The Joint Agency Commercial Imagery Evaluation (JACIE) was formed to leverage resources from several Federal agencies for the characterization of remote sensing data and to share those results across the remote sensing community (U.S. Geological Survey, 2024).
Remote sensing data and the quality of that data are vital to (1) understanding the physical world and (2) supporting the science and engineering applications that strive to advance that understanding. The growing number of remotely sensed data sources offers users more choices. Understanding the characteristics and capabilities of current and new data sources, along with the quality of data they provide, is an important function of the multi-agency JACIE team. By performing data-quality analysis of civil and commercial remote sensing data and information products, the JACIE team provides the remote sensing community with awareness and independent verification of image data quality.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243038","usgsCitation":"Clauson, J., Anderson, C., Vrabel, J., 2024, Joint Agency Commercial Imagery Evaluation (JACIE): U.S. Geological Survey Fact Sheet 2024-3038, 2 p., https://doi.org/10.3133/fs20243038.","productDescription":"2 p.","numberOfPages":"2","ipdsId":"IP-170388","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":462703,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3038/covrthb.jpg"},{"id":462704,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3038/fs20243038.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":462705,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3038/fs20243038.xml"},{"id":462706,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3038/images"},{"id":462707,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243038/full"}],"contact":"USGS EROS Cal/Val Center of Excellence (ECCOE) Project Team
U.S. Geological Survey
Earth Resources Observation and Science
47914 252nd Street
Sioux Falls, SD 57198
Email: eccoe@usgs.gov
In 2020, Colorado experienced the most severe wildfire season in recorded history, with wildfires burning 625 357 acres across the state. Two of the largest fires burned parts of Rocky Mountain National Park (RMNP), and a study was initiated to address concerns about potential effects on drinking water quality from mobilization of ash and sediment. The study took advantage of a wealth of pre-fire data from adjacent burned and unburned basins in western RMNP. Pre- and post-fire data collection included discrete sample collection and high-frequency water-quality measurements using in-stream sensors. Kruskal–Wallis tests on discrete data indicated that specific conductance, base cations, sulphate, chloride, nitrate, and total dissolved nitrogen concentrations increased post-fire, whereas silica and dissolved organic carbon (DOC) did not (p ≤ 0.05). In-stream sensors captured large spikes in concentrations of nutrients, turbidity, and DOC in the burned basin that were missed by discrete sampling. Sensor data indicated nitrate and turbidity increased by up to one and two orders of magnitude, respectively, from pre-event concentrations during storms, and DOC increased up to 3.5×. Empirical regression equations were developed using pre-fire data and applied to the post-fire period to estimate expected stream chemistry in the absence of fire (a ‘no-fire’ scenario). Overlays of actual post-fire chemistry showed the timing and magnitude of differences between observed and ‘estimated’ chemistry. For most solutes, observed post-fire concentrations were notably greater than expected under the ‘no-fire’ scenario, and differences were greatest during storm events. Comparison of data from the burned and unburned basins indicated DOC concentrations were affected by climate as well as fire. Results from this study demonstrate the importance of both pre-fire data and high-frequency data for characterizing dynamic hydrochemical responses in wildfire-affected areas.
Designated in 1978, Kaloko-Honokōhau National Historical Park is located on the west coast of the Island of Hawaiʻi. The Kaloko-Honokōhau National Historical Park encompasses about 1,200 acres of coastal land and nearshore ecosystems, which include wetlands, anchialine pools (landlocked bodies of brackish water with hydrologic connections to the ocean), fishponds, a fishtrap, and coral reefs. These nearshore ecosystems are dependent on groundwater discharge with a freshwater component and provide habitat for threatened and endangered, endemic species, such as the orangeblack Hawaiian damselfly (Megalagrion xanthomelas) and the Hawaiian coot (ʻAlae keʻokeʻo, Fulica alai). The populations of these native species, however, are threatened because of habitat loss related to urban development and environmental changes. Kaloko-Honokōhau National Historical Park is within the Keauhou aquifer system and the North Kona District, which experienced a 52 percent resident-population increase between 2000 and 2020 and a 41 percent visitor increase between 2008 and 2019. To support the current water demand associated with this growing population, groundwater is the primary source of freshwater used in the North Kona District, with about 15 million gallons of groundwater withdrawn from the Keauhou aquifer system per day since 2009. With anticipated development, future (2015–35) groundwater withdrawal from the Keauhou aquifer system is projected to be about 55 percent greater than recent (2012–14) withdrawal. Because Kaloko-Honokōhau National Historical Park is located within a coastal aquifer, natural and human-induced changes can affect the quality and quantity of groundwater, which can threaten groundwater-dependent ecosystems.
To improve understanding of recent groundwater conditions, the U.S. Geological Survey, in cooperation with the National Park Service, undertook this study to document correlations between hydrologic time-series datasets from sites in and near Kaloko-Honokōhau National Historical Park using the nonparametric (distribution-free) Kendall’s tau statistical test.
For the statistical analyses, dependent variables representing the groundwater system include groundwater level, the groundwater-level difference between pairs of sites, and specific conductance, and independent variables include datasets of sea level, rainfall, and groundwater withdrawal. About 34 percent of the 140 non-time-lagged Kendall’s tau statistical tests evaluated in this report are statistically significant (p-value ≤ 0.050) with generally weak (0.1 ≤ tau ≤ 0.2) to moderate (0.2 ≤ tau ≤ 0.3) correlations. Groundwater levels measured at monitoring sites have the strongest correlation with the multivariate El Niño–Southern Oscillation index and withdrawal from production wells at the nearby Kohanaiki Private Club Community. Specific conductance is not consistently and significantly correlated with the independent hydrologic variables investigated in this report.
Because the relations between hydrologic variables are commonly not instantaneous, a second set of correlations was evaluated after applying a range of time lags to the independent variable datasets. Relative to the non-time-lagged case (the set of correlations that did not use time-lagged independent variables), some of the time-lagged independent variables improved correlations with some of the dependent variables. For a particular independent variable, similar time lags were expected between the independent variable and dependent variable at all four monitoring sites. However, different time lags among the four sites sometimes produced the strongest correlations.
This study identified several correlations that are statistically significant and hydrologically plausible, but the correlations could indicate that multiple concurrent factors are controlling the observed groundwater-system response, which might be better addressed using multivariate analyses. This study only investigates bivariate correlations, which may not explain all the variance in the data. The correlations analyzed in this report are limited by the quantity of available hydrologic data in the area near Kaloko-Honokōhau National Historical Park and are based on 14 years of time-series data, which were aggregated to a relatively coarse monthly temporal resolution that represents the minimum resolution common to all datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245084","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Okuhata, B.K., and Oki, D.S., 2024, Correlation analysis of groundwater and hydrologic data, Kaloko-Honokōhau National Historical Park, Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2024–5084, 38 p., https://doi.org/10.3133/sir20245084.","productDescription":"ix, 38 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-154287","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":462626,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5084/covrthb.jpg"},{"id":462627,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5084/sir20245084.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":462628,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5084/sir20245084.xml"},{"id":462629,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5084/images"},{"id":462630,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245084/full"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaloko-Honokōhau National Historical Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.083351222759,\n 19.721737271204077\n ],\n [\n -156.083351222759,\n 19.65199485292854\n ],\n [\n -155.98955903724118,\n 19.65199485292854\n ],\n [\n -155.98955903724118,\n 19.721737271204077\n ],\n [\n -156.083351222759,\n 19.721737271204077\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
The Earth surface Mineral dust source InvesTigation (EMIT) is an imaging spectrometer launched to the International Space Station in July 2022 to measure the mineral composition of Earth’s dust-producing regions. We present a systematic accuracy assessment of the EMIT surface reflectance product in two parts. First, we characterize the surface reflectance product’s overall performance using multiple independent vicarious calibration field experiments with hand-held and automated field spectrometers. We find that the EMIT surface reflectance product has a standard error of ±1.0% in absolute reflectance units for temporally coincident observations. Discrepancies rise to ±2.7 % for spectra acquired at different dates and times of day, which we attribute mainly to changes in solar geometry. Second, we develop an error budget that explains the differences between EMIT and in-situ field spectrometer data. We find that uncertainties in spatial footprints, field spectroscopy, and the EMIT-reported measurement were sufficient to explain discrepancies in most cases. Our approach did not detect any systematic calibration or reflectance errors in the timespan considered. Together, these findings demonstrate that a space-based imaging spectrometer can acquire high-quality spectra across a wide range of observational and atmospheric conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2024.114450","usgsCitation":"Coleman, R.W., Thompson, D.R., Brodrick, P.G., Ben-Dor, E., Cox, E., Perez Garcıa-Pando, C., Hoefen, T.M., Kokaly, R.F., Meyer, J.M., Ochoa, F., Okin, G.S., Pearlshtien, D.H., Swayze, G.A., and Green, R.O., 2024, An accuracy assessment of the surface reflectance product from the EMIT imaging spectrometer: Remote Sensing of Environment, v. 315, 114450, 10 p., https://doi.org/10.1016/j.rse.2024.114450.","productDescription":"114450, 10 p.","ipdsId":"IP-157064","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":499955,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2024.114450","text":"Publisher Index Page"},{"id":499810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Smith Creek Playa","volume":"315","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coleman, Red Willow","contributorId":366345,"corporation":false,"usgs":false,"family":"Coleman","given":"Red","middleInitial":"Willow","affiliations":[{"id":87384,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA.","active":true,"usgs":false}],"preferred":false,"id":955672,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, David R.","contributorId":366346,"corporation":false,"usgs":false,"family":"Thompson","given":"David","middleInitial":"R.","affiliations":[{"id":87384,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA.","active":true,"usgs":false}],"preferred":false,"id":955673,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brodrick, Philip G.","contributorId":366347,"corporation":false,"usgs":false,"family":"Brodrick","given":"Philip","middleInitial":"G.","affiliations":[{"id":87384,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA.","active":true,"usgs":false}],"preferred":false,"id":955674,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ben-Dor, Eyal","contributorId":366217,"corporation":false,"usgs":false,"family":"Ben-Dor","given":"Eyal","affiliations":[{"id":87385,"text":"University of Tel Aviv, Tel Aviv, Israel","active":true,"usgs":false}],"preferred":false,"id":955682,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cox, Evan 0000-0002-1434-7000","orcid":"https://orcid.org/0000-0002-1434-7000","contributorId":223068,"corporation":false,"usgs":true,"family":"Cox","given":"Evan","affiliations":[],"preferred":true,"id":955679,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perez Garcıa-Pando, 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0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":205165,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond","email":"","middleInitial":"F.","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":955675,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Meyer, John Michael 0000-0003-2810-9414","orcid":"https://orcid.org/0000-0003-2810-9414","contributorId":297062,"corporation":false,"usgs":true,"family":"Meyer","given":"John","email":"","middleInitial":"Michael","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":955678,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ochoa, Francisco","contributorId":352606,"corporation":false,"usgs":false,"family":"Ochoa","given":"Francisco","affiliations":[{"id":33607,"text":"University of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":955680,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Okin, Gregory S.","contributorId":366354,"corporation":false,"usgs":false,"family":"Okin","given":"Gregory","middleInitial":"S.","affiliations":[{"id":87398,"text":"University of California Los Angeles, Los Angeles, CA USA","active":true,"usgs":false}],"preferred":false,"id":955681,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pearlshtien, Daniela Heller","contributorId":366355,"corporation":false,"usgs":false,"family":"Pearlshtien","given":"Daniela","middleInitial":"Heller","affiliations":[{"id":87385,"text":"University of Tel Aviv, Tel Aviv, Israel","active":true,"usgs":false}],"preferred":false,"id":955683,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Swayze, Gregg A. 0000-0002-1814-7823","orcid":"https://orcid.org/0000-0002-1814-7823","contributorId":239533,"corporation":false,"usgs":true,"family":"Swayze","given":"Gregg","email":"","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":955676,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Green, Robert O.","contributorId":366356,"corporation":false,"usgs":false,"family":"Green","given":"Robert","middleInitial":"O.","affiliations":[{"id":87384,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA.","active":true,"usgs":false}],"preferred":false,"id":955685,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70259422,"text":"70259422 - 2024 - Predicting characteristic length scales of barrier island segmentation in microtidal environments","interactions":[],"lastModifiedDate":"2024-10-08T11:41:41.240056","indexId":"70259422","displayToPublicDate":"2024-10-05T06:38:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Predicting characteristic length scales of barrier island segmentation in microtidal environments","docAbstract":"Segmented barrier islands can be found in regions with small tidal ranges. In contrast to tidally dominated barriers, where inlet dynamics are thought to control island length scales, the controls on barrier island length scales in wave-dominated environments have not been quantified. These microtidal barriers typically have a curved shoreline, suggesting the influence of wave-driven alongshore sediment transport. Microtidal barriers are also typically hydrodynamically isolated from one another, as weak tidal flows limit interactions between adjoining barriers. To better understand the controls on and scales of barrier segmentation in the relative absence of tides, here we develop a theoretical framework to estimate the alongshore length scales at which a barrier will either breach or heal following a disturbance in the barrier morphology. The non-dimensional framework compares the timescales of overwash (advective) and alongshore sediment transport (diffusive) processes along barrier island chains. We then apply this framework to modern barrier islands in the microtidal Gulf of Mexico using wave hindcast data and the lengths, widths, heights, and lagoon depths measured from remotely sensed geospatial data and topobathymetric data. We find that most of these barriers are currently longer than their critical length scale, often as a result of coastal restoration efforts. Our critical length scale analysis suggests that most of the Gulf of Mexico barriers are vulnerable to segmentation despite coastal restoration efforts intended to protect fisheries and the mainland coasts.
As part of a cooperative investigation between the U.S. Geological Survey and the Alabama Department of Agriculture and Industries, a network of 22 groundwater wells were sampled from 2014 through 2020 for about 230 pesticide and pesticide degradate compounds. Wells were located in three regions of intensive agricultural land use in Alabama: Baldwin County, the Wiregrass region, and the Tennessee River valley region.
Metolachlor sulfonic acid, a degradate of the herbicide metolachlor, was the most frequently detected compound, occurring in about 70 percent of the samples. Three other compounds, metolachlor, atrazine, and 2-chloro-4-isopropylamino-6-amino-s-triazine, were also detected in more than half of the samples. Metolachlor and its degradates accounted for 33 of the 50 greatest compound concentrations study-wide, including the maximum pesticide concentration across all compounds (62,500 nanograms per liter). The frequency and magnitude of detections of many specific pesticide compounds varied among the three regions, but all detected pesticide concentrations were well below the U.S. Environmental Protection Agency maximum contaminant levels and applicable human health benchmarks.
Sample results were combined with results of previous (2009–13) sampling to provide a continuous time-series of data for 2009–20. More than half of the 289 pesticide compounds analyzed during 2009–20 were not detected in any samples. Only four compounds were detected at great enough frequency throughout the 10 sampling years to evaluate patterns of change through time. Metolachlor and its degradate, metolachlor sulfonic acid, were frequently detected in all regions. Atrazine and its degradate, 2-chloro-4-isopropylamino-6-amino-s-triazine, were also detected in wells from all regions, but the variability and magnitude of concentrations were greatest in the Tennessee River valley region. No apparent temporal pattern in concentrations was found.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245069","issn":"2328-0328","collaboration":"Prepared in cooperation with the Alabama Department of Agriculture and Industries","usgsCitation":"Gill, A.C., 2024, Pesticide occurrence in shallow groundwater in three regions of agricultural land use: Baldwin County, the Wiregrass region, and the Tennessee River valley region of Alabama, 2009–20: U.S. Geological Survey Scientific Investigations Report 2024–5069, 50 p., https://doi.org/10.3133/sir20245069.","productDescription":"Report: viii, 50 p.; 2 Data Releases","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-124243","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":499053,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117645.htm","text":"Baldwin County and Wiregrass region region","linkFileType":{"id":5,"text":"html"}},{"id":462612,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS NWIS Data Release","linkHelpText":"- USGS water data for the Nation"},{"id":462618,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245069/full","description":"SIR 2024-5069 HTML"},{"id":462609,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5069/sir20245069.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5069 XML"},{"id":462608,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5069/sir20245069.pdf","size":"5.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5069"},{"id":462607,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5069/images"},{"id":462606,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5069/coverthb.jpg"},{"id":499052,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117644.htm","text":"Tennessee Valley region","linkFileType":{"id":5,"text":"html"}},{"id":462611,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96OOHNZ","text":"USGS Data Release","linkHelpText":"- Pesticide concentration and related water-quality data for selected groundwater sites near areas of agricultural land use in Alabama, 2009–2020"}],"country":"United States","state":"Alabama","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.23168853448631,\n 30.191218364802225\n ],\n [\n -87.23168853448631,\n 31.202019682928764\n ],\n [\n -88.02270415948634,\n 31.202019682928764\n ],\n [\n -88.02270415948634,\n 30.191218364802225\n ],\n [\n -87.23168853448631,\n 30.191218364802225\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.69335845636155,\n 31.032720603772802\n ],\n [\n -85.0234365813612,\n 31.032720603772802\n ],\n [\n -85.0234365813612,\n 32.11842944019466\n ],\n [\n -86.69335845636155,\n 32.11842944019466\n ],\n [\n -86.69335845636155,\n 31.032720603772802\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.28675227176826,\n 35.04758320679862\n ],\n [\n -88.28675227176826,\n 34.115944393543984\n ],\n [\n -86.59485774051834,\n 34.115944393543984\n ],\n [\n -86.59485774051834,\n 35.04758320679862\n ],\n [\n -88.28675227176826,\n 35.04758320679862\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"The National Volcano Early Warning System (NVEWS) was authorized and partially funded by the U.S. Government in 2019. In response, the U.S. Geological Survey (USGS) Volcano Hazards Program asked its scientists to reflect on and summarize their views of best practices for volcano monitoring. The goal was to review and update the recommendations of a previous report (Moran and others, 2008) and to provide a more detailed analysis of capabilities and instrumentation for monitoring networks for U.S. volcanoes. This Scientific Investigations Report and its chapters reflect those USGS scientists’ views and summaries and will serve as a guide for future network upgrades funded through NVEWS.
Given the well-documented hazards posed by volcanoes to population centers and aviation (for example, Blong, 1984; Scott, 1989; Neal and others, 1997, 2019; Guffanti and others, 2010; Shroder and Papale, 2014; Prata and Rose, 2015; Palmer, 2020), volcano monitoring is critical for ensuring public safety and for mitigating the impacts of volcanic activity. Accurate and timely forecasts are facilitated by well-designed monitoring networks that are in place long enough to allow for background behavior to be recognized and understood. Because precursory signals may be limited and unrest may progress rapidly to an eruption, our goal is to deploy monitoring systems that enable detection of the reactivation of dormant volcanoes as early as possible, allowing for public safety and risk mitigation. NVEWS planning is also informed by the results of Ewert and others (2005, 2018), whereby 161 U.S. volcanoes are currently categorized and ranked commensurate with their relative threat.
In each chapter, author(s) considered the need for some redundancy of instrumentation and telemetry, given the likelihood of occasional equipment failure, particularly in extreme and remote environments. Establishing digital telemetry networks requires advanced planning, sighting, radio-shot testing, and, inevitably, troubleshooting in the field. This is harder to achieve rapidly during a crisis; thus, an important goal for monitoring U.S. volcanoes is to establish digital telemetry backbones with redundancy and extra capacity to absorb additional instruments should a volcano begin to exhibit signs of unrest (fig. A1). The National Telecommunications and Information Administration (NTIA) imposed new regulations in the United States, eliminating the use of older analog radios for many purposes, which had been one previous means for redundant data delivery. However, the resulting conversion from analog to digital systems usefully enables stations to accommodate new and multivariate real-time data streams (for example, Global Navigation Satellite System [GNSS] receivers, infrasound arrays, gas spectrometers, visible and infrared cameras, and broadband seismometers).
We note that other USGS and broader national and international hazard programs can leverage NVEWS instrumentation plans. Examples of this include the following:
Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Habitat management can directly impact Setophaga discolor (Prairie Warbler) abundance and distribution. Despite regional declines, Prairie Warbler populations at Camp Edwards (Bourne, MA) have increased. To investigate habitat-management effects on Prairie Warbler populations at Camp Edwards, we used a dynamic-occupancy model to analyze a long-term monitoring dataset collected across 84 point-count sites from 2013 to 2022. The model results indicated that Prairie Warbler colonization and extinction probabilities were impacted by management (measured in years since disturbance). Colonization probability was highest initially after disturbance, then subsequently decreased for ∼50 years, and extinction probability also decreased for ∼25 years. Both probabilities remained low before increasing at ∼75 years since disturbance. The increase in colonization probability >75 years since disturbance may have been an artifact of our study design and incomplete disturbance records. We also found that latitude and longitude significantly affected colonization probability, likely a result of how habitat types are distributed across the base. These results inform how Prairie Warblers respond to long-term management, suggesting that habitat management could improve colonization rates and sustain Prairie Warbler populations.
","language":"English","publisher":"Eagle Hill Institute","doi":"10.1656/045.031.0315","usgsCitation":"Gordon, A., Drummey, D., Tur, A., Curtis, A., McCumber, J., Akresh, M., and DiRenzo, G.V., 2024, Long-term monitoring reveals management effects on Prairie Warbler colonization, local extinction, and detection in a Massachusetts pine barren: Northeastern Naturalist, v. 31, no. 3, p. 418-434, https://doi.org/10.1656/045.031.0315.","productDescription":"17 p.","startPage":"418","endPage":"434","ipdsId":"IP-159827","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.6,\n 41.77\n ],\n [\n -70.6,\n 41.64\n ],\n [\n -70.5,\n 41.64\n ],\n [\n -70.5,\n 41.77\n ],\n [\n -70.6,\n 41.77\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gordon, Andrew B. Jr.","contributorId":348604,"corporation":false,"usgs":false,"family":"Gordon","given":"Andrew B.","suffix":"Jr.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":923651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drummey, Donovan","contributorId":348607,"corporation":false,"usgs":false,"family":"Drummey","given":"Donovan","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tur, Anthony","contributorId":348610,"corporation":false,"usgs":false,"family":"Tur","given":"Anthony","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923653,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Curtis, Annie E.","contributorId":348613,"corporation":false,"usgs":false,"family":"Curtis","given":"Annie E.","affiliations":[{"id":81976,"text":"Massachusetts Army National Guard","active":true,"usgs":false}],"preferred":false,"id":923654,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCumber, Jacob C.","contributorId":348616,"corporation":false,"usgs":false,"family":"McCumber","given":"Jacob C.","affiliations":[{"id":81976,"text":"Massachusetts Army National Guard","active":true,"usgs":false}],"preferred":false,"id":923655,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Akresh, Michael E.","contributorId":348619,"corporation":false,"usgs":false,"family":"Akresh","given":"Michael E.","affiliations":[{"id":83385,"text":"Antioch University","active":true,"usgs":false}],"preferred":false,"id":923656,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":923657,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70259166,"text":"sir20245062M - 2024 - Special topic—Rapid-response instrumentation","interactions":[{"subject":{"id":70259166,"text":"sir20245062M - 2024 - Special topic—Rapid-response instrumentation","indexId":"sir20245062M","publicationYear":"2024","noYear":false,"chapter":"M","displayTitle":"Special Topic—Rapid-Response Instrumentation","title":"Special topic—Rapid-response instrumentation"},"predicate":"IS_PART_OF","object":{"id":70259167,"text":"sir20245062 - 2024 - Recommended capabilities and instrumentation for volcano monitoring in the United States","indexId":"sir20245062","publicationYear":"2024","noYear":false,"title":"Recommended capabilities and instrumentation for volcano monitoring in the United States"},"id":1}],"isPartOf":{"id":70259167,"text":"sir20245062 - 2024 - Recommended capabilities and instrumentation for volcano monitoring in the United States","indexId":"sir20245062","publicationYear":"2024","noYear":false,"title":"Recommended capabilities and instrumentation for volcano monitoring in the United States"},"lastModifiedDate":"2024-10-17T20:45:04.668726","indexId":"sir20245062M","displayToPublicDate":"2024-10-04T10:30:24","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5062","chapter":"M","displayTitle":"Special Topic—Rapid-Response Instrumentation","title":"Special topic—Rapid-response instrumentation","docAbstract":"Based on the reports of Ewert and others (2005, 2018) and Moran and others (2008), most U.S. volcanoes are currently under-monitored and are likely to remain so until the goals of the National Volcano Early Warning System are fulfilled. In addition, volcanoes determined to have low to moderate threat levels (Ewert and others 2005, 2018) could awaken suddenly and, as a result, may need to have instrumentation installed rapidly. For these reasons, equipment caches would ideally be readily available for rapid response in the event of unrest at under-monitored volcanoes or during a volcanic crisis. Given that volcanoes in Alaska and Hawai‘i are frequently active, it is likely that several U.S. volcanoes could experience unrest simultaneously, as happened in 2018, 2019, and 2020, when unrest or eruptions occurred at Great Sitkin Volcano, Alaska; Mauna Loa, Hawai‘i; Mount Cleveland, Alaska; Semisopochnoi Island, Alaska; Shishaldin Volcano, Alaska; Mount Veniaminof, Alaska, as well as the most destructive documented eruption of Kīlauea, Hawai‘i. Therefore, we recommend that sufficient numbers of seismometers, infrasound sensors, Global Navigation Satellite System (GNSS) receivers, remote cameras, gas-monitoring instruments, and airborne and ground-based remote-sensing systems be made available and placed in a state of readiness at each observatory with the capability of bringing a level-2 monitoring network to near level-4 readiness. These rapid-response caches would ideally include sufficient equipment to provide real-time data telemetry, including satellite telemetry, where available, applicable, and appropriate. Rapid-response caches would be maintained in a state of readiness so that instruments can be deployed within several hours to days. Although the primary focus of the caches would be to enable rapid increases to a volcano observatory’s real-time monitoring capabilities, not all scenarios of volcanic unrest are conducive to rapid deployment of real-time data telemetry. Non-telemetered, campaign instruments, particularly seismometers and GNSS stations, can also be deployed to aid in detection of early signs of volcanic unrest given the data can be recovered in a timely fashion.
Given the geographic separation of the U.S. Geological Survey Volcano Science Center’s (VSC) four volcano observatory offices, the logistical difficulties in shipping equipment rapidly between them in response to unrest, the possible scenario that a volcano could reawaken with just hours or days of precursory unrest, and the difference in operating environments (for example, tropical Hawai‘i compared to subarctic Alaska), we recommend three rapid-response instrument caches—for Hawai‘i, Alaska, and the lower 48 States. For the lower 48 States, a single cache shared among the Cascades Volcano Observatory, Yellowstone Volcano Observatory, and the California Volcano Observatory could be warehoused in California or Washington. Although these rapid-response caches would be located at one of the observatories, they would ideally be owned and maintained by VSC, and together form a flexible VSC-wide instrument pool. To maintain continuity of monitoring capabilities, this rapid-response cache could also serve to replace instruments destroyed during an on-going eruption. However, to retain eruption-response readiness, we recommend instruments in the rapid-response cache not be permanently reallocated to an observatory’s monitoring network unless they are replaced.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062M","usgsCitation":"Flinders, A.F., 2024, Special topic—Rapid-response instrumentation, chap. M of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–M, 4 p., https://doi.org/10.3133/sir20245062M.","productDescription":"iii, 4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-153111","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462409,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/m/covrthbm.jpg"},{"id":462410,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/m/sir20245062m.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Unoccupied aircraft systems (UAS) increasingly support volcano monitoring and eruption response activities in the United States and abroad (James and others, 2020). Advances in UAS platforms and miniaturization of sensors over the past decade have expanded the use of this technology for a wide range of applications within volcanology (Jordan, 2019; James and others, 2020). UAS can greatly enhance existing ground-, aerial-, and satellite-based observation and in situ monitoring networks at volcanoes by providing new avenues for data collection in terms of access, resolution, and timing. UAS can collect data in difficult and hazardous environments, reducing risk to occupied aircraft and (or) ground crews; support the generation of dense time series of data through frequent, low-cost, high-resolution surveys; and provide real-time, on-demand measurements at volcanic systems for indicators such as gas, thermal output, and topographic change without the need to wait for contracted aerial flight services or satellite orbit intervals.
During the 2018 response to the Kīlauea eruption on the Island of Hawaiʻi, UAS were used extensively and successfully to monitor, track, investigate, and (or) warn of ongoing volcanic activity (fig. L1; Neal and others, 2019). Throughout the eruption, the UAS team was able to provide data products rapidly to emergency managers for situational awareness and to scientists for quantitative hazard assessment (Diefenbach and others, 2018). Over the course of 4 months, more than 1,200 UAS missions were flown and yielded critical data that included (1) live video to emergency operations centers in Hilo and Honolulu for situational awareness; (2) gas emission rates, compositions, and concentrations; (3) repeat nadir videos over sections of the lava channel to support measurements of lava effusion rate; (4) oblique videos for hazards assessment and outreach; and (5) photogrammetry surveys to create very high-resolution topographic models and orthophoto mosaics (Diefenbach and others, 2018). In coming years, the U.S. Geological Survey (USGS) Volcano Hazards Program (VHP) plans to expand its fleet of UAS, associated sensors, and remote pilots to enhance volcano monitoring and response capabilities.
Currently (2023), USGS operational capabilities are restricted to small class UAS (sUAS; less than [<] 55 pounds) that are limited in range, payload capacity, and flight duration. Additionally, USGS-piloted platforms are restricted to the U.S. Department of the Interior Office of Aviation Services approved fleet, which includes a limited number of small and medium multi-rotor aircraft and vertical take-off and landing fixed-wing aircraft (https://www.doi.gov/aviation/uas/fleet). Each type of platform has advantages and disadvantages. Small rotor-wing quadcopters are fast to deploy, can be carried in a backpack, and are highly maneuverable, but are typically only equipped with a small camera and have a minimal flight range. Medium rotor-wing hexacopters can carry larger payloads (< 20 kilograms [kg]) and varied sensors, but, with the drawback of minimal flight time (<30 minutes), they typically have similar range capabilities to their smaller counterparts and are not as easily deployable. Fixed-wing platforms provide relatively long endurance (<60 minutes) and range and, with the vertical take-off and landing capabilities, can launch and land in relatively small spaces; however, they have less maneuverability and hovering capability than the rotor-wing platforms. Although the 2018 Kīlauea response showed the benefit of the current UAS fleet, all platforms have limited range [<10 kilometers (km)], such that operators must be stationed relatively close to the region of interest. To expand UAS monitoring capabilities, VHP staff have been working closely with industry partners and the National Aeronautics and Space Administration to develop a next-generation UAS for volcano monitoring (Kern and others, 2020). This ruggedized, mid-range (>20 km), multiparametric (gas and photogrammetry) UAS has been developed to meet volcano monitoring needs, particularly at less accessible, more dangerous stratovolcanoes. It is expected in the coming years that additional UAS platforms with new and smaller sensors will expand our capabilities to meet the Nation’s volcano monitoring objectives.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062L","usgsCitation":"Diefenbach, A.K., 2024, Special topic—Unoccupied aircraft systems, chap. L of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–L, 5 p., https://doi.org/10.3133/sir20245062L.","productDescription":"iii, 5 p.","numberOfPages":"5","onlineOnly":"N","ipdsId":"IP-149693","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462411,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/l/covrthbl.jpg"},{"id":462412,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/l/sir20245062l.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Installation of instrument packages in deep (several hundred to several thousand meters) boreholes near volcanoes is relatively expensive (a few million to tens of millions of U.S. dollars), but can provide a low-noise, high-quality source of geophysical (seismic, strain, tilt, and pore pressure), physical (temperature and water level), and geochemical data. Observations from instruments at depth have the potential to provide insights into processes associated with magma intrusion, unrest, and eruption that would not otherwise be possible (Lowenstern and others, 2017; Eichelberger, 2020). Examples of instrumented boreholes in volcanic areas include the 3-kilometer (km)-deep Long Valley Exploratory Well (LVEW) in California (for example, Priest and others, 1998; Prejean and Ellsworth, 2001; Fischer and others, 2003; Roeloffs and others, 2003; Sorey and others, 2003), the 1,262 meter-deep NSF Well (commonly referred to as the “Keller Well”) within the summit caldera of Kīlauea, Hawaiʻi (Keller and others, 1979; Myren and others, 2006), and the Caribbean Andesite Lava Island-volcano Precision Seismo-geodetic Observatory (CALIPSO) project at Soufrière Hills, Montserrat, which includes a series of four 200-meter (m)-deep holes (for example, Mattioli and others, 2004; Voight and others, 2006). The Plate Boundary Observatory (PBO) of the National Science Foundation’s Earthscope project placed seismometers, tiltmeters, strainmeters, and pore-pressure sensors at depths of 100 to 250 m in more than 100 boreholes scattered in western North America, including at Mount St. Helens, Washington, and Yellowstone Caldera, Wyoming. The total cost for an instrumented PBO borehole ranged from $250,000 to $270,000 U.S. dollars (USD) and a few thousand USD are required annually for maintenance (David Mencin, UNAVCO, written commun., October 2020).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062K","usgsCitation":"Hurwitz, S., and Lowenstern, J.B., 2024, Special topic—Boreholes, chap. K of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062K.","productDescription":"iii, 5 p.","numberOfPages":"5","onlineOnly":"N","ipdsId":"IP-148975","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/k/covrthbk.jpg"},{"id":462414,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/k/sir20245062k.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Explosive eruptions create plumes of volcanic ash and gas that can rise more than 30,000 feet (9.1 kilometers [km]) above sea level within minutes of eruption onset. The resulting clouds disperse under prevailing winds and may cause hazardous conditions hundreds to thousands of kilometers from the volcano, including in international airspace. Rapid detection and characterization of explosive activity is vital to mitigate the wide-ranging effects of volcanic ash. Ashfall thicknesses as small as a millimeter or so on the ground can affect infrastructure, agriculture, and air quality, requiring extensive clean-up procedures (Schuster, 1981; Warrick and others, 1981, U.S. Geological Survey, 2022). Volcanic clouds also pose substantial threats to aircraft. Since 1953, 88 encounters between airplanes and ash clouds have been documented worldwide (International Civil Aviation Organization, 2015, appendix F), resulting in aircraft damage and, in 9 cases, engine failure (Guffanti and others, 2010). In 1982, two large passenger planes suffered complete engine failure owing to eruptions in Indonesia (Global Volcanism Program, 1982) and a similar incident occurred over Alaska in 1989 (Casadevall, 1994). In all three cases, they were able to restart some engine capability and land safely once they emerged from the ash clouds, although with substantial damage (Guffanti and others, 2010).
The clear threat to aviation has led to establishment of nine Volcanic Ash Advisory Centers (VAAC) around the world to monitor and rapidly disseminate information about volcanic eruptions to the aviation community. U.S. Geological Survey (USGS) volcano observatories issue the Volcano Observatory Notice for Aviation that informs of preeruptive unrest or eruptive activity. When ash-producing eruptions do occur, volcano observatories work closely with their regional VAAC to ensure consistency and accuracy in eruption onset time, cloud altitude, ash production, and duration as reported in Volcanic Ash Advisories. Explosive volcanism in the United States and Commonwealth of the Northern Mariana Islands prompts 50–100 such advisories in any given year (table J1). This collaborative effort is greatly aided by USGS detection and monitoring of eruption clouds to ensure a timely and coordinated response.
To support these efforts to provide guidance on ash transport and fallout, the USGS developed the Ash3d volcanic ash dispersion model (https://vsc-ash.wr.usgs.gov/ash3d-gui) (Schwaiger and others, 2012). Automated simulations are run daily by the USGS for volcanoes that are in elevated states of unrest, and in response mode when eruptions occur. During eruptions, the model output is provided to local National Weather Service Weather Forecast Offices to guide them in the issuance of their information products (such as special weather statements, ashfall advisories, or ashfall warnings), as well as to State and local governments and the public. Characterization of the eruption source is needed to estimate the parameters used to initialize the Ash3d model, and by the Anchorage and Washington VAACs to initialize other dispersion models that inform forecasts for the airborne volcanic cloud. The source parameters that can be provided by observation during an eruption include eruption start time, eruption cloud height over time, and eruption duration. Other, nonobservable source parameters, such as mass eruption rate and grain-size distribution, are based on empirical correlations and study of historical deposits. The goal is to provide a time series of cloud heights, mass eruption rates, and particle-size distributions that accurately reflects current conditions. When feasible, the USGS also provides guidance on the nature of ongoing eruptions and forecasts future activity using petrologic monitoring of collected tephra samples.
The aims of providing accurate observable parameters are achieved through analysis of (1) near-real-time meteorological satellite data, (2) ground-based cameras (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–G, 11 p., https://doi.org/10.3133/sir20245062g.\">chapter G, this volume; Orr and others, 2024), (3) weather radar, (4) volcanic lightning detection, and (5) ground-based ash sensors and sampling. Explosive eruptions can be detected by a variety of geophysical monitoring, including infrasound (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–C, 11 p., https://doi.org/10.3133/sir20245062c.\">chapter C, this volume; Lyons and others, 2024) and seismicity (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–B, 9 p., https://doi.org/10.3133/sir20245062b.\">chapter B, this volume; Thelen and others, 2024). However, those methods cannot quantify the altitude, ash content, and dispersal dynamics of resulting volcanic clouds. Ideally, all available sources of monitoring data are synthesized to develop a coherent understanding of eruptive activity. The guidance summarized here provides a framework for characterizing volcanic clouds in the atmosphere and tracking the evolution of explosive eruption dynamics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062J","usgsCitation":"Schneider, D.J., and Van Eaton, A.R., 2024, Special topic—Eruption plumes and clouds, chap. J of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–J, 12 p., https://doi.org/10.3133/sir20245062J.","productDescription":"iii, 12 p.","numberOfPages":"12","onlineOnly":"N","ipdsId":"IP-154938","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462415,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/j/covrthbj.jpg"},{"id":462416,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/j/sir20245062j.pdf","text":"Report","size":"14 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Submarine volcanoes produce much of the same seismicity and eruptive activity as subaerial volcanoes and can pose hazards to society. Although they can be monitored with similar techniques and methods as described in other chapters of this volume, their submerged location brings unique challenges. This chapter addresses these challenges and provides recommendations for monitoring volcanoes fully or partly in marine environments to meet the capabilities described in other chapters of this volume.
The United States and its territories host dozens of submarine volcanoes with most (around 60) in the Commonwealth of the Northern Mariana Islands. Approximately 20 of the Northern Mariana Islands submarine volcanoes are known to be hydrothermally active, and 10 have confirmed eruptions since the 1950s (for example, Baker and others, 2008; Tepp and others, 2019a). Nine of those volcanoes were considered by the National Volcanic Threat Assessment (Ewert and others, 2018) to have a combination of eruptive type and summit depth that poses a higher risk of hazardous eruptions, although only one was listed as a moderate (level 3) threat. Other notable submarine volcanoes of interest to the United States that have historically erupted are Axial Seamount off the Washington State coast, Kamaʻehuakanaloa in Hawaiʻi, and Vailuluʻu seamount in American Samoa. All of these, however, have a low risk of hazards because of their depth (greater than 600 meters below sea level) and eruptive type and so are not included in the National Volcanic Threat Assessment. In addition to submarine volcanoes, the submerged flanks of island volcanoes can also be a source of hazardous submarine eruptions—for example, the 1877 eruption of Mauna Loa, Hawai‘i, in Kealakekua Bay (Wanless and others, 2006).
The most notable submarine eruption in recent times was the 2022 eruption of Hunga Tonga–Hunga Haʻapai in Tonga, which was one of the largest eruptions on Earth in the past 100 years. It created a massive volcanic plume, unprecedented shock waves, and far-reaching tsunami (Lynett and others, 2022). Other recent submarine eruptions in the Pacific Ocean Basin have produced subaerial plumes that reached aircraft heights (Carey and others, 2014) and large pumice rafts that can affect marine traffic and harbors (for example, Jutzeler and others, 2014; Kornei, 2019). These examples illustrate the potential hazards of major submarine eruptions. Yet, submarine volcanoes are largely unmonitored, and many eruptions occur that are unnoticed or only identified hours or days afterward.
Within U.S. territory, submarine volcanoes in the Northern Mariana Islands have been known to produce eruptive activity that can affect society. Reports from fishermen and other marine vessels in the Northern Mariana Islands have noted underwater explosions, sea-surface discoloration, and bubbling water, all of which are known to be signs of submarine volcanic activity. South Sarigan seamount, located about 160 kilometers (km) north of Saipan, erupted in 2010 from greater than 150 meters below the sea surface, resulting in a gas and ash plume that reached more than 11.9 km into the atmosphere (for example, Searcy, 2013; Embley and others, 2014), high enough to affect international air traffic. Precursory and co-eruptive seismicity was detected on the regional Northern Mariana Islands seismic network (Searcy, 2013) and on global monitoring instruments (Green and others, 2013).
Monitoring of submarine volcanoes is best accomplished with marine-based instrumentation, which is also useful for monitoring small island volcanoes that may not have the land area necessary for comprehensive subaerial monitoring. The primary marine-based instrumentation used for submarine volcanoes includes ocean-bottom pressure sensors to assess sea-floor deformation, ocean-bottom seismometers (OBSs) to detect seismicity, and both moored and ocean-bottom hydrophones to detect submarine explosions. Other sensors offer important monitoring data, such as turbidity, temperature, and chemistry of hydrothermal emissions. Marine-based instruments are typically deployed in campaign-style networks with no real-time telemetry owing to cost considerations and technical limitations. However, when necessary, marine instruments can be operated in real time using cables to transmit data to land-based facilities; other technologies for this purpose are in use or in development, such as acoustic transmission from the instrument to a moored buoy (Matsumoto and others, 2016) and a winch-based system with a satellite antenna that is part of the instrument mooring (Matsumoto and others, 2019). Emerging technologies for marine-based monitoring may be considered as part of a future monitoring plan. These technologies include ocean gliders and floats with on-board hydrophones that have been used to record earthquakes and submarine eruptions (for example, Matsumoto and others, 2013; Sukhovich and others, 2015) and fiber-optic cables that have been used as strainmeters to detect earthquakes (for example, Marra and others, 2018; Lindsey and others, 2019). Land-based instruments and satellites can also provide some capability for monitoring submarine volcanoes, but they provide more limited observations than marine-based instrumentation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062I","usgsCitation":"Tepp, G., 2024, Monitoring marine eruptions, chap. I of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–I, 7 p., https://doi.org/10.3133/sir20245062I.","productDescription":"iii, 7 p.","numberOfPages":"7","onlineOnly":"N","ipdsId":"IP-149126","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462443,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/i/covrthbi.jpg"},{"id":462444,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/i/sir20245062i.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Volcanic unrest can trigger appreciable change to surface waters such as streams, springs, and volcanic lakes. Magma degassing produces gases and soluble salts that are absorbed into groundwater that feeds streams and lakes. As magma ascends, the amount of heat and degassing will increase, and so will any related geochemical and thermal signal. Subsurface magma movement can cause pressurization that alters hydrostatic head and may induce groundwater discharge. Fluid-pressure changes have been linked to distal volcano-tectonic earthquakes (White and McCausland, 2016; Coulon and others, 2017) and phreatic eruptions (for example, Yamaoka and others, 2016). Clearly, changes in groundwater and surface waters are both indicators of unrest and clues to how and where magma is rising toward the surface. Where possible, it is prudent to incorporate real-time hydrologic data into multiparameter monitoring of restless volcanoes. Hydrologic dynamics can also be tracked by changes in groundwater levels that are commonly measured in shallow boreholes (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. \">chapter K, this volume, on boreholes; Hurwitz and Lowenstern, 2024).
Although inferred to be common, relatively few volcano-hydrology anomalies are well documented, and many are essentially anecdotal (Newhall and others, 2001), reflecting the fact that high-resolution time series remain rare. Extreme examples include the 2008 eruption of Nevado del Huila, Colombia, where relatively minor phreatomagmatic eruptions were accompanied by expulsion of as much as 300 million cubic meters of groundwater from fissures high on the volcano (Worni and others, 2011), generating large lahars. Substantial decreases in flow rate from springs about 8 kilometers from the summit of Mayon Volcano, Philippines, have been noted before most eruptions in the 20th century (Newhall and others, 2001). Stream monitoring at Redoubt Volcano in 2009 allowed Werner and others (2012) to recognize that groundwater was unable to absorb (or scrub) the high flux of volcanic gas and that a high CO2/SO2 precursor signal had been evident for 5 months prior to the eruption. A key to better interpreting hydrologic anomalies—or even identifying them—is therefore obtaining adequate baseline data.
Most hydrologic monitoring at U.S. volcanoes has been accomplished by intermittent sampling surveys with annual or less frequent sampling (for example, https://hotspringchem.wr.usgs.gov/index.php). More frequent sampling, however, generally is needed to establish reliable baselines. A recent hydrologic and hydrothermal monitoring experiment at 25 sites and 10 of the 12 level 4 (very high threat) volcanoes in the U.S. portion of the Cascade Range demonstrated that there is sufficient temporal variability in hydrothermal fluxes, even during quiescent periods, that one-time measurements will commonly have limited interpretive value (Crankshaw and others, 2018). Thus, surveys are best augmented with data from streamgages (for example, Evans and others, 2004; Bergfeld and others, 2008). Streamflow (water discharge) data allow measured temperature and specific conductance to be converted to heat and solute mass fluxes, which could be insightful parameters for detecting anomalous activity (McCleskey and others, 2012). At the Yellowstone Caldera, long-term monitoring of river solutes has allowed calculation of the chloride flux, a proxy for heat discharge (Hurwitz and others, 2007; McCleskey and others, 2016) from the subsurface magma. This is readily accomplished because data from streamgages are continuously recorded and archived by the U.S. Geological Survey (USGS) National Water Information System (NWIS) (USGS, 2024).
Similar studies on stratovolcanoes or shield volcanoes would be scientifically useful, and yet are logistically challenging, requiring streamgages on numerous radial drainages complemented by either frequent manual sampling or numerous deployments of equipment to measure water temperature and specific conductance as a proxy for water chemistry. Another challenge is that some volcanic areas, especially shield volcanoes, are characterized by near-surface porous rocks and soils, such that surface streams are rare and replaced by distant, dilute large-volume springs with only a trace of any original volcanically sourced water (Manga, 2001; Hurwitz and others, 2021).
Volcanic lakes are worthy of special attention for monitoring efforts, as their temperature and composition can provide evidence of increased flux of volatile-rich fluids from below. Quantifying changes in volatile and heat release from magma can be simpler in lakes than for volcanoes with radial drainages and no major lakes. Moreover, volcanic lakes pose a range of hazards themselves, including phreatomagmatic eruptions, debris flows, flank collapse, tsunamis, and toxic gas release (Mastin and Witter, 2000; Delmelle and others, 2015; Manville, 2015; Rouwet and others, 2015)—hazards that have historically been responsible for substantial loss of life at many volcanoes worldwide (Manville, 2015). Catastrophic CO2 release at Lake Nyos, Cameroon, in 1986 suffocated about 1,750 people and about 3,500 livestock and was probably triggered by a large landslide into the gas-saturated lake (Kling and others, 1987; Evans and others, 1993). Gas-charged springs in Soda Bay within Clear Lake (California) have caused almost a dozen deaths to bathers in the past hundred years (ABC News, 2000). A 2005 example of lake overturn and abundant gas release was documented at Mount Chiginagak in Alaska (Schaefer and others, 2008) but did not result in any human casualties. Although thermally stratified lakes, which promote trapping of exsolved magmatic gas, tend to develop in tropical regions, the phenomenon can also arise where salinity creates meromixis (a condition in which a lake does not mix completely), as occurs in Mono Lake, California (Jellison and Melack, 1993; Jellison and others, 1998).
If magma erupts or flows into a lake, the interaction between hot magma and cold water can be explosive (Mastin and others, 2004; Zimanowski and others, 2015) and substantially expand the area affected by the eruption. Another hazard is the breaching of crater rims by landslides triggered by volcanic and (or) seismic activity. Under some circumstances, substantial volumes of water can be displaced, leading to large floods and lahars. Late Holocene lake flooding from Aniakchak Crater in the Alaska Peninsula (Waythomas, 2022) and from Paulina Lake in Newberry Crater, Oregon (Chitwood and Jensen, 2000), caused by the failure of outlet sills, testify to the substantial hazards at lake-filled calderas.
Several volcanic systems in the United States host lakes known to receive heat and gas from underlying magma. These lakes vary widely in area, depth, and chemical composition. Lakes are present at level 4 volcanoes, including Crater Lake and Newberry Volcano in Oregon; Yellowstone Caldera in Wyoming; Long Valley Caldera, Clear Lake volcanic field, Medicine Lake, and Salton Buttes in California; and Aniakchak Crater, Mount Katmai, Fisher Caldera, Mount Okmok, and Kaguyak Crater, among others, in Alaska. A water lake was present in Halemaʻumaʻu, the crater of Kīlauea, Hawai‘i (fig. F1), from October 2019 to December 2020. Level 3 volcanoes with lakes include Mono Lake volcanic field (Calif.), Mount Bachelor (Ore.), Ukinrek Maars and Mount Chiginagak (Alaska), and Soda Lake (Nevada). In addition, there are lakes at many levels 1 and 2 volcanoes. In the United States, there are no strongly acidic lakes that receive abundant input of magmatic gas, such as those found at Mount Ruapehu (New Zealand), Ijen and Kelud (Indonesia), and Poás (Costa Rica). Nevertheless, many contain fluids that provide clues to magmatic processes below.
Since publication of a previous report on recommended instrumentation for volcano monitoring (Moran and others, 2008), continuous hydrologic monitoring has become increasingly feasible. However, changes in water pressure, temperature, and chemistry remain, in general, poorly studied phenomena at volcanoes (Sparks, 2003; National Academies of Sciences, Engineering, and Medicine, 2017). Recent efforts by the USGS have included the temporary study of Cascade Range volcanoes, which included frequent (15 minute to hourly) temporal sampling of temperature, depth, and conductivity (Crankshaw and others, 2018; Ingebritsen and Evans, 2019). At Yellowstone Caldera, many streamgages have now added thermistors and specific conductance sensors, allowing estimation of time-dependent chloride flux as a proxy for variations in subsurface heat flux (McCleskey and others, 2012, 2016). Efforts to better understand lakes have also accelerated, with bathymetric mapping and sampling carried out at several locations in the United States. Especially thorough work was done at Yellowstone Lake thanks to the Hydrothermal Dynamics of Yellowstone Lake (HD-YLAKE, https://hdylake.org) project, funded primarily by the National Science Foundation. In addition to geophysical surveys and recovery of cores and other samples, HD-YLAKE investigations included remotely operated vehicle (ROV) investigations of hydrothermal vents on the lake floor (fig. F2). Data collected by the ROV provided a better understanding of the thermal and chemical influx from lake-bottom hydrothermal systems (Sohn and others, 2017).
In this chapter, we focus on detecting changes in the chemistry, temperature, discharge, or water levels of streams, springs, and lakes that can be caused by seismicity, volumetric strains, or increases in gas flux associated with ascending magma. There is unavoidable overlap with other chapters of this report. Samples of water and gas can also be obtained in boreholes (of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. \">chapter K, this volume; Hurwitz and Lowenstern, 2024), both shallow and deep. Gas monitoring (of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–E, 11 p., https://doi.org/10.3133/sir20245062e.\">chapter E, this volume; Lewicki and others, 2024) relies in part on samples from springs and wells, particularly where measurable gas plumes are absent. Water acts as a trigger and lubricant for landslides and sediment-rich floods, and so hydrology has obvious relevance for lahar monitoring, as discussed in of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–H, 6 p., https://doi.org/10.3133/sir20245062h. \">chapter H (this volume; Thelen and others, 2024). Shared situational awareness among scientists engaged in geophysical, gas, and hydrologic monitoring will improve overall understanding of the volcanic hazard.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062F","usgsCitation":"Ingebritsen, S.E., and Hurwitz, S., 2024, Streams, springs, and volcanic lakes for volcano monitoring, chap. F of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–F, 9 p., https://doi.org/10.3133/sir20245062F.","productDescription":"iii, 9 p.","numberOfPages":"9","onlineOnly":"N","ipdsId":"IP-149695","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462449,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/f/covrthbf.jpg"},{"id":462450,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/f/sir20245062f.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
As magma rises through the crust, decreasing pressure conditions allow volatiles to exsolve from the magma. These volatiles then migrate upward through the crust, where they can be stored at shallower levels or escape to the atmosphere. Rising magma also heats rock masses beneath volcanic centers, causing water in shallow aquifers and hydrothermal systems to boil and release additional gases and steam (see chapter F, this volume; Ingebritsen and Hurwitz, 2024). The chemistry and quantity of gases that reach the surface during periods of quiescence or volcanic unrest can reveal that gas-rich magma is ascending, crystallizing, or alternatively stalling, with important implications for volcanic hazard (for example, Sutton and others, 1992; Aiuppa and others, 2007, 2021; Werner and others, 2009, 2011, 2012; Moretti and others, 2013; de Moor and others, 2016; Lewicki and others, 2019; Edmonds and others, 2022; Kern and others, 2022; Kunrat and others, 2022).
Most volcanoes in Alaska and the western United States are characterized by weak degassing, with one or more low-temperature fumaroles (typically near the local boiling temperature of water) and connect to a deeper and sometimes extensive hydrothermal system (for example, McGee and others, 2001; Symonds and others, 2003a, b). Hydrothermal systems will affect the chemistry of rising gases exsolved from deeper magma (Symonds and others, 2001), including sulfur dioxide (SO2), hydrogen chloride (HCl), and water vapor (for example, Doukas and Gerlach, 1995; Gerlach and others, 1998, 2008; Symonds and others, 2001; Werner and others, 2013). As an example, depending on factors such as temperature, pressure, and oxidation state, rising SO2 will react with groundwater to form hydrogen sulfide (H2S) gas, dissolved sulfate (SO42−), or elemental sulfur (Christenson, 2000; Symonds and others, 2001; Werner and others, 2008). The reaction and dissolution of SO2 into shallow groundwater is commonly referred to as scrubbing, and can reduce the likelihood that ascending, degassing magma can be detected. Carbon dioxide, however, in addition to exsolving from magma early in the ascent process, is not easily removed by hydrothermal fluids (Lowenstern, 2001). As scrubbing and other processes take place, the SO2/H2S, CO2/SO2, and CO2/H2S ratios may change. High rates of SO2 emission indicate that magma has moved to relatively shallow levels in the volcano and that the system has heated up enough to establish dry pathways from depth to the surface. Monitoring multiple gas species and the total output of those species is thereby useful for volcano monitoring during both periods of quiescence, to establish background degassing conditions, and during unrest, when gas geochemistry and emission rates can provide information on changing conditions, such as magma ascent.
To provide context for multidisciplinary volcano forecasts, we focus on the following two key required capabilities: (1) characterizing baseline geochemistry and gas discharge from volcanoes and volcanic regions and (2) monitoring changes in gas geochemistry and discharge to inform forecasts of volcanic eruptions and their effects. Sufficient baseline data must be collected to identify and interpret anomalous degassing associated with volcanic unrest (for example, Sorey and others, 1998; Rouwet and others, 2014). Differences in volcano type, baseline degassing rates, local hydrology, and geography (for example, high versus low latitude) will result in a different baseline for each volcano. Volcanoes of any threat level that exhibit one or more degassing phenomena would ideally be monitored by techniques needed to establish baseline degassing data, with the sampling frequency of baseline data dictated by the threat level (table E1). Additional monitoring techniques become necessary during periods of unrest.
In general, three of the most important techniques for gas monitoring are (1) direct sampling of fumarole, spring, and soil gases for laboratory geochemical measurements, (2) measurements of the chemical composition of the volcanic plume and emission rates of major gas species (for example, H2O, CO2, SO2, and H2S) by satellite, airborne, or ground-based techniques, and (3) measurements of diffuse emissions of CO2 and other gases through soils. Various methods and instruments may be useful both for baseline studies and during unrest.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062E","usgsCitation":"Lewicki, J.L., Kern, C., Kelly, P.J., Nadeau, P.A., Elias, T., and Clor, L.E., 2024, Volcanic gas monitoring, chap. E of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–E, 11 p., https://doi.org/10.3133/sir20245062E.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"N","ipdsId":"IP-150252","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462452,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/e/sir20245062e.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":462451,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/e/covrthbe.jpg"}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
When magma accumulates or migrates, it can cause pressurization and related ground deformation. Characterization of surface deformation provides important constraints on the potential for future volcanic activity, especially in combination with seismic activity, gas emissions, and other indicators. A wide variety of techniques and instrument types have been applied to the study of ground deformation at volcanoes (sidebar, p. 2; Dzurisin, 2000, 2003, 2007). Geodetic instruments include continuously recording Global Navigation Satellite System (GNSS; of which the United States’ Global Positioning System is one example) stations (fig. D1), borehole tiltmeters, and interferometric synthetic aperture radar (InSAR) measurements (from satellites, occupied and unoccupied aircraft systems, and ground-based sensors). Additional geodetic measurements like continuous- and survey-mode gravity (fig. D2) can contribute substantially to interpreting these data. Borehole strainmeters (see chapter K, this volume, by Hurwitz and Lowenstern, 2024) also have outstanding utility for monitoring deformation, although because of cost and permitting challenges, we do not include them as part of standard volcano monitoring networks for U.S. volcanoes. Still other techniques like light detection and ranging (lidar), structure from motion, and optical satellite data can be used to derive gross topographic changes, which can be used to map volcanic deposits, infer eruption rates, and gain insights into the source processes associated with eruptive activity (see chapter G, this volume, on tracking surface changes caused by volcanic activity; Orr and others, 2024).
Experience has shown that no single geodetic monitoring technique is adequate to detect and track the entire range of ground-motion patterns that occur at volcanoes, primarily because of the temporal and spatial diversity of volcano deformation (fig. D3). Similarly, the magnitude of surface deformation varies widely. Geodetic monitoring strategies should therefore include multiple techniques and instrument types to cover a wide range of spatial and temporal scales.
In identifying recommendations for geodetic instrumentation for volcano monitoring networks, we attempted to maximize the diversity of instrument types to measure the full range of deformation signals and minimize their expense and number; thus, we do not include several well-known deformation-monitoring techniques in our recommendations. Extensometers, for example, measure strains over distances of a few meters and have an excellent record of success in detecting changes in preeruptive localized ground motion across existing cracks, including at Mount St. Helens, Washington (Iwatsubo and others, 1992), and Piton de la Fournaise, Réunion Island (Peltier and others, 2006). Despite being relatively inexpensive, extensometers are best used primarily when localized ground displacements (for example, ground cracks) need to be tracked, and are not necessary at all volcanoes.
In considering volcano deformation monitoring strategies, two complicating factors are deserving of special attention. First, not all deformation is driven by subsurface magmatic activity—for example, at many large stratovolcanoes (for example, Mount Rainier), flank collapses and landslides are significant geologic hazards (Reid and others, 2001) that may occur even in the absence of magmatic activity. Monitoring the stability of volcanoes is thus another critical application of geodetic monitoring networks to inform hazard assessment. One of the most famous examples of edifice instability is the large flank collapse that initiated the May 18, 1980, eruption of Mount St. Helens. Deformation monitoring had detected a bulge on the north flank of the mountain in April 1980 that was expanding by several meters per day (Lipman and others, 1981). Given that flank collapses can happen at any time during a period of volcanic unrest (or even outside a period of unrest), the capability to assess edifice stability is critical.
Second, although volcanoes are commonly treated as idealized structures that erupt from single points, like centralvent stratovolcanoes, many are characterized by long rift zones from which eruptions may originate, and distributed volcanic fields are characterized by broadly spaced vents. For example, linear dikes are common at Kīlauea, Mauna Loa, and between Mount Shasta and Medicine Lake in California. At Kīlauea, one of these linear dikes emerged more than 40 kilometers (km) away from the summit of the volcano during the lower East Rift Zone eruption in 2018. Other volcanic fields, like Lassen volcanic center, California, or the San Francisco Volcanic Field, Arizona, have many small vents spread over a wide area. Although the instrumentation guidelines presented in this chapter remain phrased for central-vent volcanoes, they should be modified as needed in the context of the eruptive characteristics of each individual volcanic system.
Spatial analysis of geodetic network coverage could help to ensure adequate instrumentation in areas where volcanism can occur over a broad area as opposed to a central vent. As an example, consider the adjacent volcanoes Mount Shasta and Medicine Lake. If station locations are chosen based only on the distance from the centers of the volcanoes, then any geodetic anomalies between the two volcanoes—an area of potential volcanism as indicated by the presence of volcanic features—may remain undetected by ground-based instrumentation. The spatial analysis is accomplished via a grid of pressure point sources (Mogi, 1958) evenly distributed across the map area, at a depth of 5 km in this example (fig. D4). Each source is inflated until predicted deformations exceed the GNSS white noise uncertainty estimates at one site (Langbein, 2017; Murray and Svarc, 2017). This volume of detectable magma provides a measure of the quality of the coverage (fig. D4). The results indicate that, as of 2022, there is a large area between Mount Shasta and Medicine Lake volcano with existing mapped dikes in which a substantial amount of magma could intrude without being detected geodetically. Applying this style of analysis to individual volcanic systems can provide a guide for designing network geometry given the expected locations of future eruptions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062D","usgsCitation":"Montgomery-Brown, E.K., Anderson, K.R., Johanson, I.A., Poland, M.P., and Flinders, A.F., 2024, Ground deformation and gravity for volcano monitoring, chap. D of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–D, 11 p., https://doi.org/10.3133/sir20245062D.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"N","ipdsId":"IP-152739","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462454,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/d/sir20245062d.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":462453,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/d/covrthbd.jpg"}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Volcanic eruptions produce acoustic waves when volcanic gases and hot material rapidly expand in the atmosphere. Volcanic activity can produce acoustic signals with a wide range of frequencies, from very long period (>10 seconds) to audible (>20 hertz [Hz]), but the most energetic band is typically in the infrasound from 0.5 to 20 Hz. Studies of volcanic infrasound and the deployment of infrasound for volcano monitoring have increased rapidly in the past two decades as sensors have improved and as analytical tools have become more widely available. Improved sensors and tools have led to a growing diversity of eruptive activity being recorded and characterized, from Hawaiian to Plinian eruption styles at scales from local to global (Johnson and Ripepe, 2011; Fee and Matoza, 2013). Infrasound sensors on volcanoes are most commonly deployed locally with seismic stations, and the combination of co-located seismic and infrasound is more useful for characterizing unrest and detecting changes in activity than either data stream alone (for example, Lyons and others, 2016; Fee and others, 2017a; Matoza and others, 2018). At local (<15 kilometers [km]) to regional (15–250 km) distances from volcanoes, arrays of infrasound sensors are commonly deployed to detect coherent signals, constrain the direction to the source, and provide information on eruption dynamics; thus, infrasound is well suited to regional monitoring of volcanoes when local sensor networks are not feasible. A common usage of infrasound data in an observatory is to provide rapid confirmation that an explosion has occurred (for example, Coombs and others, 2018), although near-real-time eruption intensity quantification is also possible (Fee and others, 2010a; Ripepe and others, 2018; fig. C1). Infrasound is well suited to this task because it is not affected by clouds or precipitation and can propagate long distances with little attenuation. However, wind and ocean noise also produce infrasound, and spatiotemporal variability in the atmosphere can affect the propagation of infrasound, so care must be taken when deploying, analyzing, and interpreting the data. In addition to detecting and monitoring explosive activity, investigations of infrasound records from eruptions help constrain source processes, which in turn enhance syneruptive forecasting capabilities (for example, Fee and others, 2017b; Lyons and others, 2019).
The following is a description of the capabilities recommended for real-time monitoring of eruptive phenomena with infrasound. Infrasound is also beginning to be used for tracking hazardous surface flows that occur on volcanoes, including pyroclastic density currents (Ripepe and others, 2010), lahars (Johnson and Palma, 2015), debris flows (Marchetti and others, 2019), snow avalanches (Havens and others, 2014), and lava flows (Patrick and others, 2019). Please refer to the chapter on lahars (this volume; Thelen and others, 2024a) for more information on this application.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062C","usgsCitation":"Lyons, J.J., Fee, D., Thelen, W.A., Iezzi, A.M., and Wech, A.G., 2024, Infrasound for volcano monitoring, chap. C of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–C, 11 p., https://doi.org/10.3133/sir20245062C.","productDescription":"iii, 11 p.","numberOfPages":"11","onlineOnly":"N","ipdsId":"IP-150991","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462455,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/c/covrthbc.jpg"},{"id":462456,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/c/sir20245062c.pdf","text":"Report","size":"14 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Translocating amphibians to alternative, suitable habitat is a climate adaptation strategy aimed at minimizing the risk of extinction due to projected global warming and drying. Projected conditions could undermine their physiological performance, and thus survival and reproduction. Translocations minimize risks of extinction by increasing spatial redundancy across climate-resilient habitats, particularly for dispersal-limited species. However, outcomes of amphibian translocation attempts are poorly documented, and their effectiveness remains unclear. We released and tracked 34 Eleutherodactylus coqui to determine early postrelease survival of a control (nontranslocated) group (n = 14) and experimental (translocated) group (n = 20) moved 0.8 km from their capture location in west-central Puerto Rico in 2021. We defined “initial” as the first 17 d postrelease, a period during which we hypothesized that experimental individuals would have lower survival rates because they transitioned from known-familiar to novel-unfamiliar habitat. We found no evidence in the data to support our hypothesis. Daily survival rates were better explained by a model with no group effect but negatively influenced by in situ temperature. However, the effect of in situ temperature (proxy of operative temperature) was weak (95% confidence intervals overlapped 0). After 17 d, all but one of the recaptured frogs lost weight for a combined weight loss of 0.28 ± 0.13 g. However, weight loss was significantly higher in translocated frogs (0.81 ± 0.33 g). Average daily movements did not hinder survival even though experimental individuals traveled farther (～ eight times) than control ones. Our findings suggested that managed translocations have the potential to become a useful conservation tool, not an additive source of mortality. We outline challenges that remain before translocations of Eleutherodactylus species can be broadly applied.
","language":"English","publisher":"BioOne","doi":"10.1655/Herpetologica-D-24-00001.1","usgsCitation":"Chaparro, R., Rivera-Burgos, A., Eaton, M.J., Terando, A., Martinez, E., and Collazo, J.A., 2024, Postrelease survival of Eleutherodactylus coqui: Advancing managed translocations as an adaptive tool for climate-vulnerable anurans: Herpetologica, v. 80, no. 4, p. 314-320, https://doi.org/10.1655/Herpetologica-D-24-00001.1.","productDescription":"7 p.","startPage":"314","endPage":"320","ipdsId":"IP-157773","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487927,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1655/herpetologica-d-24-00001.1","text":"Publisher Index Page"},{"id":485331,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.02528621431577,\n 18.25\n ],\n [\n -67.02528621431577,\n 18.086320441291832\n ],\n [\n -66.90560779954839,\n 18.086320441291832\n ],\n [\n -66.90560779954839,\n 18.25\n ],\n [\n -67.02528621431577,\n 18.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"80","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Chaparro, Rafael","contributorId":354406,"corporation":false,"usgs":false,"family":"Chaparro","given":"Rafael","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":935586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rivera-Burgos, Ana C.","contributorId":354407,"corporation":false,"usgs":false,"family":"Rivera-Burgos","given":"Ana C.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":935587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eaton, Mitchell J. 0000-0001-7324-6333","orcid":"https://orcid.org/0000-0001-7324-6333","contributorId":213526,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":935588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":935589,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martinez, Eloy","contributorId":354408,"corporation":false,"usgs":false,"family":"Martinez","given":"Eloy","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":935590,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":935591,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259395,"text":"70259395 - 2024 - Arctic fishes reveal patterns in radiocarbon age across habitats and with recent climate change","interactions":[],"lastModifiedDate":"2024-11-22T16:13:19.150412","indexId":"70259395","displayToPublicDate":"2024-10-04T06:30:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5456,"text":"Limnology and Oceanography Letters","active":true,"publicationSubtype":{"id":10}},"title":"Arctic fishes reveal patterns in radiocarbon age across habitats and with recent climate change","docAbstract":"Climate change alters the sources and age of carbon in Arctic food webs by fostering the release of older carbon from degrading permafrost. Radiocarbon (14C) traces carbon sources and age, but data before rapid warming are rare and limit assessments over time. We capitalized on 14C data collected ~ 40 years ago that used fish as natural samplers by resampling the same species today. Among resampled fish, those using freshwater food webs had the oldest 14C ages (> 1000 yr BP), while those using marine food webs had the youngest 14C ages (near modern). One migratory species encompassed the entire range of 14C ages because juveniles fed in freshwater streams and adults fed in offshore marine habitats. Over ~ 40 yr, average 14C ages of freshwater and marine feeding fish shifted closer to atmospheric values, suggesting a potential influence from “greening of the Arctic.”
Data from wadeable streams collected by monitoring programs are used to assess watershed condition status and trends. Federally managed programs collect a suite of similar habitat measurements using compatible methods and produce individual program datasets for their prescribed geographic and temporal range. We identified four programs that produce similar data: the Bureau of Land Management Assessment, Inventory, and Monitoring lotic division, the U.S. Environmental Protection Agency National Aquatic Resource Surveys National Rivers and Streams Assessment survey section, the Federal interagency Aquatic and Riparian Effectiveness Monitoring Program, and the PacFish/InFish Biological Opinion Monitoring Program. Their datasets answer agency-specific management questions and fulfill reporting requirements, but the datasets are not released in full, or at all, and in some cases, there was no method to integrate data from the four programs to provide data at a larger spatial scale.
The Pacific Northwest Aquatic Monitoring Partnership (PNAMP) led a working group of experts from the four monitoring programs to determine data compatibility, develop a Stream Habitat Metrics Integration (SHMI) data exchange standard, and integrate compatible wadeable stream data. The resulting SHMI data exchange standard contains a data mapping file used to transform data from the source program data to a conformed format based on a controlled vocabulary. After extensive discussions assessing and comparing program collection and analyses methods, the working group found 26 stream habitat metrics to be sufficiently comparable to be integrated into a meaningful dataset. Furthermore, a subset of PIBO MP data previously available only by request and AREMP data available only as a proprietary ESRI ArcGIS geodatabase were made publicly available in non-proprietary formats via the integrated SHMI dataset.
A selection of data from the four programs determined to be compatible among 14 datasets were filtered, transformed, standardized, and combined using R code to create the integrated SHMI dataset containing about 12,000 locations, 19,000 events, and 200,000 measurements from 2000 to 2022.
This report describes the SHMI data exchange standard and its development, the metric compatibility assessment, and the data integration process, so that others may reuse the SHMI data exchange standard and its components as well as the data integration processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm16B2","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency, Bureau of Land Management, and the U.S. Forest Service","usgsCitation":"Scully, R.A., Dlabola, E.K., Bayer, J.M., Heaston, E., Courtwright, J., Snyder, M.N., Hockman-Wert, D., Saunders, W.C., Blocksom, K.A., Hirsch, C., and Miller, S.W., 2024, A data exchange standard for wadeable stream habitat monitoring data: U.S. Geological Survey Techniques and Methods, book 16, chap. B2, 28 p., https://doi.org/10.3133/tm16B2.","productDescription":"Report: vii, 28 p.; Data Release; Software Release","onlineOnly":"Y","ipdsId":"IP-139478","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":497946,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117643.htm","linkFileType":{"id":5,"text":"html"}},{"id":462494,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/16/b2/images"},{"id":462493,"rank":5,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9KON2PK","text":"USGS software release","description":"USGS software release","linkHelpText":"- SHMI-DES"},{"id":462492,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J3P7SN","text":"USGS data release","description":"USGS data release","linkHelpText":"Wadeable stream habitat data integrated from multiple monitoring programs for the US from 2000–2022"},{"id":462491,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm16B2/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"TM 16-B2"},{"id":462490,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/16/b2/tm16b2.pdf","text":"Report","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 16-B2"},{"id":462495,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/16/b2/tm16b2.XML"},{"id":462489,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/16/b2/tm16b2.jpg"}],"contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th Street, Suite 400
Corvallis, Oregon 97330
The dust cycle facilitates the exchange of particles among Earth's major systems, enabling dust to traverse ecosystems, cross geographic boundaries, and even move uphill against the natural flow of gravity. Dust in the atmosphere is composed of a complex and ever-changing mixture that reflects the evolving human footprint on the landscape. The emission, transport, and deposition of dust interacts with and connects Critical Zone processes at all spatial and temporal scales. Landscape properties, land use, and climatic factors influence the wind erosion of soil and nutrient loss, which alters the long-term ecological dynamics at erosional locations. Once in the atmosphere, dust particles influence the amount of solar radiation reaching Earth, and interact with longwave (terrestrial) radiation, with cascading effects on the climate system. Finally, the wet and dry deposition of particles influences ecosystem structure, composition, and function over both short and long-term scales. Tracking dust particles from source to sink relies on monitoring and measurement of the geochemical composition and size distribution of the particles, space-borne and ground-based remote sensing, and dust modeling. Dust is linked to human systems via land use and policies that contribute to dust emissions and the health-related consequences of particulate loads and composition. Despite the significant influence dust has in shaping coupled natural-human systems, it has not been considered a key component of the Critical Zone. Here, we demonstrate that dust particles should be included as a key component of the Critical Zone by outlining how dust interacts with and shapes Earth System processes from generation, through transport, to deposition. We synthesize current understanding from global research and identify critical data and knowledge gaps while showcasing case studies from North America.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2024.104942","usgsCitation":"Brahney, J., Heindel, R.C., Gill, T.E., Carling, G., Gonzalez-Olalla, J.M., Hand, J.L., Mallia, D.V., Munroe, J.S., Perry, K., Putman, A.L., Skiles, S.M., Adams, B.R., Aanderud, Z.T., Aarons, S.M., Aguirre, D., Ardon-Dryer, K., Blakowski, M.A., Creamean, J.M., Fernandez, D.P., Foroutan, H., Gaston, C.J., Hahnenberger, M., Hoch, S.W., Jones, D.K., Kelly, K.E., Lang, O.I., Lemonte, J., Reynolds, R.L., Singh, R.P., Sweeney, M., and Merrill, T.K., 2024, Dust in the Critical Zone: North American case studies: Earth-Science Reviews, v. 258, 104942, 34 p., https://doi.org/10.1016/j.earscirev.2024.104942.","productDescription":"104942, 34 p.","ipdsId":"IP-165083","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":486931,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index 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0000-0002-4396-4438","orcid":"https://orcid.org/0000-0002-4396-4438","contributorId":296383,"corporation":false,"usgs":false,"family":"Sweeney","given":"Mark","email":"","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":917857,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Merrill, Thorn K.","contributorId":345920,"corporation":false,"usgs":false,"family":"Merrill","given":"Thorn","email":"","middleInitial":"K.","affiliations":[{"id":82741,"text":"Department of Atmospheric Sciences, University of Utah, Salt Lake City UT 84112","active":true,"usgs":false}],"preferred":false,"id":917858,"contributorType":{"id":1,"text":"Authors"},"rank":31}]}} ,{"id":70267212,"text":"70267212 - 2024 - Boundary spanning increases knowledge and action on invasive species in a changing climate","interactions":[],"lastModifiedDate":"2025-05-16T16:08:58.623772","indexId":"70267212","displayToPublicDate":"2024-10-01T11:04:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9977,"text":"Ecological Solutions and Evidence","active":true,"publicationSubtype":{"id":10}},"title":"Boundary spanning increases knowledge and action on invasive species in a changing climate","docAbstract":"Long‐term standardized monitoring programs are fundamental to assessing how fish populations respond to anthropogenic stressors. Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes that are associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark–recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the past two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually.
We investigated the feasibility of using two analytical methods, N‐mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River using the historical long‐term data set; these methods allow abundance to be estimated when a mark–recapture estimate cannot be obtained due to hydrologic conditions.
When compared with abundance estimates from mark–recapture methods, N‐mixture models most often resulted in negatively biased abundance estimates, whereas mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N‐mixture models produced negatively biased estimates when tested against true abundance values from simulated data sets. The bias in the N‐mixture model estimates was caused by poor model fit and variation in capture probability. The bias in the mean capture probability estimates was caused by heterogeneity in capture probability, likely caused by variable environmental conditions, which were not accounted for in the models.
N‐mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining the utility of long‐term data sets.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/nafm.11026","usgsCitation":"Briggs, M., Glassic, H.C., Guy, C.S., Opitz, S., Rotella, J., and Schmetterling, D., 2024, Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA: North American Journal of Fisheries Management, v. 44, no. 5, p. 947-961, https://doi.org/10.1002/nafm.11026.","productDescription":"15 p.","startPage":"947","endPage":"961","ipdsId":"IP-172896","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.11026","text":"Publisher Index Page"},{"id":486239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"upper Yellowstone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.27790695947738,\n 45.77750283140904\n ],\n [\n -111.27790695947738,\n 45.00210734009434\n ],\n [\n -110.23435146225052,\n 45.00210734009434\n ],\n [\n -110.23435146225052,\n 45.77750283140904\n ],\n [\n -111.27790695947738,\n 45.77750283140904\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Michelle A.","contributorId":355621,"corporation":false,"usgs":false,"family":"Briggs","given":"Michelle A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glassic, Hayley Corrine 0000-0001-6839-1026","orcid":"https://orcid.org/0000-0001-6839-1026","contributorId":305858,"corporation":false,"usgs":true,"family":"Glassic","given":"Hayley","email":"","middleInitial":"Corrine","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":937792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":937793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Opitz, Scott T.","contributorId":355622,"corporation":false,"usgs":false,"family":"Opitz","given":"Scott T.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":937794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rotella, Jay J.","contributorId":355623,"corporation":false,"usgs":false,"family":"Rotella","given":"Jay J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmetterling, David A.","contributorId":355624,"corporation":false,"usgs":false,"family":"Schmetterling","given":"David A.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":937796,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259320,"text":"70259320 - 2024 - Preliminary observations of the April 5th, 2024, Mw4.8 New Jersey earthquake","interactions":[],"lastModifiedDate":"2024-10-04T11:54:34.426806","indexId":"70259320","displayToPublicDate":"2024-10-01T06:52:32","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"Preliminary observations of the April 5th, 2024, Mw4.8 New Jersey earthquake","docAbstract":"On 5 April 2024, 10:23 a.m. local time, a moment magnitude 4.8 earthquake struck Tewksbury Township, New Jersey, about 65 km west of New York City. Millions of people from Virginia to Maine and beyond felt the ground shaking, resulting in the largest number (>180,000) of U.S. Geological Survey (USGS) “Did You Feel It?” reports of any earthquake. A team deployed by the Geotechnical Extreme Events Reconnaissance Association and the National Institute of Standards and Technology documented structural and nonstructural damage, including substantial damage to a historic masonry building in Lebanon, New Jersey. The USGS National Earthquake Information Center reported a focal depth of about 5 km, consistent with a lack of signal in Interferometric Synthetic Aperture Radar data. The focal mechanism solution is strike slip with a substantial thrust component. Neither mechanism’s nodal plane is parallel to the primary northeast trend of geologic discontinuities and mapped faults in the region, including the Ramapo fault. However, many of the relocated aftershocks, for which locations were augmented by temporary seismic deployments, form a cluster that parallels the general northeast trend of the faults. The aftershocks lie near the Tewksbury fault, north of the Ramapo fault.
Objective
Long-term standardized monitoring programs are fundamental to assessing how fish populations respond to anthropogenic stressors. Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark-recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the last two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually.
Methods
We investigated the feasibility of using two analytical methods, N-mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River; these methods allow abundance to be estimated when a mark-recapture estimate cannot be obtained due to hydrologic conditions.
Result
When compared to abundance estimates from mark-recapture methods, N-mixture models most often resulted in negatively biased abundance estimates while mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N-mixture models produced negatively biased estimates compared to true abundance values from simulated datasets. Bias in N-mixture model estimates was caused by poor model fit and variation in capture probability. Bias in mean capture probability estimates was caused by heterogeneity in capture probability that was not accounted for in the models.
Conclusion
N-mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining long-term datasets.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/nafm.11026","usgsCitation":"Briggs, M., Glassic, H.C., Guy, C.S., Opitz, S., Rotella, J., and Schmetterling, D., 2024, Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA: North American Journal of Fisheries Management, v. 44, no. 5, p. 947-961, https://doi.org/10.1002/nafm.11026.","productDescription":"15 p.","startPage":"947","endPage":"961","ipdsId":"IP-160733","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488197,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.11026","text":"Publisher Index Page"},{"id":485823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"upper Yellowstone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.25391301846597,\n 45.79233815794254\n ],\n [\n -111.25391301846597,\n 45.00638613042193\n ],\n [\n -110.01308523101437,\n 45.00638613042193\n ],\n [\n -110.01308523101437,\n 45.79233815794254\n ],\n [\n -111.25391301846597,\n 45.79233815794254\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Michelle A.","contributorId":354954,"corporation":false,"usgs":false,"family":"Briggs","given":"Michelle A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":936730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glassic, Hayley Corrine 0000-0001-6839-1026","orcid":"https://orcid.org/0000-0001-6839-1026","contributorId":305858,"corporation":false,"usgs":true,"family":"Glassic","given":"Hayley","email":"","middleInitial":"Corrine","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":936731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Opitz, Scott T.","contributorId":354955,"corporation":false,"usgs":false,"family":"Opitz","given":"Scott T.","affiliations":[{"id":61825,"text":"Montana Fish","active":true,"usgs":false}],"preferred":false,"id":936733,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rotella, Jay J.","contributorId":354956,"corporation":false,"usgs":false,"family":"Rotella","given":"Jay J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":936734,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmetterling, David A.","contributorId":354957,"corporation":false,"usgs":false,"family":"Schmetterling","given":"David A.","affiliations":[{"id":61825,"text":"Montana Fish","active":true,"usgs":false}],"preferred":false,"id":936735,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}