The associated uncertainties of future climate projections are one of the biggest obstacles to overcome in studies exploring the potential regional impacts of future climate shifts. In remote and climatically complex regions, the limited number of available downscaled projections may not provide an accurate representation of the underlying uncertainty in future climate or the possible range of potential scenarios. Consequently, global downscaled projections are now some of the most widely used climate datasets in the world. However, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we explore the utility of two such global datasets (CHELSA and WorldClim2) in providing plausible future climate scenarios for regional climate change impact studies. Our analysis was based on three steps: (1) standardizing a baseline period to compare available global downscaled projections with regional observation-based datasets and regional downscaled datasets; (2) bias correcting projections using a single observation-based baseline; and (3) having controlled differences in baselines between datasets, exploring the patterns and magnitude of projected climate shifts from these datasets to determine their plausibility as future climate scenarios, using Hawaiʻi as an example region. Focusing on mean annual temperature and precipitation, we show projected climate shifts from these commonly used global datasets not only may vary significantly from one another but may also fall well outside the range of future scenarios derived from regional downscaling efforts. As species distribution models are commonly created from these datasets, we further illustrate how a substantial portion of variability in future species distribution shifts can arise from the choice of global dataset used. Hence, projected shifts between baseline and future scenarios from these global downscaled projections warrant careful evaluation before use in climate impact studies, something rarely done in the existing literature.
This map of fractures, scarps, faults, and landslides was completed to identify areas in Glacier Bay National Park and Preserve that may present a landslide-generated tsunami hazard. To address the potential of landslide and tsunami hazards in the park, the National Park Service (NPS) and the US Geological Survey (USGS) partnered to conduct a multi-year hazard assessment of Glacier Bay National Park and Preserve. To produce the map described in this report, we used the newly acquired (2019-2020) light detection and ranging (LiDAR) 0.5 to 1.0 m digital elevation models (DEMs) that cover all the coastal areas of the park and extend up to the ridgetops in places with steep slopes. A bare earth DEM was used to identify and map areas of incipient landslides (i.e., fractures and scarps), fault scarps, and areas where landslides have clearly occurred in the past (i.e., areas where scars and deposits are clearly visible). This map provides a baseline data set that can be used to aid forecasts of where landslides are most likely to occur in the future.
","language":"English","publisher":"National Park Service","doi":"10.36967/2300706","collaboration":"National Park Service","usgsCitation":"Hults, C., Coe, J.A., and Avdievitch, N.N., 2023, Fractures, scarps, faults, and landslides mapped using LiDAR, Glacier Bay National Park and Preserve, Alaska, iv, 14 p., https://doi.org/10.36967/2300706.","productDescription":"iv, 14 p.","ipdsId":"IP-147660","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":423385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Glacier Bay National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -139.23040794383448,\n 59.9305550708161\n ],\n [\n -139.23040794383448,\n 57.252640525398476\n ],\n [\n -134.22064231883454,\n 57.252640525398476\n ],\n [\n -134.22064231883454,\n 59.9305550708161\n ],\n [\n -139.23040794383448,\n 59.9305550708161\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hults, Chad","contributorId":332290,"corporation":false,"usgs":false,"family":"Hults","given":"Chad","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":889926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":889927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Avdievitch, Nikita N. 0000-0002-2507-2962","orcid":"https://orcid.org/0000-0002-2507-2962","contributorId":225492,"corporation":false,"usgs":true,"family":"Avdievitch","given":"Nikita","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":889928,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70250639,"text":"70250639 - 2023 - Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability","interactions":[],"lastModifiedDate":"2024-10-15T17:20:38.127462","indexId":"70250639","displayToPublicDate":"2023-12-01T07:22:20","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"seriesTitle":{"id":18745,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":19}},"title":"Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability","docAbstract":"Topography provides information about the structural controls of the Great Basin and therefore information that may be used to identify favorable structural settings for geothermal systems. The Nevada Machine Learning Project (NVML) tested the use of a digital elevation map (DEM) of topography as an input feature to predict geothermal system favorability. A recent study re-examines the NVML data, identifying the DEM as the most important feature, showing a broad uniform pattern of high-favorability in the lower-elevation west and low-favorability in the higher elevation east of their study area in north-central Nevada. This regional elevation trend conflicts with the geologic notion that local relative topography should be used to identify geologic structures associated with favorable structural settings for hydrothermal upflow. Specifically, local relative topography gives information about position in the mountains, in the valleys, or at the transitions between, aiding in identification of faults and fault intersections. As part of U.S. Geological Survey efforts to engineer features that are useful for predicting geothermal resources, we construct a detrended elevation map that emphasizes local relative topography and highlights features that geologists use for identifying geothermal systems (i.e., providing machine learning algorithms with features that may improve predictive skill by emphasizing the information used by geologists). Herein, we describe the removal of the regional trend in elevation to emphasize the basin-and-range scale structural features, creating detrended elevation maps.\nRegional elevation trends were estimated using a local linear regression and subtracted from the actual elevation using a 30-m DEM. In an effort to optimize the detrended surface, alternate versions were produced with different rates of smoothness resulting in three detrended elevation maps. The resulting elevation trend surfaces (a proxy for crustal thickness) are compared with conductive heat flow maps, and a general pattern was observed of a negative correlation between heat flow and regional elevation in many areas, indicating that thinner crust may be causing elevated heat flow in some areas and thicker crust may cause the observed heat flow lows. Because these detrended elevation maps emphasize geologic structure and relative displacement, these products may also be useful for other geologic research including mineral exploration, hydrologic research, and defining geologic provinces.","language":"English","publisher":"Geothermal Rising","usgsCitation":"DeAngelo, J., Burns, E.R., Mordensky, S.P., and Lindsey, C.R., 2023, Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability, v. 47, p. 1694-1702.","productDescription":"9 p.","startPage":"1694","endPage":"1702","ipdsId":"IP-155138","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":423865,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":423843,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034786","linkFileType":{"id":5,"text":"html"}}],"volume":"47","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":890682,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":192154,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":890683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":890684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lindsey, Cary Ruth 0000-0001-5693-9664","orcid":"https://orcid.org/0000-0001-5693-9664","contributorId":292016,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":890685,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251485,"text":"70251485 - 2023 - Modeling extreme water levels in the Salish Sea: The importance of including remote sea level anomalies for application in hydrodynamic simulations","interactions":[],"lastModifiedDate":"2024-02-13T13:12:40.92934","indexId":"70251485","displayToPublicDate":"2023-12-01T07:05:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Modeling extreme water levels in the Salish Sea: The importance of including remote sea level anomalies for application in hydrodynamic simulations","docAbstract":"A variety of classification approaches are used to facilitate understanding, prediction, monitoring, and the management of lakes. However, broad-scale applicability of current approaches is limited by either the need for in situ lake data, incompatibilities among approaches, or a lack of empirical testing of approaches based on ex situ data. We developed a new geographic classification approach for 476,697 lakes ≥ 1 ha in the conterminous U.S. based on lake archetypes representing end members along gradients of multiple geographic features. We identified seven lake archetypes with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. Individual lakes were assigned weights for each of the seven archetypes such that groups of lakes with similar combinations of archetype weights tended to cluster spatially (although not strictly contiguous) and to have similar limnological properties (e.g., concentrations of nutrients, chlorophyll a (Chl a), and dissolved organic carbon). Further, archetype lake classification improved commonly measured limnological relationships (e.g., between nutrients and Chl a) compared to a global model; a discrete archetype classification slightly outperformed an ecoregion classification; and considering lakes as continuous mixtures of archetypes in a more complex model further improved fit. Overall, archetype classification of US lakes as continuous mixtures of geographic features improved understanding and prediction of lake responses to limnological drivers and should help researchers and managers better characterize and forecast lake states and responses to environmental change.
","language":"English","publisher":"Wiley","doi":"10.1002/lno.12457","usgsCitation":"Lapierre, J., Webster, K.E., Hanks, E., Wagner, T., Soranno, P.A., McCullough, I., Reinl, K.L., Domka, M., and Lotting, N.R., 2023, A continuous classification of the 476,697 lakes of the conterminous US based on geographic archetypes: Limnology and Oceanography, v. 69, no. 12, p. 2759-2773, https://doi.org/10.1002/lno.12457.","productDescription":"15 p.","startPage":"2759","endPage":"2773","ipdsId":"IP-145952","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":441521,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.12457","text":"Publisher Index Page"},{"id":433110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Montana, America’s fourth largest State with an area of 147,040 square miles, is defined by its diverse terrain. The western two-fifths of the State falls within the Rocky Mountains and the eastern three-fifths is in the Great Plains. Because of its location along the Continental Divide, the rivers in Montana drain into either the Pacific Ocean or the Gulf of Mexico. Montana is often called the Treasure State due to its mineral wealth, which includes oil, gas, and coal, but the State’s primary economic activity is agriculture. Other economic activities include natural resources conservation, water supply and quality, infrastructure and construction management, flood risk management, and geologic resource assessment and hazard mitigation. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233041","usgsCitation":"Carlson, T., 2023, The 3D Elevation Program—Supporting Montana’s economy: U.S. Geological Survey Fact Sheet 2023–3041, 2 p., https://doi.org/10.3133/fs20233041","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146062","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":422958,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3041/images/"},{"id":422955,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3041/fs20233041.pdf","text":"Report","size":"995 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3041"},{"id":422956,"rank":3,"type":{"id":39,"text":"HTML 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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Studying how species interact with their environment and other co-occurring species are 2 main aspects of ecology. For marine turtles, ocean currents drive migratory routes and may determine the location of surrounding foraging grounds. As a result, circumglobal species like the hawksbill turtle Eretmochelys imbricata and green turtle Chelonia mydas adapt to diverse foraging habitats and employ varied feeding strategies. Dietary specializations may reduce competition for available food and space resources between co-occurring hawksbill and green turtles in US Virgin Island shallow reef habitats. This study analyzed isotopic data from immature hawksbill (n = 49; range: 18.7-49.8 cm straight carapace length [SCL]) and green turtles (n = 225; range: 24.1-69.4 cm SCL) to examine foraging niche. We used nitrogen stable isotope (δ15N) values as an indicator of trophic positioning and carbon stable isotope (δ13C) values as a habitat variable. Turtles were hand-captured across an 8 yr period (2012-2019), which facilitated the distinction of isotopic patterns in both the environment and among individual turtles. Understanding variations in habitat, community dynamics, and dietary consumption allowed us to utilize a 5 point framework to translate isotopic space to foraging niche. We found that the site’s relatively stable environmental conditions allow for isotopic overlap between hawksbill and green turtles despite the specialized feeding strategies each species employs. We also underscore the need to evaluate species-specific tissue turnover estimates as evidenced by the influence of tropical storms on recaptured turtle isotopic signatures. These findings inform our understanding of resource use for these imperiled species at our study site and are useful for future global isotopic comparisons.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01276","usgsCitation":"Moorehouse, M.A., Baldwin, J.D., and Hart, K., 2023, Hawksbill and green turtle niche overlap in a marine protected area, US Virgin Islands: Endangered Species Research, v. 52, p. 265-283, https://doi.org/10.3354/esr01276.","productDescription":"19 p.","startPage":"265","endPage":"283","ipdsId":"IP-142711","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":441523,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01276","text":"Publisher Index Page"},{"id":435114,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ED29DJ","text":"USGS data release","linkHelpText":"Stable isotope signatures from green turtles and hawksbills at Buck Island Reef National Monument, U.S. Virgin Islands from 2012-2019"},{"id":423325,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"U.S. Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -65.06621906831514,\n 18.4297449274477\n ],\n [\n -65.06621906831514,\n 17.60831911760907\n ],\n [\n -64.46197361578831,\n 17.60831911760907\n ],\n [\n -64.46197361578831,\n 18.4297449274477\n ],\n [\n -65.06621906831514,\n 18.4297449274477\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moorehouse, Melissa A. 0000-0001-5891-4110","orcid":"https://orcid.org/0000-0001-5891-4110","contributorId":332281,"corporation":false,"usgs":false,"family":"Moorehouse","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":79441,"text":"United States Military Academy","active":true,"usgs":false}],"preferred":false,"id":889869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldwin, John D.","contributorId":210505,"corporation":false,"usgs":false,"family":"Baldwin","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":15312,"text":"Florida Atlantic University","active":true,"usgs":false}],"preferred":false,"id":889870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":889871,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70251909,"text":"70251909 - 2023 - Micropaleontological evidence of a submarine fan in the lower Coaledo Formation, Southwestern Oregon, USA","interactions":[],"lastModifiedDate":"2025-01-27T16:22:06.901739","indexId":"70251909","displayToPublicDate":"2023-11-30T06:57:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2294,"text":"Journal of Foraminiferal Research","active":true,"publicationSubtype":{"id":10}},"title":"Micropaleontological evidence of a submarine fan in the lower Coaledo Formation, Southwestern Oregon, USA","docAbstract":"The middle Eocene lower Coaledo Formation was interpreted as ten shoaling upward delta-margin cycles based on sediments and macrofauna. The strata, however, contains deep-water foraminifers. Explanations to resolve this anomaly included reworking, bathymetric range extension, or upward migration of water masses. Paleoecology analysis of foraminifers indicates that the few shelf species are poorly preserved whereas the well-preserved lower bathyal species dominate, and planktic organisms are present. Evidence for reworking, bathymetric range extension, or upward migration of water masses was not found in any of the cycles. The paleoecologic utility of hummocky cross-bedded sandstones is questioned as these features are controversial. In addition, there is no evidence of sea-level changes or tectonic activity to accommodate the bathymetric changes needed. Deposition of the lower Coaledo Formation on a submarine fan at lower bathyal depths eliminates the need to explain bathymetric anomalies or lack of tectonic movement.
The U.S. Geological Survey (USGS) operates the Core Research Center (CRC) in Denver, Colorado, USA, a public access repository of rock cores from over 9800 wells and drill cuttings from over 53 000 wells, primarily from states in or adjacent to the Rocky Mountain Region. Annually, approximately 1400 visitors use the collection for traditional and innovative research.
The CRC has an online, searchable database which includes downloadable core photos, analytical data, and thin-section images. When visitors sample for analyses, the results must be returned to the CRC for public dissemination providing immediate, free access to users while sparing the finite, irreplaceable collection from redundant testing. A representative quantity of every core depth is preserved in perpetuity.
Studies on CRC materials, paired with new extraction methods, have unlocked new productive deposits. Materials drilled and curated decades ago remain in high demand while materials receiving little attention today may be crucial for future research.
The collection provides immediate, inexpensive access to subsurface materials at a fraction of the cost of new drilling, sparing money, time and environmental impacts.
The “National Field Manual for the Collection of Water-Quality Data” (NFM) provides guidelines and procedures for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation’s surface-water and groundwater resources. This chapter, NFM A6.8, provides guidance and protocols for the use of multiparameter instruments for routine field measurements, which includes storage and maintenance of equipment, calibration, troubleshooting, and procedures for measurement and reporting. It updates and supersedes USGS Techniques of Water-Resources Investigations, book 9, chapter A6.8, version 1.1, by Jacob Gibs, Franceska D. Wilde, and Heather A. Heckathorn. The use of multiparameter instruments for conducting field measurements has become routine. The methods described here are specific to the use of multiparameter instruments. The field methods described in this chapter are applicable to most natural waters.
Before 2017, the NFM chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0) at https://doi.org/10.3133/tm9A0. The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.er.usgs.gov/. Comments, questions, and suggestions related to the NFM can be addressed to nfm@usgs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm9A6.8","usgsCitation":"U.S. Geological Survey, 2023, Use of multiparameter instruments for routine field measurements (ver. 1.1, June 2025): U.S. Geological Survey Techniques and Methods, book 9, chap. A6.8, 13 p., https://doi.org/10.3133/tm9A6.8. [Supersedes USGS Techniques of Water-Resources Investigations, book 9, chap. A6.8, version 1.1; and USGS Techniques and Methods, book 9, chap. A6.8, version 1.0.]","productDescription":"v, 13 p.","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118565","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":489432,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/09/a6.8/tm9a6.8.XML","linkFileType":{"id":8,"text":"xml"},"description":"T&M 9-A6.8 XML"},{"id":489431,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm9A6.8/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T&M 9-A6.8 HTML"},{"id":422959,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/tm9A0","text":"Techniques and Methods 9-A0","linkHelpText":"- General introduction for the “National Field Manual for the Collection of Water-Quality Data”"},{"id":422687,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/mission-areas/water-resources/science/national-field-manual-collection-water-quality-data-nfm","text":"National Field Manual for the Collection of Water-Quality Data (NFM)"},{"id":489433,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/09/a6.8/images/"},{"id":422685,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/09/a6.8/versionHist.txt","size":"3.97 KB","linkFileType":{"id":2,"text":"txt"},"description":"T&M 9-A6.8 version history"},{"id":422684,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/09/a6.8/tm9a6.8.pdf","text":"Report","size":"687 KB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 9-A6.8"},{"id":422683,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/09/a6.8/coverthb2.jpg"}],"edition":"Version 1.0: November 28, 2023; Version 1.1: June 12, 2025","contact":"Water Mission Area
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Email: nfm@usgs.gov
","tableOfContents":"Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We first define each of the major C pools and fluxes and provide rationale for their importance to wetland C dynamics. For each approach, we clarify what component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as where and when an approach is typically used, who can conduct the measurements (expertise, training requirements), and how approaches are conducted, including considerations on equipment complexity and costs. Finally, we review key covariates and ancillary measurements that enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-023-01722-2","usgsCitation":"Bansal, S., Creed, I., Tangen, B., Bridgham, S.D., Desai, A.R., Krauss, K., Neubauer, S.C., Noe, G.E., Rosenberry, D.O., Trettin, C.C., Wickland, K., Allen, S.T., Arias-Ortiz, A., Armitage, A.R., Baldocchi, D., Banerjee, K., Bastviken, D., Berg, P., Bogard, M.J., Chow, A.T., Conner, W.H., Craft, C., Creamer, C., Delsontro, T., Duberstein, J., Eagle, M.J., Fennessey, M.S., Finkelstein, S.A., Goeckede, M., Grunwald, S., Halibisky, M., Herbert, E.R., Jahangir, M., Johnson, O., Jones, M.C., Kelleway, J., Knox, S., Kroeger, K.D., Kuehn, K., Lobb, D., Loder, A., Ma, S., Maher, D., McNicol, G., Meier, J., Middleton, B.A., Mills, C.T., Mistry, P., Mitra, A., Mobilian, C., Nahlik, A.M., Newman, S., O’Connell, J., Oikawa, P., Post van der Burg, M., Schutte, C.A., Song, C., Stagg, C.L., Turner, J., Vargas, R., Waldrop, M., Wallin, M., Wang, Z.A., Ward, E., Willard, D., Yarwood, S.A., and Zhu, X., 2023, Practical guide to measuring wetland carbon pools and fluxes: Wetlands, v. 43, 105, 169 p., https://doi.org/10.1007/s13157-023-01722-2.","productDescription":"105, 169 p.","ipdsId":"IP-138829","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":441538,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13157-023-01722-2","text":"Publisher Index Page"},{"id":424194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","noUsgsAuthors":false,"publicationDate":"2023-11-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":891562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Creed, Irena F.","contributorId":81209,"corporation":false,"usgs":false,"family":"Creed","given":"Irena F.","affiliations":[{"id":27655,"text":"Department of Biology, University of Western Ontario, London, ON Canada","active":true,"usgs":false}],"preferred":false,"id":891563,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":891564,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bridgham, Scott D.","contributorId":177413,"corporation":false,"usgs":false,"family":"Bridgham","given":"Scott","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":891565,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Desai, Ankur R. 0000-0002-5226-6041","orcid":"https://orcid.org/0000-0002-5226-6041","contributorId":20622,"corporation":false,"usgs":false,"family":"Desai","given":"Ankur","email":"","middleInitial":"R.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":891566,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":223022,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":891567,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Neubauer, Scott C","contributorId":169723,"corporation":false,"usgs":false,"family":"Neubauer","given":"Scott","email":"","middleInitial":"C","affiliations":[{"id":25575,"text":"Dept. of Biology, Virginia Commonwealth University, Richmond, VA","active":true,"usgs":false}],"preferred":false,"id":891568,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":37277,"text":"WMA - 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A variety of forward models are used, from analytical point sources to numerical simulations that consider complex magma system geometries, topography, and material properties. Various inversion methods can then be used to relate observed volcano data to models. Ideally, forward models should be verified through intercomparison, to check for implementation errors and quantify the error induced by any approximations used. Additionally, forward models and inversion methods should be validated through tests with synthetic and/or real data, to determine their ability to match data and estimate parameter values within uncertainty. However, to date, there have not been comprehensive verification and validation efforts in volcano geodesy. Here, we report on the first phase of the Drivers of Volcano Deformation (DVD) exercises, which were designed to build community involvement through web-based exercises involving calculations of static elastic displacement around pressurized magma reservoirs. The forward model exercises begin with a spherical reservoir in a homogeneous half space, then introduce topography, heterogeneous elastic properties, and spheroidal geometries. The inversion exercises provide synthetic noisy surface displacement data for a spherical reservoir in a homogeneous half space and assess consistency in estimates of reservoir location and volume/pressure change. There is variability in the results from both forward modeling and inversions, which highlights the strengths and limitations of different forward models, as well as the importance of inversion method choice and uncertainty quantification. This first phase of the DVD exercises serves as a community resource and will facilitate further efforts to develop standards of reproducibility.
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Oceanography","active":true,"usgs":false}],"preferred":false,"id":888766,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Zhan, Yan","contributorId":328590,"corporation":false,"usgs":false,"family":"Zhan","given":"Yan","affiliations":[{"id":78417,"text":"Univ. of Breman","active":true,"usgs":false}],"preferred":false,"id":888767,"contributorType":{"id":1,"text":"Authors"},"rank":25}]}} ,{"id":70250161,"text":"70250161 - 2023 - Deer management generally reduces densities of nymphal Ixodes scapularis, but not prevalence of infection with Borrelia burgdorferi sensu stricto","interactions":[],"lastModifiedDate":"2023-11-24T12:54:19.730004","indexId":"70250161","displayToPublicDate":"2023-11-24T06:43:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5082,"text":"Ticks and Tick-borne Diseases","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Deer management generally reduces densities of nymphal Ixodes scapularis, but not prevalence of infection with Borrelia burgdorferi sensu stricto","title":"Deer management generally reduces densities of nymphal Ixodes scapularis, but not prevalence of infection with Borrelia burgdorferi sensu stricto","docAbstract":"Human Lyme disease–primarily caused by the bacterium Borrelia burgdorferi sensu stricto (s.s.) in North America–is the most common vector-borne disease in the United States. Research on risk mitigation strategies during the last three decades has emphasized methods to reduce densities of the primary vector in eastern North America, the blacklegged tick (Ixodes scapularis). Controlling white-tailed deer populations has been considered a potential method for reducing tick densities, as white-tailed deer are important hosts for blacklegged tick reproduction. However, the feasibility and efficacy of white-tailed deer management to impact acarological risk of encountering infected ticks (namely, density of host-seeking infected nymphs; DIN) is unclear. We investigated the effect of white-tailed deer density and management on the density of host-seeking nymphs and B. burgdorferi s.s. infection prevalence using surveillance data from eight national parks and park regions in the eastern United States from 2014–2022. We found that deer density was significantly positively correlated with the density of nymphs (nymph density increased by 49% with a 1 standard deviation increase in deer density) but was not strongly correlated with the prevalence of B. burgdorferi s.s. infection in nymphal ticks. Further, while white-tailed deer reduction efforts were followed by a decrease in the density of I. scapularis nymphs in parks, deer removal had variable effects on B. burgdorferi s.s. infection prevalence, with some parks experiencing slight declines and others slight increases in prevalence. Our findings suggest that managing white-tailed deer densities alone may not be effective in reducing DIN in all situations but may be a useful tool when implemented in integrated management regimes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ttbdis.2023.102202","usgsCitation":"Martin, A., Buttke, D., Raphael, J., Taylor, K., Maes, S., Parise, C.M., Ginsberg, H., and Cross, P., 2023, Deer management generally reduces densities of nymphal Ixodes scapularis, but not prevalence of infection with Borrelia burgdorferi sensu stricto: Ticks and Tick-borne Diseases, v. 14, no. 5, 102202, 15 p., https://doi.org/10.1016/j.ttbdis.2023.102202.","productDescription":"102202, 15 p.","ipdsId":"IP-146009","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":441555,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ttbdis.2023.102202","text":"Publisher Index Page"},{"id":435115,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LSI8K9","text":"USGS data release","linkHelpText":"Blacklegged tick nymph densities, tickborne pathogen prevalence, and white-tailed deer densities in eight national parks in the eastern United States from 2014-2022"},{"id":422883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.04185662081417,\n 38.07679669581978\n ],\n [\n -77.04185662081417,\n 40.08490092740237\n ],\n [\n -77.97369181209007,\n 40.08490092740237\n ],\n [\n -77.97369181209007,\n 38.07679669581978\n ],\n [\n -77.04185662081417,\n 38.07679669581978\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Martin, Alynn 0000-0002-6603-2385","orcid":"https://orcid.org/0000-0002-6603-2385","contributorId":224233,"corporation":false,"usgs":true,"family":"Martin","given":"Alynn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":888616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buttke, Danielle","contributorId":225082,"corporation":false,"usgs":false,"family":"Buttke","given":"Danielle","affiliations":[],"preferred":false,"id":888617,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Raphael, Jordan","contributorId":218631,"corporation":false,"usgs":false,"family":"Raphael","given":"Jordan","email":"","affiliations":[{"id":39877,"text":"National Park Service, Fire Island National Seashore","active":true,"usgs":false}],"preferred":false,"id":888618,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, Kelsey","contributorId":194103,"corporation":false,"usgs":false,"family":"Taylor","given":"Kelsey","email":"","affiliations":[],"preferred":false,"id":888619,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maes, Sarah","contributorId":331731,"corporation":false,"usgs":false,"family":"Maes","given":"Sarah","email":"","affiliations":[],"preferred":false,"id":888641,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parise, Christina M.","contributorId":331732,"corporation":false,"usgs":false,"family":"Parise","given":"Christina","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":888642,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ginsberg, Howard 0000-0002-4933-2466","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":15473,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":888620,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":218820,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":888621,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70250157,"text":"70250157 - 2023 - Multi-omic responses of fish exposed to complex chemical mixtures in the Shenandoah River watershed","interactions":[],"lastModifiedDate":"2023-11-24T12:43:08.489657","indexId":"70250157","displayToPublicDate":"2023-11-24T06:35:50","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Multi-omic responses of fish exposed to complex chemical mixtures in the Shenandoah River watershed","docAbstract":"To evaluate relationships between different anthropogenic impacts, contaminant occurrence, and fish health, we conducted in situ fish exposures across the Shenandoah River watershed at five sites with different land use. Exposure water was analyzed for over 500 chemical constituents, and organismal, metabolomic, and transcriptomic endpoints were measured in fathead minnows. Adverse reproductive outcomes were observed in fish exposed in the upper watershed at both wastewater treatment plant (WWTP) effluent- and agriculture-impacted sites, including decreased gonadosomatic index and altered secondary sex characteristics. This was accompanied with increased mortality at the site most impacted by agricultural activities. Molecular biomarkers of estrogen exposure were unchanged and consistent with low or non-detectable concentrations of common estrogens, indicating that alternative mechanisms were involved in organismal adverse outcomes. Hepatic metabolomic and transcriptomic profiles were altered in a site-specific manner, consistent with variation in land use and contaminant profiles. Integrated biomarker response data were useful for evaluating mechanistic linkages between contaminants and adverse outcomes, suggesting that reproductive endocrine disruption, altered lipid processes, and immunosuppression may have been involved in these organismal impacts. This study demonstrated linkages between human-impact, contaminant occurrence, and exposure effects in the Shenandoah River watershed and showed increased risk of adverse outcomes in fathead minnows exposed to complex mixtures at sites impacted by municipal wastewater discharges and agricultural practices.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2023.165975","usgsCitation":"Bertolatus, D., Barber, L., Martyniuk, C.J., Zhen, H., Collette, T.W., Ekman, D.R., Jastrow, A., Rapp, J., and Vajda, A.M., 2023, Multi-omic responses of fish exposed to complex chemical mixtures in the Shenandoah River watershed: Science of the Total Environment, v. 902, 165975, 13 p., https://doi.org/10.1016/j.scitotenv.2023.165975.","productDescription":"165975, 13 p.","ipdsId":"IP-096955","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":467073,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10592118","text":"External Repository"},{"id":422882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia, West Virginia","otherGeospatial":"Shenandoah River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.9727012249313,\n 39.59725275152107\n ],\n [\n -78.22802064680776,\n 39.25705449140892\n ],\n [\n -78.4769570831382,\n 39.26199675696881\n ],\n [\n -79.23014937767479,\n 38.676407590053856\n ],\n [\n -79.70249030814699,\n 38.621572296491735\n ],\n [\n -80.34078886283893,\n 37.70317136166855\n ],\n [\n -80.41738468940203,\n 37.31836727052378\n ],\n [\n -80.20036318080633,\n 36.763022451379456\n ],\n [\n -79.68334135150582,\n 36.660683818457315\n ],\n [\n -79.07057473900181,\n 37.23710331035643\n ],\n [\n -77.99185018157169,\n 37.97537751570279\n ],\n [\n -77.42376446789602,\n 38.73120090646128\n ],\n [\n -77.50674328000592,\n 39.25705449140892\n ],\n [\n -77.9727012249313,\n 39.59725275152107\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"902","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bertolatus, David 0000-0002-6829-9454","orcid":"https://orcid.org/0000-0002-6829-9454","contributorId":220848,"corporation":false,"usgs":false,"family":"Bertolatus","given":"David","email":"","affiliations":[{"id":16824,"text":"University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":888604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barber, Larry B. 0000-0002-0561-0831","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":218953,"corporation":false,"usgs":true,"family":"Barber","given":"Larry B.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":888605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martyniuk, Christopher J. 0000-0003-0921-4796","orcid":"https://orcid.org/0000-0003-0921-4796","contributorId":331726,"corporation":false,"usgs":false,"family":"Martyniuk","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":79275,"text":"Center for Environmental and Human Toxicology and the Department of Physiological Sciences, University of Florida Genetics Institute, College of Veterinary Medicine, Gainesville, FL","active":true,"usgs":false}],"preferred":false,"id":888606,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhen, Huajun","contributorId":217485,"corporation":false,"usgs":false,"family":"Zhen","given":"Huajun","email":"","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":888607,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collette, Timothy W.","contributorId":217482,"corporation":false,"usgs":false,"family":"Collette","given":"Timothy","email":"","middleInitial":"W.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":888608,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ekman, Drew R.","contributorId":217483,"corporation":false,"usgs":false,"family":"Ekman","given":"Drew","email":"","middleInitial":"R.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":888609,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jastrow, Aaron 0000-0002-1250-4666","orcid":"https://orcid.org/0000-0002-1250-4666","contributorId":304149,"corporation":false,"usgs":false,"family":"Jastrow","given":"Aaron","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":888610,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rapp, Jennifer","contributorId":222688,"corporation":false,"usgs":true,"family":"Rapp","given":"Jennifer","email":"","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":888611,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vajda, Alan M.","contributorId":156301,"corporation":false,"usgs":false,"family":"Vajda","given":"Alan","email":"","middleInitial":"M.","affiliations":[{"id":6713,"text":"University of Colorado, Boulder CO","active":true,"usgs":false}],"preferred":false,"id":888612,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70250273,"text":"70250273 - 2023 - A community convention for ecological forecasting: Output files and metadata version 1.0","interactions":[],"lastModifiedDate":"2023-11-30T13:09:22.449203","indexId":"70250273","displayToPublicDate":"2023-11-23T07:04:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"A community convention for ecological forecasting: Output files and metadata version 1.0","docAbstract":"This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma-separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross-forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short-term dissemination and long-term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process.
The authors used decision and modeling analyses to evaluate management alternatives for a decision on whether to permit Cervus canadensis (elk) feeding on two sites on Bridger-Teton National Forest, Dell Creek and Forest Park. Supplemental feeding of elk could increase the transmission of chronic wasting disease (CWD) locally and disease spread regionally, potentially impacting elk populations over time with wider implications for Odocoileus hemionus (mule deer) and Odocoileus virginianus (white-tailed deer) populations and hunting, tourism, and regional revenue. Supplemental feeding is thought to improve overwinter elk survival and reduce the commingling of elk with cattle during months when brucellosis transmission risk is highest. We worked with the U.S. Department of Agriculture Forest Service to identify their fundamental objectives and associated performance metrics related to this feedground decision. We then developed disease and habitat selection models to quantify the effect of four management alternatives on select performance metrics. The four alternatives were to continue to permit feeding, phaseout permits to feed in three years, permit feeding on an emergency basis, or stop permitting feeding. In this report, we present methods and summarized results on disease and habitat selection models and summaries of other performance metrics analyzed by BIO-WEST, Inc. and Cirrus Ecological Solutions as part of an Environmental Impact Statement.
Data from Wyoming Game and Fish Department (WGFD) supported the assumption that supplemental elk feeding allows for larger elk populations in a region. We documented that herd units (HU) without feedgrounds had 23 percent lower densities of elk per area of winter range when compared against HUs with feedgrounds, after accounting for differences in sightability of elk during counts on and off feedgrounds. Thus, throughout our analyses, we assumed feedground closures would reduce elk carrying capacity resulting in an average decline of previously fed elk population segments by 23 percent (5th and 95th percentiles = [11 percent, 35 percent]) by year 20. Most of that decline occurred within the first few years after a feedground ceases to operate. We used a panel of CWD experts to help estimate CWD trans-mission in fed and unfed elk population segments. In aggregate, the expert panel estimated that median values of direct and indirect transmission of CWD are expected to be 1.9 and 4 times higher, respectively, in fed elk populations compared to unfed elk. We used these disease transmission estimates in combination with local elk demographic rates and carrying capacity estimates to project disease and population dynamics.
In year 20, we predicted CWD prevalence would increase to 42 percent (5th and 95th percentiles = [29 percent, 55 percent]), and 13 percent (5th and 95th percentiles = [4 percent, 26 percent]) on average for fed and unfed elk population segments, respectively, given a starting prevalence of 1.6 percent. The prevalence estimates for the unfed elk population segments are in the range of previous observations of CWD in elk in the western United States. The average CWD prevalence from 2016 to 2018 in the unfed elk population of Wind Cave National Park in South Dakota was 18 percent overall but up to 30 percent in some regions (Sargeant and others, 2021). Meanwhile, CWD prevalence in the Iron Mountain and Laramie Peak elk herds in Wyoming from 2016 to 2018 was 14 percent and 7 percent, respectively, despite being present since at least 2002 (Wyoming Game and Fish Department, 2020b).
From 2016 to 2020, elk that were fed at Dell Creek and Forest Park constituted on average 12–20 percent of the total elk on their respective HUs. As a result, the differences between management alternatives are modest when considering the closure of only one feedground on a HU. The no feeding alternative for Forest Park resulted in a CWD prevalence of 17 percent (SD = 7 percent) in the Afton HU compared to 20 percent (SD = 7 percent) with continued feeding by year 20. In the Upper Green River HU, no feeding on Dell Creek resulted in a CWD prevalence of 27 percent (SD = 6 percent) compared to 30 percent (SD = 5 percent) with continued feeding. In terms of disease-associated mortality, we predicted the closure of Forest Park and Dell Creek feedgrounds would reduce the total number of CWD mortalities by 9 percent in the Upper Green River HU and 26 percent in the Afton HU during the 20-year timespan.
Our spatial analyses predicted that management alternative effects vary by HU as a function of private property and other wildlife winter ranges proximity relative to feedground location. The predicted number of elk abortions on private land, as a proxy for brucellosis risk to cattle, may increase by 8–21 percent in the absence of feeding at Dell Creek and Forest Park.
Eight feedgrounds are located on Bridger-Teton National Forest, all of which have permits that have expired or will expire prior to 2028. In addition, WGFD could change their management of feedgrounds given new information; therefore, we also assessed the cumulative effects of continued feeding, phaseout, and no feeding management alternatives across five HUs south of Jackson, Wyoming (Afton HU, Fall Creek HU, Piney HU, Pinedale HU, and Upper Green River HU). These five HUs ranged from about 41 to 85 percent of the elk herd using feedgrounds, which corresponded to a CWD prevalence at year 20 of 23–34 percent if all feedgrounds in those five HUs remained open relative to 12 to 14 percent if all feedgrounds were closed. We predicted feedground closures may result in immediate reductions in population size relative to alternatives that continue feeding (for example, continued feeding and emergency feeding alternatives); however, over longer periods of time, CWD-associated mortality leads to larger population reductions. The no feeding alternative resulted in higher elk population sizes compared to the continued feeding alternative after about 10 years of implementation. Delayed action under a phaseout alternative resulted in increasing the CWD prevalence to 20 percent relative to 12 to 14 percent, on average, without feeding on HUs with a large population of fed elk such as the Upper Green River HU.
Summarizing our cumulative results across all five of the analyzed HUs, we predicted continued feeding will lead to fewer elk by year 20 (mean = 8,300, standard deviation [SD] = 740) compared to no feeding at U.S. Department of Agri-culture Forest Service sites (10,700, SD = 890). The closure of all feedgrounds was projected to result in the largest elk populations at year 20 (12,500, SD = 980). No feeding at all sites also resulted in the largest cumulative harvest of 57,700 (SD = 2,600) compared to 51,100 (SD = 3,800) for continued feeding at all current feedground sites on the five HUs. Continued feeding also resulted in the lowest brucellosis costs to producers ($194,600, SD = $11,500) compared to no feeding on all feedgrounds ($243,000, SD = $13,700). Assuming moderate reductions in hunter interest because of increasing CWD prevalence in elk, we predicted that no feeding resulted in regional revenues generated by hunting activities of $190 million (SD = $10 million) compared to $173 million (SD = $10 million) for continued feeding over the 20-year timeframe.
Recent CWD detections in mule deer and elk in Grand Teton National Park has elevated the importance of the cur-rent decision on whether, and how, to permit elk feeding on Dell Creek and Forest Park and the management of the other feedgrounds. Aggressive male harvest has slowed, but not stopped, the increasing prevalence of CWD in mule deer (Conner and others, 2021). It is unclear whether harvest management can be an effective tool to slow the spread of CWD in elk. There are also no effective treatments or vaccines for CWD, and it is unlikely that any will be developed that can be easily deployed in the near future. Thus, reducing artificial aggregations is one of the few management approaches suggested by the Western Association of Fish and Wildlife Agencies (Almberg and others, 2017).
Future surveillance and monitoring can be designed to resolve uncertainties that can improve future decision-making. If feedgrounds close, research could quantify elk population reductions in the absence of feeding, the redistribution of fed elk to other places, or the consequences of elk movement on private property. If feedgrounds remain open, research could assess how rapidly CWD spreads in artificial aggregations of elk; however, surveillance programs would need to be designed with sufficient power to detect initial changes of CWD prevalence. Delaying action on feedground management was projected to be costly. Results of the phaseout alternative relative to the no feeding alternative suggested a 3-year delay was enough for substantial long-term changes in CWD prevalence. The long-term persistence of infectious CWD prions in the environment suggests that feedground management decisions may have long-lasting consequences.
Our results indicated tradeoffs in the ability of a management agency to achieve all their objectives, and all management alternatives resulted in significant reductions in elk population size. This report contains the foundational elements for formal decision analysis methods, which can be implemented to help decision makers transparently evaluate the consequences of decision alternatives and identify the set of actions that best achieve agency and stakeholder priorities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231015","collaboration":"Prepared in cooperation with U.S. Department of Agriculture, National Park Service, U.S. Fish and Wildlife Service, and Wyoming Game and Fish Department","usgsCitation":"Cook, J.D., Cross, P.C., Tomaszewski, E.M., Cole, E.K., Campbell Grant, E.H., Wilder, J.M., and Runge, M.C., 2023, Evaluating management alternatives for Wyoming Elk feedgrounds in consideration of chronic wasting disease (ver. 2.0, November 2023): U.S. Geological Survey Open-File Report 2023–1015, 50 p., https://doi.org/10.3133/ofr20231015.","productDescription":"Report: ix, 50 p.; Software Release","onlineOnly":"Y","ipdsId":"IP-145385","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":499766,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114473.htm","linkFileType":{"id":5,"text":"html"}},{"id":422707,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2023/1015/versionHist.txt","size":"4.0kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2023-1015 history file"},{"id":422706,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1015/ofr20231015.pdf","text":"Report","size":"7.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1015"},{"id":419233,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1015/coverthb2.jpg"},{"id":422704,"rank":2,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9R7XWO1","text":"USGS software release—","linkHelpText":"Simulating chronic wasting disease on Wyoming elk feedgrounds (version 2.0)."}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.03672229293583,\n 43.73180346838649\n ],\n [\n -111.03672229293583,\n 42.40523773968059\n ],\n [\n -109.27478197144448,\n 42.40523773968059\n ],\n [\n -109.27478197144448,\n 43.73180346838649\n ],\n [\n -111.03672229293583,\n 43.73180346838649\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: March 2023: Version 2.0: November 2023","contact":"Director, Northern Rocky Mountain Science Center
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A 21-layer three-dimensional transient groundwater-flow model of the New Jersey Coastal Plain was developed and calibrated by the U.S. Geological Survey (USGS) in cooperation with the New Jersey Department of Environmental Protection to simulate groundwater-flow conditions during 1980–2013, incorporating average annual groundwater withdrawals and average annual groundwater recharge. This model is the third version of the New Jersey Coastal Plain regional groundwater-flow model that was initially developed as part of the USGS Regional Aquifer System Analysis (RASA) program. The model simulates groundwater flow in 11 aquifers and 10 intervening confining units of the New Jersey Coastal Plain to provide a regional overview of groundwater conditions. Averaged groundwater withdrawal data for 1980 to 2013 were used in the model. The 11 aquifers in New Jersey are, from shallowest to deepest, the Holly Beach water-bearing zone and the confined Cohansey aquifer in Cape May County; the Rio Grande water-bearing zone; the Atlantic City 800-foot sand; the Piney Point, Vincentown, and Wenonah-Mount Laurel aquifers; the Englishtown aquifer system; and the upper, middle, and lower aquifers of the Potomac-Raritan-Magothy (PRM) aquifer system.
The model was developed with the MODFLOW–2005 numerical code and the UCODE parameter estimation technique and calibrated using water-level and base-flow observations. A total of 3,453 water-level observations from 392 wells in New Jersey and 48 wells in Delaware from 1983 to 2013 were used in model calibration, which includes historical water-level trends for 29 wells in New Jersey during 1980–2013 presented in time-series hydrographs. In addition, derived observations also were included by calculating the vertical gradient at 33 pairs of nested observation wells in New Jersey, for a total of 210 observations. Changes in water levels over time were calculated for 134 wells in New Jersey and four wells in Delaware where water levels had varied substantially (approximately 10 ft) over the 30-year span of synoptic water-level measurements, for a total of 767 observations. A total of 1,485 base-flow observations in 47 surface-water basins in New Jersey from 1980 to 2013 were used in model calibration.
Updates to the groundwater-flow model include the conversion to a fully three-dimensional model from the previous quasi-three-dimensional model. The new model will allow for potential future uses such as particle tracking or simulation of variable-density groundwater flow that could not be accomplished with earlier versions of the model. Spatially and temporally variable recharge estimated by using a soil-water balance model resulted in a spatially and temporally finer discretization. The Rio Grande water-bearing zone was added to the model as an aquifer layer to refine estimates of simulated flow in Atlantic and Cape May Counties, New Jersey. Hydrogeologic parameters were updated to include the confining units in New Jersey and corresponding hydrogeologic units in Delaware and eastern Maryland.
The simulated water levels for the New Jersey Coastal Plain aquifers were compared to water-level measurements made during 1980–2013. The average residual for 4,243 water-level observations for New Jersey (simulated water levels minus measured water levels) is 1.5 feet. The simulated water-level contours for the confined aquifers for 2013 were compared to potentiometric surfaces produced from water levels measured during 2013. Simulated water levels generally matched the 2013 potentiometric surfaces of the confined aquifers in the areas of large withdrawals. Hydrographs of wells in the confined Coastal Plain aquifers of New Jersey show that simulated water levels generally match the magnitude and seasonal variation of the observed water levels. Hydrographs of base flow for the 47 streamgaging stations in New Jersey indicate that most of the simulated and estimated data match reasonably well.
Groundwater withdrawals are an important resource for water supply, agricultural, industrial, and commercial needs in the New Jersey Coastal Plain. Groundwater withdrawals from the New Jersey Coastal Plain aquifers have resulted in persistent, regionally extensive cones of depression in the Englishtown aquifer system and Wenonah-Mount Laurel aquifer in Ocean and Monmouth Counties; Wenonah-Mount Laurel and upper, middle, and lower PRM aquifers in Camden County; and Atlantic City 800-foot sand in Atlantic County. Because hydrologic stresses and water-management needs change with time, periodic updates to the groundwater-flow model are required to provide current information about hydrologic conditions in the New Jersey Coastal Plain and to maintain its usefulness as a tool to manage water resources and develop water-resource strategies. The current updates will support the continued application of this model as a tool for evaluating the regional effects of changes in groundwater withdrawals and of current and potential future water-management strategies on groundwater levels in the New Jersey Coastal Plain.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235066","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Gordon, A.D., and Carleton, G.B., 2023, Updates to the regional groundwater-flow model of the New Jersey Coastal Plain, 1980–2013: U.S. Geological Survey Scientific Investigations Report 2023–5066, 116 p., https://doi.org/10.3133/sir20235066","productDescription":"Report: xii, 116 p.; Data Release","numberOfPages":"116","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127396","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":500947,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115639.htm","linkFileType":{"id":5,"text":"html"}},{"id":422695,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5066/images/"},{"id":422693,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235066/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5066"},{"id":422696,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W6RXFC","text":"USGS data release","linkHelpText":"MODFLOW-2005 model used to simulate the regional groundwater flow system in the updated New Jersey Coastal Plain model, 1980-2013"},{"id":422694,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5066/sir20235066.XML"},{"id":422692,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5066/sir20235066.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5066"},{"id":422691,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5066/coverthb.jpg"}],"country":"United States","otherGeospatial":"New Jersey Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.49018324613056,\n 41.03712838002892\n ],\n [\n -75.25922621488034,\n 41.417217443631785\n ],\n [\n -77.41254652738019,\n 39.17183412365296\n ],\n [\n -75.22626723050551,\n 37.8132834585617\n ],\n [\n -72.98505629300531,\n 40.4043207917766\n ],\n [\n -74.49018324613056,\n 41.03712838002892\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
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Because of its geography, Missouri is frequently subject to natural disasters. Ice storms, severe thunderstorms, tornadoes, and flooding are all common occurrences. Since 1990, Missouri has received 40 Federal major disaster declarations. Floods and droughts severely affect the State’s agriculture, which is a leading industry. Another potential major hazard is the New Madrid seismic zone (NMSZ), located in southeastern Missouri. Because Missouri is a major producer of lead, manufacturing and mining are very important to the State’s economy, as are restoring and reclaiming lands damaged by historical mining activities. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"During 2020–22, the U.S. Geological Survey, in cooperation with the Edwards Aquifer Authority, revised a previous publication that described the geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Bexar and Comal Counties, Texas. This report presents the refined maps and descriptions of geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Bexar and Comal Counties that resulted from additional field data. Two informal geologic units and their corresponding informal hydrostratigraphic unit (HSU) names are introduced in this report; these informal units were identified during geologic mapping work done in counties adjoining the study area. Hydrostratigraphically, the rocks exposed in the study area represent a section of the upper confining unit to the Edwards aquifer, the Edwards aquifer, the upper zone of the Trinity aquifer, the middle zone of the Trinity aquifer, and the lower confining unit to the middle zone of the Trinity aquifer. The Washita, Eagle Ford, Austin, and Taylor Groups are generally considered to be the upper confining unit to the Edwards aquifer. The Edwards aquifer was subdivided into nine informally named HSUs (from top to bottom) as follows: I, II, III, IV, V, VI, VII, Seco Pass, and VIII. The upper zone of the Trinity aquifer was subdivided into five informal HSUs and two subunits (from top to bottom) as follows: cavernous, Camp Bullis, upper evaporite, fossiliferous (subunits: upper and lower), and lower evaporite. The middle zone of the Trinity aquifer was subdivided into nine named HSUs (from top to bottom) as follows: Bulverde, Little Blanco, Twin Sisters, Doeppenschmidt, Herff Falls (where present), Rust, Honey Creek, Hensell, and Cow Creek. The middle zone of the Trinity aquifer is underlain by the confining Hammett HSU. Groundwater recharge and flow paths in the study area are influenced not only by the hydrostratigraphic characteristics of the individual HSUs but also by faults and fractures.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The Mississippi Alluvial Plain, located in the south-central United States, is undergoing long-term groundwater-level declines within the surficial Mississippi River Valley alluvial aquifer (hereinafter referred to as “alluvial aquifer”), which has raised concerns about future groundwater availability. In some parts of the alluvial aquifer, groundwater availability for common uses such as irrigation, public supply, and domestic use is limited by quality (for example, high salinity) rather than quantity of water stored in the aquifer. The Mississippi Alluvial Plain region has an abundance of water-quality measurements in the alluvial aquifer and deeper aquifers; however, large areas lack direct measurements of salinity to evaluate regional groundwater availability. Statistical models can interpolate between wells to fill in spatial data gaps. In 2021, the U.S. Geological Survey trained two boosted regression tree (BRT) machine-learning models on specific conductance data available between 1942 and 2020 to predict spatially continuous surfaces of groundwater salinity at multiple depths for the alluvial aquifer and deeper aquifers. Well construction information, water levels, and surficial variables such as geomorphology and soils were included as explanatory variables in this baseline model. Additionally, subsurface electrical resistivity data from the first aquifer-wide aerial electromagnetic (AEM) survey for the region were incorporated to create a geophysical model. This work expands on prior BRT salinity predictions of the alluvial aquifer and extends predictions south to the Gulf of Mexico, where groundwater salinity is high. AEM survey data were not available for the southern extent of the alluvial aquifer at the time of modeling. A BRT model was trained without (baseline) and with (geophysical) AEM variables to test the ability of the models to predict salinity where explanatory data are missing and response data are sparse. Additionally, model sensitivity to AEM survey data was evaluated to better understand how AEM variables influence specific conductance predictions. Model performance was improved with the addition of geophysical data, which added three-dimensional information, thereby improving salinity predictions at depth. Groundwater specific conductance predictions can help inform other geophysical investigations in the southern extent of the study area, where high groundwater specific conductance can obfuscate changes in aquifer sediment resistivity and could limit groundwater resources for agricultural, public supply, and domestic uses.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, suite 100
Nashville, TN 37211