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":70234150,"text":"70234150 - 2022 - Defining fine-scaled population structure among continuously distributed populations","interactions":[],"lastModifiedDate":"2022-10-17T15:47:18.330622","indexId":"70234150","displayToPublicDate":"2022-08-01T08:27:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Defining fine-scaled population structure among continuously distributed populations","docAbstract":"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
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.
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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 Sundstrom, 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 Sundstrom","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 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impacts","interactions":[],"lastModifiedDate":"2022-08-02T13:41:56.206949","indexId":"70234156","displayToPublicDate":"2022-07-28T08:08:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Fifty years of Landsat science and impacts","docAbstract":"Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and the ability to acquire and work with large datasets was limited. The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs. Innovations driven by the Landsat program have paved the way for subsequent science, application, and policy support activities. The economic and scientific value of the knowledge gained through the Landsat program has been long recognized, and despite periods of funding uncertainty, has resulted in the program's 50 years of continuity, as well as substantive and ongoing improvements to payload and mission performance. Free and open access to Landsat data, enacted in 2008, was unprecedented for medium spatial resolution Earth observation data and substantially increased usage and led to a proliferation of science and application opportunities. Here, we highlight key developments over the past 50 years of the Landsat program that have influenced and changed our scientific understanding of the Earth system. Major scientific and programmatic impacts have been realized in the areas of agricultural crop mapping and water use, climate change drivers and impacts, ecosystems and land cover monitoring, and mapping the changing human footprint. The introduction of Landsat collection processing, coupled with the free and open data policy, facilitated a transition in Landsat data usage away from single images and towards time series analyses over large areas and has fostered the widespread use of science-grade data. The launch of Landsat-9 on September 27, 2021, and the advanced planning of its successor mission, Landsat-Next, underscore the sustained institutional support for the program. Such support and commitment to continuity is recognition of both the historic impact the program, and the future potential to build upon Landsat's remarkable 50-year legacy.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113195","usgsCitation":"Wulder, M., Roy, D., Radeloff, V., Loveland, T., Anderson, M.C., Johnson, D.M., Healey, S., Zhu, Z., Scambos, T.A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C., Masek, J.G., Hermosilla, T., White, J.C., Belward, A.S., Schaaf, C., Woodcock, C.E., Huntington, J., Lymburner, L., Hostert, P., Gao, F., Lyapustin, A., Pekel, J., Strobl, P., Eric Vermote, and Cook, B., 2022, Fifty years of Landsat science and impacts: Remote Sensing of Environment, v. 280, 113195, 21 p., https://doi.org/10.1016/j.rse.2022.113195.","productDescription":"113195, 21 p.","ipdsId":"IP-140037","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":446999,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113195","text":"Publisher Index 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Feng","contributorId":294427,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","affiliations":[{"id":18168,"text":"USDA ARS","active":true,"usgs":false}],"preferred":false,"id":848036,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Lyapustin, Alexi","contributorId":294428,"corporation":false,"usgs":false,"family":"Lyapustin","given":"Alexi","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":848037,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Pekel, Jean-Francois","contributorId":294429,"corporation":false,"usgs":false,"family":"Pekel","given":"Jean-Francois","email":"","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":848038,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Strobl, Peter","contributorId":294430,"corporation":false,"usgs":false,"family":"Strobl","given":"Peter","email":"","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":848039,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Eric Vermote","contributorId":294431,"corporation":false,"usgs":false,"family":"Eric Vermote","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":848040,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Cook, Bruce D.","contributorId":294432,"corporation":false,"usgs":false,"family":"Cook","given":"Bruce D.","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":848041,"contributorType":{"id":1,"text":"Authors"},"rank":28}]}} ,{"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.
Translocation, the intentional movement of animals from one location to another, is a common management practice for the gopher tortoise (Gopherus polyphemus). Although the inadvertent spread of pathogens is a concern with any translocation effort, waif tortoises—individuals that have been collected illegally, injured and rehabilitated or have unknown origins—are generally excluded from translocation efforts due to heightened concerns of introducing pathogens and subsequent disease to naïve populations. However, repurposing these long-lived animals for species recovery is desirable when feasible, and introducing waif tortoises may bolster small populations facing extirpation. The objective of this study was to assess the health of waif tortoises experimentally released at an isolated preserve in Aiken County, SC, USA. Our assessments included visual examination, screening for 14 pathogens using conventional or quantitative polymerase chain reaction (qPCR) and haematological evaluation. Of the 143 individuals assessed in 2017 and 2018, most individuals (76%; n = 109 of 143) had no overt clinical evidence of disease and, when observed, clinical findings were mild. In both years, we detected two known tortoise pathogens, Mycoplasma agassizii and Mycoplasma testudineum, at a prevalence of 10.2–13.9% and 0.0–0.8%, respectively. Additionally, we found emydid Mycoplasma, a bacterium commonly found in box turtles (Terrapene spp.), in a single tortoise that showed no clinical evidence of infection. The presence of nasal discharge was an important, but imperfect, predictor of Mycoplasma spp. infection in translocated tortoises. Hemogram data were comparable with wild populations. Our study is the first comprehensive effort to assess pathogen prevalence and hemogram data of waif gopher tortoises following translocation. Although caution is warranted and pathogen screening necessary, waif tortoises may be an important resource for establishing or augmenting isolated populations when potential health risks can be managed.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coac051","usgsCitation":"McKee, R.K., Buhlmann, K.A., Moore, C.T., Allender, M., Stacy, N.I., and Tuberville, T., 2022, Island of misfit tortoises: Waif gopher tortoise health assessment following translocation: Conservation Physiology, v. 10, no. 1, coac051, 18 p., https://doi.org/10.1093/conphys/coac051.","productDescription":"coac051, 18 p.","ipdsId":"IP-133988","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447004,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coac051","text":"Publisher Index Page"},{"id":432572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"McKee, Rebecca K.","contributorId":341474,"corporation":false,"usgs":false,"family":"McKee","given":"Rebecca","email":"","middleInitial":"K.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":908478,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buhlmann, Kurt. 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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
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A reported fuel release in November 2021 at the Red Hill Bulk Fuel Storage Facility within the naval reservation at Red Hill led to the shutdown of several production wells in the Hālawa area, O‘ahu, Hawai‘i. Red Hill Shaft—one of the high-capacity production wells that shut down—was reactivated on January 29, 2022. Submersible pressure transducers were deployed at 20 wells in the Hālawa area to measure groundwater levels and evaluate the regional groundwater-level response to the resumption of groundwater withdrawals from Red Hill Shaft. Groundwater levels measured in wells from January 17 to March 3, 2022, ranged between 16 and 20 feet at all sites and generally between 17 and 19 feet at most sites. Average groundwater-level decreases measured in wells 10 days after the January 29, 2022, resumption of withdrawal from Red Hill Shaft ranged from about 0.1 to 0.4 foot. In general, greatest decreases in groundwater levels occurred in wells closest to Red Hill Shaft.
The groundwater-level data contain uncertainty because of several potential sources of error associated with (1) the accuracy of the measuring tapes and submersible pressure transducers used, (2) the accuracy of the measuring-point altitude at the top of each well, (3) the stability of the submersible pressure transducers’ suspension depth in each well, (4) well plumbness and alignment, and (5) human error. Because of the potential sources of error, comparability of groundwater-level data may be affected. Some sources of uncertainty, including the accuracy of measuring-point altitudes, can be addressed and lead to improved accuracy and comparability of groundwater levels. Data collected for this study are available in the U.S. Geological Survey National Water Information System database.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221069","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, Groundwater-level monitoring from January 17 to March 3, 2022, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1069, 29 p., https://doi.org/10.3133/ofr20221069.","productDescription":"Report: vi, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-141201","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":501822,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113349.htm","linkFileType":{"id":5,"text":"html"}},{"id":404436,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221018","text":"Open-File Report 2022-1018","description":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, December 23, 2021, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1018, 10 p., https://doi.org/10.3133/ofr20221018.","linkHelpText":"- December 23, 2021, Red Hill Synoptic Groundwater-Level Survey, Hālawa Area, O‘ahu, Hawai‘i"},{"id":404435,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221048","text":"Open-File Report 2022-1048","description":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, January 18, 2022, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1048, 11 p., https://doi.org/10.3133/ofr20221048.","linkHelpText":"- January 18, 2022, Red Hill Synoptic Groundwater-Level Survey, Hālawa Area, O‘ahu, Hawai‘i"},{"id":404434,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the nation","description":"U.S. Geological Survey, 2022, USGS water data for the nation: U.S. Geological Survey National Water Information System database, https://doi.org/10.5066/F7P55KJN."},{"id":404433,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1069/ofr20221069.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":404432,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1069/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hālawa Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.96142578124997,\n 21.317522325157526\n ],\n [\n -157.85156249999997,\n 21.317522325157526\n ],\n [\n -157.85156249999997,\n 21.409605198965597\n ],\n [\n -157.96142578124997,\n 21.409605198965597\n ],\n [\n -157.96142578124997,\n 21.317522325157526\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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Understanding age and growth of fishes is critical for making meaningful management decisions. Obtaining useful information is dependent on using the best structure (e.g., scale, otolith). The objective of this study was to evaluate precision and reader confidence in age estimates from sagittal otoliths (i.e., whole, sectioned) and scales for Yellowstone Cutthroat Trout Oncorhynchus clarkii bouvieri collected from Henrys Lake, Idaho. We also sought to compare growth estimates among structures sampled during annual gill net surveys in May 2019 and 2020. We removed sagittal otoliths and scales from 416 Yellowstone Cutthroat Trout. Two readers without prior knowledge of fish length independently aged scales, whole otoliths, and sectioned otoliths. Each reader also provided a confidence rating of 0 (not confident) to 3 (completely confident). Percent exact agreement between readers was highest for sectioned otoliths (85.3%), followed by scales (68.5%) and whole otoliths (66.1%). Average confidence rating was highest for sectioned (mean ± SD = 2.2 ± 0.6) and whole (1.4 ± 0.5) otoliths and lowest for scales (1.0 ± 0.2). Among structures, percent exact agreement (i.e., consensus age) was highest between whole and sectioned otoliths (66.7%), followed by scales and sectioned otoliths (58.9%). Exact agreement was lowest between scales and whole otoliths (51.2%). Differences in back-calculated length at age estimates between sectioned otoliths and scales were minimal, particularly for ages 1–4. Although sectioned otoliths required more time to prepare than scales or whole otoliths, sectioned otoliths produced the most precise age estimates for Yellowstone Cutthroat Trout, with the highest reader confidence.
Tephra is a unique volcanic product with an unparalleled role in understanding past eruptions, long-term behavior of volcanoes, and the effects of volcanism on climate and the environment. Tephra deposits also provide spatially widespread, high-resolution time-stratigraphic markers across a range of sedimentary settings and thus are used in numerous disciplines (e.g., volcanology, climate science, archaeology). Nonetheless, the study of tephra deposits is challenged by a lack of standardization that inhibits data integration across geographic regions and disciplines. We present comprehensive recommendations for tephra data gathering and reporting that were developed by the tephra science community to guide future investigators and to ensure that sufficient data are gathered for interoperability. Recommendations include standardized field and laboratory data collection, reporting and correlation guidance. These are organized as tabulated lists of key metadata with their definition and purpose. They are system independent and usable for template, tool, and database development. This standardized framework promotes consistent documentation and archiving, fosters interdisciplinary communication, and improves effectiveness of data sharing among diverse communities of researchers.
","language":"English","publisher":"Springer","doi":"10.1038/s41597-022-01515-y","usgsCitation":"Wallace, K.L., Bursik, M., Kuehn, S., Kurbatov, A., Abbot, P., Bonadonna, C., Cashman, K., Davies, S., Jensen, B.J., Lane, C., Plunkett, G., Smith, V., Tomlinson, E., Thordarsson, T., and Walker, J.D., 2022, Community established best practice recommendations for tephra studies— From collection through analysis: Scientific Data, v. 9, 447, 11 p., https://doi.org/10.1038/s41597-022-01515-y.","productDescription":"447, 11 p.","ipdsId":"IP-120110","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467172,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-022-01515-y","text":"Publisher Index 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NY","active":true,"usgs":false}],"preferred":false,"id":917116,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuehn, Stephen 0000-0002-2918-980X","orcid":"https://orcid.org/0000-0002-2918-980X","contributorId":345616,"corporation":false,"usgs":false,"family":"Kuehn","given":"Stephen","email":"","affiliations":[{"id":82658,"text":"Concord University, WV","active":true,"usgs":false}],"preferred":false,"id":917117,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kurbatov, Andrei 0000-0002-9819-9251","orcid":"https://orcid.org/0000-0002-9819-9251","contributorId":345617,"corporation":false,"usgs":false,"family":"Kurbatov","given":"Andrei","email":"","affiliations":[{"id":82659,"text":"University of Maine, ME","active":true,"usgs":false}],"preferred":false,"id":917118,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Abbot, Peter 0000-0002-6347-9499","orcid":"https://orcid.org/0000-0002-6347-9499","contributorId":345618,"corporation":false,"usgs":false,"family":"Abbot","given":"Peter","email":"","affiliations":[{"id":38843,"text":"University of Bern, Switzerland","active":true,"usgs":false}],"preferred":false,"id":917119,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bonadonna, Contanza 0000-0002-2368-2193","orcid":"https://orcid.org/0000-0002-2368-2193","contributorId":339895,"corporation":false,"usgs":false,"family":"Bonadonna","given":"Contanza","email":"","affiliations":[{"id":62805,"text":"Université de Genève","active":true,"usgs":false}],"preferred":false,"id":917120,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cashman, Katharine 0000-0001-9312-8377","orcid":"https://orcid.org/0000-0001-9312-8377","contributorId":345619,"corporation":false,"usgs":false,"family":"Cashman","given":"Katharine","email":"","affiliations":[{"id":38325,"text":"University of Bristol, 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0000-0001-9206-3903","orcid":"https://orcid.org/0000-0001-9206-3903","contributorId":345621,"corporation":false,"usgs":false,"family":"Lane","given":"Christine","email":"","affiliations":[{"id":52574,"text":"University of Cambridge, UK","active":true,"usgs":false}],"preferred":false,"id":917124,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Plunkett, Gill 0000-0003-1014-3454","orcid":"https://orcid.org/0000-0003-1014-3454","contributorId":288522,"corporation":false,"usgs":false,"family":"Plunkett","given":"Gill","email":"","affiliations":[{"id":61787,"text":"Queen’s University Belfast","active":true,"usgs":false}],"preferred":false,"id":917125,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Smith, Victoria 0000-0003-0878-5060","orcid":"https://orcid.org/0000-0003-0878-5060","contributorId":345622,"corporation":false,"usgs":false,"family":"Smith","given":"Victoria","email":"","affiliations":[{"id":82660,"text":"Research Laboratory for Archaeology and the History of Art, University of Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":917126,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Tomlinson, Emma 0000-0002-0646-6640","orcid":"https://orcid.org/0000-0002-0646-6640","contributorId":288524,"corporation":false,"usgs":false,"family":"Tomlinson","given":"Emma","email":"","affiliations":[{"id":61788,"text":"Trinity College Dublin","active":true,"usgs":false}],"preferred":false,"id":917127,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Thordarsson, Thor","contributorId":345623,"corporation":false,"usgs":false,"family":"Thordarsson","given":"Thor","email":"","affiliations":[{"id":35076,"text":"University of Iceland, Reykjavík, Iceland","active":true,"usgs":false}],"preferred":false,"id":917128,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Walker, J. Douglas 0000-0002-3706-2729","orcid":"https://orcid.org/0000-0002-3706-2729","contributorId":345624,"corporation":false,"usgs":false,"family":"Walker","given":"J.","email":"","middleInitial":"Douglas","affiliations":[{"id":82661,"text":"University of Kansas, Lawrence, KS, USA","active":true,"usgs":false}],"preferred":false,"id":917129,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70236242,"text":"70236242 - 2022 - Geological reservoir characterization of a gas hydrate prospect associated with the Hydrate-01 Stratigraphic Test Well, Alaska North Slope","interactions":[],"lastModifiedDate":"2022-08-31T11:51:24.970277","indexId":"70236242","displayToPublicDate":"2022-07-26T06:49:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12564,"text":"Journal of Energy and Fuels","active":true,"publicationSubtype":{"id":10}},"title":"Geological reservoir characterization of a gas hydrate prospect associated with the Hydrate-01 Stratigraphic Test Well, Alaska North Slope","docAbstract":"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.
We use amplitude ratios from narrowband-filtered earthquake seismograms to measure variations of seismic attenuation over time, providing unique insights into the dynamic state of stress in the Earth’s crust at depth. Our dataset from earthquakes of the 2016-2017 Central Apennines sequence allows us to obtain high-resolution time histories of seismic attenuation (frequency band: 0.5-30 Hz) characterized by strong earthquake dilatation-induced fluctuations at seismogenic depths, caused by the cumulative elastic stress drop after the sequence, as well as damage-induced ones at shallow depths caused by energetic surface waves.
Cumulative stress drop causes negative dilatation, reduced permeability, and seismic attenuation, whereas strong-motion surface waves produce an increase in crack density, and so in permeability and seismic attenuation. In the aftermath of the main shocks of the sequence, we show that the M ≥ 3.5 earthquake occurrence vs. time and distance is consistent with fluid diffusion: diffusion signatures are associated with changes in seismic attenuation during the first days of the Amatrice, Visso-Norcia, and Capitignano sub-sequences. We hypothesize that coseismic permeability changes create fluid diffusion pathways that are at least partly responsible for triggering multi-mainshock seismic sequences. Here we show that anelastic seismic attenuation fluctuates coherently with our hypothesis.
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":404243,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5062/coverthb.jpg"},{"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":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":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":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":404251,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5062/sir20225062.XML"},{"id":404252,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5062/images"}],"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
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":70234143,"text":"70234143 - 2022 - Barium enrichment in the non-spinose planktic foraminifer, Globorotalia truncatulinoides","interactions":[],"lastModifiedDate":"2022-08-02T11:46:22.195509","indexId":"70234143","displayToPublicDate":"2022-07-23T06:42:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Barium enrichment in the non-spinose planktic foraminifer, Globorotalia truncatulinoides","docAbstract":"Observations of elevated barium-to-calcium ratios (Ba/Ca) in Globorotalia truncatulinoides have been attributed to contaminant phases, deep calcification depth and diagenetic processes. Here we investigate intra- and inter-test Ba/Ca variability in the non-spinose planktic foraminifer, G. truncatulinoides, from a sediment trap time series in the northern Gulf of Mexico to gain insights into the environmental influences on barium enrichment in this and other non-spinose species. We use laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) to differentiate between the elemental composition of the crust and lamellar calcite in non-encrusted (<150 m calcification depth) and encrusted (>150 m calcification depth) specimens of G. truncatulinoides. We find that the Ba/Ca ratio in lamellar calcite is between two and three orders of magnitude higher (10–280 μmol/mol) than that of the crust (0–3 μmol/mol). We include seasonal water column profiles of the Ba/Ca ratio in the northern Gulf of Mexico and determine that the vertical gradient in seawater barium concentration cannot account for the intra-test Ba/Ca variations in G. truncatulinoides. We find the Ba/Ca ratio of the crust to be within the range observed in co-occurring spinose species of foraminifera (pink and white chromotypes of Globigerinoides ruber, and Orbulina universa) while the range of Ba/Ca in lamellar calcite is consistent with co-occurring non-spinose foraminifera (Pulleniatina obliquiloculata, Globorotalia menardii, G. tumida, and Neogloboquadrina dutertrei). Our data are consistent with the hypothesis that G. truncatulinoides calcifies in a marine snow aggregate microenvironment that is enriched in barium relative to ambient seawater. We suggest that G. truncatulinoides crust is formed after the rhizopodia retract and the foraminifer detaches from its marine snow substrate.
The U.S. Geological Survey (USGS) evaluated nitrate and orthophosphate concentrations in groundwater for temporal trends (monotonic and step trends) for the middle Snake River region (Cassia, Gooding, Jerome, Lincoln, Minidoka, and Twin Falls Counties) in south-central Idaho using the Regional Kendall test (monotonic trends) and the Wilcoxon signed rank test (step trends). The study evaluated two trend periods: 2000–09 and 2010–19/20. The study area was divided into six hydrogeologic zones (HZs) that had similar geologic and hydrologic characteristics and that correlated with county boundaries where possible. Two well networks sampled by the USGS National Water Quality Program within the HZs were also evaluated.
The northern Gooding County HZ had statistically significant increasing nitrate concentration trends for both the monotonic and step trends in the early trend period, while the Cassia and Jerome/Southern Gooding County HZs only had one of the statistical tests with statistically significant increasing nitrate concentrations. The Minidoka County HZ had conflicting results between the two statistical tests for the early time period with a statistically significant increasing monotonic trend in nitrate concentration and a statistically significant decreasing step trend. The differing results between these two statistical tests indicates the significance of concentration data during the middle of the time period. Both the Lincoln and Twin Falls County HZs did not have statistically significant trends for either test during either time period as well as the Northern Gooding County HZ for the latter time period. The Minidoka County HZ had statistically significant nitrate trends for both tests in the latter time period along with one of the trend tests for the Cassia and Jerome/Southern Gooding County HZ. Most of the nitrate concentration trend rates are low from 0.01 to 0.12 milligram per liter per year (mg/L/year) with the northern Gooding County HZ having the highest trend rate during the early time period of 0.28 mg/L/year for the step trend and 0.55 mg/L/year for the monotonic trend.
All the HZs and both well networks had statistically significant increasing orthophosphate-concentrations trends in groundwater for the early time period except for the Lincoln County HZ and the step-trend for the Minidoka County HZ. Orthophosphate concentration trend rates for the early period were low, ranging from 0.001 to 0.015 mg/L/year. Only two HZs and the well networks had enough orthophosphate concentration data available in the latter time period to do statistical analysis. The two HZs (Minidoka and Southern Gooding/Jerome County) both have decreasing orthophosphate concentration trends, with only the monotonic trend for the Southern Gooding/Jerome County HZ being statistically significant at 90 percent with a rate of −0.001 mg/L/year.
Groundwater levels in two well networks in the eastern Snake River Plain aquifer were also evaluated for trends (monotonic and step), with both networks having statistically significant declining groundwater levels for the 1993–2009 trend period. The latter trend period (2010–20) had statistically significant declining groundwater levels for the A&B well network and statistically significant increasing groundwater levels for the Jerome/Gooding well network, which is downgradient from an aquifer recharge area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225060","collaboration":"Prepared in cooperation with the Idaho Department of Environmental Quality and the Middle Snake Regional Water Resource Commission","usgsCitation":"Skinner, K.D., 2022, Trends in groundwater levels, and orthophosphate and nitrate concentrations in the Middle Snake River Region, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2022–5060, 18 p., https://doi.org/10.3133/sir20225060.","productDescription":"vii, 18 p.","onlineOnly":"Y","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":404369,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5060"},{"id":404371,"rank":5,"type":{"id":31,"text":"Publication 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Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
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.
Solutions for emerging infectious disease and bioterror threats can be improved by incorporating integrated biodefense strategies, including improved surveillance for animal and zoonotic diseases, strong national leadership, and effective management tools. Active biosurveillance for disease events is key to early detection, warning, and overall situational awareness and enables better communication, coordination, decision making, and data-driven responses. The national biosurveillance infrastructure has well-established channels for human and domestic animal health data through the Centers for Disease Control and Prevention and U.S. Department of Agriculture, and State, county, and local authorities. Wildlife disease information, however, has been more challenging to acquire and access, in part, due to the comparatively small infrastructure and resources dedicated to wildlife health and also because regulatory authority for wildlife and wildlife health is split among Federal, State, Tribal, and indigenous natural resource authorities. To address these issues, the Wildlife Health Information Sharing Partnership-event reporting system (WHISPers; https://whispers.usgs.gov) was developed by the U.S. Geological Survey National Wildlife Health Center to promote collaboration and sharing of wildlife health information and to provide situational awareness and timely information about wildlife disease threats. WHISPers is a free science gateway and data portal that provides interactive query, display, reporting, and export capabilities for wildlife health event summary information.
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U.S. Geological Survey
6006 Schroeder Road
Madison, WI 53711-6223
Urban development alters stormflow characteristics and is associated with increasing flood risks. The long-term evaluation of stormflow characteristics that exacerbate floods, such as peak stormflow and time-to-peak stormflow at varying levels of urbanization across different hydroclimates, is limited. This study investigated the long-term (1980s to 2010s) effects of increasing urbanization on key stormflow characteristics using observed 15 min streamflow data across six broad hydroclimate representative urban watersheds in the conterminous United States. The results indicate upward trends in peak stormflow and downward trends in time-to-peak stormflow at four out of six watersheds. The watershed in the Great Plains region had the largest annual increasing (decreasing) percent change in peak stormflow (time-to-peak stormflow). With the current change rates, peak stormflow in the Great Plains region watershed is expected to increase by 55.4% and have a 2.71 h faster time-to-peak stormflow in the next decade.
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Although small in land area, Rhode Island claims the largest estuary in New England in the 147-square-mile Narragansett Bay. Locals and visitors feast on clams caught in the bay, trek to glimpse shorebirds, or boat to 1 of 30 islands.
As with any coastal State, the natural wonders of Rhode Island face threats related to sea level rise and warming ocean temperatures. State agencies also work to fend off foes like the invasive Lymantria dispar (Linnaeus, 1758; spongy moth) and protect the forests that cover more than one-half of Rhode Island.
The U.S. Geological Survey Landsat Program, with 50 years of recurring Earth observations from space, offers a unique and freely available public data source for the study of land and coastal change across Rhode Island and the United States. Here are just a few of the ways Landsat imagery has been used to benefit the State.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Delaware’s status as the first State to ratify the U.S. Constitution is a well-known point of pride. “The First State” is among Delaware’s nicknames, alongside “the Blue Hen State,” “the Diamond State,” and “the Small Wonder,” the last of which relates to Delaware’s diminutive land area—larger only than Rhode Island.
Less well known, perhaps, is Delaware’s geographic distinction as the State with the lowest average elevation. Most of its land area rises no more than 80 feet above sea level. In fact, about 32,000 acres of Cypress Swamp, sometimes called the Great Cypress Swamp, stretch across its southern border.
These low elevations put Delaware at particular risk of sea level rise associated with climate change. Sea levels are rising more quickly than average for the Mid-Atlantic Region, which includes Delaware. The State has seen its coastal waters rise more than 1 foot over the past century.
The Landsat Program’s 50-year archive of repeat Earth observations offers an indispensable record of land change along the Nation’s coastlines. Imagery collected by Landsat satellites can inform studies of the coastline losses, flooding extents, and land cover conversions that affect climate resilience in Delaware. Landsat data also can support plans to mitigate those effects. Here are a few examples of the ways Delaware benefits from Landsat.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
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
At its widest point, a mere 80 miles separate the eastern and western borders of New Hampshire. Its northern and southern borders are just 175 miles apart. Even so, few States can boast as much rugged natural beauty per mile as the Nation’s fifth smallest.
Nestled within New Hampshire are 93 State parks teeming with moose, Ursus americanus (Pallas, 1780; black bears), coyotes, beavers, river otters, and foxes. The largest section of White Mountain National Forest cuts across north-central New Hampshire, drawing visitors to its lakes, streams, mountain peaks, and hardwood forests. New Hampshire also is home to Lake Winnipesaukee, the State’s largest lake, notable for its floating post offices, the annual “ice-out” contest that sees residents vying to guess the date its surface ice dissipates, and its supporting role in films such as “On Golden Pond” and “What About Bob?” However, the scenic forests of New Hampshire face challenges in the form of invasive species such as Lymantria dispar (Linnaeus, 1758; spongy moth), Adelges piceae (balsam woolly adelgid), and Agrilus planipennis (emerald ash borer). In recent years, New Hampshire’s lakes and streams have seen more cyanobacterial blooms as well.
The U.S. Geological Survey Landsat Program offers a consistent, reliable, and historically unmatched source of Earth observations that can aid in the mapping, monitoring, and management of New Hampshire’s land and water resources. Here are a few ways Landsat data have been used in the Granite State.
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Hampshire\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192