-earthquake recovery management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1862P","collaboration":"Jointly published with the Saudi Geological Survey [as Saudi Geological Survey Special Report SGS–SP–2021–1]","usgsCitation":"Zahran, H.M., Sokolov, V., and Stewart, I.C.F., 2023, Seismic hazard assessment for areas of volcanic activity in western Kingdom of Saudi Arabia, chap. P of Sisson, T.W., Calvert, A.T., and Mooney, W.D., eds., Active volcanism on the Arabian Shield—Geology, volcanology, and geophysics of northern Harrat Rahat and vicinity, Kingdom of Saudi Arabia: U.S. Geological Survey Professional Paper 1862 [also released as Saudi Geological Survey Special Report SGS–SP–2021–1], 18 p., https://doi.org/10.3133/pp1862P.","productDescription":"v, 18 p.","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-126761","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":423992,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1862/p/coverthbp.jpg"},{"id":423993,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1862/p/pp1862p.pdf","text":"Report","size":"6.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1862-P"}],"country":"Kingdom of Saudi Arabia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 36.79510250167232,\n 26.52078234630855\n ],\n [\n 36.79510250167232,\n 21.77775441707078\n ],\n [\n 42.479119302334084,\n 21.77775441707078\n ],\n [\n 42.479119302334084,\n 26.52078234630855\n ],\n [\n 36.79510250167232,\n 26.52078234630855\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025