This remote sensing section is based on Nagler et al. (in preparation for the journal Hydrological Processes) and is a summary of the USGS preliminary findings to date.
This report documents the changes in green foliage density (greenness) as measured by satellite vegetation index (VI) data and corresponding evapotranspiration (ET) in the riparian corridor of the Colorado River delta associated with the Minutes 319 and 323 environmental water deliveries using time-series data from 2013 through 2018. The report focuses on what happened only within the riparian corridor’s seven reaches since the 2014 flows, and despite being a continuation of measuring greenness and ET after the 2017 end of Minute 319, this study continued the tracking of these two variables, greenness and ET, in these original riparian corridor focal areas. Two spatial scales are used here: (1) Landsat satellite imagery at 30 m pixels and (2) the EOS-1 satellite sensor the Moderate Resolution Imaging Spectrometer (MODIS) with a resolution of 250 m pixels. The focal period includes 2013 (prepulse flow) and the years 2014-2018, with a focus on imagery collected from the Summer growing seasons 2014 through 2018 (one-year, pre-pulse and several post-pulse years, respectively).
This report re-creates the 2013-2017 Landsat-based results from Jarchow et al. (2017a, b) by using the same region of interest (ROI). The report now provides revised and re-created results using all new imagery acquisition and processing techniques, as well as extraction code, created by the Vegetation Index and Phenology (VIP) Lab of the Biosystems Engineering Department of the University of Arizona (UofA). In 2018, methods employed by the VIP lab (and not ArcGIS) were used. ArcGIS was only used in the newly processed data to display the final difference maps. The entire spatial tile data from NASA was downloaded and processed at the VIP Lab using satellite imagery at two resolutions: 250 m MODIS and 30 m Landsat using three sensors, Landsat 5, Landsat 7 ETM+ and Landsat 8 Operational Land Imager (OLI), with added scenes for each year based on new clear atmosphere requirements. The VIP lab clipped the river boundary and seven riparian reaches from the previously existing ROI used in Jarchow et al. (2017 a, b) for the analyses done under Minute 319. The NASA image datasets for this riparian corridor ROI in seven reaches were re-processed to produce additional vegetation index (VI) information for years 2013 to 2018 for this report. At the same time, the report acquired and processed imagery from 2000- 2018 (data outside the scope of this report and data not shown here). The additional VIs (NDVI, scaled NDVI, EVI, EVI2) were analyzed so that new assessments of greenness and ET could be produced from the imagery datasets following methods in Nagler et al. (2013). These VI choices were based on previous performance comparisons between biophysical ground-based data and radiometric satellite-based data collected from this riparian ecosystem (Nagler et al., 2001) as well as performance related to ET estimation (Nagler et al., 2005a, b) and current advancements in VIs such as EVI2.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Minute 323: Colorado River limitrophe and delta environmental flows monitoring interim report for 2018","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"International Boundary and Water Commission United States and Mexico","usgsCitation":"Nagler, P.L., Barreto-Munoz, A., Jarchow, C., and Didan, K., 2022, Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018, 10 p.","productDescription":"10 p.","startPage":"39","endPage":"48","ipdsId":"IP-114755","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":407594,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.17517089843749,\n 31.587894464070395\n ],\n [\n -114.3621826171875,\n 31.587894464070395\n ],\n [\n -114.3621826171875,\n 32.99484290420988\n ],\n [\n -115.17517089843749,\n 32.99484290420988\n ],\n [\n -115.17517089843749,\n 31.587894464070395\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barreto-Munoz, Armando","contributorId":131000,"corporation":false,"usgs":false,"family":"Barreto-Munoz","given":"Armando","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":853314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarchow, Christopher J. 0000-0002-0424-4104","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":211737,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher J.","affiliations":[{"id":38314,"text":"USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":853315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":853316,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70234354,"text":"70234354 - 2022 - Rural turtles: Estimating the occupancy of Northwestern Pond Turtles and non-native red-eared sliders in agricultural habitats in California's Sacramento Valley and Sacramento-San Joaquin River Delta","interactions":[],"lastModifiedDate":"2022-08-09T12:19:57.100101","indexId":"70234354","displayToPublicDate":"2022-08-01T07:16:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2901,"text":"Northwestern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Rural turtles: Estimating the occupancy of Northwestern Pond Turtles and non-native red-eared sliders in agricultural habitats in California's Sacramento Valley and Sacramento-San Joaquin River Delta","docAbstract":"The Northwestern Pond Turtle (Actinemys marmorata; WPT) was once widespread throughout the Sacramento Valley and the Sacramento-San Joaquin River Delta. Much of its historical range has been converted into agricultural land, reducing and altering aquatic habitat and surrounding uplands. Red-eared Sliders (Trachemys scripta elegans; RES) have been introduced throughout much of the existing WPT range, particularly near urban centers, potentially competing with WPT for resources. Previous surveys for turtles in central California have primarily focused on rivers, lakes, and protected wetlands. Little is known about where WPT and RES occur in the vast expanses of agricultural land across the Sacramento Valley and Sacramento-San Joaquin River Delta. Using aquatic hoop nets, we surveyed 142 locations (102 irrigation canal sites, 39 wetlands, 1 tidally influenced slough) across 8 counties during the summers of 2018 and 2019. Both species were detected in agricultural habitats. Using occupancy modeling, we estimated that WPT occur at 44 (95% CRI = 38–53) of our trapping sites and RES occur at 51 (41–66) sampled sites. Co-occurrence of these 2 species was rare; the species were found together at only 6 sites. RES were primarily found in restored wetlands near major roads and the Sacramento metropolitan area, whereas WPT were more commonly found farther from urban areas in wider canals. Our work provides a picture of how WPT and RES occupy this modified agroecosystem that can inform future conservation efforts.
The locations of many faults in and near the Rialto-Colton groundwater subbasin are not precisely known because the spatial density of existing lithologic and hydrologic data used to infer the locations of faults can be sparse. The U.S. Geological Survey, in cooperation with the San Bernardino Valley Municipal Water District, analyzed structural control of groundwater flow in and near the Rialto-Colton groundwater subbasin using Interferometric Synthetic Aperture Radar (InSAR) methods. Faults commonly are barriers to groundwater flow, and the high spatial resolution of InSAR imagery can be used to infer the locations of buried faults where groundwater pumping occurs. InSAR results have revealed three areas in and near the Rialto-Colton groundwater subbasin where buried faults are interpreted as groundwater-flow barriers: the northwestern area about 3 miles northwest of the City of Rialto, the San Jacinto fault area west of the City of San Bernardino, and the southeastern area about 2 miles southeast of the City of Colton. The InSAR results were combined with knowledge gained from previous studies to better define the location and extent of faults acting as groundwater-flow barriers. New data about faults acting as groundwater-flow barriers can be incorporated into future conceptual and hydrologic models of the Rialto-Colton groundwater subbasin and provide water managers information to help effectively manage groundwater resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221030","collaboration":"Prepared in cooperation with the San Bernardino Valley Municipal Water District","programNote":"Water Availability and Use Science Program","usgsCitation":"Brandt, J.T., 2022, Mapping structural control through analysis of land-surface deformation for the Rialto-Colton groundwater subbasin, San Bernardino County, California, 1992–2010: U.S. Geological Survey Open-File Report 2022–1030, 11 p., https://doi.org/10.3133/ofr20221030.","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-084965","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":501769,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113347.htm","linkFileType":{"id":5,"text":"html"}},{"id":403230,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1030/images"},{"id":403228,"rank":1,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1030/ofr20221030.xml"},{"id":403229,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1030/ofr20221030.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":403232,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"Data release","description":"U.S. Geological Survey, 2014, Web interface: U.S. Geological Survey National Water Information System web page, accessed June 11, 2014, at https://doi.org/10.5066/F7P55KJN.","linkHelpText":"Web interface: U.S. Geological Survey National Water Information System web page"},{"id":404520,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1030/covrthb.jpg"},{"id":404546,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221030/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1030"}],"country":"United States","state":"California","county":"San Bernardino County","otherGeospatial":"Rialto-Colton groundwater subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.51319885253905,\n 34.01851844336969\n ],\n [\n -117.2138214111328,\n 34.01851844336969\n ],\n [\n -117.2138214111328,\n 34.19362958613085\n ],\n [\n -117.51319885253905,\n 34.19362958613085\n ],\n [\n -117.51319885253905,\n 34.01851844336969\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The electrification of volcanic plumes has been described intermittently since at least the time of Pliny the Younger and the 79 AD eruption of Vesuvius. Although sometimes disregarded in the past as secondary effects, recent work suggests that the electrical properties of volcanic plumes reveal intrinsic and otherwise inaccessible parameters of explosive eruptions. An increasing number of volcanic lightning studies across the last decade have shown that electrification is ubiquitous in volcanic plumes. Technological advances in engineering and numerical modelling, paired with close observation of recent eruptions and dedicated laboratory studies (shock-tube and current impulse experiments), show that charge generation and electrical activity are related to the physical, chemical, and dynamic processes underpinning the eruption itself. Refining our understanding of volcanic plume electrification will continue advancing the fundamental understanding of eruptive processes to improve volcano monitoring. Realizing this goal, however, requires an interdisciplinary approach at the intersection of volcanology, atmospheric science, atmospheric electricity, and engineering. Our paper summarizes the rapid and steady progress achieved in recent volcanic lightning research and provides a vision for future developments in this growing field.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01591-3","usgsCitation":"Cimarelli, C., Behnke, S., Genareau, K., Méndez Harper, J., and Van Eaton, A.R., 2022, Volcanic electrification: Recent advances and future perspectives: Bulletin of Volcanology, v. 84, 78, 10 p., https://doi.org/10.1007/s00445-022-01591-3.","productDescription":"78, 10 p.","ipdsId":"IP-139794","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467171,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-022-01591-3","text":"Publisher Index Page"},{"id":466638,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"84","noUsgsAuthors":false,"publicationDate":"2022-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cimarelli, Corrado","contributorId":257017,"corporation":false,"usgs":false,"family":"Cimarelli","given":"Corrado","affiliations":[{"id":47800,"text":"Ludwig Maximilian University of Munich","active":true,"usgs":false}],"preferred":false,"id":924031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Behnke, Sonja A","contributorId":184085,"corporation":false,"usgs":false,"family":"Behnke","given":"Sonja A","affiliations":[],"preferred":false,"id":924032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Genareau, Kimberly","contributorId":345648,"corporation":false,"usgs":false,"family":"Genareau","given":"Kimberly","affiliations":[{"id":82675,"text":"The University of Alabama, Tuscaloosa, AL","active":true,"usgs":false}],"preferred":false,"id":924033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Méndez Harper, Joshua","contributorId":349123,"corporation":false,"usgs":false,"family":"Méndez Harper","given":"Joshua","affiliations":[{"id":83438,"text":"University of Oregon, Eugene, USA","active":true,"usgs":false}],"preferred":false,"id":924034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924035,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70234181,"text":"70234181 - 2022 - Spatiotemporal changes in influenza A virus prevalence among wild waterfowl inhabiting the continental United States throughout the annual cycle","interactions":[],"lastModifiedDate":"2022-08-03T12:09:42.114227","indexId":"70234181","displayToPublicDate":"2022-07-29T07:05:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal changes in influenza A virus prevalence among wild waterfowl inhabiting the continental United States throughout the annual cycle","docAbstract":"Avian influenza viruses can pose serious risks to agricultural production, human health, and wildlife. An understanding of viruses in wild reservoir species across time and space is important to informing surveillance programs, risk models, and potential population impacts for vulnerable species. Although it is recognized that influenza A virus prevalence peaks in reservoir waterfowl in late summer through autumn, temporal and spatial variation across species has not been fully characterized. We combined two large influenza databases for North America and applied spatiotemporal models to explore patterns in prevalence throughout the annual cycle and across the continental United States for 30 waterfowl species. Peaks in prevalence in late summer through autumn were pronounced for dabbling ducks in the genera Anas and Spatula, but not Mareca. Spatially, areas of high prevalence appeared to be related to regional duck density, with highest predicted prevalence found across the upper Midwest during early fall, though further study is needed. We documented elevated prevalence in late winter and early spring, particularly in the Mississippi Alluvial Valley. Our results suggest that spatiotemporal variation in prevalence outside autumn staging areas may also represent a dynamic parameter to be considered in IAV ecology and associated risks.
We examine the real‐time earthquake detection and alerting behavior of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm and compare PLUM’s performance with the real‐time performance of the current source‐characterization‐based ShakeAlert System. In the United States (U.S.), PLUM uses a two‐station approach to detect earthquakes. Once a detection is confirmed, observed modified Mercalli intensity (MMI) distributions are forecast onto a regular grid, in which the preferred alert regions are grid cells with MMI 4.0+ forecasts. Although locations of dense station coverage allow PLUM to detect small (M < 4.5) earthquakes typically not considered for EEW in the U.S., a PLUM detection on a small earthquake does not always generate an alert. This is because PLUM alerts are determined by current shaking distributions. If the MMI 4.0+ shaking subsides prior to detection confirmation by shaking at a second neighboring station, the prior MMI 4.0+ information will not be in the alert forecasts. Of the 432 M 3.0+ U.S. West Coast earthquakes in 2021, 33 produced ground motions large enough to be detected by PLUM. Twenty‐four generated MMI 4.0+ PLUM alerts, whereas ShakeAlert issued public EEW alerts for 13 of these earthquakes. We compare PLUM and ShakeAlert alert regions with ShakeMap and “Did You Feel It?” intensity distributions. Because PLUM alert regions surround stations observed to have strong ground motions (regardless of earthquake magnitude), PLUM alerts reliably include locations that experience significant shaking. This is not necessarily the case for ShakeAlert alert regions when there are large errors in magnitude or epicenter estimates. For two of the largest earthquakes in our real‐time dataset, the M 6.0 Antelope Valley and M 5.1 Petrolia earthquakes, the inclusion of PLUM would have improved real‐time ShakeAlert performance. Our results indicate that incorporation of PLUM into ShakeAlert will improve the robustness of the EEW system.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220022","usgsCitation":"Saunders, J.K., Minson, S.E., Baltay Sundstrom, A.S., Bunn, J.J., Cochran, E.S., Kilb, D.L., O’Rourke, C.T., Hoshiba, M., and Kodera, Y., 2022, Real-time earthquake detection and alerting behavior of PLUM ground-motion-based early warning in the United States: Bulletin of the Seismological Society of America, v. 112, no. 5, p. 2668-2688, https://doi.org/10.1785/0120220022.","productDescription":"21 p.","startPage":"2668","endPage":"2688","ipdsId":"IP-135990","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":407607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, 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\"}}]}","volume":"112","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Saunders, Jessie Kate 0000-0001-5340-6715","orcid":"https://orcid.org/0000-0001-5340-6715","contributorId":290634,"corporation":false,"usgs":true,"family":"Saunders","given":"Jessie","email":"","middleInitial":"Kate","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baltay, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853348,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bunn, Julian J 0000-0002-3798-298X","orcid":"https://orcid.org/0000-0002-3798-298X","contributorId":297107,"corporation":false,"usgs":false,"family":"Bunn","given":"Julian","email":"","middleInitial":"J","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":853349,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853350,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kilb, Deborah L.","contributorId":216380,"corporation":false,"usgs":false,"family":"Kilb","given":"Deborah","email":"","middleInitial":"L.","affiliations":[{"id":37799,"text":"SCRIPPS","active":true,"usgs":false}],"preferred":false,"id":853351,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O’Rourke, Colin T 0000-0001-5403-4685","orcid":"https://orcid.org/0000-0001-5403-4685","contributorId":290635,"corporation":false,"usgs":true,"family":"O’Rourke","given":"Colin","email":"","middleInitial":"T","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853352,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoshiba, Mitsuyuki","contributorId":216382,"corporation":false,"usgs":false,"family":"Hoshiba","given":"Mitsuyuki","email":"","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":853353,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kodera, Yuki","contributorId":290636,"corporation":false,"usgs":false,"family":"Kodera","given":"Yuki","email":"","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":853354,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236053,"text":"70236053 - 2022 - Comparisons of the NGA-Subduction ground motion models","interactions":[],"lastModifiedDate":"2022-10-17T16:04:02.593176","indexId":"70236053","displayToPublicDate":"2022-07-28T06:53:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Comparisons of the NGA-Subduction ground motion models","docAbstract":"In this article, ground-motion models (GMMs) for subduction earthquakes recently developed as part of the Next Generation Attenuation-Subduction (NGA-Sub) project are compared. The four models presented in this comparison study are documented in their respective articles submitted along with this article. Each of these four models is based on the analysis of the large NGA-Sub database. Three of the four current models are developed for a global version as well as separate regionalized models. The fourth model was developed based on earthquakes only from Japan, and as such is applicable only for Japan. As part of this comparison study, a general discussion on the parameterization of the four models and the regionalization of the three models is provided. The specific strengths and or weaknesses or the technical decisions and justifications of any one model are not part of this comparison. A selected suite of deterministic attenuation curves and spectra are presented for the models along with a selected suite of currently used subduction models. A limited number of comparisons are presented in this article with a larger number of comparisons and the digital values provided in the electronic attachment. In addition to these scenario calculation comparisons, the results from a standard probabilistic seismic hazard analysis (PSHA) for two sites located in the Pacific Northwest Region in the state of Washington are presented. These calculations highlight the potential impact of using the new GMMs. Based on the comparisons presented here, a general understanding of these new GMMs can be obtained with the expectation that the implementation of a specific seismic hazard study should incorporate similar and additional comparisons and sensitivity studies pertinent to the site of interest.
The southern Rocky Mountains of northern New Mexico and southern Colorado preserve the Oligocene to Pleistocene record of North American continental arc to rift volcanism. The 35–23 million year old (Ma) southern Rocky Mountain volcanic field (SRMVF), spectacularly preserved in the San Juan Mountains of southern Colorado, records the evolution of large andesitic stratovolcanoes to complex caldera clusters, from which at least 22 major ignimbrite sheets (each 150–5,000 cubic kilometers) were erupted. Outflow deposits of the SRMVF preserved along the broadly uplifted northwest flank of the northern Rio Grande rift basin (the San Luis Valley) provide critical structural and temporal constraints on the inception of crustal extension. Coincident with waning stages of SRMVF caldera-forming volcanism (~25.4 Ma), extensional tectonism was accompanied by a transition from bimodal early Miocene to intermediate-composition late Miocene and dominantly basaltic Pliocene rift volcanism of the Taos Plateau in the southern San Luis Basin. Concomitant rift volcanism in the Española Basin and bordering Jemez Mountains of northern New Mexico records a similar Miocene eruptive history dominated by intermediate-composition volcanism that transitioned locally to Pliocene rift-related basaltic volcanism of the Cerros del Rio volcanic field and culminated in eruptions of the iconic rhyolitic Pleistocene Bandelier Tuff and formation of the Valles Caldera along the northwestern rift-basin margin.
This 6-day, 7-night field trip will focus, in broadly equal proportions, on rift-related extensional volcanism of the Jemez Mountains and Taos Plateau regions during the first half of the trip, and on caldera-forming volcanism of the southern Rocky Mountain volcanic field during the second half of the trip. The 35-million-year volcanic history of the region highlighted by new geologic mapping, high-resolution geochronology, petrologic, geochemical, and geophysical data facilitates discussion of (1) the magmatic response to the tectonic transition from subducted-slab arc to continental-rift volcanism; (2) the nature and temporal evolution of rift magmas; (3) fault controls on the spatial evolution of rift magmatism; (4) the diversity of continental-arc ignimbrite volcanism and associated lavas; (5) ignimbrite caldera structure and associated intrusions in three-dimension; (6) the role of recycled crystal mush and magmatic cumulates during growth of Cordilleran batholiths; and (7) high-precision geochronologic contributions to interpretation of relations between regional tectonic and volcanic processes. Most stops will be along roads, but there will be moderate hikes on trails of less than 1-hour duration covering 1–2 kilometers (0.6–1.2 miles) with modest elevation gain of <150 meters (<492 feet).
The route will progress in reverse stratigraphic order, starting in the Jemez Mountains of New Mexico and proceed northward to San Luis Basin and San Luis Hills before turning west to the southeast and central San Juan Mountains. Our last full day takes us to the little-visited and only recently mapped, Bonanza caldera of the northeastern San Juan Mountains and on the final day, we leave the San Luis Valley to briefly explore the Tertiary subvolcanic plutons of the Collegiate Range along the west side of the Arkansas Valley rift valley, en route to Denver.
The authors of all daily contributions acknowledge the helpful reviews by Amy Gilmer and Joe Colgan and thank Christine Chan and Jeremy Havens for assistance with figures, tables, and guidebook text.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175022R","usgsCitation":"Thompson, R.A., Turner, K.J., Lipman, P.W., Wolff, J.A., and Dungan, M.A., 2022, Field-trip guide to continental arc to rift volcanism of the southern Rocky Mountains—Southern Rocky Mountain, Taos Plateau, and Jemez Mountains volcanic fields of southern Colorado and northern New Mexico: U.S. Geological Survey Scientific Investigations Report 2017-5022-R, 346 p., https://doi.org/10.3133/sir20175022R.","productDescription":"Report: xix, 346 p.; Data Release","numberOfPages":"346","onlineOnly":"Y","ipdsId":"IP-103158","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":501942,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113348.htm","linkFileType":{"id":5,"text":"html"}},{"id":404505,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5022/r/sir20175022r.pdf","text":"Report","size":"51 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":404504,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5022/r/covrthb.jpg"},{"id":404506,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BKL3ZE","text":"Data Release","description":"Turner, K.J., Thompson, R.A., and Cosca, M.A., 2022, Data release for geochronology and geochemistry of volcanic rocks in the Southern Rocky Mountains and Taos Plateau volcanic fields and other Oligocene to Pleistocene volcanic rocks within the southern San Luis Basin and San Juan Mountains, southern Colorado and northern New Mexico: U.S. Geological Survey data release, https://doi.org/10.5066/P9BKL3ZE.","linkHelpText":"Data release for geochronology and geochemistry of volcanic rocks in the Southern Rocky Mountains and Taos Plateau volcanic fields and other Oligocene to Pleistocene volcanic rocks within the southern San Luis Basin and San Juan Mountains, southern Colorado and northern New Mexico"}],"country":"United States","state":"Colorado, New Mexico","otherGeospatial":"Southern Rocky Mountain, Taos Plateau, and Jemez Mountains Volcanic Fields","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.52319335937497,\n 35.51434313431818\n ],\n [\n -105.10620117187499,\n 35.51434313431818\n ],\n [\n -105.10620117187499,\n 37.76202988573206\n ],\n [\n -107.52319335937497,\n 37.76202988573206\n ],\n [\n -107.52319335937497,\n 35.51434313431818\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
The morphology of the summit of Nevado del Ruiz volcano (Colombia) and its active Arenas crater is the product of complex interactions between effusive and explosive eruptions, and the dynamics of the summit glacier. Here, we document the morphologic evolution of the summit of Nevado del Ruiz, and the growth of its dome, from a variety of methods: monitoring data (2010 to 2021), photogrammetry, remote sensing, and quantitative modeling. The present morphology of Arenas crater, with small terraces limited by the walls of the crater, various vents of ash emission, and zones of fumarolic activity, has been shaped by the activity following the eruptions of 1845, 1985, 1989, 2012 and the volcanic unrest of the last 10 years. The latest emplacement of a lava dome at the bottom of the main crater began in 2015. The dome grew, with fluctuations in its extrusion rate between ~0.19 m3/s (November 2015) and 0.02 m3/s (February 2018), until December 2019, reaching a diameter of ~130 m, a maximum height of ~60 m, and a volume of 1.7 ± 0.2 × 106 m3.
Geological reservoir characterization is essential for accurate evaluation of gas production performance from gas hydrate reservoirs. Particularly, the understanding of reservoir architecture and heterogeneity is of great importance since these are considered as major controls on fluid hydrodynamic and thermodynamic conditions. This study deals with well log and three-dimensional (3-D) vertical seismic profile (VSP) data acquired from the Hydrate-01 Stratigraphic Test Well within the 7-11-12 prospect, Prudhoe Bay Unit, Alaska North Slope and reports on the results of geological/geophysical evaluation related to the geological structure and reservoir properties of the 7-11-12 prospect. The structural trends of the target reservoirs, based on well correlations, are mostly consistent with the predrill prediction using the surface seismic data, and infer the existence of subseismic faults cutting through the Hydrate-01 well. The 3-D VSP data confirm a down-to-the-east normal fault that offsets the reservoir units across the Hydrate-01 well, which is concordant with the well identification of the same fault, and indicate a northeast-dipping relay structure associated with the overstepping normal faults. The edge enhancement attribute associated with discontinuity generated from the 3-D VSP data shows small faults/fractures, possibly as part of a complex fault network within the imaged normal fault system. These results reveal that the 3-D VSP data provide detailed structural information that is not present from the surface seismic data. The Hydrate-01 well log data confirm the occurrence of gas hydrate at high saturation in the two targeted sand units (B1 and D1 sands), and the comparison to a nearby pre-existing well (7-11-12 well) shows the same general trend in gas hydrate saturation as a map of seismic impedance generated from surface seismic data. The well log data also suggest that the base of gas hydrate occurrence in the Hydrate-01 and 7-11-12 wells is almost aligned at the same depth in both of the targeted B1 and D1 sand reservoirs. Especially for the D1 sand in the Hydrate-01 well, the resistivity logs show a sharp transition from high gas hydrate saturation to fully water-saturated within the D1 sand, suggesting a common gas hydrate/water contact. The results of this study will be used to construct the geological models needed for reservoir simulation studies and they can provide important insights into the geological factors that control the occurrence of gas hydrate on the Alaska North Slope.
Municipal water supply for Albuquerque, New Mexico, is provided, in part, through diversion of surface water from the Rio Grande by way of the San Juan-Chama Drinking Water Project diversion structure. Changes in surface-water quality along the Rio Grande and its tributaries upstream from the San Juan-Chama Drinking Water Project diversion structure are not well characterized. This study describes the methods and results of an analysis of surface-water-quality trends for selected constituents in the Rio Grande upstream from Albuquerque. Trends were evaluated for differing time periods ranging from 2004 to 2019 by using the Seasonal Kendall Tau (SKT) test and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model.
Water-quality data at three long-term sites were used for the trend analyses in this study, with the Cochiti and Alameda sites along the Rio Grande and the Jemez Canyon Dam site along the Jemez River, a tributary of the Rio Grande. The proximity of the Cochiti and Jemez Canyon Dam sites to dams is a drawback to the analysis because it is difficult to differentiate between the influence of dam management and the influence of streamflow on water-quality trends. The data used also did not fully meet desired levels of seasonal sampling density and had shorter periods of record than typically used for trend analysis, and this should be considered in the interpretation of these results.
Study results indicate that concentrations, and thereby fluxes, are influenced by changes in streamflow at the Alameda site. Most trends from the WRTDS results, obtained by using flow-normalization, were downward for constituents at the Alameda site. Most constituents that were analyzed for trends by using SKT did not have a significant trend at any of the sites included in this study, indicating either that the water quality in the Middle Rio Grande Basin has been stable during the study period or that not enough samples were collected during different seasons to characterize the range of concentration variability with streamflow. The SKT test results indicate upward trends in concentrations of the following constituents: aluminum and antimony at the Alameda site, nitrate and nitrate plus nitrite at the Cochiti site, and potassium and antimony during the spring season at Jemez Canyon Dam. The SKT test results indicate a downward trend in cobalt at the Cochiti site that is subject to bias in the cobalt concentrations. SKT test results also indicate small, downward trends in Kjeldahl nitrogen at the Alameda and Cochiti sites.
Concentrations of water-quality constituents were also compared to Federal and State water-quality standards to provide context and relevance to the results. No concentrations were above the national primary or secondary drinking water standards at the Alameda and Cochiti sites, but the Jemez Canyon Dam site did have concentrations above the U.S. Environmental Protection Agency primary drinking water standard for arsenic and above the national secondary drinking water standards for dissolved solids and aluminum. The Alameda and Cochiti sites are on reaches of the Rio Grande that are listed as impaired for gross alpha particles and the Alameda site is on a reach of the Rio Grande that is listed as impaired for Escherichia coli, but there were no consistent changes in concentrations of these constituents at the impaired locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225062","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Flickinger, A.K., and Shephard, Z.M., 2022, Water-quality trends in surface waters of the Jemez River and Middle Rio Grande Basin from Cochiti to Albuquerque, New Mexico, 2004–19: U.S. Geological Survey Scientific Investigations Report 2022–5062, 33 p., https://doi.org/10.3133/sir20225062.","productDescription":"Report: vi, 33 p.; 4 Appendixes; Dataset","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-125261","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":503379,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113346.htm","linkFileType":{"id":5,"text":"html"}},{"id":404252,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5062/images"},{"id":404251,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5062/sir20225062.XML"},{"id":404248,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5062/sir20225062_appendixes.zip","text":"Appendixes 1–4","size":"17.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022–5062, appendixes 1–4"},{"id":404246,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5062/sir20225062_appendixes.xlsx","text":"Appendixes 1–4","size":"59.7 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022–5062, appendixes 1–4"},{"id":404245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5062/sir20225062.pdf","text":"Report","size":"4.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5062"},{"id":404250,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":404243,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5062/coverthb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.24829101562499,\n 33.8430453147447\n ],\n [\n -105.16113281249999,\n 33.8430453147447\n ],\n [\n -105.16113281249999,\n 36.589068371399115\n ],\n [\n -108.24829101562499,\n 36.589068371399115\n ],\n [\n -108.24829101562499,\n 33.8430453147447\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
New lithologic and detrital zircon (DZ) U-Pb data from Devonian–Triassic strata on St. Lawrence Island in the Bering Sea and from the western Brooks Range of Alaska suggest affinities between these two areas. The Brooks Range constitutes part of the Arctic Alaska–Chukotka microplate, but the tectonic and paleogeographic affinities of St. Lawrence Island are unknown or at best speculative. Strata on St. Lawrence Island form a Devonian–Triassic carbonate succession and a Mississippian(?)–Triassic clastic succession that are subdivided according to three distinctive DZ age distributions. The Devonian–Triassic carbonate succession has Mississippian-age quartz arenite beds with Silurian, Cambrian, Neoproterozoic, and Mesoproterozoic DZ age modes, and it exhibits similar age distributions and lithologic and biostratigraphic characteristics as Mississippian-age Utukok Formation strata in the Kelly River allochthon of the western Brooks Range. Consistent late Neoproterozoic, Cambrian, and Silurian ages in each of the Mississippian-age units suggest efficient mixing of the DZ prior to deposition, and derivation from strata exposed by the pre-Mississippian unconformity and/or Endicott Group strata that postdate the unconformity. The Mississippian(?)–Triassic clastic succession is subdivided into feldspathic and graywacke subunits. The feldspathic subunit has a unimodal DZ age mode at 2.06 Ga, identical to Nuka Formation strata in the Nuka Ridge allochthon of the western Brooks Range, and it records a distinctive depositional episode related to late Paleozoic juxtaposition of a Paleoproterozoic terrane along the most distal parts of the Arctic Alaska–Chukotka microplate. The graywacke subunit has Triassic maximum depositional ages and abundant late Paleozoic grains, likely sourced from fringing arcs and/or continent-scale paleorivers draining Eurasia, and it has similar age distributions to Triassic strata from the Lisburne Peninsula (northwestern Alaska), Chukotka and Wrangel Island (eastern Russia), and the northern Sverdrup Basin (Canadian Arctic), but, unlike the Devonian–Triassic carbonate succession and feldspathic subunit of the Mississippian(?)–Triassic clastic succession, it has no obvious analogue in the western Brooks Range allochthon stack. These correlations establish St. Lawrence Island as conclusively belonging to the Arctic Alaska–Chukotka microplate, thus enhancing our understanding of the circum-Arctic region in late Paleozoic–Triassic time.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02490.1","usgsCitation":"Amato, J.M., Dumoulin, J.A., Gottlieb, E.S., and Moore, T.E., 2022, Detrital zircon ages from upper Paleozoic–Triassic clastic strata on St. Lawrence Island, Alaska: An enigmatic component of the Arctic Alaska–Chukotka microplate: Geosphere, v. 18, no. 5, p. 1492-1523, https://doi.org/10.1130/GES02490.1.","productDescription":"32 p.","startPage":"1492","endPage":"1523","ipdsId":"IP-134575","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":447026,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02490.1","text":"Publisher Index Page"},{"id":435756,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PILIK","text":"USGS data release","linkHelpText":"Location Data for Petrographic Samples and Isotopic and Age Data from Detrital Zircon Grains from Selected Rock Samples from St. Lawrence Island and the Western Brooks Range, Alaska"},{"id":408489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"St. Lawrence Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -171.968994140625,\n 62.88520467163244\n ],\n [\n -168.50830078125,\n 62.88520467163244\n ],\n [\n -168.50830078125,\n 63.86487567533106\n ],\n [\n -171.968994140625,\n 63.86487567533106\n ],\n [\n -171.968994140625,\n 62.88520467163244\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Amato, Jeffrey M.","contributorId":247883,"corporation":false,"usgs":false,"family":"Amato","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[{"id":49682,"text":"Dept of Geolgical Sciences, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":854897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":854898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gottlieb, Eric S. 0000-0002-4904-9492","orcid":"https://orcid.org/0000-0002-4904-9492","contributorId":291239,"corporation":false,"usgs":false,"family":"Gottlieb","given":"Eric","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":854899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Thomas E. 0000-0002-0878-0457 tmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-0878-0457","contributorId":127538,"corporation":false,"usgs":true,"family":"Moore","given":"Thomas","email":"tmoore@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":854900,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70235715,"text":"70235715 - 2022 - Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace-of-life","interactions":[],"lastModifiedDate":"2022-08-16T11:42:49.081347","indexId":"70235715","displayToPublicDate":"2022-07-25T06:41:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace-of-life","docAbstract":"Pathogen transmission depends on host density, mobility and contact. These components emerge from host and pathogen movements that themselves arise through interactions with the surrounding environment. The environment, the emergent host and pathogen movements, and the subsequent patterns of density, mobility and contact form an ‘epidemiological landscape’ connecting the environment to specific locations where transmissions occur. Conventionally, the epidemiological landscape has been described in terms of the geographical coordinates where hosts or pathogens are located. We advocate for an alternative approach that relates those locations to attributes of the local environment. Environmental descriptions can strengthen epidemiological forecasts by allowing for predictions even when local geographical data are not available. Environmental predictions are more accessible than ever thanks to new tools from movement ecology, and we introduce a ‘movement-pathogen pace of life’ heuristic to help identify aspects of movement that have the most influence on spatial epidemiology. By linking pathogen transmission directly to the environment, the epidemiological landscape offers an efficient path for using environmental information to inform models describing when and where transmission will occur.
Movement behavior is an important contributor to habitat selection and its incorporation in disease risk models has been somewhat neglected. The habitat preferences of host individuals affect their probability of exposure to pathogens. If preference behavior can be incorporated in ecological niche models (ENMs) when data on pathogen distributions are available, then variation in such behavior may dramatically impact exposure risk. Here we use data from the anthrax endemic system of Etosha National Park, Namibia, to demonstrate how integrating inferred movement behavior alters the construction of disease risk maps. We used a Maximum Entropy (MaxEnt) model that associated soil, bioclimatic, and vegetation variables with the best available pathogen presence data collected at anthrax carcass sites to map areas of most likely Bacillus anthracis (the causative bacterium of anthrax) persistence. We then used a hidden Markov model (HMM) to distinguish foraging and non-foraging behavioral states along the movement tracks of nine zebra (Equus quagga) during the 2009 and 2010 anthrax seasons. The resulting tracks, decomposed on the basis of the inferred behavioral state, formed the basis of step-selection functions (SSFs) that used the MaxEnt output as a potential predictor variable. Our analyses revealed different risks of exposure during different zebra behavioral states, which were obscured when the full movement tracks were analyzed without consideration of the underlying behavioral states of individuals. Pathogen (or vector) distribution models may be misleading with regard to the actual risk faced by host animal populations when specific behavioral states are not explicitly accounted for in selection analyses. To more accurately evaluate exposure risk, especially in the case of environmentally transmitted pathogens, selection functions could be built for each identified behavioral state and then used to assess the comparative exposure risk across relevant states. The scale of data collection and analysis, however, introduces complexities and limitations for consideration when interpreting results.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-022-00331-8","usgsCitation":"Dougherty, E., Seidel, D., Blackburn, J., Turner, W.C., and Getz, W., 2022, A framework for integrating inferred movement behavior into disease risk models: Movement Ecology, v. 10, 31, 15 p., https://doi.org/10.1186/s40462-022-00331-8.","productDescription":"31, 15 p.","ipdsId":"IP-138565","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-022-00331-8","text":"Publisher Index Page"},{"id":480820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Namibia","otherGeospatial":"Etosha National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 15.655851549884602,\n -18.75382547428198\n ],\n [\n 15.655851549884602,\n -19.249215511439346\n ],\n [\n 16.38844485420617,\n -19.249215511439346\n ],\n [\n 16.38844485420617,\n -18.75382547428198\n ],\n [\n 15.655851549884602,\n -18.75382547428198\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Dougherty, Eric R.","contributorId":349723,"corporation":false,"usgs":false,"family":"Dougherty","given":"Eric R.","affiliations":[],"preferred":false,"id":924662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seidel, Dana P.","contributorId":349724,"corporation":false,"usgs":false,"family":"Seidel","given":"Dana P.","affiliations":[],"preferred":false,"id":924663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blackburn, Jason K.","contributorId":349725,"corporation":false,"usgs":false,"family":"Blackburn","given":"Jason K.","affiliations":[],"preferred":false,"id":924664,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924275,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Getz, Wayne M.","contributorId":287152,"corporation":false,"usgs":false,"family":"Getz","given":"Wayne M.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":924665,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70235705,"text":"70235705 - 2022 - Early treatment of white-nose syndrome is necessary to stop population decline","interactions":[],"lastModifiedDate":"2022-10-17T15:49:29.799347","indexId":"70235705","displayToPublicDate":"2022-07-22T07:02:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Early treatment of white-nose syndrome is necessary to stop population decline","docAbstract":"Magnetotelluric (MT) imaging results from mineral provinces in Australia and in the United States show an apparent spatial relationship between crustal-scale electrical conductivity anomalies and major magmatic-hydrothermal iron oxide-apatite/iron oxide-copper-gold (IOA-IOCG) deposits. Although these observations have driven substantial interest in the use of MT data to image ancient fluid pathways, the exact cause of these anomalies has been unclear. Here, we interpret the conductors to be the result of graphite precipitation from CO2-rich magmatic fluids during cooling. These fluids would have exsolved from mafic magmas at mid- to lower-crustal depths; saline magmatic fluids that could drive mineralization were likely derived from related, more evolved intrusions at shallower crustal levels. In our model, the conductivity anomalies then mark zones that once were the deep roots of ancient magmatic-hydrothermal mineral systems.
Mountain pine beetle (MPB) is a native disturbance agent across most pine forests in the western US. Climate changes will directly and indirectly impact frequencies and severities of MPB outbreaks, which can then alter fuel characteristics and wildland fire dynamics via changes in stand structure and composition. To investigate the importance of MPB to past and future landscape dynamics, we used the mechanistic, spatially explicit ecosystem process model FireBGCv2 to quantify interactions among climate, MPB, wildfire, fire suppression, and fuel management under historical and projected future climates for three western US landscapes. We compared simulated FireBGCv2 output from three MPB modules (none, simple empirical, and complex mechanistic) using three focus variables and six exploratory variables to evaluate the importance of MPB to landscape dynamics.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-022-00137-4","usgsCitation":"Keane, R., Bentz, B., Holsinger, L.M., Saab, V., and Loehman, R.A., 2022, Modeled interactions of mountain pine beetle and wildland fire under future climate and management scenarios for three western US landscapes: Fire Ecology, v. 18, 12, 18 p., https://doi.org/10.1186/s42408-022-00137-4.","productDescription":"12, 18 p.","ipdsId":"IP-139683","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":447042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-022-00137-4","text":"Publisher Index Page"},{"id":412113,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.98363968463241,\n 49.37174471222207\n ],\n [\n -124.98363968463241,\n 41.36819847856913\n ],\n [\n -109.9167960885596,\n 41.36819847856913\n ],\n [\n -109.9167960885596,\n 49.37174471222207\n ],\n [\n -124.98363968463241,\n 49.37174471222207\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2022-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Keane, Robert","contributorId":187606,"corporation":false,"usgs":false,"family":"Keane","given":"Robert","affiliations":[],"preferred":false,"id":861970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bentz, Barbara","contributorId":146954,"corporation":false,"usgs":false,"family":"Bentz","given":"Barbara","affiliations":[],"preferred":false,"id":861971,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holsinger, Lisa M.","contributorId":187607,"corporation":false,"usgs":false,"family":"Holsinger","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":6679,"text":"US Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":861972,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saab, Victoria","contributorId":301089,"corporation":false,"usgs":false,"family":"Saab","given":"Victoria","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":861973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":861974,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70233493,"text":"sir20225056 - 2022 - Light attenuation and erosion characteristics of fine sediments in a highly turbid, shallow, Great Basin Lake—Malheur Lake, Oregon, 2017–18","interactions":[],"lastModifiedDate":"2026-04-23T16:38:02.572523","indexId":"sir20225056","displayToPublicDate":"2022-07-21T13:47:09","publicationYear":"2022","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":"2022-5056","displayTitle":"Light Attenuation and Erosion Characteristics of Fine Sediments in a Highly Turbid, Shallow, Great Basin Lake—Malheur Lake, Oregon, 2017–18","title":"Light attenuation and erosion characteristics of fine sediments in a highly turbid, shallow, Great Basin Lake—Malheur Lake, Oregon, 2017–18","docAbstract":"Malheur Lake is a large, shallow, turbid lake in southeastern Oregon that fluctuates widely in surface area in response to yearly precipitation and climatic cycles. High suspended-sediment concentrations (SSCs) likely are negatively affecting the survival of aquatic plants by reducing the intensity of solar radiation reaching the plants, thus inhibiting photosynthesis. This study was designed to determine the types of suspended material, the erodibility of the lakebed, the attenuation of photosynthetically active radiation (PAR) through the water column, and the effects of wind and precipitation on SSC.
Two sites in the lake were monitored for approximately 5 months during the summer growing season each year (2017–19). At these sites, turbidity, chlorophyll a fluorescence (a surrogate for concentration), and underwater PAR measurements were collected continuously, and discrete samples were collected every 2 weeks and analyzed for SSC, loss on ignition, and chlorophyll a concentration. Underwater PAR profile measurements were collected during site visits, and a nearby meteorological station recorded terrestrial PAR and wind speeds.
About 18 percent of suspended material in the water was organic and mostly detrital. Nearly 100 percent of all suspended material was fine material (less than 63 micrometers), and more than 90 percent of the surficial lakebed material was fine material. The high concentrations of fine material in the water column can be expected to strongly attenuate light.
SSC was significantly higher at both sites in 2018 compared to 2017 and 2019; the interannual differences were mostly due to the lower amount of precipitation in 2018, which resulted in shallower lake depths. Three years of SSC values multiplied by water depth showed a seasonal pattern: concentrations were often highest in early spring, lowest in summer, and intermediate in autumn.
Episodic wind events with speeds of 5–10 meters per second caused rapid increases in turbidity above background that lasted for a few days. However, a baseline SSC value multiplied by water depth (estimated to be 0.11 kilograms per square meter) was present between wind events and even under ice, suggesting a persistent suspension of very fine, highly erodible material. Terrestrial and underwater PAR measurements were used to develop a relation between PAR attenuation and turbidity that can be used in modeling restoration scenarios. Calculated bottom shear stress caused by wind-generated waves ranged from 0 to 0.4 pascals (Pa). Erosion experiments indicated variability in the bottom sediments from the two lake sites, but much of the lakebed is highly erodible at a threshold of 0.05 to 0.1 Pa.
Restoration actions may target the persistent turbidity (for example, the use of flocculation) or transient turbidity (for example, construction of wave-reduction barriers), with a goal of attaining approximately 36 micromoles photons per square meter per second of PAR at the lakebed to promote emergence of sago pondweed and other desirable plants. Currently, that threshold often is reached from 4 to 34 centimeters (cm) below the water surface in 1 meter water depth, depending on wind conditions, but halving persistent turbidity would increase the upper end of the range to 55 cm. Additional studies regarding the effects of (1) sediment drying on resuspension and (2) nutrient inputs and internal cycling on phytoplankton populations would help determine the most appropriate restoration strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225056","usgsCitation":"Wood, T.M., and Smith, C.D., 2022, Light attenuation and erosion characteristics of fine sediments in a highly turbid, shallow, Great Basin Lake—Malheur Lake, Oregon, 2017–18: U.S. Geological Survey Scientific Investigations Report 2022–5056, 51 p., https://doi.org/10.3133/sir20225056.","productDescription":"Report: x, 51 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-123319","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":503371,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113310.htm","linkFileType":{"id":5,"text":"html"}},{"id":404302,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5056/sir20225056.XML"},{"id":404301,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5056/images"},{"id":404298,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225056/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5056"},{"id":404296,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5056/coverthb.jpg"},{"id":404297,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5056/sir20225056.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5056"},{"id":404299,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92ZBWJ5","text":"USGS data release","description":"USGS data release","linkHelpText":"Phytoplankton data for Malheur Lake, Oregon, 2018-20"},{"id":404300,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95FMN8F","text":"USGS data release","description":"USGS data release","linkHelpText":"Photosynthetically active radiation measurements collected at Malheur Lake, Oregon, 2017-18"}],"country":"United States","state":"Oregon","otherGeospatial":"Malheur Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.08218383789062,\n 43.113014204188914\n ],\n [\n -118.42300415039062,\n 43.113014204188914\n ],\n [\n -118.42300415039062,\n 43.49178653083377\n ],\n [\n -119.08218383789062,\n 43.49178653083377\n ],\n [\n -119.08218383789062,\n 43.113014204188914\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
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The Rio Grande Transboundary Integrated Hydrologic Model (RGTIHM) was developed through an interagency effort between the U.S. Geological Survey and the Bureau of Reclamation to provide a tool for analyzing the hydrologic system response to the historical evolution of water use and potential changes in water supplies and demands in the Hatch Valley (also known as Rincon Valley in the study area) and Mesilla Basin, New Mexico and Texas, United States, and northern Chihuahua, Mexico. Reclamation operates the Rio Grande Project (RGP) to store and deliver surface water for irrigation and municipal use within the study area and in the El Paso Valley south of the El Paso Narrows.
Biases in the RGTIHM’s simulation of streamflow and aquifer storage depletion and the availability of new estimates of historical agricultural consumptive use in the study area initiated an update and recalibration of the RGTIHM. In addition to the new estimates of historical agricultural consumptive use, updates were made to more accurately represent the natural system and included adjustments to the initial groundwater levels; streamflow rating tables; Rio Grande, canal, and drain streambed elevations; tributary streambed elevations; surface-water inflows and diversions; RGP surface-water deliveries and canal waste; on-farm efficiency; the routing of surface-water runoff within the MODFLOW Farm Process; and general head boundaries used to simulate interbasin groundwater flow. Model settings, including the assignment of hydraulic conductivity and storage properties to model layers and the MODFLOW solver package, were adjusted to improve numerical stability, and the model was recalibrated to better simulate the natural system. The updated and recalibrated RGTIHM demonstrates a robust ability to simulate the spatially and temporally variable measurements, estimates, or reports of hydraulic head, surface-water flows, agricultural pumping, RGP surface-water deliveries and canal waste, and decadal aquifer storage changes, with improvements over the previous version of the model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225045","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Ritchie, A.B., Galanter, A.E., Flickinger, A.K., Shephard, Z.M., and Ferguson, I.M., 2022, Update and recalibration of the Rio Grande Transboundary Integrated Hydrologic Model, New Mexico and Texas, United States, and northern Chihuahua, Mexico: U.S. Geological Survey Scientific Investigations Report 2022–5045, 28 p., https://doi.org/10.3133/sir20225045.","productDescription":"Report: vi, 28 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-132740","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":404022,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PLDXV","text":"USGS data release","linkHelpText":"MODFLOW One-Water Hydrologic Flow Model (MF-OWHM) used to simulate conjunctive use in the Hatch Valley and Mesilla Basin, New Mexico and Texas, United States, and northern Chihuahua, Mexico"},{"id":404024,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5045/sir20225045.xml"},{"id":404023,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5045/images"},{"id":404021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5045/sir20225045.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5045"},{"id":404020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5045/coverthb.jpg"},{"id":502400,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113311.htm","linkFileType":{"id":5,"text":"html"}}],"country":"Mexico, United States","state":"Chihuahua, New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108,\n 31.5\n ],\n [\n -106,\n 31.5\n ],\n [\n -106,\n 33.25\n ],\n [\n -108,\n 33.25\n ],\n [\n -108,\n 31.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
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Louisiana holds a unique historical, economic, and cultural position in the national consciousness. Its off-shore oil operations help fuel the U.S. economy. The Port of South Louisiana is the busiest in the United States by cargo volume; the nearby Port of New Orleans is the sixth busiest. The former French and Spanish colony served as a key connection to the Caribbean long before U.S. independence, and Louisiana’s multinational effects soon melded into a Creole culture that had an outsized effect on America.
That heritage remains a powerful draw for the tourism industry in Louisiana. Gulf Coast breezes carry the aromas of tropical flowers, sweet beignets, and savory crawfish through the 13 colorful blocks of New Orleans’ French Quarter. Interwoven with the sounds of jazz, rock, country music, and zydeco, the city’s charms delight more than 18 million visitors each year.
The proximity of the Gulf Coast and the city’s elevation, however—just 6.5 feet above sea level—also offer an ominous warning of the ever-present threat of climate change and natural disaster. Hurricane Katrina battered New Orleans in 2005, an incident tied to more than 1,800 deaths that marks one of the most notorious U.S. weather-related tragedies in the 21st century. Environmental changes have amplified threats from tropical storms. Through more frequent and powerful storms, sea level rise threatens low-lying areas such as Lake Charles and creates unpredictable weather patterns that threaten the cities and agricultural operations to the north.
Landsat data offer rich information that can aid in early warning, disaster response, and the monitoring of recovery from natural disasters. Its historic, unparalleled 50-year archive of repeat Earth observations also serves to guide resiliency plans and feeds modeling that can help States like Louisiana prepare for coming coastal and inland change. Here are just a few examples of how Landsat has been used to study and understand Louisiana.
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"nation\":\"USA \"}}]}","edition":"Version 1.0: July 20, 2022; Version 1.1: March 19, 2025","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The rolling plains of Nebraska occupy a storied place in the American psyche. For those living outside the Midwest, the Cornhusker State may be seen as a symbol of the Nation’s heartland, cropped border to border, with country churches and barely standing barns to be found around every turn of its gravel roads.
Although the pioneer history and agricultural heritage of the 37th State lend credence to this idyllic view, Nebraska’s varied landscapes and modern economy make the reality of life in the State more complex than its rural image would suggest.
Agriculture remains Nebraska’s top industry, but manufacturing now represents 12 percent of the State’s gross domestic product. The financial services and insurance industries account for 8 percent of the gross domestic product in Nebraska, which is the headquarters of Berkshire Hathaway and Mutual of Omaha, the latter of which is named after Nebraska’s largest city, which grew nearly 12 percent between 2010 and 2020. Cropland is indeed a prominent feature in Nebraska, but the State also is home to 8 State parks, 5 national parks, 2 national forests, and 3 national grasslands. The grasses and dunes of the Nebraska Sand Hills that stretch across the north-central quarter of the State are a National Natural Landmark.
Data from the Landsat satellite program contribute to the study and management of Nebraska’s land in myriad ways, from monitoring crop productivity and aiding in rangeland management to tracking damage from floods, droughts, or hurricanes. Land cover maps produced using data pulled from the 50-year Landsat archive can offer important insights into urban growth, land use trends, and land change patterns. Here are a few examples of how Landsat has been used in Nebraska.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Hypocenter estimation at active volcanoes improves our understanding of their magmatic systems and indicates changing conditions at depth for continuously monitored volcanoes. The most active volcano in the Cascades Range, Mount St. Helens, has a multi-decadal earthquake catalog and it shows an apparent change in the depth distribution of seismicity before and after the 2004–2008 dome-building eruption and unrest sequence. We use two new resources to evaluate the accuracy of hypocenters and consequently the change in depth distribution of seismicity before and after the 2004–2008 eruption. First, we deployed a dense array of 136 three-component nodal seismographs for one month in 2017, including sub-arrays on the newly extruded dome and crater floor. Second, for events recorded during this month, we located their hypocenters using a three-dimensional (3D) wavefront-tracking location solver and a recently developed tomography model derived from active and passive source data. The relocated hypocenters are generally shallower and more concentrated beneath the crater compared to their catalog locations. The mean hypocenter movement from catalog locations is 2.86 km, with an averaged depth shift of 2.53 km upward. Comparison between 2017 hypocenters located using all of the available phase picks and those located using only the catalog picks from the permanent network suggests the improved hypocenters mostly resulted from the location solver and the 3D velocity model, with smaller changes due to the dense three-component array. Applying the same hypocenter estimation method to phase picks for all events from 1997 to 2004 and 2008–2021, we found a concentration of seismicity between sea level and ~ 1 km above it before and after the 2004–2008 eruption. The new results suggest that the seismogenic structure in the shallow magmatic system quickly re-equilibrated to its earlier state after the dome-building eruption.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2022.107629","usgsCitation":"Zhang, H., Glasgow, M., Schmandt, B., Thelen, W., Moran, S.C., and Thomas, A., 2022, Revisiting the depth distribution of seismicity before and after the 2004–2008 eruption of Mount St. Helens: Journal of Volcanology and Geothermal Research, v. 430, 107629, 10 p., https://doi.org/10.1016/j.jvolgeores.2022.107629.","productDescription":"107629, 10 p.","ipdsId":"IP-142680","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467174,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2022.107629","text":"Publisher Index Page"},{"id":463341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Untied States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.34522424954554,\n 46.31416654047442\n ],\n [\n -122.34522424954554,\n 46.084925777087534\n ],\n [\n -122.05723487869246,\n 46.084925777087534\n ],\n [\n -122.05723487869246,\n 46.31416654047442\n ],\n [\n -122.34522424954554,\n 46.31416654047442\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"430","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Han","contributorId":345700,"corporation":false,"usgs":false,"family":"Zhang","given":"Han","email":"","affiliations":[],"preferred":false,"id":917263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glasgow, Margaret 0000-0001-5637-5918","orcid":"https://orcid.org/0000-0001-5637-5918","contributorId":345691,"corporation":false,"usgs":false,"family":"Glasgow","given":"Margaret","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":917264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmandt, Brandon","contributorId":202750,"corporation":false,"usgs":false,"family":"Schmandt","given":"Brandon","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":917265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thelen, Weston 0000-0003-2534-5577","orcid":"https://orcid.org/0000-0003-2534-5577","contributorId":215530,"corporation":false,"usgs":true,"family":"Thelen","given":"Weston","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917266,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moran, Seth C. 0000-0001-7308-9649 smoran@usgs.gov","orcid":"https://orcid.org/0000-0001-7308-9649","contributorId":224629,"corporation":false,"usgs":true,"family":"Moran","given":"Seth","email":"smoran@usgs.gov","middleInitial":"C.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917267,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thomas, Amanda","contributorId":195086,"corporation":false,"usgs":false,"family":"Thomas","given":"Amanda","affiliations":[],"preferred":false,"id":917268,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70233537,"text":"70233537 - 2022 - Predicting larval alewife transport in Lake Michigan using hydrodynamic and Lagrangian particle dispersion models","interactions":[],"lastModifiedDate":"2022-09-15T14:17:56.583793","indexId":"70233537","displayToPublicDate":"2022-07-20T06:53:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Predicting larval alewife transport in Lake Michigan using hydrodynamic and Lagrangian particle dispersion models","docAbstract":"Several species of fish in large lakes and marine environments have a pelagic larval stage, and are subject to variable transport that can ultimately regulate survival and recruitment success. Alewife, Alosa pseudoharengus, are subject to transport by complex coastal currents during their pelagic larval stage (~ 30 d). We assessed backward-trajectory simulations, consisting of a Lagrangian particle dispersion model linked to the Finite Volume Community Ocean Model, to estimate likely hatch locations of aged larval alewife collected from locations on both the eastern and western sides of Lake Michigan during July 2015. We used four deployments of three satellite-tracked drifter buoys in coastal waters to assess model skill in estimating the origin of a drifter from its final location. We found that the trajectories of drifters varied greatly, depending on wind events and associated coastal transport processes, including upwelling/downwelling and coastal jet currents. In 2 of 12 cases, the backward trajectory simulations failed to predict the drifter origin, associated with transport of 170 km in a narrow coastal jet current. In the remaining 10 cases, the known drifter origin was within 3.5 km of the spatial patch of predicted possible origins for a scenario of horizontal diffusivity (188 m2 s−1) consistent with the offshore model grid resolution. Modeled backward trajectories estimated that alewife originated from the same side of the lake where they were collected, within ~ 100 km of the collection site. Our paper demonstrates the utility of hydrodynamic models to estimate a region of origin for aged larval fish.