Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Informed analysis of policies related to food security, global climate change, wetland ecology, environmental nutrient flux, element cycling, groundwater weathering, continental denudation, human health, etc. depends to a large extent on quantitative estimates of solute mass fluxes into and out of all global element pools including the enigmatic global aquifer systems. Herein for the first time, we proffer the mean global solute concentration of all major and selected minor and trace solutes in the active groundwater that represents 99% of liquid fresh water on Earth. Concentrations in this significant element pool have yielded to a geospatial machine learning kNN-nearest neighbors’ algorithm with numerous geospatial predictors utilizing a large new lithology/climate/aquifer age/elevation based solute database. The predicted concentrations are consistent with traditional solute ratios, concentrations, and thermodynamic saturation indices.
In southeastern New York, the villages of Ellenville, Wurtsboro, Woodridge, the hamlet of Mountain Dale, and surrounding communities in the Neversink River and Rondout Creek drainage basins rely on wells that pump groundwater from valley-fill glacial aquifers for public water supply. Glacial aquifers are vulnerable to contamination because they are highly permeable and have a shallow depth to water table. To protect the quality of these water resources, water managers need accurate information about the areas that contribute recharge to production wells that pump from these aquifers. The New York State Department of Environmental Conservation and the New York State Department of Health designated eight priority wells in this region for which water supply protection is of primary concern.
The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health, began an investigation in 2019 with the general objectives of (1) improving understanding of regional groundwater-flow system, (2) delineating areas contributing recharge to eight priority production wells, and (3) quantifying the uncertainty of these contributing areas in a probabilistic way that can be used to inform decision-making related to priority well source-water protection. To complete these objectives, a MODFLOW 6 groundwater model was created encompassing the eight priority wells and the surrounding flow system, which includes parts of the Neversink River and Rondout Creek Basins in Sullivan County and Ulster County, New York. The model was built using Python tools (such as flopy, modflow-setup, and sfrmaker) that facilitate transparent and repeatable model development using existing datasets. The model parameters were estimated with a stepwise approach using an iterative ensemble smoother implementation of the Parameter ESTimation software PEST++ (version 5.0.0). We evaluated initial “best guess” parameter bounds with a prior Monte Carlo analysis. Results of the first prior Monte Carlo analysis were used to make informed adjustments to model parameter bounds (typically resulting in expanded bounds), and a second prior Monte Carlo analysis was run to identify improved ranges for model parameters during history matching.
The history matching effort produced an ensemble of parameter values for the groundwater-flow model that spans the range of values within prior uncertainty bounds. The ensemble is informed by the historical observation data, within a reasonable range of uncertainty on those observations. This history-matched ensemble was used in a particle tracking Monte Carlo analysis to delineate the areas contributing recharge to priority wells. The groundwater-flow and particle tracking (MODPATH7) models were run once for each ensemble member. Deterministic contributing areas computed for each ensemble member were aggregated to produce maps showing the probability that a location contributes recharge to priority wells. Finally, the particle tracking Monte Carlo analysis was repeated for six pumping scenarios, representing a wide range of possible pumping levels, to incorporate uncertainty in future pumping rates related to population growth or other management decisions. Increasing pumping rates generally led to larger contributing recharge areas and larger areas of high probability that a location contributes recharge to priority wells. These maps show the overall uncertainty of the areas contributing recharge to priority wells in the study area and provide a tool for risk-based decision making for protection of well source water.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215112","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health","usgsCitation":"Corson-Dosch, N.T., Fienen, M.N., Finkelstein, J.S., Leaf, A.T., White, J.T., Woda, J., and Williams, J.H., 2022, Areas contributing recharge to priority wells in valley-fill aquifers in the Neversink River and Rondout Creek drainage basins, New York: U.S. Geological Survey Scientific Investigations Report 2021–5112, 50 p., https://doi.org/10.3133/sir20215112.","productDescription":"Report: ix, 50 p.; 2 Data Releases","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125165","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":399109,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":399101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5112/coverthb.jpg"},{"id":399102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5112/sir20215112.pdf","text":"Report","size":"22.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5112"},{"id":399104,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5112/sir20215112.XML"},{"id":399105,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5112/images/"},{"id":399107,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://ny.water.usgs.gov/maps/neversink/","text":"Neversink-Rondout Source Water Mapper"},{"id":399982,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20215112/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5112"},{"id":399106,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225024","text":"Scientific Investigations Report 2022–5024","linkHelpText":"- Data Sources and Methods for Digital Mapping of Eight Valley-Fill Aquifer Systems in Upstate New York"},{"id":399108,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HWSOHP","text":"USGS data release","linkHelpText":"Groundwater model archive and workflow for Neversink/Rondout Basin, New York, source water delineation"},{"id":502119,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112974.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Neversink River and Rondout Creek Drainage Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.8663330078125,\n 41.40153558289846\n ],\n [\n -74.1961669921875,\n 41.40153558289846\n ],\n [\n -74.1961669921875,\n 41.99216023337633\n ],\n [\n -74.8663330078125,\n 41.99216023337633\n ],\n [\n -74.8663330078125,\n 41.40153558289846\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
In the Chenango River Basin of central New York, unconfined and confined glacial valley-fill aquifers are an important source of drinking-water supplies. The risk of contaminating water withdrawn by wells that tap these aquifers might be reduced if the areas contributing recharge to the wells are delineated and these areas protected from land uses that might affect the water quality. The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health, began an investigation in 2019 to improve understanding of groundwater flow and delineate areas contributing recharge to 16 production wells clustered in three study areas in the basin as part of an effort to protect the source of water to these wells. Areas contributing recharge were delineated on the basis of numerical steady-state groundwater-flow models representing long-term average hydrologic conditions.
In the Cortland study area, four water suppliers operate 10 production wells that withdraw a total average rate of 2,480 gallons per minute from an unconfined aquifer consisting of well-sorted sand and gravel deposits. Simulated areas contributing recharge to these wells at their average pumping rates covered a total area of 6.93 square miles. Simulated areas contributing recharge extend upgradient from the wells to upland till deposits and to groundwater divides. Some simulated areas contributing recharge include isolated areas remote from the wells. Short simulated groundwater traveltimes from recharging locations to discharging wells indicated that the wells are vulnerable to contamination from land-surface activities; 50 percent of the traveltimes were 10 years or less. Land cover in some of the areas contributing recharge included a substantial amount of urban and agriculture land use.
The groundwater-flow model of the Cortland study area was calibrated to available hydrologic data by inverse modeling using nonlinear regression. The parameter variance-covariance matrix from model calibration was used to create parameter sets that reflect the uncertainty of the parameter estimates and the correlation among parameters to evaluate the uncertainty associated with the single, predicted contributing areas to the wells. This analysis led to contributing areas expressed as a probability distribution. Because of the effects of parameter uncertainty, the size of the probabilistic contributing areas was larger than the size of the single, predicted contributing area for the wells. Thus, some areas not in the single, predicted contributing area might actually be in the contributing area, including additional areas of urban and agriculture land use that have the potential to contaminate groundwater. Additional areas that might be in the contributing area included recharge originating near the pumping wells that have relatively short groundwater-flow paths and traveltimes.
In each of the Greene and Cincinnatus study areas, one water supplier operates three wells that are screened near the top of the bedrock surface in a confined aquifer consisting of poorly to well-sorted sand and gravel deposits. This confined aquifer is overlain by a lacustrine confining unit of very fine sand, silt, and clay, which in turn is overlain by a thin unconfined aquifer of sand and gravel. The groundwater-flow models for these two areas were manually calibrated because of the limited hydrologic data. Simulated areas contributing recharge to the Greene study area wells covered a total area of 0.35 square mile for the average pumping rate of 170 gallons per minute. The contributing areas extended southeastward of the wells to the groundwater divide in the till uplands. The contributing areas also included remote, isolated areas on the opposite side of the Chenango River from the wells primarily in the till uplands. For the Cincinnatus study area wells, which have a low average pumping rate (34 gallons per minute), the simulated contributing areas totaled 0.06 square mile and were on the same side of the river as the wells, but they are isolated areas remote from the wells primarily in the till-covered bedrock uplands. Land cover in these contributing areas for both study areas is primarily agriculture and forested, with the contributing areas to the Greene study area wells also including some urban land uses. Because the Greene and Cincinnatus study area wells are screened relatively deep and some flow paths to the wells partly travel through the confining unit, which impedes the connection with surface sources of recharge, overall groundwater traveltimes are greater than for wells in the Cortland study area. Fifty percent of Cortland study area wells, but only 9 and 44 percent of Greene and Cincinnatus study area wells, respectively, have groundwater traveltimes of 10 years or less.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215083","collaboration":"Prepared in cooperation with New York State Department of Environmental Conservation and New York State Department of Health","usgsCitation":"Friesz, P.J., Williams, J.H., Finkelstein, J.S., and Woda, J.C., 2022, Areas contributing recharge to selected production wells in unconfined and confined glacial valley-fill aquifers in Chenango River Basin, New York (ver. 1.1, 2026): U.S. Geological Survey Scientific Investigations Report 2021–5083, 48 p., https://doi.org/10.3133/sir20215083.","productDescription":"Report: vi, 48 p.; 2 Data Releases; Database","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126791","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":502109,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112975.htm","linkFileType":{"id":5,"text":"html"}},{"id":500551,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5083/versionHist.txt","size":"892 B","linkFileType":{"id":2,"text":"txt"}},{"id":398545,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/sir20225024","text":"Scientific Investigations Report 2022–5024","linkHelpText":"- Data Sources and Methods for Digital Mapping of Eight Valley-Fill Aquifer Systems in Upstate New York"},{"id":398544,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":398543,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":398541,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5083/images/"},{"id":398540,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5083/sir20215083.XML"},{"id":398539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5083/sir20215083.pdf","text":"Report","size":"18.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5083"},{"id":398538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5083/coverthb3.jpg"},{"id":399980,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215083/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5083"},{"id":398542,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HU2G1K","text":"USGS data release","linkHelpText":"MODFLOW -NWT groundwater-flow models used to delineate areas contributing recharge to selected production wells in unconfined and confined glacial valley-fill aquifers in Chenango River Basin, New York"}],"country":"United States","state":"New York","otherGeospatial":"Chenango River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125000001,\n 42.13896840458089\n ],\n [\n -75.16845703125,\n 42.13896840458089\n ],\n [\n -75.16845703125,\n 42.90011265525331\n ],\n [\n -76.11328125000001,\n 42.90011265525331\n ],\n [\n -76.11328125000001,\n 42.13896840458089\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: May 2022; Version 1.1: April 2026","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Digital hydrogeologic maps were developed in eight study areas in upstate New York by the U.S. Geological Survey in cooperation with the New York State Department of Environmental Conservation. The digital maps define the hydrogeologic framework of the valley-fill aquifers and surrounding till-covered uplands in the vicinity of the villages of Ellenville and Wurtsboro and hamlets of Woodbourne and South Fallsburg in Sullivan and Ulster Counties, town of Greene in Chenango County, city of Cortland and town of Cincinnatus in Cortland County, city of Jamestown in Chautauqua County, city of Olean and village of Ellicottville in Cattaraugus County, and villages of Fishkill and Wappinger Falls in Dutchess County. The hydrogeologic framework provided the foundation for groundwater-flow models that were used in the delineation of areas contributing groundwater flow to production wells screened in four of the eight valley-fill aquifers considered in this study. The hydrogeologic framework for the other four study areas was developed for potential future use in groundwater contributing-area studies.
Data used in the creation of all digital surfaces and thicknesses included published surficial geology; aquifer maps and hydrogeologic sections; light detection and ranging (lidar) datasets; the Soil Survey Geographic Database; and lithologic well logs from the National Water Information System, New York State Department of Environmental Conservation, New York State Department of Transportation, and Empire State Organized Geologic Information System databases. Digital maps of the surficial geology; thickness of the surficial sand and gravel aquifers; and tops of the confining lacustrine silt and clay units, confined sand and gravel aquifers, and bedrock surfaces were created by using ArcGIS (a geographic information system). All surfaces and thicknesses were generated by using one of the following ArcGIS interpolation tools: Topo to Raster, Natural Neighbors, Kriging, or Empirical Bayesian Kriging. The datasets developed in this study provide a greater understanding of the underlying hydrogeologic framework in glacial valley-fill aquifers and can be applied in the evaluation of groundwater-supply development and protection.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225024","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Finkelstein, J.S., Woda, J.C., and Williams, J.H., 2022, Data sources and methods for digital mapping of eight valley-fill aquifer systems in upstate New York: U.S. Geological Survey Scientific Investigations Report 2022–5024, 21 p., https://doi.org/10.3133/sir20225024.","productDescription":"Report: v, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122133","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":398708,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":399981,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225024/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5024"},{"id":398710,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20215112","text":"Scientific Investigations Report 2021–5112","linkHelpText":"- Areas Contributing Recharge to Priority Wells in Valley-fill Aquifers in the Neversink River and Rondout Creek Drainage Basins, New York"},{"id":398707,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5024/images/"},{"id":398706,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5024/sir20225024.XML"},{"id":398705,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5024/sir20225024.pdf","text":"Report","size":"4.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5024"},{"id":398704,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5024/coverthb2.jpg"},{"id":398709,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20215083","text":"Scientific Investigations Report 2021–5083","linkHelpText":"- Areas Contributing Recharge to Selected Production Wells in Unconfined and Confined Glacial Valley-Fill Aquifers in Chenango River Basin, New York"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.200439453125,\n 40.59727063442024\n ],\n [\n -73.223876953125,\n 40.59727063442024\n ],\n [\n -73.223876953125,\n 43.48481212891603\n ],\n [\n -79.200439453125,\n 43.48481212891603\n ],\n [\n -79.200439453125,\n 40.59727063442024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The U.S. Geological Survey completed gravity transects in 2015, 2018, and 2019 over four features: the Bright Angel Fault, Bright Angel Monocline, Tusayan Graben, and Redlands Ranch Fault Zone in the Coconino Plateau, Coconino County, Arizona, to determine if the existence and width of high porosity (low density) zones could be inferred from the resulting gravity contrasts, which could be used to update groundwater models of the region. Faults and other geological structures in the Coconino Plateau are commonly thought to play a role in the movement of groundwater in the area, but limited data exist to constrain their influence. Some groundwater models of the region have used zones of enhanced permeability and porosity along or near features to model their effect on groundwater flow but have not shown sensitivity to the width of the zones used. Enhanced porosity zones in the subsurface, such as those included along or near features in some groundwater models of the region, could create small mass deficiencies detectable by microgravity methods. However, 3 of the 4 gravity transects, the Bright Angel Fault, Bright Angel Monocline, and Tusayan Graben, showed no negative gravity anomaly over the features that could indicate the presence of a low-density zone. Only the Redlands Ranch Fault Zone that had nearby collapse features showed a negative gravity anomaly that was modeled as a zone of 0.017 increased porosity about 800 meters wide, corresponding to the relative dimension and enhanced porosity used in groundwater models of the area. This study was unable to verify the existence of enhanced porosity zones at the selected locations along the other features. However, faults and other features may affect groundwater flow in different ways at different locations, and this work does not preclude the existence of enhanced porosity zones at other places along these faults.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225031","usgsCitation":"Wildermuth, L.M., 2022, Gravity surveys for estimating possible width of enhanced porosity zones across structures on the Coconino Plateau, Coconino County, north-central Arizona: U.S. Geological Survey Scientific Investigations Report 2022–5031, 22 p., https://doi.org/10.3133/sir20225031.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","ipdsId":"IP-121579","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":399839,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZYHEBB","text":"Data from “Gravity surveys for estimating possible width of enhanced porosity zones across structures on the Coconino Plateau, Coconino County, north-central Arizona”","description":"Wildermuth, L.M., 2021, Data from “Gravity surveys for estimating possible width 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Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Effective management and restoration of salt marshes and other vegetated intertidal habitats require objective and spatially integrated metrics of geomorphic status and vulnerability. The unvegetated-vegetated marsh ratio (UVVR), a recently developed metric, can be used to establish present-day vegetative cover, identify stability thresholds, and quantify vulnerability to open-water conversion over a range of spatial scales. We developed a Landsat-based approach to quantify the within-pixel vegetated fraction and UVVR for coastal wetlands of the conterminous United States, at 30-m resolution for 2014–2018. Here we present the methodology used to generate the UVVR from spectral indices, along with calibration, validation, and spatial autocorrelation assessments. We then demonstrate multiple applications of the data across varying spatial scales: first, we aggregate the UVVR across individual states and estuaries to quantify total vegetated wetland area for the nation. On the state level, Louisiana and Florida account for over 50% of the nation’s total, while on the estuarine level, the Chesapeake Bay Estuary and selected Louisiana coastal areas each account for over 6% of the nation’s total vegetated wetland area. Second, we present cases where this dataset can be used to track wetland change (e.g., expansion due to restoration and loss due to stressors). Lastly, we propose a classification methodology that delineates areas vulnerable to open-water expansion based on the 5-year mean and standard deviation of the UVVR. Calculating the UVVR for the period-of-record back to 1985, as well as regular updating, will fill a critical gap for tracking national status of salt marshes and other vegetated habitats through time and space.
No abstract available.
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Surface water in the basin is used for a variety of social, economic, and ecological benefits. While some surface water uses compete with one another, others are complementary or jointly produce multiple beneficial outcomes. The objective of this study is to conduct a baseline economic assessment of surface water in the Basin as it relates to wet meadow pasture production and outdoor recreation. 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\"}}]}","issue":"Spring 2022","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marti, Mackenzie K. 0000-0001-8817-4969 mmarti@usgs.gov","orcid":"https://orcid.org/0000-0001-8817-4969","contributorId":289738,"corporation":false,"usgs":true,"family":"Marti","given":"Mackenzie","email":"mmarti@usgs.gov","middleInitial":"K.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wavra, Harper N. 0000-0001-5688-902X","orcid":"https://orcid.org/0000-0001-5688-902X","contributorId":292171,"corporation":false,"usgs":true,"family":"Wavra","given":"Harper","email":"","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Medenblik, Andrea 0000-0003-2806-7541","orcid":"https://orcid.org/0000-0003-2806-7541","contributorId":216586,"corporation":false,"usgs":true,"family":"Medenblik","given":"Andrea","email":"","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843994,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241893,"text":"70241893 - 2022 - On the role of climate in monthly baseflow changes across the continental United States","interactions":[],"lastModifiedDate":"2023-03-30T13:35:11.224943","indexId":"70241893","displayToPublicDate":"2022-05-01T08:27:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"On the role of climate in monthly baseflow changes across the continental United States","docAbstract":"Baseflow is the portion of streamflow that comes from groundwater and subsurface sources. Although baseflow is essential for sustaining streams during low flow and drought periods, we have little information about how and why it has changed over large regions of the continental United States. The objective of this study was to evaluate how changes in the climate system have affected observed monthly baseflow records at 3,283 USGS gauges over the last 30 years (1989–2019). We developed a statistical modeling framework to determine the relationship between monthly baseflow and monthly climate predictors (i.e., precipitation, temperature, and antecedent wetness). Overall, we found that baseflow trends and the factors influencing them vary by region and month. In the US Northeast, increases were detected earlier in the year (February and March) and in the summer (May and June), and were likely due to increasing precipitation, warmer temperature, and subsequent changes in snowmelt. Increasing baseflow in the US Pacific Northwest and Midwest were associated with increases in precipitation and antecedent wetness throughout the year. Decreasing trends were located in the US Southeast and Southwest. Baseflow trends in the US Southeast were only detected in March, possibly as a result of decreased precipitation during the spring. On the other hand, decreases in baseflow in the Central Southwestern United States occurred throughout the year. These trends were associated with a lack of precipitation and increases in temperature. Finally, we examined the relationship between monthly baseflow trends and changes in total water storage using monthly Gravity Recovery and Climate Experiment mascon products from the Jet Propulsion Laboratory. In this study, trends in total water storage were strongly associated with baseflow trends across the United States. The spatial and temporal variability in baseflow response to climate reported here can aid water managers in adapting to future climate change.
The Arctic Ocean has experienced orbital and millennial-scale climate oscillations over the last 500 kilo-annum (ka) involving massive changes in global sea level and components of the Arctic cryosphere, including sea-ice cover, land-based ice sheets and ice shelves. Although these climate events are only partially understood, micropaleontological studies utilizing ostracodes and benthic foraminifera have demonstrated that major changes in faunas have occurred at different timescales that signify ecosystem regime changes linked to sea-ice cover, surface productivity, bottom temperature and other factors. In addition to faunal changes characterizing glacial-interglacial cycles, Arctic sediments contain several unusual faunal events that cannot be explained by orbital-scale sea level and cryospheric changes. One indicator of such events involves the ostracode Rabilimis mirabilis (Brady 1868), a shallow-water species that inhabits continental shelves in the modern Arctic. We conducted studies of the stratigraphic distribution of R. mirabilis in cores from the Northwind, Mendeleev, Lomonosov, and Alpha Ridges; the Siberian and North American (Beaufort Sea) continental margins; and the Lincoln Sea off North Greenland and in the northern Greenland Sherard Osborn Fjord. Evidence from these records suggests that this species occurs as a fossil in deeper water sediment cores on the upper parts of submarine ridges (mainly 700-900 meters water depth, mwd), in significant numbers (from 1%to 50% of total ostracodes) during Marine Isotope Stages (MIS) 5a (125-109 ka), MIS 4 (71-57 ka), and MIS 3 (57-29 ka). Furthermore, it occurs in cores from various depths on the Siberian margin, the Beaufort and Lincoln Seas during MIS 1 (the Holocene, approx. 11-0 ka). These occurrences involve well-preserved, stratigraphically consistent adult and juvenile populations, which are autochthonous in nature and not caused by downslope transport or ice rafting. Based on their age and associated paleoceanographic conditions in the Arctic, we interpret these R. mirabilis events as signifying basin-ward migration during abrupt changes in growth and decay of massive ice shelves and may be useful as biostratigraphic markers.
The Ogallala aquifer is the primary source of water for agricultural and municipal purposes in the Texas Panhandle. Because most of the groundwater in the Texas Panhandle is withdrawn from the Ogallala aquifer, information on the quality of groundwater in the Ogallala aquifer in this part of Texas is useful for resource characterization. During 2012–13, the U.S. Geological Survey in cooperation with the North Plains Groundwater Conservation District (NPGCD), collected and analyzed water-quality samples from 30 groundwater monitoring wells in the Texas Panhandle. The results of the initial 2012–13 synoptic sampling were published in 2014 to help provide an initial characterization of the spatial and temporal variability of water quality in the NPGCD management area. This report documents the results of a followup synoptic sampling completed between March 2019 and July 2020 by the U.S. Geological Survey, in cooperation with the NPGCD, to further characterize the spatial and temporal characteristics of groundwater in the NPGCD management area; measurements of the depth to water, in feet below land surface, and water-quality samples were obtained from the same 30 monitoring wells that were sampled during 2012–13. The water-quality samples were analyzed for major ions, nutrients, trace elements, and selected organic compounds. Results from the 2019–20 synoptic sampling were compared to drinking-water standards and to the results from the 2012–13 synoptic sampling.
Between the 2012–13 and 2019–20 sampling periods, the depth to water increased in 28 of the 30 wells, with a median difference of 18.17 feet. Results from major ion analyses indicate that most of the groundwater samples collected during 2019–20 were classified as magnesium-bicarbonate type, the same water type indicated for most samples during 2012–13. Dissolved-solids concentrations for the wells sampled during 2019–20 ranged from 260 to 774 milligrams per liter (mg/L) with a median dissolved-solids concentration of 316 mg/L, which was slightly higher than the median dissolved-solids concentration of 311 mg/L for the 2012–13 sampling period. Of the four nutrients analyzed, nitrate was the dominant nitrogen species, with a median nitrate concentration of 2.25 mg/L for the 2019–20 sampling period, which was a slight increase relative to the median nitrate concentration of 2.05 mg/L for the 2012–13 sampling period. Accounting for variability in analyses, median major ion concentrations and median concentrations for nutrient species were similar during the 2012–13 and 2019–20 sampling periods. None of the trace element concentrations exceeded any maximum contaminant level or secondary drinking-water standards. Median concentrations of trace elements from the 2012–13 sampling period were compared to those from the 2019–20 sampling period for constituents in cases where at least 50 percent of concentrations measured in the samples were detected at concentrations greater than the highest applicable laboratory reporting level, and variability in analyses was accounted for. Comparison results indicated that that median concentrations of two trace elements (lithium and uranium) increased, whereas median concentrations for two of the other trace elements measured (barium and molybdenum) decreased. Atrazine and deethylatrazine were the only organic compounds detected; both were detected in four of the six samples collected from different wells and analyzed for organic compounds. Concentrations of atrazine and deethylatrazine detections were all less than 0.05 micrograms per liter.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225026","collaboration":"Prepared in cooperation with the North Plains Groundwater Conservation District","usgsCitation":"Mobley, C.A., and Ging, P.B., 2022, Depth to water and water quality in groundwater wells in the Ogallala aquifer within the North Plains Groundwater Conservation District, Texas Panhandle, 2019–20, and comparison to 2012–13 conditions: U.S. Geological Survey Scientific Investigations Report 2022–5026, 25 p., https://doi.org/10.3133/sir20225026.","productDescription":"Report: vii, 25 p.; Data release; Dataset","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-130725","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":399917,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225026/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":399500,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J57VYB","text":"USGS data release","linkHelpText":"Water-quality and depth to water for groundwater wells primarily completed in the Ogallala aquifer within the North Plains Groundwater Conservation District, Texas Panhandle, 2012–13 and 2019–20"},{"id":399499,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5026/images"},{"id":399498,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5026/sir20225026.XML"},{"id":399497,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5026/sir20225026.pdf","text":"Report","size":"6.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5026"},{"id":399496,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5026/coverthb.jpg"},{"id":399501,"rank":6,"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"}],"country":"United States","state":"Texas","otherGeospatial":"Ogallala aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.062744140625,\n 35.37113502280101\n ],\n [\n -101.75537109375,\n 35.36217605914681\n ],\n [\n -101.788330078125,\n 35.764343479667176\n ],\n [\n -99.986572265625,\n 35.71975793933433\n ],\n [\n -99.99755859375,\n 36.474306755095235\n ],\n [\n -103.062744140625,\n 36.474306755095235\n ],\n [\n -103.062744140625,\n 35.37113502280101\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Through stable isotope measurements of total mercury (HgT), identification of crucial processes and transformations affecting different sources of mercury (Hg) has become possible. However, attempting to use HgT stable isotopes to track bioaccumulation of Hg sources among different food web compartments can be challenging, if not impossible, when tissues have varying methylmercury (MeHg) contents. We measured HgT and MeHg stable isotope ratios within the lower Fox River to examine how these values differed across the food web and if isotope values in biota were influenced by legacy contamination. We showed that seston, invertebrates, and fish had a large range of δ202HgT (−0.74 to 0.15 ‰, n = 11) due to varying MeHg contents in tissues but a commonly conserved MeHg isotope value (δ202MeHgave = 0.01 ± 0.12 ‰, 1 standard deviation, n = 11). We also examined some mathematical approaches to estimate the MeHg isotope values, which were mostly comparable to measured MeHg isotope values in the Fox River, with some exceptions. In this study, we observed that the MeHg isotope values can elucidate links between different food web compartments and provide insight on aquatic Hg cycling that can be masked by the sole use of HgT isotopes in contaminated sites.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acsestwater.1c00285","usgsCitation":"Rosera, T., Janssen, S., Tate, M., Lepak, R., Ogorek, J.M., DeWild, J.F., Krabbenhoft, D.P., and Hurley, J., 2022, Methylmercury stable isotopes: New insights on assessing aquatic food web bioaccumulation in legacy impacted regions: ACS ES&T Water, v. 2, no. 5, p. 701-709, https://doi.org/10.1021/acsestwater.1c00285.","productDescription":"9 p.","startPage":"701","endPage":"709","ipdsId":"IP-125115","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":400056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"lower Fox River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.08494567871094,\n 44.449467536006935\n ],\n [\n -88.04237365722656,\n 44.4440753677203\n ],\n [\n -87.98812866210938,\n 44.532737755596294\n ],\n [\n -88.01353454589844,\n 44.54693080488455\n ],\n [\n -88.08494567871094,\n 44.449467536006935\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosera, Tylor 0000-0002-3611-4654","orcid":"https://orcid.org/0000-0002-3611-4654","contributorId":221507,"corporation":false,"usgs":true,"family":"Rosera","given":"Tylor","email":"","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tate, Michael T. 0000-0003-1525-1219 mttate@usgs.gov","orcid":"https://orcid.org/0000-0003-1525-1219","contributorId":3144,"corporation":false,"usgs":true,"family":"Tate","given":"Michael T.","email":"mttate@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841662,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lepak, Ryan F. 0000-0003-2806-1895","orcid":"https://orcid.org/0000-0003-2806-1895","contributorId":210990,"corporation":false,"usgs":false,"family":"Lepak","given":"Ryan F.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":841663,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ogorek, Jacob M. 0000-0002-6327-0740 jmogorek@usgs.gov","orcid":"https://orcid.org/0000-0002-6327-0740","contributorId":4960,"corporation":false,"usgs":true,"family":"Ogorek","given":"Jacob","email":"jmogorek@usgs.gov","middleInitial":"M.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841664,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeWild, John F. 0000-0003-4097-2798 jfdewild@usgs.gov","orcid":"https://orcid.org/0000-0003-4097-2798","contributorId":2525,"corporation":false,"usgs":true,"family":"DeWild","given":"John","email":"jfdewild@usgs.gov","middleInitial":"F.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841665,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":841667,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hurley, James P.","contributorId":147931,"corporation":false,"usgs":false,"family":"Hurley","given":"James P.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":841668,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70231213,"text":"70231213 - 2022 - Identifying monitoring information needs that support the management of fish in large rivers","interactions":[],"lastModifiedDate":"2022-05-03T11:37:15.435279","indexId":"70231213","displayToPublicDate":"2022-04-29T06:33:35","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Identifying monitoring information needs that support the management of fish in large rivers","docAbstract":"Management actions intended to benefit fish in large rivers can directly or indirectly affect multiple ecosystem components. Without consideration of the effects of management on non-target ecosystem components, unintended consequences may limit management efficacy. Monitoring can help clarify the effects of management actions, including on non-target ecosystem components, but only if data are collected to characterize key ecosystem processes that could affect the outcome. Scientists from across the U.S. convened to develop a conceptual model that would help identify monitoring information needed to better understand how natural and anthropogenic factors affect large river fishes. We applied the conceptual model to case studies in four large U.S. rivers. The application of the conceptual model indicates the model is flexible and relevant to large rivers in different geographic settings and with different management challenges. By visualizing how natural and anthropogenic drivers directly or indirectly affect cascading ecosystem tiers, our model identified critical information gaps and uncertainties that, if resolved, could inform how to best meet management objectives. Despite large differences in the physical and ecological contexts of the river systems, the case studies also demonstrated substantial commonalities in the data needed to better understand how stressors affect fish in these systems. For example, in most systems information on river discharge and water temperature were needed and available. Conversely, information regarding trophic relationships and the habitat requirements of larval fishes were generally lacking. This result suggests that there is a need to better understand a set of common factors across large-river systems. We provide a stepwise procedure to facilitate the application of our conceptual model to other river systems and management goals.
Large lahars pose substantial threats to people and property downstream from Mount Rainier volcano in Washington State. Geologic evidence indicates that these threats exist even during the absence of volcanic activity and that the threats are highest in the densely populated Puyallup and Nisqually River valleys on the west side of the volcano. However, the precise character of these threats can be difficult to anticipate.
To help predict depths and rates of possible lahar inundation in the area, this report presents the results of simulations of hypothetical future lahars that originate high on the west side of Mount Rainier and travel downstream into the Puyallup and Nisqually River valleys. Many of the results portrayed as still images in the figures of this report are also available as animated files that can be accessed at the web address provided in the figure captions. We simulated eight scenarios, including worst-case scenarios in which the simulated lahars are similar in size and mobility to the approximately 260 million cubic meter (Mm3; 340 million cubic yard) Electron Mudflow lahar that descended from Mount Rainier and inundated the Puyallup River valley about 500 years ago. The other six scenarios place the worst-case scenarios in perspective by simulating lahars that originate from the same source areas but have smaller volumes or lesser mobilities.
We perform our simulations using an open-source software package that we developed called D-Claw. The numerical model composing the kernel of D-Claw solves a system of five hyperbolic partial differential equations that describe the depth-averaged dynamics of static or flowing grain-fluid mixtures interacting with three-dimensional topography. In D-Claw, the volume fraction occupied by solid grains is a dependent variable that can freely evolve, enabling simulation of landslide liquefaction and of lahar interaction with static bodies of water. The latter feature facilitates a seamless simulation of a lahar in the Nisqually River valley entering Alder Lake reservoir.
In the event of an approximately 260 Mm3 high-mobility lahar originating on the west side of Mount Rainier, our results point to two areas of pronounced hazard. One area, comprising the densely populated lowlands of Orting, Washington, and environs, could be inundated by lahars originating from either the Sunset Amphitheater or Tahoma Glacier headwall areas. In the worst-case scenario we consider for the Orting lowlands, which involves a 260 Mm3 high-mobility lahar originating from a landslide in the Sunset Amphitheater, a flow front approximately 4 meters deep and traveling about 4 meters per second reaches the Orting lowlands about 1 hour after the onset of slope failure. After passing through the Orting lowlands, the simulated lahar slows down and comes to rest in the valleys surrounding Sumner and Puyallup. A second area of pronounced hazard is the stretch of the Nisqually River valley beginning in Mount Rainier National Park and extending downstream to Alder Lake reservoir and Alder Dam. This area would be substantially affected in the worst-case scenario that involves a 260 Mm3 high-mobility lahar originating from the Tahoma Glacier headwall area—the locality identified by a previous study as the sector of Mount Rainier most prone to large-scale gravitational collapse. The simulated lahar passes through the area of Ashford, Washington, within about 20 minutes of the onset of slope failure and reaches the head of Alder Lake within about 50 minutes. The lahar ultimately displaces enough reservoir water to cause overtopping of the 100 meter (330 foot) tall Alder Dam, but consequences of such dam overtopping are not addressed in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211118","usgsCitation":"George, D.L., Iverson, R.M., and Cannon, C.M., 2022, Modeling the dynamics of lahars that originate as landslides on the west side of Mount Rainier, Washington: U.S. Geological Survey Open-File Report 2021–1118, 54 p., https://doi.org/10.3133/ofr20211118.","productDescription":"Report: vii, 54 p.;16 Companion Files","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-123581","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":399834,"rank":18,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig27.gif","text":"Supplemental animation for figure 27","size":"5 MB gif"},{"id":399833,"rank":17,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig26(T-52-HM).gif","text":"Supplemental animation for figure 26 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gif"},{"id":399819,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supplemental_animations.zip","text":"Supplemental animation files","size":"190 MB","linkFileType":{"id":6,"text":"zip"},"description":"Zip file containing all supplemental files","linkHelpText":"- Zip file containing all supplemental files."},{"id":399818,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118.pdf","text":"Report","size":"25 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":399817,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1118/covrthb.jpg"},{"id":501533,"rank":20,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112955.htm","linkFileType":{"id":5,"text":"html"}},{"id":399835,"rank":19,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig28.gif","text":"Supplemental animation for figure 28","size":"3 MB gif"},{"id":399827,"rank":11,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig21.gif","text":"Supplemental animation for figure 21","size":"12 MB gif"},{"id":399826,"rank":10,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig20.gif","text":"Supplemental animation for figure 20","size":"8 MB gif"},{"id":399825,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig18.gif","text":"Supplemental animation for figure 18","size":"7 MB gif"},{"id":399829,"rank":13,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig23.gif","text":"Supplemental animation for figure 23","size":"36 MB gif"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683
Migratory behavior among ungulates in the Western United States occurs in response to changing forage quality and quantity, weather patterns, and predation risk. As snow melts and vegetation green-up begins in late spring and early summer, many migratory ungulates leave their winter range and move to higher elevation summer ranges to access high-quality forage and areas with vegetative cover for protection during fawning. Ungulates remain on these ranges until the fall when increasing snowfall and decreasing temperatures trigger them to migrate back to their lower elevation winter ranges. While researchers have begun to assess the effects of physical barriers such as roads and energy infrastructure on migration, less attention has been paid to understanding how changing climate conditions might affect ungulate movements and range habitats. Does earlier spring green-up make ungulates leave their winter ranges sooner? Do persistent drought conditions reduce the carrying capacity of seasonal range habitats or lead to shifts in migration pathways? These and other questions remain largely unanswered but could have cascading effects on ungulate population dynamics and migratory behavior.
In February 2018, the Secretary of the Interior signed Department of the Interior Secretarial Order 3362 (SO3362), “Improving Habitat Quality in Western Big-Game Winter Range and Migration Corridors.” The order, which focuses on elk, mule deer, and pronghorn in 11 Western States, directs the Bureau of Land Management (BLM), the U.S. Fish and Wildlife Service (FWS), the National Park Service (NPS), and the U.S. Geological Survey (USGS) to partner with State wildlife agencies on their priorities and objectives for identifying and conserving ungulate migration corridors and winter-range habitat. The USGS Climate Adaptation Science Centers (CASCs) were established to help managers of the Nation’s fish, wildlife, waters, and lands understand the effects of climate change and adapt to changing conditions. To support the recent Department of the Interior (DOI) emphasis on ungulate migration corridors and winter-range habitat, this report assesses current information on how climate change could affect elk, mule deer, and pronghorn migration. The report synthesizes the drivers of migration, outlines what is known about how climate change might affect these drivers, and summarizes management priorities and science needs related to ungulate migration corridors and range habitat.
A review of the literature on ungulate migration shows that the core drivers of spring migration are the timing of spring green-up and snowmelt, and the core driver of fall migration is winter severity. After exploring what is known about how these drivers affect or could be affected by climate change, several pathways through which ungulate migration could be altered were identified: (1) ungulates alter migration timing to better track plant phenology or in response to changes in winter conditions; (2) ungulates change their migration route or distance traveled during migration to accommodate changes in environmental conditions; and (3) ungulate populations that are currently migratory may begin to demonstrate interannual variability in whether they migrate, depending on environmental conditions and density-dependence, and may remain resident for sets of consecutive years.
Through discussions with managers, physical barriers to movement such as roads and fences were identified as a core concern. In addition, the primary research needs of States are the acquisition and analysis of data on ungulate movements, to refine delineation of winter range, summer range, and corridors, and to support a better understanding of how ungulates use these habitats. When it comes to understanding climate effects, managers were more concerned with understanding the vulnerability of winter- and summer-range habitats than the vulnerability of migration corridors because of the influence of summer and winter forage on ungulate condition and reproductive success. Managers were also concerned about how forage quality and quantity might change because of stressors such as drought, wildfire, and invasive species and how they might need to alter habitat-treatment strategies as a result.
More baseline data are needed before effective projections of ungulate migration, at a West-wide scale under climate change, can be made. These data needs include (1) more clearly defined corridors and seasonal range habitats; (2) a comprehensive understanding of the ecological drivers of migration across ungulate species and populations; and (3) the identification of environmental thresholds for key variables that influence migration, above which ungulates alter migratory behavior.
The CASCs have several opportunities to play a role in addressing these needs. The CASCs could initiate projects to identify past and potential future changes and trends in key variables known to affect ungulate migration, such as plant phenology, forage quality, or winter severity. However, it would be difficult to use this information to determine what those trends mean for ungulate migration due to the lack of knowledge about environmental thresholds for ungulates. Additional projects would be required to compare multiple years of movement data with key variables to define thresholds. Once available, information on environmental thresholds could be integrated with projections of key variables to forecast the likelihood that the migration routes or the distance traveled could change—another area in which the CASCs could contribute.
A more immediate role for the CASCs would be to carry out synthesis projects. One such project could summarize the “state of the science” on the drivers of ungulate migration. Although there are dozens of population- and location-specific studies on this topic, collating this information could help highlight trends in migration drivers that span species and geographies: a necessary first step toward determining the extent to which migration drivers could be affected by climate change. A second project could focus on what is known about how climate variability and change affect ungulate life-histories, population dynamics, and migration in the Western United States. The goal of this effort could be to identify knowledge clusters and information gaps that require further investigation. Together, these synthesized products could focus future scientific activities on the most pressing issues of ungulate migration and climate change in the Western United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1493","programNote":"Climate Adaptation Science Center and Land Change Science Program","usgsCitation":"Malpeli, K.C., 2022, Ungulate migration in a changing climate—An initial assessment of climate impacts, management priorities, and science needs: U.S. Geological Survey Circular 1493, 32 p., https://doi.org/10.3133/cir1493.","productDescription":"viii, 32 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119845","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":399812,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1493/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Circular 1493"},{"id":399744,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1493/cir1493.XML"},{"id":399743,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1493/cir1493.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1493"},{"id":399742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1493/coverthb.jpg"},{"id":399745,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1493/images/"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-104.053249,41.001406],[-102.124972,41.002338],[-102.051292,40.749591],[-102.04192,37.035083],[-102.979613,36.998549],[-103.002247,36.911587],[-103.064423,32.000518],[-106.565142,32.000736],[-106.577244,31.810406],[-106.750547,31.783706],[-108.208394,31.783599],[-108.208573,31.333395],[-111.000643,31.332177],[-114.813613,32.494277],[-114.722746,32.713071],[-117.118868,32.534706],[-117.50565,33.334063],[-118.088896,33.729817],[-118.428407,33.774715],[-118.519514,34.027509],[-119.159554,34.119653],[-119.616862,34.420995],[-120.441975,34.451512],[-120.608355,34.556656],[-120.644311,35.139616],[-120.873046,35.225688],[-120.884757,35.430196],[-121.851967,36.277831],[-121.932508,36.559935],[-121.788278,36.803994],[-121.880167,36.950151],[-122.140578,36.97495],[-122.419113,37.24147],[-122.511983,37.77113],[-122.425942,37.810979],[-122.168449,37.504143],[-122.144396,37.581866],[-122.385908,37.908136],[-122.301804,38.105142],[-122.484411,38.11496],[-122.492474,37.82484],[-122.972378,38.020247],[-123.103706,38.415541],[-123.725367,38.917438],[-123.851714,39.832041],[-124.373599,40.392923],[-124.063076,41.439579],[-124.536073,42.814175],[-124.150267,43.91085],[-123.962887,45.280218],[-123.996766,46.20399],[-123.548194,46.248245],[-124.029924,46.308312],[-124.06842,46.601397],[-123.97083,46.47537],[-123.84621,46.716795],[-124.022413,46.708973],[-124.108078,46.836388],[-123.86018,46.948556],[-124.138035,46.970959],[-124.425195,47.738434],[-124.672427,47.964414],[-124.727022,48.371101],[-123.981032,48.164761],[-122.748911,48.117026],[-122.637425,47.889945],[-123.15598,47.355745],[-122.527593,47.905882],[-122.578211,47.254804],[-122.725738,47.33047],[-122.691771,47.141958],[-122.796646,47.341654],[-122.863732,47.270221],[-122.67813,47.103866],[-122.364168,47.335953],[-122.429841,47.658919],[-122.230046,47.970917],[-122.425572,48.232887],[-122.358375,48.056133],[-122.512031,48.133931],[-122.424102,48.334346],[-122.689121,48.476849],[-122.425271,48.599522],[-122.796887,48.975026],[-104.048736,48.999877],[-104.053249,41.001406]]],[[[-119.789798,34.05726],[-119.5667,34.053452],[-119.795938,33.962929],[-119.916216,34.058351],[-119.789798,34.05726]]],[[[-118.524531,32.895488],[-118.573522,32.969183],[-118.369984,32.839273],[-118.524531,32.895488]]],[[[-118.500212,33.449592],[-118.32446,33.348782],[-118.593969,33.467198],[-118.500212,33.449592]]],[[[-122.519535,48.288314],[-122.66921,48.240614],[-122.400628,48.036563],[-122.419274,47.912125],[-122.744612,48.20965],[-122.664928,48.374823],[-122.519535,48.288314]]],[[[-122.800217,48.60169],[-122.883759,48.418793],[-123.173061,48.579086],[-122.949116,48.693398],[-122.743049,48.661991],[-122.800217,48.60169]]]]},\"properties\":{\"name\":\"Arizona\",\"nation\":\"USA \"}}]}","contact":"Director, National Climate Adaptation Science Center (CASC)
U.S. Geological Survey
Mail Stop 516
12201 Sunrise Valley Drive
Reston, VA 20192
Henrys Lake, Idaho, is a renowned trophy trout fishery that faces an uncertain future following the establishment of Utah Chub (UTC) Gila atraria. Utah Chub were first documented in the lake in 1993 and have become abundant over the past two decades. Little is known about the ecology of UTC, but they typically have negative effects on salmonids in systems where they have been introduced. We sought to fill knowledge gaps in UTC ecology and provide insight on potential interactions with Yellowstone Cutthroat Trout (YCT) Oncorhynchus clarkii bouvieri. Ninety-four YCT and 95 UTC were radio-tagged in spring 2019 and 2020 to better understand potential interactions between YCT and UTC in Henrys Lake. Fish were located via mobile tracking and fixed receivers from June to December 2019 and 2020. In June of both years, YCT and UTC were concentrated in nearshore habitats. As water temperatures increased, UTC were documented in deeper water (mean ± SD = 3.6 ± 1.4 m) and YCT became more concentrated in areas with cold water (e.g., mouths of tributaries, in-lake springs). In July and August, large congregations of UTC were observed. Yellowstone Cutthroat Trout were detected in tributaries from June to August, but no UTC were detected in the tributaries. By late fall (November–December), YCT were located along the shoreline and UTC were detected in the middle of the lake. Both YCT and UTC were observed in areas with dense vegetation. Macrophytes likely provided a food source for UTC and cover from predators for both species. Locations of YCT were negatively related to warm water temperatures, whereas UTC were positively associated with warm water temperatures. Results from this research fill knowledge gaps in UTC and YCT interactions as well as provide valuable insight on the ecology of UTC and adfluvial Cutthroat Trout populations. Furthermore, distribution patterns and habitat selectivity of YCT and UTC in Henrys Lake can be used to inform management decisions for fishery improvement and YCT conservation.
The United States Department of Energy, the MH21-S Research Consortium of Japan, and the United States Geological Survey are collaborating to enable gas hydrate scientific drilling and extended-duration reservoir response testing on the Alaska North Slope. To feasibly execute such a test, a location is required that is accessible from existing roads and gravel pads and that can be occupied without disrupting ongoing industry operations. A review of potential locations meeting these criteria determined the likely occurrence of gas hydrate in two fine-grained marginal-marine sands of Tertiary age in the vicinity of the inactive “Kuparuk State 7-11-12” exploration pad in the western Prudhoe Bay Unit (PBU). Existing well and seismic data for that site were insufficient to preclude the potential for free gas occurrence within the deeper (and most prospective) target sand. Therefore, with support from the PBU Working Interest Owners, Alaska Department of Natural Resources, and Petrotechnical Resources Alaska, the Hydrate-01 Stratigraphic Test Well (STW) was drilled in December 2018 to confirm the suitability of the site for future gas hydrate scientific testing. The Hydrate-01 well was successfully drilled to −3290 ft (1003 m) subsea vertical depth at a bottom hole location of approximately 900 ft (∼275 m) east of the surface location. The drilling program featured acquisition of a full suite of logging while drilling data, the collection of side-wall pressure cores, and the installation of distributed temperature and distributed acoustic sensor fiber-optic cables. The log data acquired confirmed the occurrence of gas hydrate at high saturation in two target sands. Integrated evaluation of log and sidewall core data provide petrophysical and geomechanical property information that allow for potential reservoir response to depressurization to be simulated. The deeper “B1 sand” is deemed to be most favorable for reservoir response testing as a result of confirmed gas hydrate occurrence in sediments of high intrinsic permeability, location within 100 ft (30 m) of the base of gas hydrate stability, and minimal risk for direct communication with permeable water-bearing (hydrate-free) zones. The shallower “D1 sand” provides a secondary target that is differentiated by colder in situ temperatures and the interpreted direct hydraulic communication to a lower section of non-hydrate-bearing, water-saturated sand. The Hydrate-01 log data also confirm the occurrence of at least one sub-seismic fault in close proximity to the B1 sand reservoir. To better image the distribution of the gas-hydrate-bearing reservoir sections and associated faults, a three-dimensional (3D) vertical seismic profile was conducted in early 2019 using the distributed acoustic sensors installed as part of the Hydrate-01 STW completion. Detailed two-dimensional (2D) and 3D geologic models have been constructed to enable numerical simulations to inform the planning for potential future scientific tests of reservoir response to depressurization at the site.
The Yellowstone Volcano Observatory (YVO) monitors volcanic and hydrothermal activity associated with the Yellowstone magmatic system, carries out research into magmatic processes occurring beneath Yellowstone Caldera, and issues timely warnings and guidance related to potential future geologic hazards. This report summarizes the activities and findings of YVO during the year 2021, focusing on the Yellowstone volcanic system. Highlights of YVO research and related activities during 2021 include deployments of seismometers in Norris Geyser Basin and Upper Geyser Basin to investigate geyser plumbing systems, semipermanent Global Positioning System array deployment from May to October, geological studies of post-glacial hydrothermal activity, refining the ages of Yellowstone volcanic units and updating existing maps of geologic deposits, installation of a new continuous gas monitoring station near Mud Volcano, sampling of thermal waters around Yellowstone National Park to monitor water chemistry over space and time, and assessment of thermal output based on satellite imagery and chloride flux in rivers.
Steamboat Geyser, in Norris Geyser Basin, continued the pattern of frequent eruptions that began in 2018 with 20 water eruptions in 2021—a significant decrease from the 48 eruptions that occurred in both 2019 and 2020. Total seismicity—2,773 located earthquakes—was elevated compared to the 1,722 earthquakes located in 2020, but not significantly outside the historical average of about 1,500–2,500 earthquakes per year. Overall subsidence of the caldera floor, ongoing since late 2015 or early 2016, continued at rates of a few centimeters (1–2 inches) per year, whereas deformation in the Norris Geyser Basin area was below detection levels. Satellite deformation measurements indicate the possibility of slight uplift amounting to about 1 centimeter (less than 1 inch) along the north caldera rim, south of Norris Geyser Basin. The deformation is similar to that which occurred in the late 1990s. Throughout 2021, the aviation color code for Yellowstone Caldera remained at “green” and the volcano alert level remained at “normal.”
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1494","usgsCitation":"Yellowstone Volcano Observatory, 2022, Yellowstone Volcano Observatory 2021 annual report: U.S. Geological Survey Circular 1494, 48 p., https://doi.org/10.3133/cir1494.","productDescription":"v, 48 p.","ipdsId":"IP-137750","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":399786,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1494/coverthb.jpg"},{"id":399787,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1494/circ1494.pdf","text":"Report","size":"20.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1494"},{"id":501194,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112956.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.26953125,\n 43.13306116240612\n ],\n [\n -108.73168945312499,\n 43.13306116240612\n ],\n [\n -108.73168945312499,\n 45.10454630976873\n ],\n [\n -111.26953125,\n 45.10454630976873\n ],\n [\n -111.26953125,\n 43.13306116240612\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Yellowstone Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court, Suite 100
Vancouver, WA 98683
Email: yvowebteam@usgs.gov
","tableOfContents":"Barkley Canyon is one of the few known sites worldwide with the occurrence of thermogenic gas seepage and formation of structure-II and structure-H gas hydrate mounds on the seafloor. This site is the location of continuous seafloor monitoring as part of the Ocean Networks Canada (ONC) cabled observatory off the west coast off Vancouver Island, British Columbia, Canada. We combine repeat remotely operated vehicle (ROV) seafloor video observations, mapping with an autonomous underwater vehicle (AUV), ship-, ROV-, and AUV-based identification of gas flares, as well as seismic and Chirp data to investigate the distribution of fluid migration pathways. Geologically, the site with the prominent gas hydrate mounds and associated fluid seepage is covering an area of ∼0.15 km2 and is situated on a remnant of a rotated fault block that had slipped off the steep flanks of the north-east facing canyon wall. The gas hydrate mounds, nearly constant in dimension over the entire observation period, are associated with gas and oil seepage and surrounded by debris of chemosynthetic communities and authigenic carbonate. The formation of gas hydrate at and near the seafloor requires additional accommodation space created by forming blisters at the seafloor that displace the regular sediments. An additional zone located centrally on the rotated fault block with more diffuse seepage (∼0.02 km2 in extent) has been identified with no visible mounds, but with bacterial mats, small carbonate concretions, and clam beds. Gas venting is seen acoustically in the water column up to a depth of ∼300 m. However, acoustic water-column imaging during coring and ROV dives showed rising gas bubbles to much shallower depth, even <50 m, likely a result of degassing of rising oil droplets, which themselves cannot be seen acoustically. Combining all observations, the location of the gas hydrate mounds is controlled by a combination of fault-focused fluid migration from a deeper reservoir and fluid seepage along more permeable strata within the rotated slope block. Fluids must be provided continuously to allow the sustained presence of the gas hydrate mounds at the seafloor.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2022.852853","usgsCitation":"Reidel, M., Scherwath, M., Romer, M., Paull, C., Lundsten, E., Caress, D.W., Brewer, P., Pohlman, J., Lapham, L.L., Chapman, N., Whiticar, M., Spence, G.D., Enkin, R., and Douglas, K., 2022, Barkley Canyon gas hydrates: A synthesis based on two decades of seafloor observation and remote sensing: Frontiers in Earth Science, v. 10, 852853, 25 p., https://doi.org/10.3389/feart.2022.852853.","productDescription":"852853, 25 p.","ipdsId":"IP-137853","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2022.852853","text":"Publisher Index Page"},{"id":399810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Barkley Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.6667,\n 48\n ],\n [\n -125.8,\n 48\n ],\n [\n -125.8,\n 48.5\n ],\n [\n -126.6667,\n 48.5\n ],\n [\n -126.6667,\n 48\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Reidel, M.","contributorId":290694,"corporation":false,"usgs":false,"family":"Reidel","given":"M.","email":"","affiliations":[{"id":62473,"text":"GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany","active":true,"usgs":false}],"preferred":false,"id":841589,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scherwath, M.","contributorId":290695,"corporation":false,"usgs":false,"family":"Scherwath","given":"M.","affiliations":[{"id":62475,"text":"Ocean Networks Canada, University of Victoria, Victoria, BC, Canada","active":true,"usgs":false}],"preferred":false,"id":841590,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romer, M.","contributorId":290696,"corporation":false,"usgs":false,"family":"Romer","given":"M.","email":"","affiliations":[{"id":62476,"text":"MARUM - Center for Environmental Sciences and Department of Geosciences at the University of Bremen, Bremen, Germany","active":true,"usgs":false}],"preferred":false,"id":841591,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paull, C. 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Macroinvertebrate investigations in research and monitoring require consistent and reliable field and laboratory procedures. Comprehensive standard operating procedures for sampling macroinvertebrates from depressional wetlands, which can range from riverine floodplain lakes to wetlands of any size and hydrologic regime, remain relatively sparse. This report provides step-by-step protocols for efficient use of time and resources while collecting and processing aquatic macroinvertebrate samples; for example, a single wetland can typically be field surveyed in less than 1 hour, and the samples can be processed in the laboratory in less than 2 hours. Samples can be collected from inside a motorboat or canoe or while wading. This procedures manual describes dip netting to collect macroinvertebrates from the wetland bottom and water column separately to facilitate investigations of habitat use by species occupying different areas of the wetland. This report also provides descriptive supplemental materials and data sheets to assist with the preparation of survey maps, the acquisition of field and laboratory equipment, and the calculation of macroinvertebrate densities from the wetland bottom and water column. These procedures can be applied to most macroinvertebrate species and communities that inhabit a variety of wetland sizes and types. Uses and applications can range from elementary and secondary environmental education to rigorous scientific evaluations of community abundance, diversity, distribution, or species-habitat relations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221029","collaboration":"Prepared in collaboration with Minnesota Department of Natural Resources and Bemidji State University","usgsCitation":"Keith, B.R., Carleen, J.D., Larson, D.M., Anteau, M.J., and Fitzpatrick, M.J., 2022, Protocols for collecting and processing macroinvertebrates from the benthos and water column in depressional wetlands: U.S. Geological Survey Open-File Report 2022–1029, 22 p., https://doi.org/10.3133/ofr20221029.","productDescription":"vi, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-127838","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":399709,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221029/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":399703,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1029/ofr20221029.pdf","text":"Report","size":"4.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1029"},{"id":399702,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1029/coverthb.jpg"},{"id":399705,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1029/images"},{"id":399704,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1029/ofr20221029.XML"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Utah’s list of notable features runs long, but scenery rises to the top. The Colorado River does not simply run through southeastern Utah; it meanders through steep canyons of the eroded sedimentary rock that colors the sweeping vistas of the Colorado Plateau. Stone arches, spires, hoodoos, cliffs, and bridges in hues of red enchant residents and tourists. Mountain ranges extending through the State add dynamic views—and skiing opportunities.
The Great Salt Lake in northern Utah is the largest saltwater lake in the Western Hemisphere. The western part of Utah, including the Great Salt Lake, lies in the Great Basin, a multi-State drainage area with no outlet. Because the lake has no outlet to flush out any salt, evaporation produces a higher concentration of salts in the water or soils, called salinity. The lake lacks fish but supports algae and brine shrimp, and extensive wetlands around the lake attract millions of migratory birds.
Landsat imagery is useful for showing surface changes, such as the fluctuating water levels of the shallow Great Salt Lake. The lake flooded in the 1980s, but the southern part dropped to its lowest level in recorded history in 2021. Landsat data also can take a much deeper look at land and water conditions. Here are several ways Landsat benefits Utah.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192