Despite its biological importance in shaping both individual fitness and population structure, much remains to be learned about the hatchling emergence ecology of most freshwater turtles. Here, we provide some of the first details on these early life stages for the Ouachita map turtle (Graptemys ouachitensis) obtained during 2015–2017 along the lower Wisconsin River, Iowa County, Wisconsin, and integrate our results into related research within the genus Graptemys. Dedicated trail cameras over in situ turtle nests provided otherwise difficult to obtain observational data relevant to natural hatchling emergence without disturbing nests or hatchlings. In contrast to some earlier reports for Graptemys, hatchling emergence was mostly diurnal and synchronous, primarily in the morning soon after soil temperatures began to rise from overnight low values. Data suggest a temperature change model of cueing hatchling emergence, which may represent a local or regional adaptation to reduce nocturnal predation risks, mostly from raccoons (Procyon lotor), or may simply reflect default diurnal hatchling activity patterns when not affected by thermal constraints. Aside from predation, hatchlings on this small study site are affected by vegetative shading, leading to relatively long times to first emergence periods (mean, 82.3 d), low mean nest temperatures (25.9°C), and a likely male-biased sex ratio. These findings highlight the value of hatchling emergence studies in revealing important influences on population viability and in guiding appropriate habitat management in conservation efforts.
Despite its importance to individual fitness and population dynamics, the dispersal behaviors of most neonate freshwater turtles after nest emergence are poorly known. We studied the initial dispersal tendencies of neonate Ouachita map turtles (Graptemys ouachitensis) exiting natural nests during 2015–2017 along the Wisconsin River, Wisconsin. Overall, dispersal was nonrandom, and hatchlings largely oriented toward the nearest substantial vegetative cover, a woodland north of the nesting area. However, variation sometimes occurred in routes taken among hatchlings within a clutch. Directional changes within an individual's dispersal track, including route reversals, were also observed. As our work appears to be the first to use standalone trail cameras as a primary data-gathering tool for a hatchling dispersal study, it highlights the potential benefits and limitations of this technique for similar research.
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Stewart B. McKinney National Wildlife Refuge in Connecticut. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of two marsh management units within the refuge and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that would be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that would maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to approximately $1,190,000, but that further expenditures may yield diminishing return on investment. Management actions in optimal portfolios at total costs less than $1,190,000 included controlling avian predators in both management units, managing stormwater on lands adjacent to one marsh management unit, and removing a tide gate and breaching a dike to improve tidal flow in the other marsh management unit. The management benefits were derived from expected increases in the numbers of spiders (as an indicator of trophic health) and tidal marsh obligate birds, and an expected decrease in the use of herbicides to control invasive vegetation. The prototype presented here provides a framework for decision making at the Stewart B. McKinney National Wildlife Refuge that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201139","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Low, L.E. , Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Vagos, K., and Potvin, R., 2020, Optimization of salt marsh management at the Stewart B. McKinney National Wildlife Refuge, Connecticut, through use of structured decision making: U.S. Geological Survey Open-File Report 2020–1139, 28 p., https://doi.org/10.3133/ofr20201139.","productDescription":"vi, 28 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120812","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":381645,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1139/ofr20201139.pdf","text":"Report","size":"2.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1139"},{"id":381644,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1139/coverthb.jpg"}],"country":"United States","state":"Connecticut","otherGeospatial":"Stewart B. McKinney National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.17169189453125,\n 41.15022321163024\n ],\n [\n -73.13186645507812,\n 41.13677892209895\n ],\n [\n -73.10028076171875,\n 41.14867208811923\n ],\n [\n -73.15177917480469,\n 41.18537216794189\n ],\n [\n -73.18113327026366,\n 41.17090135180691\n ],\n [\n -73.17169189453125,\n 41.15022321163024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708–4039
With the advent of highly precise total stations and modern surveying instrumentation, trigonometric leveling has become a compelling alternative to conventional leveling methods for establishing vertical-control networks and for perpetuating a datum to field sites. Previous studies of trigonometric-leveling measurement uncertainty proclaim that first-, second-, and third-order accuracies may be achieved if strict leveling protocols are rigorously observed. Common field techniques to obtain quality results include averaging zenith angles and slope distances observed in direct and reverse instrument orientation (F1 and F2, respectively), multiple sets of reciprocal observations, quality meteorological observations to correct for the effects of atmospheric refraction, and electronic distance measurements that generally do not exceed 500 feet. In general, third-order specifications are required for differences between F1 and F2 zenith angles and slope distances; differences between redundant instrument-height measurements; section misclosure determined from reciprocal observations; and closure error for closed traverse. For F1 observations such as backsight check and check shots, the construction-grade specification is required for elevation differences between known and observed values.
Recommended specifications for trigonometric-leveling equipment include a total station instrument with an angular uncertainty specification less than or equal to plus or minus 5 arc-seconds equipped with an integrated electronic distance measurement device with an uncertainty specification of less than or equal to plus or minus 3 millimeters plus 3 parts per million. A paired data collector or integrated microprocessor should have the capability to average multiple sets of measurements in direct and reverse instrument orientation. Redundant and independent measurements by the survey crew and automated or manual reduction of slant heights to the vertical equivalent are recommended to obtain quality instrument heights. Horizontal and vertical collimation tests should be conducted daily during trigonometric-leveling surveys, and electronic distance-measurement instruments should be tested annually on calibrated baselines maintained by the National Geodetic Survey. Specifications that were developed by the National Geodetic Survey for geodetic leveling have been adapted by the U.S. Geological Survey (USGS) for the purpose of developing standards for trigonometric leveling, which are identified as USGS Trigonometric Level I (TL I), USGS Trigonometric Level II (TL II), USGS Trigonometric Level III (TL III), and USGS Trigonometric Level IV (TL IV). TL I, TL II, and TL III surveys have a combination of first, second, and third geodetic leveling specifications that have been modified for plane leveling. The TL III category also has specifications that are adapted from construction-grade standards, which are not recognized by the National Geodetic Survey for geodetic leveling. A TL IV survey represents a leveling approach that does not generally meet criteria of a TL I, TL II, or TL III survey.
Site conditions, such as highly variable topography, and the need for cost-effective, rapid, and accurate data collection in response to coastal or inland flooding have emphasized the need for an alternative approach to conventional leveling methods. Trigonometric leveling and the quality-assurance methods described in this manual will accommodate most site and environmental conditions, but measurement uncertainty is potentially variable and dependent on the survey method. Two types of closed traverse surveys have been identified as reliable methods to establish and perpetuate vertical control: the single-run loop traverse and double-run spur traverse. Leveling measurements for a double-run spur traverse are made in the forward direction from the origin to the destination and are then retraced along the same leveling route in the backward direction, from the destination to the origin. Every control point in a double-run spur traverse is occupied twice. Leveling measurements for a single-run loop traverse are made in the forward direction from the origin point to the destination, and then from the destination to the origin point, along a different leveling route. The only point that is redundantly occupied for the single-run loop traverse is the origin. An open traverse method is also considered an acceptable approach to establish and perpetuate vertical control if the foresight prism height is changed between measurement sets to ensure at least two independent observations. A modified version of leap-frog leveling is recommended for all traverse surveys because it reduces measurement uncertainty by forcing the surveying instrumentation into a level and centered condition over the ground point as the instrumentation is advanced to the objective. Sideshots are considered any radial measurement made from the total station that is not part of a traverse survey. F1 and F2 observations are recommended for sideshots measurements for projects that require precise elevations. Quality-assurance measurements made in F1 from the station to network-control points should be considered for surveys that require a high quantity of sideshots.
The accuracy of a trigonometric-leveling survey essentially depends on four components (1) the skill and experience of the surveyor, (2) the environmental or site conditions, (3) the surveying method, and (4) the quality of the surveying instrumentation. Although components one and two can sometimes be difficult to evaluate and be highly variable, the objective of this manual is to disseminate information needed to identify, maintain, and operate quality land-surveying instrumentation, and to document procedures and best practices for preparing and executing precision trigonometric-leveling surveys in the USGS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11D3","usgsCitation":"Noll, M.L., and Rydlund, P.H., 2020, Procedures and best practices for trigonometric leveling in the U.S. Geological Survey: U.S. Geological Survey Techniques and Methods, book 11, chap. D3, 94 p., https://doi.org/10.3133/tm11D3.","productDescription":"Report: vii, 93 p.; Appendix","numberOfPages":"94","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-108800","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":381587,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11d3/coverthb.jpg"},{"id":381588,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11d3/tm11d3.pdf","text":"Report","size":"6.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 11-D3"},{"id":381589,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/11d3/tm11d3_appendix1.pdf","text":"Appendix 1","size":"207 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Standard Field Form for Running Trigonometric Levels"}],"contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The Northern Goshawk (Accipiter gentilis) is an apex predator occurring across North America and Eurasia. The species has received considerable conservation focus in late-seral conifer forests of western North America, where its habitat has been substantially reduced and altered by timber harvest and is increasingly at risk from high severity fire, drought, and forest pathogens. In the Sierra Nevada range of California, management and conservation of goshawks are hampered by a lack of knowledge of their basic space use and movement ecology. We used global positioning system (GPS) loggers to investigate space use of 20 resident, adult Northern Goshawks over 3 yr (2015–2018) in the Plumas National Forest, California. Median home range sizes of male goshawks were more than twice as large as those of females, and nonbreeding-season home ranges were three times larger than breeding-season home ranges. High resolution GPS data (location interval 1–6 min) allowed quantification of daily transit distances up to 60 km for individual goshawks and revealed long-distance forays into adjacent territories and surrounding areas. Four goshawks (three males, one female) undertook forays >8 km from their nest locations, with forays lasting up to 6 d; these forays occurred during both breeding and nonbreeding seasons for both sexes. Comparing our results to current conservation approaches, we determined that USDA Forest Service goshawk Protected Activity Centers protected <25% of both the roost locations and the area used during the daytime. Conservation efforts for Northern Goshawks in the Sierra Nevada would benefit from consideration of year-round habitat needs at larger scales than previously thought.
The bedrock geology of the 7.5- by 15-minute Springfield quadrangle consists of highly deformed and metamorphosed Mesoproterozoic through Devonian metasedimentary and meta-igneous rocks. In the west, Mesoproterozoic gneisses of the Mount Holly Complex are the oldest rocks and form the eastern side of the Chester dome. The Moretown slice structurally overlies the Chester dome along the Keyes Mountain thrust fault which represents the Ordovician Taconic suture (Red Indian Line) between Laurentian and Ganderian crust. The allochthonous Cambrian through Ordovician Moretown slice includes the Moretown and Cram Hill Formations and the North River Igneous Suite. Silurian and Devonian metasedimentary and metavolcanic rocks of the Connecticut Valley trough (CVT) unconformably overlie the Moretown slice. Ordovician to Silurian and Devonian metasedimentary and meta-igneous rocks of the New Hampshire sequence structurally overlie the CVT along the Devonian, Acadian Monroe thrust fault. The oldest part of the New Hampshire sequence consists of Ordovician metamorphosed volcanic, plutonic, and sedimentary rocks of the Bronson Hill arc including the Ammonoosuc Volcanics, the Partridge Formation, and the Oliverian Plutonic Suite. The Ammonoosuc Volcanics are the base of the exposed arc section in the map area. The Bronson Hill arc rocks are exposed in fault-bounded structural belts, including the Monroe thrust sheet, the Claremont belt, the Sugar River and Unity domes, and the footwall of the Brennan Hill thrust fault. Silurian to Devonian metasedimentary rocks of the Clough Quartzite, and Fitch and Littleton Formations unconformably overlie the Bronson Hill arc rocks. Devonian granitic and pegmatitic dikes and sills of the New Hampshire Plutonic Suite intruded previously deformed rocks.
Devonian, Acadian F1 fold nappes have a sheath fold geometry and are truncated by multiple generations of faults. The Bronson Hill arc structurally overlies the CVT along the Acadian Monroe fault with preserved tectonic mélange in the footwall. Upright dome-stage F2 folds post-date amphibolite facies metamorphism and locally developed into sheath folds in high-strain zones. F3 folds exhibit sinistral rotation associated with Alleghanian lower-greenschist facies faults. Late Paleozoic Alleghanian to Mesozoic shear zones transpose stratigraphy, early structures, and peak metamorphic isograds. 40Ar/39Ar white-mica growth ages (300–250 million years before present [Ma]) indicate that retrograde deformation continued into the latest Paleozoic and earliest Mesozoic. Apatite fission track data show that brittle faults were active prior to about 100 Ma and experienced Late Cretaceous and even Paleocene re-activation.
The bedrock geology was mapped to study the tectonic history of the area and to provide a framework for ongoing characterization of the bedrock of Vermont and New Hampshire. This Scientific Investigations Map of the Springfield 7.5- x 15-minute quadrangle consists of sheets 1 and 2 as well as a geographic information system (GIS) database that includes bedrock geologic units, faults, outcrops, and structural geologic information. Sheet 1 of the report includes a bedrock geologic map, a correlation of map units, and a description of map units. Sheet 2 includes a discussion of the geology, references cited, two cross sections from the geologic map on sheet 1, a tectonic map showing major structural features, and a structural domain map showing the orientation of brittle features.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3462","collaboration":"Prepared in cooperation with the State of Vermont, Vermont Agency of Natural Resources, Vermont Geological Survey; and the State of New Hampshire, Department of Environmental Services, New Hampshire Geological Survey","usgsCitation":"Walsh, G.J., Valley, P.M., Armstrong, T.R., Ratcliffe, N.M., Merschat, A.J., and Gentry, B.J., 2020, Bedrock geologic map of the Springfield 7.5- x 15-minute quadrangle, Windsor County, Vermont, and Sullivan County, New Hampshire (ver. 1.1, June 2024): U.S. Geological Survey Scientific Investigations Map 3462, 2 sheets, scale 1:24,000, https://doi.org/10.3133/sim3462.","productDescription":"2 Sheets: 62.78 x 40.79 inches and 48.00 x 41.00 inches; Base Map; Metadata; Database; Read Me; Companion File","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091203","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":499273,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_110835.htm","linkFileType":{"id":5,"text":"html"}},{"id":429409,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3462/versionHist.txt","size":"715 B","linkFileType":{"id":2,"text":"txt"}},{"id":381115,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_openaccess.zip","text":"Open Access","size":"5.06 MB","linkFileType":{"id":6,"text":"zip"}},{"id":381114,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_basemap.zip","text":"Base Map","size":"84.1 MB","linkFileType":{"id":6,"text":"zip"}},{"id":381111,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_readme.txt","size":"11.8 KB","linkFileType":{"id":2,"text":"txt"}},{"id":381110,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_sheet2.pdf","text":"Sheet 2","size":"7.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3462"},{"id":381108,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3462/coverthb.jpg"},{"id":381113,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_metadata.zip","size":"445 KB","linkFileType":{"id":6,"text":"zip"}},{"id":381109,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_sheet1.pdf","text":"Sheet 1","size":"23.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3462"},{"id":381112,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3462/sim3462_database.zip","size":"5.38 MB","linkFileType":{"id":6,"text":"zip"}}],"country":"United States","state":"New Hampshire, Vermont","county":"Sullivan County, Windsor County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.5,\n 43.25\n ],\n [\n -72.25,\n 43.25\n ],\n [\n -72.25,\n 43.375\n ],\n [\n -72.5,\n 43.375\n ],\n [\n -72.5,\n 43.25\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 22, 2020; Version 1.1: June 4, 2024","contact":"Florence Bascom Geoscience Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Confined (or buried) aquifers of glacial origin overlain by till confining units provide drinking water to hundreds of thousands of Minnesota residents. The sustainability of these groundwater resources is not well understood because hydraulic properties of till that control vertical groundwater fluxes (leakage) to underlying aquifers are largely unknown. The U.S. Geological Survey, Iowa State University, Minnesota Geological Survey, and Minnesota Department of Health investigated hydraulic properties and groundwater flow through till confining units using field studies and heuristic MODFLOW simulations. Till confining units in the following late-Wisconsinan stratigraphic units (with locations in parentheses) were characterized: Des Moines lobe till of the New Ulm Formation (Litchfield, Minnesota), Superior lobe till of the Cromwell and Aitkin Formations (Cromwell, Minn.), and Wadena lobe till of the Hewitt Formation (hydrogeology field camp [HFC] near Akeley, Minn.). Pre-Illinoian till of the Good Thunder formation (Olivia, Minn.) was also characterized.
Hydraulic and geochemical field data were collected from sediment cores and a series of five piezometer nests. Each nest consisted of five to eight piezometers screened at short vertical intervals in hydrostratigraphic units including (if present) surficial aquifers, till confining units, confined/buried aquifers, and underlying bedrock. Till hydraulic conductivity was estimated from slug tests (horizontal [Kh]) and constant-rate aquifer tests in the confined aquifer (vertical [Kv]). Travel times through the till were evaluated with Darcy’s law and stable isotope concentrations. A series of heuristic MODFLOW simulations were used to evaluate groundwater fluxes through till across the range of till hydraulic properties and pumping rates observed at the field sites.
The field data demonstrated variability in hydraulic properties between and within till stratigraphic units horizontally and vertically. The variability in hydraulic properties within and between sites resulted in substantial differences in groundwater flux through till. A conceptual understanding that emerges from the vertical till profiles is that they are not homogeneous hydrostratigraphic units with uniform properties; rather, each vertical sequence is a heterogeneous mixture of glacial sediment with differing abilities to transmit water.
Till thicknesses varied from 60 to 166 feet, and till textures ranged from a sandy loam (Hewitt Formation, HFC site) to a silt loam/clay loam (Good Thunder formation, Olivia site). Till Kh varied by one to three orders of magnitude within each piezometer nest. Four piezometer nests had downward hydraulic gradients ranging from 0.04 to 0.56, and one nest had a slight upward hydraulic gradient of 0.02. The Cromwell, HFC, and Litchfield 1 sites were examples of “leaky” tills with high Kv (0.001 to 1.1 feet per day [ft/d]) and geometric mean Kh (0.03 to 0.07 ft/d) and extensive vertical hydraulic connectivity between the confined aquifer and the overlying till. Estimated groundwater travel times through these sites ranged from 1 to 81 years, and two of these sites had tritium throughout their till profiles. The tills at the other two sites, Olivia and Litchfield 2, were effective confining units that had low Kv (0.001 to 0.0005 ft/d) and geometric mean Kh (0.0002 to 0.004 ft/d). The till piezometers at these sites had no drawdown response to short-term (up to 10 hours for Olivia and up to 5 days for Litchfield) high-capacity pumping from the confined aquifer. Estimated groundwater travel times through the tills at these sites ranged from 165 to nearly 1,800 years, and tritium was only detected in the upper one-third of these till profiles. Across all sites, the till vertical anisotropy (ratio of Kh to Kv) ranged by four orders of magnitude from 0.05 at the Cromwell nest to 70 at the Litchfield 1 nest. Stable isotopes of oxygen and hydrogen indicate that groundwater throughout all five till profiles is younger than the last glacial advance into Minnesota at about 11,000 years ago.
The heuristic modeling demonstrated that, for understanding sustainability of groundwater pumping from confined aquifers, knowledge of till hydraulic properties is just as important as knowledge of aquifer hydraulic properties. Substantial differences in groundwater fluxes into and through till were observed across hydrogeologic settings representative of the field sites. Over long periods of time (hundreds of years), pumping-induced hydraulic gradients are established in confined aquifer systems and, even in low hydraulic conductivity tills, these pumping-induced hydraulic gradients increase leakage into and through till compared to ambient conditions.
In conclusion, groundwater flowing vertically downward through till confining units (leakage) replenishes water pumped from confined aquifers. Till hydraulic properties, such as those presented in this report, provide important information that can be used to quantify leakage rates through till. Till hydraulic properties are variable over short distances and profoundly affect leakage rates, demonstrating the importance of site-specific till hydraulic data for evaluating the sustainability of groundwater withdrawals from confined aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205127","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources and in collaboration with Iowa State University and the Minnesota Department of Health","usgsCitation":"Trost, J.J., Maher, A., Simpkins, W.W., Witt, A.N., Stark, J.R., Blum, J., and Berg, A.M., 2020, Hydrogeology and groundwater geochemistry of till confining units and confined aquifers in glacial deposits near Litchfield, Cromwell, Akeley, and Olivia, Minnesota, 2014–18: U.S. Geological Survey Scientific Investigations Report 2020–5127, 80 p., https://doi.org/10.3133/sir20205127.","productDescription":"Report: ix, 80 p.; 2 Data Releases; Dataset","numberOfPages":"94","onlineOnly":"Y","ipdsId":"IP-103595","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":381538,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS dataset","linkHelpText":"— USGS water data for the Nation"},{"id":381534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5127/coverthb.jpg"},{"id":381535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5127/sir20205127.pdf","text":"Report","size":"4.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5127"},{"id":381536,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IXC7D3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geochemical data, water-level data, and slug test analysis results from till confining units and confined aquifers in glacial deposits near Akeley, Cromwell, Litchfield, and Olivia, Minnesota, 2015–2018"},{"id":381537,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KOI6T3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Heuristic MODFLOW models used to evaluate the effects of pumping groundwater from confined aquifers overlain by till confining units"}],"country":"United States","state":"Minnesota","city":"Akeley, Cromwell, Litchfield, Olivia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.5758056640625,\n 45.084672408703945\n ],\n [\n -94.48173522949219,\n 45.084672408703945\n ],\n [\n -94.48173522949219,\n 45.16364237397378\n ],\n [\n -94.5758056640625,\n 45.16364237397378\n ],\n [\n -94.5758056640625,\n 45.084672408703945\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.91549682617188,\n 46.65320687122665\n ],\n [\n -92.84614562988281,\n 46.65320687122665\n ],\n [\n -92.84614562988281,\n 46.6965511173143\n ],\n [\n -92.91549682617188,\n 46.6965511173143\n ],\n [\n -92.91549682617188,\n 46.65320687122665\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.74918365478516,\n 46.987044654111116\n ],\n [\n -94.70970153808594,\n 46.987044654111116\n ],\n [\n -94.70970153808594,\n 47.01678007415223\n ],\n [\n -94.74918365478516,\n 47.01678007415223\n ],\n [\n -94.74918365478516,\n 46.987044654111116\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.02418518066406,\n 44.74185630294231\n ],\n [\n -94.95758056640625,\n 44.74185630294231\n ],\n [\n -94.95758056640625,\n 44.79986517347138\n ],\n [\n -95.02418518066406,\n 44.79986517347138\n ],\n [\n -95.02418518066406,\n 44.74185630294231\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Hanford, Washington (USA) is the construction site of a multi-billion-dollar high-level nuclear waste treatment facility. This site lies 200 kilometers (km) east of Mount St. Helens (MSH), the most active volcano in the contiguous United States. Tephra from a future MSH eruption could pose a hazard to the air intake and filtration systems at this plant. In this report, we present a probabilistic estimate of the amount of tephra that could fall, and the concentrations of airborne ash that could occur at the Hanford Site during a future eruption. Mount St. Helens has produced four large explosive eruptions in approximately the past 500 years, suggesting that its annual probability of eruption (P1) is roughly 4/500=0.008. Assuming that a large eruption occurs, we calculate the probability (P3|1) of a given fall deposit thickness or airborne concentration at Hanford by running about 10,000 simulations of ash-producing eruptions using the atmospheric transport model Ash3d. In each simulation, we calculate the pattern of tephra dispersal, deposit thickness at Hanford, and airborne ash concentration at ground level. As input for each simulation, we choose meteorological conditions from a randomly chosen time in the historical record between 1980 and 2010, using data from the European Centre for Medium-Range Weather Forecasting (ECMWF) Reanalysis (ERA) Interim model. The volume (dense-rock equivalent) of each simulated eruption is randomly chosen from a uniform probability distribution on a log scale from the range of magma volumes (0.008–2.3 cubic kilometers [km3]) estimated for late Holocene eruptions at MSH. Plume heights and durations of each eruption are chosen using empirical correlations between volume, height, and eruption rate, which account for the fact that larger eruptions have higher plumes and last longer. We construct summary tables of final deposit thickness (T), maximum ground-level airborne concentration (Cmax), and average ground-level airborne concentration (Cavg) during tephra-fall for each run. Each table is sorted and ranked by decreasing value of T, Cmax, or Cavg. Conditional probabilities (P3|1) are derived by dividing rank by n+1, where n is the total number of successful runs. For example, a deposit thickness of 5.10 centimeters (cm) from run 446 is ranked 123 of 9,785 successful runs, yielding P3|1=123/9,786=0.01257. Its annual probability is P=P1·P3|1=0.008×0.01257=0.000101. By interpolation, the deposit thickness (T10k) having an annual probability of 1 in 10,000 (P= 0.0001) is 5.11 cm. Analogous concentration values are Cmax,10k=3,819 and Cavg,10k=1,513 milligrams per cubic meter (mg/m3), respectively. Independent calculations using the known mass accumulation rate of the deposit (=0.001–0.006 kilograms per square meter per second [kg/m2/s]), aggregate fall velocities (u=0.3–0.8 meters per second [m/s]), and the simple formula , yield similar results, although highly variable fall velocities add significant uncertainty. This formula implies that deposit accumulation rates of millimeters (mm) to greater than 1 cm per hour, which are not uncommon during heavy ash fall, are associated with airborne concentrations of 102–103 milligrams per cubic meter (mg/m3). These concentrations are much higher than published measurements (10-3–101 mg/m3), which record only suspended particles sampled in sheltered areas. During heavy ashfall, most fine ash falls as aggregates. Whether such aggregates will be ingested into air ducts will depend on the aggregate size and fall rate, the fragility of the aggregates, the air duct geometry, intake velocity, and other factors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201133","collaboration":"Prepared in cooperation with the U.S. Department of Energy, Office of River Protection","usgsCitation":"Mastin, L.G., Van Eaton, A., and Schwaiger, H.F., 2020, A probabilistic assessment of tephra-fall hazards at Hanford, Washington, from a future eruption of Mount St. Helens: U.S. Geological Survey Open-File Report 2020–1133, 54 p., https://doi.org/10.3133/ofr20201133.","productDescription":"Report: ix, 54 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-112179","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":381546,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1133/covrthb.jpg"},{"id":381547,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1133/ofr20201133.pdf","text":"Report","size":"9.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381548,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VPFXQR","linkHelpText":"Data Used to Develop A Probabilistic Assessment of Tephra-Fall Hazards at Hanford, Washington"}],"country":"United States","state":"Washington","otherGeospatial":"Hanford","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.88281249999999,\n 46.33175800051563\n ],\n [\n -119.2950439453125,\n 46.33175800051563\n ],\n [\n -119.2950439453125,\n 46.81509864599243\n ],\n [\n -119.88281249999999,\n 46.81509864599243\n ],\n [\n -119.88281249999999,\n 46.33175800051563\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center
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1300 SE Cardinal Court
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New Mexico relies on observations from the Landsat series of satellites operated by the U.S. Geological Survey. Data from Landsat also assist New Mexico in managing its precious water resources for agriculture, recreation, and industrial and community consumption. Landsat supports a variety of public and private sector decisions across New Mexico and the Nation for effective adaptation to changing landscapes.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Information concerning the availability, use, and quality of water in Red River Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 5.76 million gallons per day (Mgal/d) of water were withdrawn in Red River Parish: about 4.23 Mgal/d from groundwater sources and 1.54 Mgal/d from surface-water sources. Withdrawals for agricultural use, composed of general irrigation, rice irrigation, and livestock uses, accounted for about 72 percent (4.15 Mgal/d) of the total water withdrawn. Other categories of use included public supply (about 18 percent of the total water withdrawn, or 1.03 Mgal/d), industry (about 7 percent, or 0.39 Mgal/d), and rural domestic (about 4 percent, or 0.20 Mgal/d). Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 1975 at more than 7.3 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203053","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Robinson, A.L., and White, V.E., 2020, Water resources of Red River Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3053, 6 p., https://doi.org/10.3133/fs20203053.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103355","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":381549,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3053/coverthb.jpg"},{"id":381550,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3053/fs20203053.pdf","text":"Report","size":"1.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3053"},{"id":381551,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"}],"country":"United States","state":"Louisiana","county":"Red River Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-93.6149,32.2376],[-93.5347,32.2377],[-93.5325,32.2378],[-93.4713,32.237],[-93.4355,32.2358],[-93.4333,32.2359],[-93.4284,32.236],[-93.4057,32.2363],[-93.3959,32.236],[-93.3558,32.2363],[-93.3417,32.236],[-93.2475,32.2362],[-93.2139,32.2358],[-93.2127,32.2308],[-93.212,32.2258],[-93.213,32.2207],[-93.2123,32.2144],[-93.2116,32.2066],[-93.2097,32.1934],[-93.202,32.1885],[-93.1987,32.1872],[-93.1954,32.1832],[-93.1937,32.18],[-93.1924,32.17],[-93.1917,32.1632],[-93.1889,32.1568],[-93.186,32.1487],[-93.1744,32.1365],[-93.1643,32.1203],[-93.1576,32.1122],[-93.1565,32.1076],[-93.1526,32.1049],[-93.1563,32.0976],[-93.1589,32.0957],[-93.1636,32.0865],[-93.1608,32.0816],[-93.1521,32.0785],[-93.1444,32.0713],[-93.1393,32.0614],[-93.138,32.0518],[-93.1422,32.0449],[-93.1432,32.0399],[-93.1469,32.0375],[-93.1506,32.0343],[-93.1554,32.0278],[-93.1557,32.0201],[-93.1556,32.0146],[-93.1582,32.0082],[-93.1522,32.0037],[-93.146,31.9942],[-93.1383,31.9866],[-93.1306,31.9803],[-93.1213,31.9727],[-93.2131,31.9732],[-93.2342,31.9733],[-93.2385,31.9728],[-93.2389,31.9682],[-93.2403,31.9071],[-93.2452,31.9084],[-93.2479,31.9101],[-93.2523,31.911],[-93.2592,31.91],[-93.2639,31.8989],[-93.2713,31.8938],[-93.2777,31.891],[-93.2799,31.8909],[-93.2852,31.8895],[-93.2933,31.8898],[-93.2993,31.8925],[-93.301,31.8956],[-93.3075,31.8964],[-93.3187,31.889],[-93.3208,31.8885],[-93.3317,31.8924],[-93.3415,31.8954],[-93.3507,31.8989],[-93.3508,31.9016],[-93.3526,31.9103],[-93.3527,31.9144],[-93.3528,31.919],[-93.3548,31.9321],[-93.3532,31.9354],[-93.3554,31.9367],[-93.3679,31.942],[-93.3696,31.9433],[-93.3692,31.9501],[-93.3748,31.9569],[-93.3727,31.962],[-93.3766,31.966],[-93.3803,31.965],[-93.3814,31.9659],[-93.4034,31.981],[-93.4079,31.9887],[-93.4139,31.9891],[-93.4175,31.9831],[-93.4229,31.9839],[-93.424,31.9853],[-93.4231,31.9903],[-93.4328,31.9897],[-93.4376,31.9887],[-93.4431,31.9904],[-93.4437,31.994],[-93.4476,31.999],[-93.452,32.0003],[-93.4558,32.0034],[-93.4597,32.0061],[-93.4609,32.0111],[-93.4621,32.017],[-93.4638,32.022],[-93.4672,32.026],[-93.4769,32.0272],[-93.4802,32.0299],[-93.4814,32.0335],[-93.479,32.0472],[-93.4798,32.0568],[-93.4848,32.0604],[-93.4896,32.0603],[-93.5049,32.0641],[-93.5071,32.0673],[-93.5068,32.075],[-93.5139,32.0804],[-93.5135,32.0858],[-93.525,32.0911],[-93.53,32.0969],[-93.541,32.104],[-93.5411,32.1068],[-93.5384,32.1082],[-93.5369,32.1132],[-93.5295,32.1166],[-93.5227,32.1258],[-93.5129,32.1264],[-93.5098,32.1297],[-93.5099,32.1333],[-93.521,32.145],[-93.5225,32.1614],[-93.5215,32.1669],[-93.5249,32.1704],[-93.5261,32.1763],[-93.5312,32.1854],[-93.5379,32.1926],[-93.5525,32.1936],[-93.5547,32.1927],[-93.5672,32.1952],[-93.5809,32.2018],[-93.594,32.2061],[-93.5985,32.2124],[-93.5969,32.2147],[-93.5982,32.2215],[-93.6049,32.2269],[-93.6061,32.2319],[-93.6132,32.2349],[-93.6149,32.2376]]]},\"properties\":{\"name\":\"Red River\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Bienville Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 13.03 million gallons per day (Mgal/d) of water were withdrawn in Bienville Parish, including about 12.88 Mgal/d from groundwater sources and 0.15 Mgal/d from surface-water sources. Withdrawals for industrial use accounted for about 78 percent (10.18 Mgal/d) of the total water withdrawn. Other categories of use included public supply, which accounted for about 18 percent of the total water withdrawn (2.33 Mgal/d); agriculture, composed of general irrigation and livestock, which accounted for about 1 percent (0.18 Mgal/d); and rural domestic (0.33 Mgal/d), which accounted for about 2 percent. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 1995 at more than 17 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203052","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Robinson, A.L., and White, V.E., 2020, Water resources of Bienville Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3052, 6 p., https://doi.org/10.3133/fs20203052.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103354","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":381552,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3052/coverthb.jpg"},{"id":381553,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3052/fs20203052.pdf","text":"Report","size":"875 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3052"},{"id":381554,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"}],"country":"United States","state":"Louisiana","county":"Bienville Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-92.8807,32.5853],[-92.8779,32.5202],[-92.8795,32.4541],[-92.8078,32.4545],[-92.7768,32.4548],[-92.7752,32.3865],[-92.7749,32.371],[-92.7746,32.3181],[-92.7739,32.3077],[-92.7732,32.2648],[-92.7751,32.2429],[-92.7739,32.2366],[-92.7824,32.2283],[-92.7861,32.2191],[-92.7963,32.214],[-92.8011,32.2085],[-92.8004,32.1984],[-92.7986,32.1916],[-92.8018,32.1866],[-92.8038,32.1783],[-92.8081,32.1742],[-92.8177,32.165],[-92.8198,32.1604],[-92.8207,32.149],[-92.9376,32.148],[-92.9407,32.148],[-93.0837,32.1488],[-93.186,32.1487],[-93.1889,32.1568],[-93.1917,32.1632],[-93.1924,32.17],[-93.1937,32.18],[-93.1954,32.1832],[-93.1987,32.1872],[-93.202,32.1885],[-93.2097,32.1934],[-93.2116,32.2066],[-93.2123,32.2144],[-93.213,32.2207],[-93.212,32.2258],[-93.2127,32.2308],[-93.2139,32.2358],[-93.2475,32.2362],[-93.3417,32.236],[-93.3558,32.2363],[-93.3959,32.236],[-93.4057,32.2363],[-93.4284,32.236],[-93.4269,32.2387],[-93.4193,32.2393],[-93.4125,32.2485],[-93.4111,32.2599],[-93.413,32.2713],[-93.4126,32.2763],[-93.4101,32.2832],[-93.4155,32.2854],[-93.4204,32.284],[-93.4226,32.2871],[-93.4228,32.2944],[-93.4302,32.3089],[-93.4244,32.314],[-93.4213,32.3227],[-93.4111,32.3493],[-93.4037,32.3572],[-93.4023,32.3649],[-93.4056,32.3703],[-93.4176,32.3729],[-93.4248,32.3773],[-93.4288,32.3854],[-93.4273,32.3923],[-93.4215,32.3997],[-93.4162,32.4016],[-93.3981,32.3951],[-93.3916,32.3947],[-93.3729,32.4105],[-93.324,32.4104],[-93.3186,32.4101],[-93.2811,32.4102],[-93.242,32.4099],[-93.2371,32.41],[-93.2366,32.4114],[-93.237,32.4273],[-93.2371,32.4346],[-93.2371,32.4555],[-93.2083,32.4555],[-93.1485,32.4564],[-93.1491,32.4587],[-93.1518,32.4595],[-93.1514,32.4627],[-93.1503,32.465],[-93.1504,32.4669],[-93.1488,32.4705],[-93.1466,32.4715],[-93.1473,32.4783],[-93.1474,32.4801],[-93.1533,32.48],[-93.1561,32.4823],[-93.1535,32.4887],[-93.1558,32.4932],[-93.1586,32.4982],[-93.1608,32.5],[-93.1642,32.5036],[-93.1671,32.5117],[-93.1666,32.5158],[-93.1695,32.5254],[-93.1714,32.5354],[-93.1721,32.5431],[-93.1684,32.5522],[-93.1696,32.5563],[-93.1724,32.5577],[-93.18,32.5863],[-93.063,32.5852],[-92.9998,32.5838],[-92.8965,32.5846],[-92.8807,32.5853]]]},\"properties\":{\"name\":\"Bienville\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
The U.S. Geological Survey, in cooperation with the Albuquerque Bernalillo County Water Utility Authority (ABCWUA), measures groundwater levels continuously (hourly) and discretely (semiannually and annually) at a network of wells and piezometers (hereafter called the observation network) within the Middle Rio Grande Basin in central New Mexico. Groundwater levels that are measured in this observation network provide a long-term hydrologic dataset that is heavily relied upon to make water management decisions. The desire to upgrade and perform maintenance on this observation network initiated this study, which assesses the spatial and temporal importance of measurements towards optimization of monitoring the observation network to reduce or redirect monitoring costs. This report describes the methods and results of the optimization assessment of this observation network, which included separate spatial and temporal methodologies and an evaluation using principal component analysis (PCA).
Results from the spatial optimization assessment can be used to help identify observation network sites that do not significantly affect the generation of winter groundwater-elevation contour maps of the production zone. Results from the temporal optimization assessment and PCA can also be consulted when deciding which sites to remove from the network, especially for sites that are monitored more frequently than annually. Results from the temporal optimization assessment can be used to inform the minimum monitoring frequency at the observation network required to capture the trends shown in higher frequency monitoring. The PCA results distinguish spatially distributed characteristic water-level trends that can inform the management decisions that are made when using the spatial and temporal optimization assessment results. Reducing the temporal frequency or spatial density of monitoring is ultimately a management decision that depends on the amount of data loss or degradation that is deemed acceptable while still meeting the network objectives of the ABCWUA. This study can also serve as a starting point to a data gap analysis of local aquifer characteristics and help guide enhanced observation network design as needs arise or in advance of future water management decisions.
The results of the spatial optimization assessment indicate that as many as about 20 specified sites can be removed from the observation network with a relatively small loss in the ability to represent the kriged groundwater-elevation surfaces of the production zone that were generated by using median groundwater elevations for two periods: the 2001 time interval and 2015 time interval. This analysis also demonstrated the importance of wells at the margin of the study area and in areas where there are large hydrologic gradients. At many of the 47 hourly monitored sites analyzed in the temporal optimization assessment, temporal trends were well represented for at least one of the reduced monitoring frequencies tested, indicating that a reduced frequency may be sufficient to adequately characterize seasonal and long-term trends. PCA and k-means clustering analysis of the 15 hourly monitored sites that are screened within the production zone indicate that the sites can be categorized into four groups, or clusters, of differing groundwater-level hydrograph characteristics. Except for one cluster, all of the clusters have the potential to be well represented by fewer index monitoring sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205007","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Ritchie, A.B., and Pepin, J.D., 2020, Optimization assessment of a groundwater-level observation network in the Middle Rio Grande Basin, New Mexico (ver. 2, December 2020): U.S. Geological Survey Scientific Investigations Report 2020–5007, 113 p., https://doi.org/10.3133/sir20205007.","productDescription":"Report: vii, 113 p.; 2 Figures","numberOfPages":"125","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-102753","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":373207,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5007/sir20205007.pdf","text":"Report","size":"7.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5007"},{"id":399629,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109787.htm"},{"id":381500,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5007/versionHist.txt","text":"Version History","description":"SIR 2020–5007 Version History"},{"id":373209,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5007/sir20205007_figure23B.pdf","text":"Figure 23B","size":"170 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5007 Figure 23B","linkHelpText":"Clustered monthly median hydrographs plotted using an independent y-axis range for all plots"},{"id":373208,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5007/sir20205007_figure23A.pdf","text":"Figure 23A","size":"163 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5007 Figure 23A","linkHelpText":"Clustered monthly median hydrographs plotted using the same fixed y-axis range for all plots"},{"id":373206,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5007/coverthb2.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Middle Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8517,\n 34.9064\n ],\n [\n -106.4542,\n 34.9064\n ],\n [\n -106.4542,\n 35.4011\n ],\n [\n -106.8517,\n 35.4011\n ],\n [\n -106.8517,\n 34.9064\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: March 17, 2020; Version 2.0: December 21, 2020","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
The U.S. Geological Survey (USGS), in cooperation with the Grand River Dam Authority, completed a high-resolution multibeam bathymetric survey to compute a new area and capacity table for Grand Lake O’ the Cherokees in northeastern Oklahoma. Area and capacity tables identify the relation between the elevation of the water surface and the volume of water that can be impounded at each water-surface elevation. The area and capacity of Grand Lake O’ the Cherokees were computed from a triangular irregular network surface created in Global Mapper Version 21.0.1. The triangular irregular network surface was created from three datasets: (1) a multibeam mapping system bathymetric survey of Grand Lake O’ the Cherokees completed during April–July 2019, (2) a previous bathymetric survey of the Neosho, Spring, and Elk Rivers, and (3) a 2010 USGS lidar-derived digital elevation model. The digital elevation model data were used in areas with land-surface elevations greater than 744 feet above the North American Vertical Datum of 1988 where the multibeam sonar data could not be collected. The 2019 multibeam sonar data were the predominant data used to compute the new area and capacity table for Grand Lake O’ the Cherokees.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3467","collaboration":"Prepared in cooperation with the Grand River Dam Authority","usgsCitation":"Hunter, S.L., Trevisan, A.R., Villa, J., and Smith, K.A., 2020, Bathymetric map, surface area, and capacity of Grand Lake O’ the Cherokees, northeastern Oklahoma, 2019: U.S. Geological Survey Scientific Investigations Map 3467, 2 sheets, https://doi.org/10.3133/sim3467.","productDescription":"2 Sheets: 36.00 x 42.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-116457","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":381444,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3467/coverthb.jpg"},{"id":381448,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3467/sim3467_sheet1.pdf","text":"Sheet 1","size":"5.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3467 Sheet 1"},{"id":381449,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3467/sim3467_sheet2.pdf","text":"Sheet 2","size":"26.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3467 Sheet 2"},{"id":381450,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KA2T3Z","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data release of bathymetric map, surface area, and capacity of Grand Lake O’ the Cherokees, northeastern Oklahoma, 2019"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Grand Lake O’ the Cherokees","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.10589599609375,\n 36.436751611390264\n ],\n [\n -94.60601806640625,\n 36.436751611390264\n ],\n [\n -94.60601806640625,\n 36.8510544475565\n ],\n [\n -95.10589599609375,\n 36.8510544475565\n ],\n [\n -95.10589599609375,\n 36.436751611390264\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Hinkley Valley, in the central to western Mojave Desert of southeastern California, has a long historical record owing to its position as a crossroads for rail and road traffic and its position adjacent to the Mojave River. Subflow in the Mojave River provided groundwater recharge that maintained water consumption and demand by way of shallow wells for local agriculture in the valley. Its crossroads position led to construction of several power-transmission lines, pipeline, and communications cable routes that transect Hinkley Valley. One of these, a natural gas pipeline and its associated compressor station, was the locus of hexavalent chromium, Cr(VI), released into, and consequent contamination of, groundwater. Understanding the movement and fate of the contaminants is a complex hydrologic and geochemical problem. Geologic mapping of the Hinkley Valley area provides framework elements for use in resolving this problem. This report provides new information on surface and subsurface geology to better constrain the origin and geometry of hydrologically important deposits in the Hinkley Valley area and describes youthful faults that may control sediment distribution and groundwater flow. The geologic map (sheet 1) presents substantial new information on surficial geology, including Pliocene deposits, but does not contain significant new work on bedrock. Bedrock investigations were specific to identifying youthful faults and representative outcrops for rocks that were penetrated by boreholes in the valley. Special attention was placed on locating and describing youthful faults. In addition, we analyzed gravity data to (1) map horizontal gradients that we interpret to reflect long-term fault traces and to (2) estimate the depth to bedrock, which is defined as Miocene and older intrusive and metamorphic rocks for the purposes of this report. The subsurface geology of Hinkley Valley was investigated by examining borehole sediment cores and rock encountered at the base of the sediment section. We analyzed the core to determine depositional environments, provenance, and age of the sediment that infilled the valley. Valleys, mountains, and basins in the Hinkley Valley area are topographically complex and incompletely named. The nearly flat floored Hinkley Valley slopes gently northward. It is framed by Mount General and the informally named “Hinkley hills” (southeast of Mount General) on the northeast and by Iron Mountain and Lynx Cat Mountain on the southwest, although breaks in the western mountains allow stream connections between Hinkley Valley and another valley to the west that is herein referred to as Hawes valley. At its south end, Hinkley Valley is traversed by the entrenched Mojave River, which passes east out of the valley past Barstow. North of Hinkley Valley, a few low hills (including Red Hill) separate the valley from a broad west-sloping piedmont that is part of the physiographic Harper Basin (of which the Harper Lake playa is the center). The lower part of this piedmont, however, is referred to as Water Valley, although it is not a distinct valley. The name derives from groundwater sourced from subflow in the Mojave River, which caused shallow water and even artesian flow in Water Valley but not in other parts of the Harper Basin. When water filled the Harper Basin to form Pleistocene Lake Harper it not only submerged Water Valley but also northern Hinkley Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3458","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board and the State Water Resources Control Board","usgsCitation":"Miller, D.M., Langenheim, V.E., and Haddon, E.K., 2020, Geologic map and borehole stratigraphy of Hinkley Valley and vicinity, San Bernardino County, California: U.S. Geological Survey Scientific Investigations Map 3458, pamphlet 23 p., 2 sheets, scale 1:24,000, https://doi.org/10.3133/sim3458.","productDescription":"Pamphlet,: iv, 23 p.; 2 Sheets ; 2 Tables; Database; Data Release; Metadata","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102109","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":381271,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_sheet2.pdf","text":"Sheet 2","size":"32 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381270,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_sheet1.pdf","text":"Sheet 1","size":"40 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381269,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_database.zip","text":"Database","size":"7.5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":381268,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_base.zip","text":"Base","size":"1.25 GB","linkFileType":{"id":6,"text":"zip"}},{"id":381267,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_metadata.txt","size":"10 KB","linkFileType":{"id":2,"text":"txt"}},{"id":381266,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_pamphlet.pdf","text":"Pamphlet","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381451,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FV5LG5","linkHelpText":"Gravity data of the Hinkley area, southern California"},{"id":381273,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_table_7.xlsx","text":"Table 7","size":"60 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":381265,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3458/covrthb.jpg"},{"id":381272,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_table_3.xlsx","text":"Table 3","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"}}],"country":"United States","state":"California","county":"San Bernadino County","otherGeospatial":"Hinkley Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.26257324218749,\n 34.80647431931937\n ],\n [\n -117.06619262695312,\n 34.80647431931937\n ],\n [\n -117.06619262695312,\n 35.060352812431496\n ],\n [\n -117.26257324218749,\n 35.060352812431496\n ],\n [\n -117.26257324218749,\n 34.80647431931937\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Surface-based 2D electrical resistivity tomography (ERT) surveys were used to characterize permafrost distribution at wetland sites on the alluvial plain north of the Tanana River, 20 km southwest of Fairbanks, Alaska, in June and September 2014. The sites were part of an ecologically-sensitive research area characterizing biogeochemical response of this region to warming and permafrost thaw, and the site contained landscape features characteristic of interior Alaska, including thermokarst bog, forested permafrost plateau, and a rich fen. The results show how vegetation reflects shallow (0–10 m depth) permafrost distribution. Additionally, we saw shallow (0–3 m depth) low resistivity areas in forested permafrost plateau potentially indicating the presence of increased unfrozen water content as a precursor to ground instability and thaw. Time-lapse study from June to September suggested a depth of seasonal influence extending several meters below the active layer, potentially as a result of changes in unfrozen water content. A comparison of several electrode geometries (dipole-dipole, extended dipole-dipole, Wenner-Schlumberger) showed that for depths of interest to our study (0–10 m) results were similar, but data acquisition time with dipole-dipole was the shortest, making it our preferred geometry. The results show the utility of ERT surveys to characterize permafrost distribution at these sites, and how vegetation reflects shallow permafrost distribution. These results are valuable information for ecologically sensitive areas where ground-truthing can cause excessive disturbance. ERT data can be used to characterize the exact subsurface geometry of permafrost such that over time an understanding of changing permafrost conditions can be made in great detail. Characterizing the depth of thaw and thermal influence from the surface in these areas also provides important information as an indication of the depth to which carbon storage and microbially-mediated carbon processing may be affected.
","language":"English","publisher":"Environmental & Engineering Geophysical Society","doi":"10.2113/JEEG19-091","usgsCitation":"Conaway, C., Johnson, C., Lorenson, T., Turetsky, M.R., Euskirchen, E., Waldrop, M., and Swarzenski, P.W., 2020, Permafrost mapping with electrical resistivity tomography in two wetland systems north of the Tanana River, Interior Alaska: Journal of Environmental & Engineering Geophysics, v. 2, no. 25, p. 199-209, https://doi.org/10.2113/JEEG19-091.","productDescription":"11 p.","startPage":"199","endPage":"209","ipdsId":"IP-102186","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":436695,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KTHH8X","text":"USGS data release","linkHelpText":"Permafrost Mapping in Two Wetland Systems North of the Tanana River in Interior Alaska 2014"},{"id":381485,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Tanana River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.19732666015625,\n 64.72374428370091\n ],\n [\n -148.4197998046875,\n 64.86527470612393\n ],\n [\n -149.117431640625,\n 64.83959712503844\n ],\n [\n -149.095458984375,\n 64.63564536799623\n ],\n [\n -149.07073974609375,\n 64.57321597426092\n ],\n [\n -148.7274169921875,\n 64.59561280029605\n ],\n [\n -148.66973876953125,\n 64.63917482390902\n ],\n [\n -148.392333984375,\n 64.66621875267623\n ],\n [\n -148.19732666015625,\n 64.72374428370091\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"25","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Conaway, Christopher H. 0000-0002-0991-033X","orcid":"https://orcid.org/0000-0002-0991-033X","contributorId":201932,"corporation":false,"usgs":true,"family":"Conaway","given":"Christopher H.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":807080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Cordell 0000-0001-8353-8030","orcid":"https://orcid.org/0000-0001-8353-8030","contributorId":212817,"corporation":false,"usgs":true,"family":"Johnson","given":"Cordell","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":807081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorenson, Thomas 0000-0001-7669-2873 tlorenson@usgs.gov","orcid":"https://orcid.org/0000-0001-7669-2873","contributorId":174599,"corporation":false,"usgs":true,"family":"Lorenson","given":"Thomas","email":"tlorenson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":807082,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Turetsky, Merritt R.","contributorId":169398,"corporation":false,"usgs":false,"family":"Turetsky","given":"Merritt","email":"","middleInitial":"R.","affiliations":[{"id":12660,"text":"University of Guelph","active":true,"usgs":false}],"preferred":false,"id":807083,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Euskirchen, Eugénie S.","contributorId":83378,"corporation":false,"usgs":false,"family":"Euskirchen","given":"Eugénie S.","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":807084,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":807085,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Swarzenski, Peter W. 0000-0003-0116-0578","orcid":"https://orcid.org/0000-0003-0116-0578","contributorId":189823,"corporation":false,"usgs":false,"family":"Swarzenski","given":"Peter","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":807086,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70216871,"text":"sir20205091 - 2020 - Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15","interactions":[],"lastModifiedDate":"2021-04-08T21:42:55.915848","indexId":"sir20205091","displayToPublicDate":"2020-12-16T09:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5091","displayTitle":"Simulation of Groundwater Flow in the Regional Aquifer System on Long Island, New York, for Pumping and Recharge Conditions in 2005–15","title":"Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15","docAbstract":"A three-dimensional groundwater-flow model was developed for the aquifer system of Long Island, New York, to evaluate (1) responses of the hydrologic system to changes in natural and anthropogenic hydraulic stresses, (2) the subsurface distribution of groundwater age, and (3) the regional-scale distribution of groundwater travel times and the source of water to fresh surface waters and coastal receiving waters. The model also provides the groundwater flow components used to define model boundaries for possible inset models used for local-scale analyses.
The three-dimensional, groundwater flow model developed for this investigation uses the numerical code MODFLOW–NWT to represent steady-state conditions for average groundwater pumping and aquifer recharge for 2005–15. The particle-tracking algorithm MODPATH, which simulates advective transport in the aquifer, was used to estimate groundwater age, delineate the areas at the water table that contribute recharge to coastal and freshwater bodies, and estimate total travel times of water from the water table to discharge locations.
A three-dimensional, 1-meter (3.3-foot) topobathymetric model was used to determine land-surface altitudes for the island and seabed altitudes for the surrounding coastal waters. The mapped extents and surface altitudes of major geologic units were compiled and used to develop a three-dimensional hydrogeologic framework of the aquifer system, including aquifers and confining units. Lithologic data from deep boreholes and previous aquifer-test results were used to estimate the three-dimensional distribution of hydraulic conductivity in principal aquifers. Natural recharge from precipitation was estimated for 2005–15 using a modified Thornthwaite-Mather methodology as implemented in a soil-water balance model. Components of anthropogenic recharge—wastewater return flow, storm water inflow, and inflow from leaky infrastructure—also were estimated for 2005–15. Groundwater withdrawals for various sources, including public water supply, industrial, remediation, and agricultural, were compiled or estimated for the same period.
These data were incorporated into the model development to represent the aquifer system geometry, boundaries, and initial hydraulic properties of the regional aquifers and confining units within the Long Island aquifer system. Average hydraulic conditions—water levels and streamflows—for 2005–15 were estimated using existing data from the U.S. Geological Survey National Water Information System database. Model inputs were adjusted to best match average hydrologic conditions using inverse methods as implemented in the parameter-estimating software PEST. The calibrated model was used to simulate average hydrologic conditions in the aquifer system for 2005–15.
About 656 cubic feet per second of water was withdrawn on average annually for 2005–15 for water supply and an average of about 349 cubic feet per second of water recharged the aquifer annually from return flow and leaky infrastructure. Parts of New York City have drawdowns exceeding 25 feet, mostly because of urbanization and associated large decreases in recharge rates. Large areas in the western part of the island have drawdowns exceeding 10 feet, mostly from large groundwater withdrawals and sewering, which largely eliminates wastewater return flow. Water-table altitudes in eastern parts of the island increased by more than 2 feet in some areas as a result of wastewater return flow in unsewered areas and changes in land use. Changes in streamflows show a similar pattern as water-table altitudes. Streamflows generally decrease in western parts of the island where there are large drawdowns and increase in eastern parts of the island where water-table altitudes increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205091","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Walter, D.A., Masterson, J.P., Finkelstein, J.S., Monti, J., Jr., Misut, P.E., and Fienen, M.N., 2020, Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15: U.S. Geological Survey Scientific Investigations Report 2020–5091, 75 p., https://doi.org/10.3133/sir20205091.","productDescription":"Report: ix, 75 p.; 3 Data Releases","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112206","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":381521,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5091/images/"},{"id":381195,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5091"},{"id":381194,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5091/coverthb2.jpg"},{"id":381192,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954DLLC","text":"USGS data release","linkHelpText":"Aquifer texture data describing the Long Island aquifer system"},{"id":381191,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KWQSEJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 used to simulate groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15"},{"id":381190,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens Counties, New York, October-November 2017"},{"id":381520,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.XML"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.102783203125,\n 40.55554790286311\n ],\n [\n -73.7017822265625,\n 40.49709237269567\n ],\n [\n -72.8778076171875,\n 40.65147128144057\n ],\n [\n -72.037353515625,\n 40.91351257612758\n ],\n [\n -71.6143798828125,\n 41.16211393939692\n ],\n [\n -71.9384765625,\n 41.178653972331674\n ],\n [\n -72.191162109375,\n 41.236511201246216\n ],\n [\n -72.65808105468749,\n 41.11660732012896\n ],\n [\n -73.4490966796875,\n 40.967455873296714\n ],\n [\n -73.751220703125,\n 40.89275342420696\n ],\n [\n -74.0203857421875,\n 40.718119379753446\n ],\n [\n -74.102783203125,\n 40.55554790286311\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
About one-quarter of the water supply for the Village of Ruidoso, New Mexico, is from groundwater pumped from wells located along North Fork Eagle Creek in the National Forest System lands of the Lincoln National Forest near Alto, New Mexico. Because of concerns regarding the effects of groundwater pumping on surface-water hydrology in the North Fork Eagle Creek Basin and the effects of the 2012 Little Bear Fire, which resulted in substantial loss of vegetation in the basin, the U.S. Department of Agriculture Forest Service, Lincoln National Forest, has required monitoring of a portion of North Fork Eagle Creek for short-term geomorphic change as part of the permitting decision that allows for the continued pumping of the production wells. The objective of this study is to address the geomorphic monitoring requirements of the permitting decision by conducting annual geomorphic surveys of North Fork Eagle Creek along the stream reach between the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station (U.S. Geological Survey [USGS] site 08387550) and the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station (USGS site 08387600). The monitoring of short-term geomorphic change in the stream reach began in June 2017 with surveys of select cross sections and surveys of all woody debris accumulations and pools found in the channel. In June 2018, the monitoring of short-term geomorphic change continued with another geomorphic survey of the stream reach (with some modification to the monitoring methods).
The 2017 and 2018 surveys were conducted by the USGS, in cooperation with the Village of Ruidoso, and were the first two in a planned series of five annual geomorphic surveys. The results of the 2017 geomorphic survey were summarized and interpreted in a previous USGS Open-File Report, and the data were published in the companion data release of that report. In this report, the results of the 2018 geomorphic survey are summarized, interpreted, and compared to the results of the 2017 survey. The data from the 2018 geomorphic survey are published in the companion data release of this report.
The study reach surveyed in June 2018 is 1.89 miles long, beginning about 260 feet upstream from the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station and ending at the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station. Large sections of the study reach are characterized by intermittent streamflow, and where streamflow is normally continuous (including at the upper and lower portions of the study reach, near the streamflow-gaging stations), the streamflow typically remains less than 2 cubic feet per second throughout the year except during seasonal high flows, which most often result from rainfall during the North American monsoon months of July, August, and September or from snowmelt runoff in March, April, and May. Between the 2017 and 2018 surveys, high-flow events resulting from both rainfall (during the North American monsoon season) and snowmelt runoff (during the winter) occurred in the study reach, and those high-flow events appeared to have caused some minor and localized geomorphic changes in the study reach, which were evaluated through comparison of the 2017 and 2018 survey results.
For the 2017 geomorphic survey of North Fork Eagle Creek, cross sections were established and surveyed at 14 locations along the study reach, and in 2018, those same 14 cross sections were resurveyed. Comparisons of the cross-section survey results indicated that minor observable geomorphic changes had occurred in 3 of the 14 cross sections. These minor observable geomorphic changes included aggradation or degradation of surface materials by about 1–2 feet in some parts of the affected cross sections.
To further assess geomorphic changes within the study reach, other features, including woody debris accumulations and pools, were surveyed in both 2017 and 2018. During the 2018 geomorphic survey, 112 distinct accumulations of woody debris and 71 pools were identified in the study reach. Charred wood or burn-marked wood was present in at least 17 of the identified woody debris accumulations (and was present in some of the woody debris accumulations identified during the 2017 survey), indicating that some of the woody debris in the channel may have been sourced from trees or forest litter that had burned during 2012 Little Bear Fire. Only 22 of the 112 woody debris accumulations identified during the 2018 survey were certain to have also been present during the 2017 survey (when 58 woody debris accumulations were identified), indicating that most of the woody debris accumulations surveyed in 2017 were likely transported during the high-flow events between the 2017 and 2018 surveys but also indicating that the flows during those events were not high enough to remove some of the more firmly anchored woody debris accumulations. Most woody debris accumulations identified in 2018 did not appear to have substantially influenced geomorphic change in the locations where they were found. However, the formation of 10 of the 71 pools identified in the study reach in 2018 appeared to have been influenced by the presence of woody debris, indicating that some woody debris accumulations may have driven local geomorphic changes. Notably, pool totals from the 2017 survey could not be accurately compared to the pool totals from the 2018 survey because of differences between the two surveys in the methods used to identify pools.
Because the study began 5 years after the 2012 Little Bear Fire, and because the period and geomorphic scope of the study have so far been limited, it cannot be said that the geomorphic changes observed between the 2017 and 2018 surveys are representative of a pattern of geomorphic change following the 2012 Little Bear Fire. Though, once geomorphic changes between the 2017 and 2018 surveys can be compared with results from geomorphic surveys planned for 2019, 2020, and 2021, it may be possible to develop an understanding of the patterns in geomorphic change following the 2012 Little Bear Fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201121","collaboration":"Prepared in cooperation with the Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., 2020, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2018: U.S. Geological Survey Open-File Report 2020–1121, 37 p., https://doi.org/10.3133/ofr20201121.","productDescription":"Report: v, 37 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-112737","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":381235,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1121/ofr20201121.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1121"},{"id":381236,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94ZQHKU","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data supporting the 2018 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":381234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1121/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5621337890625,\n 32.99023555965106\n ],\n [\n -104.7930908203125,\n 32.99023555965106\n ],\n [\n -104.7930908203125,\n 33.770015152780125\n ],\n [\n -105.5621337890625,\n 33.770015152780125\n ],\n [\n -105.5621337890625,\n 32.99023555965106\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Amphibian conservation has progressed from the identification of declines to mitigation, but efforts are hampered by the lack of nuanced information about the effects of environmental characteristics and stressors on mechanistic processes of population regulation. Challenges include a paucity of long-term data and scant information about the relative roles of extrinsic (e.g., weather) and intrinsic (e.g., density dependence) factors. We used a Bayesian formulation of an open population capture-recapture model and >30 years of data to examine intrinsic and extrinsic factors regulating two adult boreal chorus frogs (Pseudacris maculata) populations. We modelled population growth rate and apparent survival directly, assessed their temporal variability, and derived estimates of recruitment. Populations were relatively stable (geometric mean population growth rate >1) and regulated by negative density dependence (i.e., higher population sizes reduced population growth rate). In the smaller population, density dependence also acted on adult survival. In the larger population, higher population growth was associated with warmer autumns. Survival estimates ranged from 0.30–0.87, per-capita recruitment was <1 in most years, and mean seniority probability was >0.50, suggesting adult survival is more important to population growth than recruitment. Our analysis indicates density dependence is a primary driver of population dynamics for P. maculata adults.
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