The timing and location of spawning events are important data for managers seeking to control invasive grass carp populations. Ichthyoplankton tows for grass carp eggs and larvae can be used to detect spawning events; however, these samples can be highly debris-laden, and are expensive and laborious to process. An alternative method, environmental DNA (eDNA) technology, has proven effective in determining the presence of aquatic species. The objectives of this project were to assess the use of eDNA collections and quantitative eDNA analysis to assess the potential spawning of grass carp in five reservoir tributaries, and to compare those results to the more traditional method of ichthyoplankton tows. Grass carp eDNA was detected in 56% of sampling occasions and was detected in all five rivers. Concentrations of grass carp eDNA were orders of magnitude higher in June, corresponding to elevated discharge and egg presence. Grass carp environmental DNA flux (copies/h) was lower when no eggs were present and was higher when velocities and discharge increased and eggs were present. There was a positive relationship between grass carp eDNA flux and egg flux. Our results support the further development of eDNA analysis as a method to detect the spawning events of grass carp or other rheophilic spawners.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes5030027","usgsCitation":"Hayer, C., Bayless, M.F., George, A.E., Thompson, N., Richter, C.A., and Chapman, D., 2020, Use of environmental DNA to detect grass carp spawning events: Fishes, v. 5, no. 3, 27, 10 p., https://doi.org/10.3390/fishes5030027.","productDescription":"27, 10 p.","ipdsId":"IP-120266","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":455503,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes5030027","text":"Publisher Index Page"},{"id":436810,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WBOLYW","text":"USGS data release","linkHelpText":"Asian carp eDNA and egg morphology data collected from Truman Reservoir tributaries, Missouri, USA, 2014"},{"id":378085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Hayer, Cari-Ann chayer@usgs.gov","contributorId":177628,"corporation":false,"usgs":false,"family":"Hayer","given":"Cari-Ann","email":"chayer@usgs.gov","affiliations":[],"preferred":false,"id":797721,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bayless, Michael F.","contributorId":239697,"corporation":false,"usgs":false,"family":"Bayless","given":"Michael","email":"","middleInitial":"F.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":797722,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"George, Amy E. 0000-0003-1150-8646 ageorge@usgs.gov","orcid":"https://orcid.org/0000-0003-1150-8646","contributorId":3950,"corporation":false,"usgs":true,"family":"George","given":"Amy","email":"ageorge@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":797723,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":797724,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richter, Catherine A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":138994,"corporation":false,"usgs":true,"family":"Richter","given":"Catherine","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":797725,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":797726,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212537,"text":"sir20205067 - 2020 - Bathymetric surveys of Morse and Geist Reservoirs in central Indiana made with a multibeam echosounder, 2016, and comparison with previous surveys","interactions":[],"lastModifiedDate":"2020-08-28T12:29:29.790982","indexId":"sir20205067","displayToPublicDate":"2020-08-27T12:35:16","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":"2020-5067","displayTitle":"Bathymetric Surveys of Morse and Geist Reservoirs in Central Indiana made with a Multibeam Echosounder, 2016, and Comparison with Previous Surveys","title":"Bathymetric surveys of Morse and Geist Reservoirs in central Indiana made with a multibeam echosounder, 2016, and comparison with previous surveys","docAbstract":"The U.S. Geological Survey, in cooperation with Citizens Energy Group, conducted a bathymetric survey of Morse and Geist Reservoirs in central Indiana in April and May of 2016 with a multibeam echosounder. Both reservoirs serve as water supply, flood control, and recreational resources for the city of Indianapolis and the surrounding communities.
Morse and Geist Reservoirs were surveyed to create updated bathymetric maps, determine storage capacities (volume) at specified water-surface elevations, and compare current conditions to historical surveys. Bathymetric data were collected using a high-resolution multibeam echosounder, and supplemental data were collected in coves and other shallow areas using an acoustic Doppler current profiler. The data were processed and combined using HYPACK and ArcMap software to develop a triangulated irregular network, a 5-foot gridded bathymetric dataset, a reservoir capacity table, and a bathymetric contour map for each reservoir.
The computed volume of Morse Reservoir was 23,136 acre-feet (7.54 billion gallons) with a surface area of 1,439 acres (62.7 million square feet). The computed volume of Geist Reservoir was 21,146 acre-feet (6.89 billion gallons) with a surface area of 1,853 acres (80.7 million square feet).
Between 1996 and 2016, lake bottom elevations have increased by a mean of 0.32 feet in Morse Reservoir and 0.27 feet in Geist Reservoir. The data indicate higher sedimentation rates in the upper parts of each reservoir as compared to near the dam and higher sedimentation rates in Morse Reservoir (0.5 inch per year) than in Geist Reservoir (0.2 inch per year). The differences between the current and historical surveys may be due to sedimentation, differences in accuracy between previous surveys, or a combination of both.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205067","collaboration":"Prepared in cooperation with Citizens Energy Group","usgsCitation":"Boldt, J.A., and Martin, Z.W., 2020, Bathymetric surveys of Morse and Geist Reservoirs in central Indiana made with a multibeam echosounder, 2016, and comparison with previous surveys: U.S. Geological Survey Scientific Investigations Report 2020–5067, 39 p., https://doi.org/10.3133/sir20205067.","productDescription":"Report: viii, 39 p.; Data Release; Additional Reports","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-116783","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":377662,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5067/sir20205067.pdf","text":"Report","size":"31.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5067"},{"id":377911,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2020/5067/sir20205067_Morse_Reservoir_2016.pdf","text":"Bathymetric Map of Morse Reservoir near Noblesville, Indiana, 2016","size":"28.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"— High resolution file"},{"id":377912,"rank":5,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2020/5067/sir20205067_Geist_Reservoir_2016.pdf","text":"Bathymetric Map of Geist Reservoir near Fishers, Indiana, 2016","size":"23.4 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"— High resolution file"},{"id":377663,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A2ITC6","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetry of Morse and Geist Reservoirs in central Indiana, 2016"},{"id":377661,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5067/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Hamilton County, Marion County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-85.8617,40.2201],[-85.863,40.139],[-85.8624,39.9436],[-85.8625,39.9286],[-85.9369,39.9272],[-85.9379,39.87],[-85.9541,39.8696],[-85.9518,39.6969],[-85.9523,39.638],[-86.248,39.6335],[-86.3268,39.6318],[-86.3281,39.8526],[-86.328,39.8662],[-86.325,39.8662],[-86.3267,39.9238],[-86.2967,39.9246],[-86.2757,39.925],[-86.2385,39.9259],[-86.239,39.9549],[-86.2417,40.0419],[-86.242,40.1304],[-86.2424,40.1807],[-86.2435,40.2152],[-86.1285,40.2176],[-86.0135,40.2186],[-85.9015,40.2194],[-85.8617,40.2201]]]},\"properties\":{\"name\":\"Hamilton\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
This report presents an updated grade and tonnage model for tungsten skarn deposits. As a critical component of the U.S. Geological Survey’s three-part form of quantitative mineral resource assessment, robust grade and tonnage models are essential to transforming mineral resource assessments into effective tools for decision makers. Using the best data available at the time of publication, this represents the first attempt in nearly 30 years to capture current mineral inventory and cumulative production data for worldwide tungsten skarn deposits. The accuracy of modern assessments of undiscovered tungsten skarn resources is highly influenced by the use of current data on the distribution of the grades and tonnages of well-explored tungsten skarn deposits. Primary factors affecting the changes to these distributions in the model presented here compared with those of previous models are the inclusion of important deposits, especially those in China that had been omitted in previous models; expanded mineral inventories resulting from increased exploration; and changes to international reporting standards. These factors have resulted in dramatic increases in average ore tonnage and slight decreases in the average grade of tungsten skarn deposits compared with previous models. Large increases in contained metal are observed among many of the individual deposits incorporated within this model that were also included in previous tungsten skarn grade and tonnage models. This report also provides recommendations for input parameters related to grade and tonnage models to use with software tools designed to facilitate the three-part form of quantitative mineral resource assessments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205085","usgsCitation":"Green, C.J., Lederer, G.W., Parks, H.L., and Zientek, M.L., 2020, Grade and tonnage model for tungsten skarn deposits—2020 update: U.S. Geological Survey Scientific Investigations Report 2020–5085, 23 p., https://doi.org/10.3133/sir20205085.","productDescription":"vi, 23 p.","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-117570","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":377895,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5085/sir20205085.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5085"},{"id":377894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5085/coverthb.jpg"}],"contact":"Eastern Mineral and Environmental Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
954 National Center
Reston, VA 20192
The Gunnison Basin Selenium Management Program implemented a water-quality monitoring network in 2011 to measure concentrations of selenium in the lower Gunnison River Basin in Colorado. Selenium is a trace element that bioaccumulates in aquatic food chains. Selenium is essential for life, but elevated amounts can cause reproductive failure, deformities, and other harmful effects. The primary goal of the Selenium Management Program is to meet the State of Colorado water-quality standard of 4.6 micrograms per liter (µg/L) for dissolved selenium at the U.S. Geological Survey (USGS) streamflow-gaging station number 09152500—Gunnison River near Grand Junction, Colorado—herein referred to as “Whitewater.” The U.S. Geological Survey, in cooperation with the Bureau of Reclamation, has completed a review of dissolved-selenium data collected from the Selenium Management Program network during Water Year (WY) 2017 (October 1, 2016 through September 30, 2017) to further the understanding of the status and trends of selenium in the basin. This report presents the percentile values for selenium because regulatory agencies in Colorado make decisions based on the U.S. Environmental Protection Agency’s Clean Water Act section 303(d), which uses percentile values for concentrations. Also presented are dissolved-selenium loads at 14 sites in the lower Gunnison River Basin for WYs 2011–17. Annual dissolved-selenium loads were calculated for six sites with continuous U.S. Geological Survey streamflow-gaging stations. These six sites are referred to as “core” sites in this report. The remaining sites, which do not have streamflow-gaging stations, are referred to as “ancillary” sites in this report. During WY 2017, the loads calculated at the six core sites ranged from 306 pounds (lb) at Uncompahgre River at Colona to 12,600 lb at Whitewater, respectively.
By using discrete water-quality samples and the associated discharge measurements, instantaneous loads were calculated for 14 sites in WYs 2011–17 where discrete water-quality sampling took place. Median instantaneous loads ranged from 0.52 pounds per day (lb/d) at Uncompahgre River at Colona to 35.7 lb/d at Whitewater. Mean instantaneous loads ranged from 0.63 lb/d at Cummings Gulch at mouth to 35.5 lb/d at Whitewater. Most tributary sites in the basin had a median instantaneous dissolved-selenium load of less than 20.0 lb/d. In general, dissolved-selenium loads at Gunnison River main-stem sites showed an increase from upstream to downstream.
The State of Colorado’s water-quality standard for dissolved selenium of 4.6 µg/L was compared to the 85th percentiles for dissolved selenium at selected sites. Annual 85th percentiles for dissolved selenium were calculated by using estimated dissolved-selenium concentrations from linear regression models for the six core sites with U.S. Geological Survey streamflow-gaging stations. The 85th-percentile concentrations for WY 2017 based on this method ranged from 0.68 µg/L at Uncompahgre River at Colona to 140 µg/L at Loutzenhizer Arroyo at North River Road. The 85th percentiles for concentrations of dissolved selenium also were calculated from water-quality samples collected during WY 2017 from sites with sufficient data. The annual 85th-percentile concentrations based on the discrete samples ranged from 0.75 µg/L at Uncompahgre River at Colona to 106 µg/L at Loutzenhizer Arroyo at North River Road.
An analysis was completed for Whitewater to determine if an upward or downward trend exists for dissolved-selenium loads during two time periods. The first time period included all data at Whitewater, whereas the second time period focused on more recent data. The trend analysis indicates a decrease from 22,200 to 12,600 lb, which is a 43.1 percent (9,600 lb) reduction during the time period WY 1986 through WY 2017. The trend analysis for the annual dissolved-selenium load for WY 1995 through WY 2017 indicates a decrease of 6,600 lb per year, or 35.5 percent. An evaluation of laboratory bias was completed for selenium data which was used in the trend analysis. Findings indicated a potential positive bias of approximately 12 percent may exist in the data from October 2005 through August 2015.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201078","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Henneberg, M.F., 2020, Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17: U.S. Geological Survey Open-File Report 2020–1078, 21 p., https://doi.org/10.3133/ofr20201078","productDescription":"v, 21 p.","onlineOnly":"Y","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":377861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1078/ofr20201078.pdf","text":"Report","size":"1.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1078"},{"id":377860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1078/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.80584716796875,\n 39.01064750994083\n ],\n [\n -109.11895751953125,\n 38.8782049970615\n ],\n [\n -108.6328125,\n 38.10214399750345\n ],\n [\n -108.69598388671875,\n 37.77288579232439\n ],\n [\n -107.87750244140625,\n 37.309014074275915\n ],\n [\n -107.4462890625,\n 37.31338308990806\n ],\n [\n -107.1441650390625,\n 37.727280276860036\n ],\n [\n -107.18536376953125,\n 38.07620357665235\n ],\n [\n -107.26776123046875,\n 38.50304202775689\n ],\n [\n -107.50671386718749,\n 38.9380483825641\n ],\n [\n -107.6495361328125,\n 39.115144700901475\n ],\n [\n -108.80584716796875,\n 39.01064750994083\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Assessments of organisms’ vulnerability to potential climatic shifts are increasingly common. Such assessments are often conducted at the species level and focused primarily on the magnitude of anticipated climate change (i.e., climate exposure). However, wildlife management would benefit from population-level assessments that also incorporate measures of local or regional potential for organismal adaptation to change. Estimates of genetic diversity, gene flow, and landscape connectivity can address this need and complement climate exposure estimates to establish management priorities at broad to local scales. We provide an example of this holistic approach for desert bighorn sheep (Ovis canadensis nelsoni) within and surrounding lands administered by the U.S. National Park Service. We used genetic and environmental data from 62 populations across the southwestern U.S. to delineate genetic structure, evaluate relationships between genetic diversity and isolation, and estimate relative climate vulnerability for populations as a function of five variables associated with species’ responses to climate change: genetic diversity, genetic isolation, geographic isolation, forward climate velocity within a population’s habitat patch (a measure of geographic movement rate required for an organism to maintain constant climate conditions), and maximum elevation within the habitat patch (a measure of current climate stress, as lower maximum elevation is associated with higher temperature, lower precipitation, and lower population persistence). Genetic structure analyses revealed a high-level division between populations in southeastern Utah and populations in the remainder of the study area, which were further differentiated into four lower-level genetic clusters. Genetic diversity decreased with population isolation, whereas genetic differentiation increased, but these patterns were stronger for native populations than for translocated populations. Populations exhibited large variation in predicted vulnerability across the study area with respect to all variables, but native populations occupying relatively intact landscapes, such as Death Valley and Grand Canyon national parks, had the lowest overall vulnerability. These results provide local and regional context for conservation and management decisions regarding bighorn populations in a changing climate. Our study further demonstrates how assessments combining multiple factors could allow a more integrated response, such as increasing efforts to maintain connectivity and thus potential for adaptation in areas experiencing rapid climate change.
Streamflow estimates for floods with an annual exceedance probability of 0.001 or lower are needed to accurately portray risks to critical infrastructure, such as nuclear powerplants and large dams. However, extrapolating flood-frequency curves developed from at-site systematic streamflow records to very low annual exceedance probabilities (less than 0.001) results in large uncertainties in the streamflow estimates. Traditionally, methods for statistically estimating flood frequency have relied on the systematic streamflow record, which provides a time series of annual maximum flood peaks, often including some historical peaks. However, most peak-flow records are less than 100 years, and uncertainties are large when trying to extrapolate magnitudes of very low annual exceedance probability events.
Other data may be available that extend the record beyond the systematic dataset. Historical data are defined as data from outside the period of systematic records but within the period of human records. Examples of historical information include flood estimates from other agencies and newspaper accounts that can be translated to flood magnitude point estimates, interval estimates, or perception thresholds (such as a statement that an 1880 flood was the largest since 1869). Paleoflood data, which may also extend the dataset, include a broad range of information about flood occurrence or magnitude from sources like sediment deposits or tree rings.
Several assumptions are made in flood-frequency analysis, and an understanding of whether the data conform to these assumptions is desired. A particularly difficult assumption to evaluate for flood-frequency analysis is the underlying assumption that the flood series is stationary—the assumption that a time series of peak flow varies around a constant mean within a particular range of values (constant variance). As the hydrologic community’s understanding of natural systems and anthropogenic effects on streamflows has evolved, the community has come to understand that many surface-water systems exhibit one or more forms of nonstationarity, and thus the stationarity assumption is often violated to some degree. However, there is currently (2020) no consensus among hydrologists regarding the most appropriate flood-frequency-analysis methods for nonstationary systems, and this topic remains an active area of research.
A literature review was completed to summarize the state of the science of flood frequency. The literature review highlights tools available to detect nonstationarities and identifies approaches that include external information to inform flood-frequency analysis. To demonstrate methods for initial data analysis and for incorporating historical and paleoflood information in flood-frequency analysis, five sites were selected: the Red River of the North at James Avenue Pumping Station, Winnipeg, Manitoba, Canada; lower reach, Rapid Creek, South Dakota; Spring Creek, South Dakota; Cherry Creek near Melvin, Colorado; and Escalante River near Escalante, Utah. The sites were chosen for the availability of published historical and paleoflood data and for their geographic diversity and unique characteristics, which highlighted issues such as autocorrelation, change points, trends, outlier peaks, or short periods of record.
An initial data analysis that involved examining records for autocorrelation, change points, and trends was completed for all sites. The flood-frequency analysis completed for this study used version 7.2 of the U.S. Geological Survey PeakFQ program. Multiple analyses were done on each site documenting the change in the flood-frequency curve when additional historical or paleoflood data were added. When other flood-frequency studies were available, their results were compared to the results here. The comparisons in some cases simply show the effect of additional years of data, whereas other comparisons show results from probability distributions or fitting methods other than those used in PeakFQ.
For the Red River of the North, flood-frequency analysis shows that paleoflood data appear necessary to reasonably estimate very low annual exceedance probabilities. For the analysis of the lower reach of Rapid Creek and Spring Creek, paleoflood information helped put a high outlier from the systematic period in context; however, very low annual exceedance probabilities at these sites still had extraordinarily large confidence bounds. These sites also showed that paleoflood information might be transferred from one site to another, with the caveat that this is a case where we had existing paleoflood data to test the transfer of paleoflood information—this is not the case at many sites, and transferring paleoflood information requires assumptions about the comparability of floods at the sites. The Cherry Creek analysis affirmed the result of an earlier study that showed that the generalized Pareto distribution was not a good distribution for estimating very low annual exceedance probabilities. The Escalante River analysis showed that adding paleoflood information might increase uncertainty for very low annual exceedance probabilities, compared to analysis with the systematic period of record information only, when the paleoflood peaks are of much larger magnitudes than the systematic record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205065","collaboration":"Prepared in cooperation with the U.S. Nuclear Regulatory Commission","usgsCitation":"Ryberg, K.R., Kolars, K.A., Kiang, J.E., and Carr, M.L., 2020, Flood-frequency estimation for very low annual exceedance probabilities using historical, paleoflood, and regional information with consideration of nonstationarity: U.S. Geological Survey Scientific Investigations Report 2020–5065, 89 p., https://doi.org/10.3133/sir20205065.","productDescription":"Report: xii, 89 p.; 5 Tables; Appendix; Dataset","numberOfPages":"105","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088812","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":377559,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_appendix.zip","text":"Appendix 1. Data, Settings, and Output for Each Site and Scenario","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5065 Appendix 1","linkHelpText":"— Each zipped file represents the analysis for a particular site and scenario"},{"id":377557,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_7.pdf","text":"Table 7","size":"114 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 7","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under two different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 06712500 Cherry Creek near Melvin, Colorado, with comparisons to other distributions and fitting methods."},{"id":377553,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065.pdf","text":"Report","size":"5.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065"},{"id":377554,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_4.pdf","text":"Table 4","size":"139 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 4","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under 10 different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 05OJ015 Red River of the North at James Avenue Pumping Station, Winnipeg, Manitoba, Canada, as well as results from flood-frequency studies by Burn and Goel (2001) and Harden (1999)."},{"id":377697,"rank":9,"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":377555,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_5.pdf","text":"Table 5","size":"122 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 5","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for the lower reach of Rapid Creek, South Dakota, with comparisons to Harden and others (2011)."},{"id":377556,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_6.pdf","text":"Table 6","size":"112 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 6","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for Spring Creek, South Dakota, with comparisons to Harden and others (2011)."},{"id":377552,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5065/coverthb.jpg"},{"id":377558,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_8.pdf","text":"Table 8","size":"116 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 8","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 09337500 Escalante River near Escalante, Utah, with comparisons to Webb and others (1988), Webb and Rathburn (1988), and Kenney and others (2008)."}],"contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Nearly one-sixth of U.S. river basins are unable to consistently meet societal water demands while also providing sufficient water for the environment. Water scarcity is expected to intensify and spread as populations increase, new water demands emerge, and climate changes. Improving water productivity by meeting realistic benchmarks for all water users could allow U.S. communities to expand economic activity and improve environmental flows. Here we utilize a spatially detailed database of water productivity to set realistic benchmarks for over 400 industries and products. We assess unrealized water savings achievable by each industry in each river basin within the conterminous U.S. by bringing all water users up to industry- and region-specific water productivity benchmarks. Some of the most water stressed areas throughout the U.S. West and South have the greatest potential for water savings, with around half of these water savings obtained by improving water productivity in the production of corn, cotton, and alfalfa. By incorporating benchmark-meeting water savings within a national hydrological model (WaSSI), we demonstrate that depletion of river flows across Western U.S. regions can be reduced on average by 6.2–23.2%, without reducing economic production. Lastly, we employ an environmentally extended input-output model to identify the U.S. industries and locations that can make the biggest impact by working with their suppliers to reduce water use 'upstream' in their supply chain. The agriculture and manufacturing sectors have the largest indirect water footprint due to their reliance on water-intensive inputs but these sectors also show the greatest capacity to reduce water consumption throughout their supply chains.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ab9d39","usgsCitation":"Marston, L., Lamsal, G., Ancona, Z.H., Caldwell, P.V., Richter, B., Ruddell, B., Rushforth, R., and Davis, K.F., 2020, Reducing water scarcity by improving water productivity in the United States: Environmental Research Letters, v. 15, no. 9, 094033, 13 p., https://doi.org/10.1088/1748-9326/ab9d39.","productDescription":"094033, 13 p.","ipdsId":"IP-114542","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":455531,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab9d39","text":"Publisher Index Page"},{"id":377942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": 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V","contributorId":145892,"corporation":false,"usgs":false,"family":"Caldwell","given":"Peter","email":"","middleInitial":"V","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":797431,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richter, Brian","contributorId":239628,"corporation":false,"usgs":false,"family":"Richter","given":"Brian","email":"","affiliations":[],"preferred":false,"id":797432,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ruddell, Benjamin 0000-0003-2967-9339","orcid":"https://orcid.org/0000-0003-2967-9339","contributorId":239629,"corporation":false,"usgs":false,"family":"Ruddell","given":"Benjamin","email":"","affiliations":[{"id":47944,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":797433,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rushforth, Richard","contributorId":239630,"corporation":false,"usgs":false,"family":"Rushforth","given":"Richard","email":"","affiliations":[],"preferred":false,"id":797434,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Davis, Kyle F. 0000-0003-4504-1407","orcid":"https://orcid.org/0000-0003-4504-1407","contributorId":239631,"corporation":false,"usgs":false,"family":"Davis","given":"Kyle","email":"","middleInitial":"F.","affiliations":[{"id":47945,"text":"Department of Geography and Spatial Sciences & Department of Plant and Soil Sciences, University of Delaware","active":true,"usgs":false}],"preferred":false,"id":797435,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70212621,"text":"sim3459 - 2020 - Stratigraphic units of shallow unconsolidated deposits in Deadwood, South Dakota, delineated by real-time kinematic surveys","interactions":[],"lastModifiedDate":"2020-08-26T13:05:12.542236","indexId":"sim3459","displayToPublicDate":"2020-08-25T11:11:39","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3459","displayTitle":"Stratigraphic Units of Shallow Unconsolidated Deposits in Deadwood, South Dakota, Delineated by Real-Time Kinematic Surveys","title":"Stratigraphic units of shallow unconsolidated deposits in Deadwood, South Dakota, delineated by real-time kinematic surveys","docAbstract":"The City of Deadwood, South Dakota, has been working on a new archeological investigation in preparation for economic growth and expansion within the city limits, through the Deadwood Historic Preservation Office. During the excavation process, buried artifacts and historical features from the late 1800s have been uncovered. The stratigraphy of shallow unconsolidated deposits in the city of Deadwood, S. Dak., was surveyed on January 29, 2020, using real-time kinematic survey methods and described to identify variations in geologic material, thickness, and depth from the land surface in support of archeological studies by the city. The findings of the study will provide city managers and the public with reliable and impartial information for their use by advancing field or analytical methodology and understanding of hydrologic processes in the study area. The primary excavation site was surveyed, and stratigraphic units were delineated from changes in material properties or depositional environment. The primary excavation site consisted of nine stratigraphic units; however, some units were not consistent along the length of the excavation and pinched out along the cross section. Survey data points also were collected for artifacts and other sites of interest. The shallow surficial geology in the study area was affected by human construction, fires, and flooding.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3459","collaboration":"Prepared in cooperation with the City of Deadwood, South Dakota","usgsCitation":"Tatge, W.S., Medler, C.J., Eldridge, W.G., and Valder, J.F., 2020, Stratigraphic units of shallow unconsolidated deposits in Deadwood, South Dakota, delineated by real-time kinematic surveys: U.S. Geological Survey Scientific Investigations Map 3459, pamphlet 7 p., 1 sheet, https://dx.doi.org/10.3133/sim3459.","productDescription":"Pamphlet: vi, 7 p.; 1 Sheet: 42.75 x 35.40 inches; 1 Table","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119064","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":377805,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3459/sim3459_table1.csv","text":"Table 1","size":"32.7 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIM 3459 Table 1","linkHelpText":"— Survey points collected for delineation of selected stratigraphic units."},{"id":377804,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3459/sim3459_pamphlet.pdf","text":"Pamphlet","size":"2.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3459 Pamphlet"},{"id":377803,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3459/sim3459.pdf","text":"Sheet 1","size":"5.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3459","linkHelpText":"— Stratigraphic Units of Shallow Unconsolidated Deposits in Deadwood, South Dakota, Delineated by Real-Time Kinematic Surveys"},{"id":377802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3459/coverthb.jpg"}],"country":"United States","state":"South Dakota","city":"Deadwood","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.74698638916016,\n 44.364237624976326\n ],\n [\n -103.71814727783202,\n 44.364237624976326\n ],\n [\n -103.71814727783202,\n 44.38558741441454\n ],\n [\n -103.74698638916016,\n 44.38558741441454\n ],\n [\n -103.74698638916016,\n 44.364237624976326\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Over many millennia, northern peatlands have accumulated large amounts of carbon and nitrogen, thus cooling the global climate. Over shorter timescales, peatland disturbances can trigger losses of peat and release of greenhouses gases. Despite their importance to the global climate, peatlands remain poorly mapped, and the vulnerability of permafrost peatlands to warming is uncertain. This study compiles over 7,000 field observations to present a data-driven map of northern peatlands and their carbon and nitrogen stocks. We use these maps to model the impact of permafrost thaw on peatlands and find that warming will likely shift the greenhouse gas balance of northern peatlands. At present, peatlands cool the climate, but anthropogenic warming can shift them into a net source of warming.
Eastern box turtles (Terrapene carolina carolina) are widely distributed throughout the eastern United States. Although once common throughout much of its distribution, the species has experienced declines in local populations. Understanding resource selection is important for the conservation of this species; however, few data exist on resource selection for eastern box turtles in the southeastern United States. We estimated home range and resource selection for 100 individual turtles in the Blue Ridge, Ridge and Valley, and Cumberland Plateau and Mountains physiographic regions in Tennessee, USA, from 2016 to 2018. We used step‐selection functions to investigate eastern box turtle resource selection during May–August 2017 and May–August 2018 at 2 spatial scales. We classified vegetation type, measured vegetation composition and structure, recorded time since fire, and measured coarse woody debris abundance at 1,225 used telemetry locations and 1,225 associated available points. Home range sizes averaged 9.3 ha ± 3.0 (SE) using minimum convex polygon analysis, 8.25 ha ± 2.88 using 95% kernel density analysis, and 1.50 ha ± 0.56 using 50% kernel density analysis. Box turtles selected areas with greater visual obstruction at the 0–0.25‐m level, greater amounts of 10‐hour and 100‐hour fuels (timelag categories used in fire‐danger ratings), and greater litter depths compared to available locations. Box turtles were more likely to select areas with greater cover of brambles and coarser woody debris and were less likely to select areas with less vegetation cover. Vegetation type and time since last fire did not affect selection. Our data suggest that management activities that encourage greater understory vegetation cover, greater visual obstruction at the 0–0.25‐m level, and greater bramble cover will enhance habitat quality for eastern box turtles.
Continuous water-quality monitoring data and chemical analysis of surface-water and groundwater samples collected during 2017–19 in the recharge zone of the Edwards aquifer were used to develop a better understanding of the surface-water/groundwater connection in and around Bexar County in south-central Texas. This fact sheet is provided to inform water-resource managers, city planners, the scientific community, and the general public about the effects of urbanization on water quality in the Edwards aquifer recharge zone.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203028","collaboration":"Prepared in cooperation with the City of San Antonio","usgsCitation":"Opsahl, S.P., Musgrove, M., and Mecum, K.E., 2020, Effects of urbanization on water quality in the Edwards aquifer, San Antonio and Bexar County, Texas: U.S. Geological Survey Fact Sheet 2020–3028, 4 p., https://doi.org/10.3133/fs20203028.","productDescription":"Report: 4 p.; Companion Report","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-115922","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":376080,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3028/coverthb2.jpg"},{"id":376081,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3028/fs20203028.pdf","text":"Report","size":"1.27 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urbanization on the Edwards aquifer recharge zone in the greater San Antonio area raises concern about the potential adverse effects on the public water supply from development. To address this concern, the U.S. Geological Survey, in cooperation with the City of San Antonio, studied patterns of temporal and spatial changes in water quality at selected surface-water and groundwater sites in the Edwards aquifer recharge zone, with an emphasis on changes during periods of groundwater recharge. Water-quality characteristics were continuously monitored and discrete water samples were collected at two sets of paired surface-water (stream) and groundwater (well) sites during a 2-year period (2017–19) that included relatively dry conditions and a large recharge event in September 2018 when as much as 16 inches of rain fell in parts of the study area.
Continuous monitoring of water-level altitude, specific conductance, and concentrations of nitrate in two wells completed in the Edwards aquifer provided high-resolution data showing detailed changes in water quality across a broad range of hydrologic conditions. Water levels in the wells responded rapidly (within hours to days) to recharge from both small and large rainfall and runoff events; changes in groundwater quality as a consequence of the influx of surface-derived recharge were indicated by changes in values of the monitored characteristics. A broad range in measured values of the stable isotopes of water expressed as delta deuterium and delta oxygen-18 in the water samples collected from two streams (Salado and West Elm Creeks), in comparison to the tight clustering of the values of these isotopes in groundwater samples, indicates that source waters (surface waters) of widely varying chemical characteristics become homogenized within the aquifer system.
Concentrations of major ions, trace ions, and nutrient concentrations in stormwater runoff indicate a combination of land-derived and rainfall-derived constituents. The distribution of concentrations of nitrogen species (nitrite, nitrate, and nitrogen in ammonia) among sampling sites transitions from a more variable distribution in stormwater runoff to a more uniform distribution in groundwater in which the dominant form is nitrate. Differences in nitrate isotopic composition and concentration in groundwater across the study area are likely controlled by the relative contributions of natural and anthropogenic nitrogen (with the anthropogenic nitrogen component including a wastewater source) and by the process of nitrification. Among all measured constituents, pesticides detected in discrete stormwater-runoff samples provided the clearest indication that urbanization was adversely affecting water quality; specifically, the more urbanized surface-water site had a greater number of detections and greater variety of detected pesticides. Though temporal variability in the numbers and types of pesticides was evident, the overall proportion of pesticides was dominated by triazine herbicides including atrazine, atrazine degradates, and simazine. The observed hydrologic responses to rainfall and corresponding changes in water quality in wells are thought to result from the direct hydrologic connectivity of surface water and unconfined groundwater; however, patterns of groundwater-quality change indicate mixing from multiple sources such as ambient groundwater, recent surface-derived recharge, and possibly inflow from other aquifers. Therefore, understanding the connection between urbanization and groundwater quality cannot be inferred from the input of stormwater runoff alone as changes related to local and regional hydrologic conditions also need to be considered. It should be noted that a single study comparing the results from two site pairs is not able to support definitive conclusions about the full effect of urbanization on surface water/groundwater quality; however, this study does provide useful insights about the spatial and temporal variability of both stormwater runoff and unconfined groundwater that are consistent with expectations based on the current conceptual model that depicts the Edwards aquifer surface-water/groundwater system as a single water resource.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205033","collaboration":"Prepared in cooperation with the City of San Antonio","usgsCitation":"Opsahl, S.P., Musgrove, M., and Mecum, K.E., 2020, Temporal and spatial variability of water quality in the San Antonio segment of the Edwards aquifer recharge zone, Texas, with an emphasis on periods of groundwater recharge, September 2017–July 2019: U.S. Geological Survey Scientific Investigations Report 2020–5033, 37 p., https://doi.org/10.3133/sir20205033.","productDescription":"Report: x, 37 p.; Companion Report","numberOfPages":"51","onlineOnly":"Y","ipdsId":"IP-112400","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":376131,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5033/sir20205033.pdf","text":"Report","size":"1.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5033"},{"id":376132,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/fs20203028","text":"FS 2020-3028","size":"852 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–5028","linkHelpText":"— Effects of urbanization on water quality in the Edwards aquifer, San Antonio and Bexar County"},{"id":376130,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5033/coverthb.jpg"}],"country":"United States","state":"Texas","city":"San Antonio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.909912109375,\n 28.613459424004414\n ],\n [\n -97.05322265625,\n 29.635545914466675\n ],\n [\n -98.02001953125,\n 30.472348632640834\n ],\n [\n -99.744873046875,\n 29.49698759653577\n ],\n [\n -98.909912109375,\n 28.613459424004414\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Prolonged volcanic activity can induce surface weathering and hydrothermal alteration that is a primary control on edifice instability, posing a complex hazard with its challenges to accurately forecast and mitigate. This study uses a frequently active composite volcano, Mt Ruapehu, New Zealand, to develop a conceptual model of surface weathering and hydrothermal alteration applicable to long‐lived composite volcanoes. The alteration on Mt Ruapehu was classified using ground samples as non‐altered, supergene argillic, intermediate argillic, and advanced argillic. The first two classes have a paragenesis that is consistent with surficial infiltration and circulation of low‐temperature (<40°C) neutral to mildly acidic fluids, inducing chemical weathering and formation of weathering rims on rock surfaces. The intermediate and advanced argillic alteration formed from hotter (≥100°C) hydrothermal fluids with lower pH, interacting with the andesitic to dacitic host rocks. The distribution of weathering and hydrothermal alteration has been mapped with airborne hyperspectral imaging through image classification, while aeromagnetic data inversion was used to map alteration to up to 500‐m depth. The joint use of hyperspectral imaging complements the geophysical methods since it can spectrally identify hydrothermal alteration mineralogy. This study established a conceptual model of hydrothermal alteration history of Mt Ruapehu, exemplifying a long‐lived and nested active and ancient hydrothermal system. This study's combination approach can be used to indicate the most likely sources of future debris avalanches, which are a significant hazard on Ruapehu.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GC009270","usgsCitation":"Kereszturi, G., Schaefer, L.N., Miller, C.A., and Mead, S., 2020, Hydrothermal alteration on composite volcanoes: Mineralogy, hyperspectral imaging and aeromagnetic study of Mt Ruapehu, New Zealand: Geochemistry, Geophysics, Geosystems, v. 21, no. 9, e2020GC009270, 28 p., https://doi.org/10.1029/2020GC009270.","productDescription":"e2020GC009270, 28 p.","ipdsId":"IP-121751","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":455555,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/7f0a71d89f2449cb949ef5b223d16534","text":"External Repository"},{"id":382079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","otherGeospatial":"Mt Ruapehu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 175.166015625,\n -40.71395582628604\n ],\n [\n 176.57226562500003,\n -40.71395582628604\n ],\n [\n 176.57226562500003,\n -36.738884124394296\n ],\n [\n 175.166015625,\n -36.738884124394296\n ],\n [\n 175.166015625,\n -40.71395582628604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Kereszturi, Gabor 0000-0003-4336-2012","orcid":"https://orcid.org/0000-0003-4336-2012","contributorId":247601,"corporation":false,"usgs":false,"family":"Kereszturi","given":"Gabor","email":"","affiliations":[{"id":49587,"text":"Volcanic Risk Solutions, Massey University, Palmerston North, 4474, New Zealand","active":true,"usgs":false}],"preferred":false,"id":808007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaefer, Lauren N. 0000-0003-3216-7983","orcid":"https://orcid.org/0000-0003-3216-7983","contributorId":241997,"corporation":false,"usgs":true,"family":"Schaefer","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":808008,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Craig A. 0000-0001-8499-0352","orcid":"https://orcid.org/0000-0001-8499-0352","contributorId":219638,"corporation":false,"usgs":false,"family":"Miller","given":"Craig","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":808009,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mead, Stuart","contributorId":247602,"corporation":false,"usgs":false,"family":"Mead","given":"Stuart","email":"","affiliations":[{"id":49587,"text":"Volcanic Risk Solutions, Massey University, Palmerston North, 4474, New Zealand","active":true,"usgs":false}],"preferred":false,"id":808010,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212659,"text":"70212659 - 2020 - Evaluating stereo DTM quality at Jezero Crater, Mars with HRSC, CTX, and HiRISE images","interactions":[],"lastModifiedDate":"2020-08-25T15:51:05.37368","indexId":"70212659","displayToPublicDate":"2020-08-21T10:50:52","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Evaluating stereo DTM quality at Jezero Crater, Mars with HRSC, CTX, and HiRISE images","docAbstract":"We have used a high-precision, high-resolution digital terrain model (DTM) of the NASA Mars 2020 rover Perseverance landing site in Jezero crater based on mosaicked images from the Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment (MRO HiRISE) camera as a reference dataset to evaluate DTMs based on Mars Express High Resolution Stereo Camera (MEX HRSC) and MRO Context camera (CTX) images. Results are consistent with our earlier HRSC-HiRISE comparisons at the Mars Science Laboratory (MSL) Curiosity landing site in Gale crater, confirming that those results were not compromised by the small area compared and potential problems with spatial registration. Specifically, height errors are on the order of half a pixel and correspond to an image matching error of 0.2–0.3 pixel but estimates of horizontal resolution are 10–20 pixels. Products from the HRSC team pipeline at DLR are smoother but more precise vertically than those produced by using the commercial stereo package SOCET SET®. The DLR products are also homogenous in quality, whereas the SOCET products are less smoothed and have higher errors in rougher terrain. Despite this weak variation, our results are consistent with a rule of thumb of 0.2–0.3 pixel matching precision based on many prior studies. Horizontal resolution is significantly coarser than the DTM ground sample distance (GSD), which is typically 3–5 pixels.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"International archives of the photogrammetry, remote sensing, and spatial information sciences","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"XXIV ISPRS Congress 2020","conferenceDate":"Aug 31-Sep 2, 2020","conferenceLocation":"Nice, France","language":"English","publisher":"International Society for Photogrammetry and Remote Sensing","doi":"10.5194/isprs-archives-XLIII-B3-2020-1129-2020","usgsCitation":"Kirk, R.L., Fergason, R.L., Redding, B.L., Galuszka, D.M., Smith, E., Mayer, D., Hare, T.M., and Gwinner, K., 2020, Evaluating stereo DTM quality at Jezero Crater, Mars with HRSC, CTX, and HiRISE images, in International archives of the photogrammetry, remote sensing, and spatial information sciences, v. 43, no. B3, Nice, France, Aug 31-Sep 2, 2020, p. 1129-1136, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1129-2020.","productDescription":"8 p.","startPage":"1129","endPage":"1136","ipdsId":"IP-118772","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":455567,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/isprs-archives-xliii-b3-2020-1129-2020","text":"Publisher Index Page"},{"id":377829,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"43","issue":"B3","noUsgsAuthors":false,"publicationDate":"2020-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Kirk, Randolph L. 0000-0003-0842-9226 rkirk@usgs.gov","orcid":"https://orcid.org/0000-0003-0842-9226","contributorId":2765,"corporation":false,"usgs":true,"family":"Kirk","given":"Randolph","email":"rkirk@usgs.gov","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fergason, Robin L. 0000-0002-2044-1714","orcid":"https://orcid.org/0000-0002-2044-1714","contributorId":206167,"corporation":false,"usgs":true,"family":"Fergason","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Redding, Bonnie L. 0000-0001-8178-1467 bredding@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-1467","contributorId":4798,"corporation":false,"usgs":true,"family":"Redding","given":"Bonnie","email":"bredding@usgs.gov","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797224,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Galuszka, Donna M. 0000-0003-1870-1182 dgaluszka@usgs.gov","orcid":"https://orcid.org/0000-0003-1870-1182","contributorId":3186,"corporation":false,"usgs":true,"family":"Galuszka","given":"Donna","email":"dgaluszka@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797225,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Ethan 0000-0003-3896-326X","orcid":"https://orcid.org/0000-0003-3896-326X","contributorId":239562,"corporation":false,"usgs":false,"family":"Smith","given":"Ethan","affiliations":[],"preferred":false,"id":797226,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mayer, David 0000-0001-8351-1807","orcid":"https://orcid.org/0000-0001-8351-1807","contributorId":215429,"corporation":false,"usgs":true,"family":"Mayer","given":"David","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797227,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hare, Trent M. 0000-0001-8842-389X thare@usgs.gov","orcid":"https://orcid.org/0000-0001-8842-389X","contributorId":3188,"corporation":false,"usgs":true,"family":"Hare","given":"Trent","email":"thare@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797228,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gwinner, Klaus","contributorId":211338,"corporation":false,"usgs":false,"family":"Gwinner","given":"Klaus","email":"","affiliations":[],"preferred":false,"id":797229,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70212669,"text":"70212669 - 2020 - Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States","interactions":[],"lastModifiedDate":"2022-07-21T13:50:50.026883","indexId":"70212669","displayToPublicDate":"2020-08-21T10:01:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States","docAbstract":"Satellite-derived phenology metrics are valuable tools for understanding broad-scale patterns and changes in vegetated landscapes over time. However, the extraction and interpretation of phenology in ecosystems with subtle growth dynamics can be challenging. US National Park Service monitoring of evergreen pinyon-juniper ecosystems in the western US revealed an unexpected winter-peaking phenological pattern in normalized difference vegetation index (NDVI) time-series derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. In this paper, we assess the validity of the winter peaks through ground-based observation of phenology and examination of solar and satellite geometry effects. To test the premise of a true vegetation response, we analyzed NDVI values extracted from a time series of ground-based digital camera (‘phenocam’) images collected September 2017 to December 2018 in a pinyon-juniper woodland in Arizona, US. Results show pinyon and juniper growth peaked in the warm season, as did the other species in the phenocam field of view. NDVI time series from four other sensors (Landsat 7, Sentinel-2, VIIRS, and Proba-V) confirmed that winter peaks in this ecosystem are not limited to MODIS products. Examination of NDVI time series (2003–2018) derived from daily 250-m MODIS data in the broader pinyon-juniper ecosystem revealed that solar-to-sensor angle, sensor zenith angle, and forward/back-scatter reflectance explained >80% of intra-annual variability. Solar-to-sensor angle exerted the greatest control, and the direction of its correlation (positive) was the opposite of that which would be expected if it were driven by vegetation greenness. Solar-to-sensor angle is controlled seasonally by solar zenith angle and daily by variations in satellite overpass geometry. We mapped winter peaks across the western US in Google Earth Engine using 500-m MODIS MCD43A4 data, which correct for reflectance differences caused by view angle. In areas where winter vegetation peaks are ecologically improbable (i.e., locations with sub-freezing December temperatures), consistent winter peaks (≥ 14 years in 2003 to 2018) are widespread in both pinyon-juniper and non-pinyon-juniper conifer ecosystems; winter peaks are common (≥ 5 years in 2003 to 2018) across areas of shrubland. We attribute winter peaks to the positive correlation of NDVI with solar-to-sensor angle and solar zenith angle in combination with sparse, vertically oriented evergreen vegetation canopies. Increasing shadow visibility has been shown to increase overall NDVI, and the prevalence of the winter peaking in evergreen western sparse canopy ecosystems is consistent with this hypothesis. The extent of winter peaking patterns may have been previously overlooked due to temporal compositing, curve fitting, and incomplete snow screening.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.112013","usgsCitation":"Norris, J.R., and Walker, J.J., 2020, Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States: Remote Sensing of Environment, v. 249, 112013, 19 p.; Data Release, https://doi.org/10.1016/j.rse.2020.112013.","productDescription":"112013, 19 p.; Data Release","ipdsId":"IP-115826","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":455570,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2020.112013","text":"Publisher Index Page"},{"id":377823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":404210,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LNQL6L"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.984375,\n 31.316101383495624\n ],\n [\n -108.28125,\n 31.316101383495624\n ],\n [\n -108.1494140625,\n 31.690781806136822\n ],\n [\n -106.25976562499999,\n 31.765537409484374\n ],\n [\n -106.12792968749999,\n 32.02670629333614\n ],\n [\n -103.1396484375,\n 32.0639555946604\n ],\n [\n -103.0078125,\n 36.80928470205937\n ],\n [\n -102.1728515625,\n 36.98500309285596\n ],\n [\n -102.041015625,\n 41.07935114946899\n ],\n [\n -104.0185546875,\n 40.94671366508002\n ],\n [\n -104.0185546875,\n 49.095452162534826\n ],\n [\n -122.958984375,\n 49.009050809382046\n ],\n [\n -122.9150390625,\n 48.19538740833338\n ],\n [\n -124.71679687499999,\n 48.48748647988415\n ],\n [\n -124.62890625,\n 47.81315451752768\n ],\n [\n -124.1455078125,\n 46.6795944656402\n ],\n [\n -124.541015625,\n 43.389081939117496\n ],\n [\n -124.93652343749999,\n 42.32606244456202\n ],\n [\n -124.1455078125,\n 41.409775832009565\n ],\n [\n -124.93652343749999,\n 40.34654412118006\n ],\n [\n -123.92578125,\n 38.41055825094609\n ],\n [\n -121.11328124999999,\n 35.17380831799959\n ],\n [\n -120.84960937499999,\n 34.30714385628804\n ],\n [\n -117.68554687499999,\n 32.69486597787505\n ],\n [\n -117.2900390625,\n 32.54681317351514\n ],\n [\n -114.7412109375,\n 32.731840896865684\n ],\n [\n -114.7412109375,\n 32.43561304116276\n ],\n [\n -111.005859375,\n 31.353636941500987\n ],\n [\n -108.984375,\n 31.316101383495624\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"249","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Norris, Jodi R.","contributorId":190196,"corporation":false,"usgs":false,"family":"Norris","given":"Jodi","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":797242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Jessica J. 0000-0002-3225-0317 jjwalker@usgs.gov","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":169458,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica","email":"jjwalker@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":797243,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70212576,"text":"70212576 - 2020 - Climate sensitivity to decadal land cover and land use change across the conterminous United States","interactions":[],"lastModifiedDate":"2020-08-24T12:25:26.814997","indexId":"70212576","displayToPublicDate":"2020-08-21T10:01:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Climate sensitivity to decadal land cover and land use change across the conterminous United States","docAbstract":"Transitions to terrestrial ecosystems attributable to land cover and land use change (LCLUC) and climate change can affect the climate at local to regional scales. 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Ecoregions with increasing vapor pressure were found across the Great Plains, Intermountain West, and West Coast ecoregions and several of these regions in the Great Plains and West Coast were sensitive to LCLUC. A combination of changes in temperature, precipitation, and vapor pressure was used to characterize climate sensitivity associated with LCLUC forcing, and five major persistent patterns were found in some ecoregions. These findings suggest that climate conditions, especially temperature and vapor pressure, in some ecoregions are sensitive to LCLUC and such change should be better incorporated into regional climate assessments.
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,{"id":70212535,"text":"sir20205076 - 2020 - Groundwater levels in the Denver Basin bedrock aquifers of Douglas County, Colorado, 2011–19","interactions":[],"lastModifiedDate":"2020-08-21T14:15:33.731249","indexId":"sir20205076","displayToPublicDate":"2020-08-20T15:37:38","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":"2020-5076","displayTitle":"Groundwater Levels in the Denver Basin Bedrock Aquifers of Douglas County, Colorado, 2011–19","title":"Groundwater levels in the Denver Basin bedrock aquifers of Douglas County, Colorado, 2011–19","docAbstract":"Municipal and domestic water users in Douglas County, Colorado, rely on groundwater from the bedrock aquifers in the Denver Basin aquifer system as part of their water supply. The four principal Denver Basin bedrock aquifers are, from shallowest to deepest, the Dawson aquifer (divided administratively into “upper” and “lower” Dawson aquifers in Douglas County), the Denver aquifer, the Arapahoe aquifer, and the Laramie-Fox Hills aquifer. Increased groundwater pumping in response to rapid population growth and development has led to declining groundwater levels in Douglas County, where groundwater is a primary water source for densely populated and rural communities. The U.S. Geological Survey, in cooperation with the Rural Water Authority of Douglas County, began a study in 2011 to assess the groundwater resources of the Denver Basin bedrock aquifers within the county. The primary purpose of this report is to present a summary of groundwater levels measured during the study period (2011–19) and present results from statistical analyses of changes in groundwater-level elevations, reported above the land-surface datum, North American Vertical Datum of 1988, through time. During the study period, January 2011 through June 2019, discrete groundwater levels were routinely measured at 36 wells producing from Denver Basin bedrock aquifers within Douglas County. Of the 36 wells, 15 are instrumented with pressure transducers that record groundwater-level measurements at hourly intervals, and these data were temporally aggregated into time-series records. During 2011, wells were added to the monitoring network in phases, so that the start dates of the well records are noncontemporaneous. To keep temporal analysis among wells consistent, the periods of record used in statistical analyses were from February 2012 through February 2019 for the discrete data and from January 2012 through June 2019 for the time-series data.
The upper Dawson, lower Dawson, Denver, and Arapahoe aquifers had some wells with rises in calculated groundwater-level elevations, but most wells showed declines on the basis of statistically significant trends and the relative differences in static groundwater-level elevations between the February 2012 and February 2019 measurements. Neither of the two wells in the Laramie-Fox Hills aquifer showed significant trends in groundwater-level elevations, and these wells had few static discrete measurements, precluding a comparison between 2012 and 2019 static groundwater-level elevations. Of the 13 wells in the upper Dawson, lower Dawson, Denver, and Arapahoe aquifers with significant trends in discrete groundwater-level elevation measurements, the records of 12 wells demonstrated negative trends during the study period. The upper Dawson, lower Dawson, Denver, and Arapahoe aquifers had median significant trends of −0.23, −0.31, −0.92, and −2.26 feet per year, respectively. Although the Arapahoe aquifer had the greatest negative median trend, this median only represents one well with significant trends. Otherwise, the Denver aquifer had the next greatest negative trend, with a median trend of −0.92 foot per year. Significant trends in time-series groundwater-level elevations agreed with significant trends in discrete groundwater-level elevations; for all wells with statistically significant trends in discrete and in time-series groundwater-level elevation data, trend estimates from the two records were within 0.1 foot per year of each other. Potentiometric-surface maps of the upper Dawson, lower Dawson, and Denver aquifers, created using discrete static groundwater levels measured in February 2019, show that groundwater flow direction for the upper Dawson, lower Dawson, and Denver aquifers is generally from south to north. Results of this study could guide future groundwater monitoring in the county and aid in long-term planning of water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205076","collaboration":"Prepared in cooperation with the Rural Water Authority of Douglas County","usgsCitation":"Malenda, H.F., and Penn, C.A., 2020, Groundwater levels in the Denver Basin bedrock aquifers of Douglas County, Colorado, 2011–19: U.S. Geological Survey Scientific Investigations Report 2020–5076, 44 p., https://doi.org/10.3133/sir20205076.","productDescription":"Report: vii, 44 p.; Dataset","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-112835","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":377656,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5076/coverthb.jpg"},{"id":377657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5076/sir20205076.pdf","text":"Report","size":"5.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5076"},{"id":377658,"rank":3,"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"}],"country":"United States","state":"Colorado","county":"Douglas County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-104.6627,39.5665],[-104.6626,39.4762],[-104.663,39.3892],[-104.664,39.3026],[-104.6638,39.2165],[-104.6642,39.1308],[-104.8303,39.1311],[-104.9175,39.131],[-104.9371,39.1312],[-105.032,39.1311],[-105.0503,39.1312],[-105.1607,39.1306],[-105.274,39.1309],[-105.3232,39.1307],[-105.322,39.1343],[-105.3213,39.1407],[-105.3195,39.1434],[-105.3171,39.1443],[-105.3148,39.1461],[-105.3136,39.1493],[-105.3117,39.1542],[-105.3069,39.161],[-105.3051,39.1624],[-105.3015,39.1632],[-105.2985,39.1673],[-105.2961,39.1705],[-105.2908,39.1741],[-105.2872,39.1772],[-105.2841,39.1863],[-105.2817,39.1935],[-105.2768,39.2016],[-105.2744,39.2048],[-105.272,39.2052],[-105.2649,39.2061],[-105.2619,39.2074],[-105.2601,39.2097],[-105.2595,39.2156],[-105.2582,39.2283],[-105.2557,39.2332],[-105.2533,39.2378],[-105.2527,39.2396],[-105.2491,39.2395],[-105.2432,39.2395],[-105.2396,39.2399],[-105.2348,39.2431],[-105.2259,39.248],[-105.2217,39.2534],[-105.2216,39.2575],[-105.2204,39.2589],[-105.2168,39.2593],[-105.2144,39.2606],[-105.2162,39.2643],[-105.2167,39.2683],[-105.2143,39.2729],[-105.2058,39.29],[-105.2046,39.295],[-105.2016,39.2982],[-105.1938,39.3018],[-105.1938,39.304],[-105.1955,39.3081],[-105.1948,39.3126],[-105.1919,39.3131],[-105.1877,39.3158],[-105.187,39.3194],[-105.1846,39.3239],[-105.184,39.328],[-105.1815,39.3352],[-105.1767,39.3402],[-105.1718,39.3501],[-105.1694,39.3555],[-105.1658,39.36],[-105.1663,39.3682],[-105.168,39.3732],[-105.1697,39.3809],[-105.1702,39.3845],[-105.166,39.39],[-105.1654,39.3949],[-105.1671,39.399],[-105.1671,39.4031],[-105.1664,39.4049],[-105.1586,39.4094],[-105.1527,39.4116],[-105.1419,39.417],[-105.1383,39.4197],[-105.1335,39.4233],[-105.1268,39.4296],[-105.1238,39.4336],[-105.1244,39.4368],[-105.1237,39.4409],[-105.1225,39.4468],[-105.123,39.4531],[-105.1253,39.4563],[-105.1289,39.4586],[-105.1306,39.4654],[-105.1305,39.4695],[-105.1263,39.4731],[-105.1197,39.4762],[-105.1155,39.4785],[-105.1149,39.4798],[-105.1137,39.4825],[-105.1119,39.4834],[-105.1,39.4829],[-105.0928,39.4846],[-105.0874,39.4891],[-105.0843,39.4941],[-105.0818,39.5022],[-105.08,39.5059],[-105.077,39.5095],[-105.0775,39.5126],[-105.0757,39.5194],[-105.0762,39.524],[-105.0724,39.5402],[-105.0646,39.547],[-105.0609,39.5501],[-105.0609,39.5551],[-105.0561,39.5592],[-105.0494,39.5627],[-105.0452,39.5659],[-104.9408,39.5664],[-104.8292,39.5663],[-104.7182,39.5661],[-104.6627,39.5665]]]},\"properties\":{\"name\":\"Douglas\",\"state\":\"CO\"}}]}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Coastal cliffs constitute 80 percent of the world’s coastline, with seacliffs fronting a large proportion of the U.S. West Coast shoreline, particularly in California. Erosion of coastal cliffs can threaten infrastructure and human life, yet the spatial and temporal scope of cliff studies have been limited by cumbersome traditional methods that rely on the manual interpretation of seacliff features—especially seacliff toes and top edges. The Cliff Feature Delineation Tool (CFDT) and the Baseline Builder Tool are designed to increase the efficiency of deriving seacliff features from remote sensing datasets by utilizing an automated, quantitative approach that eliminates traditional interpretive methods and ensures reproducibility. This document functions as a user guide for operating the Cliff Feature Delineation Tool and Baseline Builder Tool and includes a walkthrough of data-visualization and data-review workflows for the tools’ three-dimensional (3D) cliff feature outputs. Also included is a brief overview of cliff feature delineation at the U.S. Geological Survey (USGS) and a detailed description of the tools’ algorithmic logic.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201070","usgsCitation":"Seymour, A.C., Hapke, C.J., and Warrick, J., 2020, Cliff Feature Delineation Tool and Baseline Builder version 1.0 user guide: U.S. Geological Survey Open File Report 2020–1070, 54 p.,\nhttps://doi.org/10.3133/ofr20201070.","productDescription":"Report: vi, 54 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112057","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":377578,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1070/ofr20201070.pdf","text":"Report","size":"10.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1070"},{"id":377535,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1070/coverthb2.jpg"},{"id":377532,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UKW7PO","text":"USGS software release","linkHelpText":"Cliff Feature Delineation Tool and Baseline Builder version 1.0"}],"contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Digital flood-inundation maps for about an 8-mile reach of the Little Calumet River, Illinois, were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at three USGS streamgages: Little Calumet River at South Holland, Ill. (USGS station 05536290); Little Calumet River at Munster, Indiana (USGS station 05536195); and Thorn Creek at Thornton, Ill. (USGS station 05536275). Near-real-time stages at these streamgages may be obtained on the internet from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at these sites.
Flood profiles were computed for the stream reaches using a one-dimensional unsteady flow step-backwater hydraulic model. The model performance was evaluated using historical streamflow measurements and the most current stage-discharge relations at the USGS streamgages at Little Calumet River at South Holland, Ill.; Little Calumet River at Munster, Ind.; and Thorn Creek at Thornton, Ill. The model was used to compute 24 water-surface profiles at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to about the 0.2-percent annual-exceedance probability flood (500-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.6-foot vertical accuracy and a 2-foot horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with internet information regarding current stage from USGS streamgages and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205074","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Dunn, A.P., Straub, T.D., and Manaster, A.E., 2020, Flood-inundation maps for the Little Calumet River from Lansing to South Holland, Illinois, 2020: U.S. Geological Survey Scientific Investigations Report 2020–5074, 10 p., https://doi.org/10.3133/sir20205074.","productDescription":"Report: vi, 10 p.; Data Release; Dataset","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-097182","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":377581,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99L14DN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets for the flood-inundation study of Little Calumet River from Lansing to South Holland, Illinois, 2020, 2020"},{"id":377582,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":377580,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5074/sir20205074.pdf","text":"Report","size":"2.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5074"},{"id":377579,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5074/coverthb.jpg"}],"country":"United States","state":"Illinois","city":"Lansing, South Holland","otherGeospatial":"Little Calumet River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.6295280456543,\n 41.54404730359805\n ],\n [\n -87.52584457397461,\n 41.54404730359805\n ],\n [\n -87.52584457397461,\n 41.62339874820646\n ],\n [\n -87.6295280456543,\n 41.62339874820646\n ],\n [\n -87.6295280456543,\n 41.54404730359805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
National Hydrography Dataset (NHD) stream permanence classifications (SPC; perennial, intermittent, and ephemeral) are widely used for data visualization and applied science, and have implications for resource policy and management. NHD SPC were assigned using a combination of topographic field surveys and interviews with local residents. However, previous studies indicate that non‐NHD, in situ streamflow observations (NNO) frequently disagree with NHD SPC. We hypothesized that differences in annual climate conditions between map creation years and the years NNO were collected contributed to disagreement between NNO and NHD SPC. We compared NHD SPC to 10,055 NNO (classified as “wet” or “dry”) collected in the Pacific Northwest between 1977 and 2015. Annual climate conditions were described with the Palmer Drought Severity Index (PDSI). Stream order was added as a covariate to account for different effects along the stream network. NHD SPC agreed with 80.5% of NNO. “Dry” NNO were five times more likely to disagree with NHD than “wet” NNO (p < 0.0001). Disagreement was greatest on first‐order streams. When NHD SPC were collected during a wetter period than NNO the probability of disagreement increased by a factor of 1.17 (p < 0.0001) per unit difference in PDSI. The influence of climate on disagreements between NNO and NHD SPC provides support for the continued development of dynamic models representing SPC as opposed to static NHD classifications.
Carbon dioxide (CO2) has been approved by the US Environmental Protection Agency as a new aquatic pesticide to control invasive Asian carps and other aquatic nuisance species in the United States. However, limited CO2 toxicity data could make it challenging for resource managers to characterize the potential risk to nontarget species during CO2 applications. The present study quantified the toxicity of CO2 to 2 native riverine fishes, bluegill (Lepomis macrochirus) and fathead minnow (Pimephales promelas), using 12‐h continuous flow‐through CO2 exposure at 5, 15, and 25 °C water temperatures. Resulting survival indicated that bluegill (median lethal concentration [LC50] range 91–140 mg/L CO2) were more sensitive to CO2 than fathead minnow (LC50 range 235–306 mg/L CO2) across all water temperatures. Bluegill were also more sensitive to CO2 at 5 °C (LC50 91 mg/L CO2, 95% CI 85–96 mg/L CO2) than at 25 °C (LC50 140 mg/L CO2, 95% CI 135–146 mg/L CO2). Fathead minnow showed an opposite response and were less sensitive at 5 °C (LC50 306 mg/L CO2, 95% CI 286–327 mg/L CO2) relative to 25 °C (LC50 235 mg/L CO2, 95% CI 224–246 mg/L CO2). Our results show that CO2 toxicity can differ by species and water temperature. Data from the present study may inform decisions related to the use of CO2 as a control tool. Environ Toxicol Chem 2020;39:2247–2255. Published 2020. This article is a U.S. government work and is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.4855","usgsCitation":"Cupp, A.R., Smerud, J.R., Thomas, L.M., Waller, D.L., Smith, D.L., Erickson, R.A., and Gaikowski, M., 2020, Toxicity of carbon dioxide to freshwater fishes: Implications for aquatic invasive species management: Environmental Toxicology and Chemistry (ET&C), v. 39, no. 11, p. 2247-2255, https://doi.org/10.1002/etc.4855.","productDescription":"9 p.","startPage":"2247","endPage":"2255","ipdsId":"IP-115255","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":436817,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M4VYY3","text":"USGS data release","linkHelpText":"Toxicity of carbon dioxide to two freshwater fishes data"},{"id":379795,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Cupp, Aaron R. 0000-0001-5995-2100 acupp@usgs.gov","orcid":"https://orcid.org/0000-0001-5995-2100","contributorId":5162,"corporation":false,"usgs":true,"family":"Cupp","given":"Aaron","email":"acupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":803062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smerud, Justin R. 0000-0003-4385-7437 jrsmerud@usgs.gov","orcid":"https://orcid.org/0000-0003-4385-7437","contributorId":5031,"corporation":false,"usgs":true,"family":"Smerud","given":"Justin","email":"jrsmerud@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":803063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomas, Linnea M 0000-0002-0140-1207","orcid":"https://orcid.org/0000-0002-0140-1207","contributorId":244022,"corporation":false,"usgs":true,"family":"Thomas","given":"Linnea","email":"","middleInitial":"M","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":803064,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waller, Diane L. 0000-0002-6104-810X dwaller@usgs.gov","orcid":"https://orcid.org/0000-0002-6104-810X","contributorId":5272,"corporation":false,"usgs":true,"family":"Waller","given":"Diane","email":"dwaller@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":803065,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, David L.","contributorId":192711,"corporation":false,"usgs":false,"family":"Smith","given":"David","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":803066,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":803067,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gaikowski, Mark P. 0000-0002-6507-9341 mgaikowski@usgs.gov","orcid":"https://orcid.org/0000-0002-6507-9341","contributorId":149357,"corporation":false,"usgs":true,"family":"Gaikowski","given":"Mark P.","email":"mgaikowski@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":803068,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70212507,"text":"fs20203034 - 2020 - National Land Imaging Program","interactions":[],"lastModifiedDate":"2021-06-14T19:48:27.908531","indexId":"fs20203034","displayToPublicDate":"2020-08-18T16:08:03","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3034","displayTitle":"National Land Imaging Program","title":"National Land Imaging Program","docAbstract":"Changes taking place across the Earth’s land surface have the potential to affect people, economies, and the environment on a daily basis. Our Nation’s economic security and environmental vitality rely on continuous monitoring of the Earth’s continents, islands, and coastal regions to record, study, and understand land change at local, regional, and global scales. The U.S. Geological Survey’s National Land Imaging Program helps meet this need by ensuring the continuous availability of moderate-resolution satellite imagery and other remotely sensed and geospatial data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203034","usgsCitation":"Young, S.M., 2020, National Land Imaging Program: U.S. Geological Survey Fact Sheet 2020–3034, 4 p., https://doi.org/10.3133/fs20203034.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":377618,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3034/fs20203034.pdf","text":"Report","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3034"},{"id":377630,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3034/coverthb.jpg"}],"contact":"Land Remote Sensing Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Haskell Lake is a shallow, 89-acre drainage lake in the headwaters of the Squirrel River, on the Lac du Flambeau Reservation in northern Wisconsin. The lake has long been valued by the Lac du Flambeau Band of Lake Superior Chippewa Indians (LDF Tribe) for abundant wild rice and game fish. In recent decades, however, wild rice has mostly disappeared from the lake and the fishery has declined. A petroleum contamination plume discovered in the 1990s in the shallow aquifer upgradient from the northern end of the lake poses a threat to the ecological health of the lake and the aquifer, which is the sole drinking water source for nearby residents and businesses. Understanding of the lake’s hydrology is important to the LDF Tribe as they seek to restore wild rice and maintain the ecological health of the Haskell Lake/Tower Creek watershed. An improved understanding of lithology in the area of the contamination plume, documentation of a contamination pathway from groundwater in the plume source area to Haskell Lake, and an understanding of the plume extent beneath the lake are needed to advance remediation efforts. Evaluation of the fraction of groundwater discharge that is contaminated relative to the overall lake water budget is desired as a first step towards determining the extent of ecological effects from the plume.
A cooperative study between the U.S. Geological Survey and the LDF Tribe was initiated to quantify the lake water budget and the sources of water to the lake, to provide a rough estimate of the maximum quantity of groundwater discharge to the lake that may be contaminated, and to improve the conceptual understanding of the plume extent and subsurface materials in the area of contamination. The results of this study can help inform natural resource management of the Haskell Lake/Tower Creek watershed, including planned wild rice restoration and cleanup of the contaminant plume.
During 2016–17, field data on lake and groundwater levels, gradients, fluxes, and subsurface lithology were collected using a variety of techniques that ranged from basic measurement of water levels and streamflows to distributed temperature sensing, vertical temperature profiling, and several shallow geophysical methods. The data were used to inform a MODFLOW–NWT model that simulated the contributing groundwatershed, including the water budget for Haskell Lake and Tower Creek using the Lake, Streamflow-Routing, and Unsaturated Zone-Flow Packages. Particle tracking with the MODFLOW solution (using MODPATH 6) was used to improve understanding of the downgradient extent of the contamination plume, estimate groundwater flux through the plume area, and delineate the groundwater contributing area (groundwatershed) for the lake/creek system. Linear uncertainty estimates for model results were computed during model parameter estimation using the software package PEST++.
Results indicate groundwater discharge along the perimeter of Haskell Lake, with groundwater accounting for about 22 (± 11.5) percent of the lake water budget. Field data and particle tracking results indicate discharge of the entire contamination plume to Haskell Lake. Although the exact locations where contaminated groundwater enters the lake are unknown, the downgradient extent of the plume beneath Haskell Lake is likely limited to within about 700 feet from the shore. Groundwater flux through the plume accounts for at most about 1.4 percent of total groundwater discharge to Haskell Lake, or about 0.3 percent of the lake water budget. Most groundwater discharging to Haskell Lake and Tower Creek originates as terrestrial recharge. A lesser amount originates in or passes through neighboring lakes, including Buckskin, Crawling Stone, Broken Bow, Tippecanoe, and Jerms Lakes, as well as several unnamed kettles. The average age of simulated groundwater discharge to the lake is about 20 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205024","collaboration":"Prepared in cooperation with the Lac du Flambeau Band of Lake Superior Chippewa Indians","usgsCitation":"Leaf, A.T., and Haserodt, M.J., 2020, Hydrology of Haskell Lake and investigation of a groundwater contamination plume, Lac du Flambeau Reservation, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2020–5024, 79 p., https://doi.org/10.3133/sir20205024.","productDescription":"Report: x, 70 p.; Appendices: 1.1-10.3; Data Release; Companion Report","numberOfPages":"92","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098814","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":377617,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZQGGHY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW–NWT and MODPATH models, data from aquifer tests and temperature profilers, and groundwater flux estimates used to assess groundwater/surface-water interactions in Haskell Lake, Wisconsin"},{"id":377616,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table10.1_10.3.xlsx","text":"Appendix Tables 10.1 to 10.3","size":"19.4 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Tables 10.1 to 10.3"},{"id":377615,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_9.1.xlsx","text":"Appendix Table 9.1","size":"12.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 9.1"},{"id":377614,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_8.1.xlsx","text":"Appendix Table 8.1","size":"17.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 8.1"},{"id":377611,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_5.1.xlsx","text":"Appendix Table 5.1","size":"12.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 5.1"},{"id":377607,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table1.1_1.12.xlsx","text":"Appendix Tables 1.1 to 1.12","size":"35.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Tables 1.1 to 1.12"},{"id":377606,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20205005","text":"SIR 2020–5005","size":"3.67 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"— A distributed temperature sensing investigation of groundwater discharge to Haskell Lake, Lac du Flambeau Reservation, Wisconsin, July 27–August 1, 2016"},{"id":377610,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_4.1.xlsx","text":"Appendix Table 4.1","size":"10.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 4.1"},{"id":377608,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_2.1.xlsx","text":"Appendix Table 2.1","size":"12.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 2.1"},{"id":377609,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_3.1_3.6.xlsx","text":"Appendix Tables 3.1 to 3.6","size":"24.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Tables 3.1 to 3.6"},{"id":377604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5024/coverthb.jpg"},{"id":377801,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/downloads","text":"Appendix Tables","size":"47.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5024 Appendix Tables"},{"id":377612,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_6.1_6.2.xlsx","text":"Appendix Tables 6.1 to 6.2","size":"13.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Tables 6.1 to 6.2"},{"id":377613,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_7.1.xlsx","text":"Appendix Table 7.1","size":"13.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 7.1"},{"id":377605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5024"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Haskell Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.93322372436523,\n 45.89717666670996\n ],\n [\n -89.89992141723633,\n 45.89717666670996\n ],\n [\n -89.89992141723633,\n 45.920467927558576\n ],\n [\n -89.93322372436523,\n 45.920467927558576\n ],\n [\n -89.93322372436523,\n 45.89717666670996\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Haskell Lake is a shallow, 89-acre drainage lake in the headwaters of the Squirrel River, on the Lac du Flambeau Reservation in northern Wisconsin. Historically, this lake was an important producer of wild rice for the Lac du Flambeau Band of Lake Superior Chippewa Indians (LDF Tribe); but, beginning in the late 1970s, the rice began to diminish and by the late 1990s, the lake no longer had harvestable stands. Restoring wild rice to Haskell Lake is a long-term priority for the LDF Tribe. A first step towards that effort is the cleanup of a petroleum-contamination plume in the shallow aquifer upgradient of the northern end of the lake. Knowledge of the downgradient extent of the plume and the locations where contaminated water is discharging to the lake is needed to inform cleanup efforts.
A cooperative study between the U.S. Geological Survey and the LDF Tribe was initiated to characterize the distribution of groundwater discharge to Haskell Lake in the areas downgradient of the contamination plume. A fiber optic distributed temperature sensing system was used to monitor temperatures at the sediment-water interface for a 7-day period in July and August 2016. Challenges during the investigation included data storage and power supply limitations, maintenance of calibration baths, accurate location of the cable in space, cable placement in weeds and soft sediment, the confounding effects of solar radiation, and contamination of the data by multiple sources of instrument noise. The problem of instrument noise was overcome by solving the fiber optic distributed temperature sensing calibration equation for two parameters that describe temporal variation in the source laser and the photon detectors that observe the backscatter. Early morning temperatures, when the influence of solar radiation via direct warming of the sediment-water interface is minimized, were used to evaluate groundwater discharge, similar to other studies. The results indicate a persistent, horizontal variation in temperature of as much as 5.5 degrees Celsius across the study area, with cooler temperatures interpreted to indicate spatially discrete preferential groundwater discharge. Results of the study can be used to determine locations for collecting lakebed pore water samples to better define the extent of contamination discharging to the lake.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205005","collaboration":"Prepared in cooperation with the Lac du Flambeau Band of Lake Superior Chippewa Indians","usgsCitation":"Leaf, A.T., 2020, A distributed temperature sensing investigation of groundwater discharge to Haskell Lake, Lac du Flambeau Reservation, Wisconsin, July 27–August 1, 2016: U.S. Geological Survey Scientific Investigations Report 2020–5005, 17 p., https://doi.org/10.3133/sir20205005.","productDescription":"Report: vi, 17 p.; Data Release; Companion Report","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100793","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376503,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5005/coverthb.jpg"},{"id":376504,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5005/sir20205005.pdf","text":"Report","size":"3.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5005"},{"id":376505,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X2OHNX","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Distributed lakebed temperature data, Haskell Lake, Lac du Flambeau Reservation, Wisconsin, July 27–August 1, 2016"},{"id":377597,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20205024","text":"SIR 2020–5024","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5024","linkHelpText":"— Hydrology of Haskell Lake and investigation of a groundwater contamination plume, Lac du Flambeau Reservation, Wisconsin"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Haskell Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.93322372436523,\n 45.89717666670996\n ],\n [\n -89.89992141723633,\n 45.89717666670996\n ],\n [\n -89.89992141723633,\n 45.920467927558576\n ],\n [\n -89.93322372436523,\n 45.920467927558576\n ],\n [\n -89.93322372436523,\n 45.89717666670996\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Long‐distance migration presents complex conservation challenges, and migratory species often experience shortfalls in conservation due to the difficulty of identifying important locations and resources throughout the annual cycle. In order to prioritize habitats for conservation of migratory wildlife, it is necessary to understand how habitat needs change throughout the annual cycle, as well as to identify key habitat sites and features that concentrate large numbers of individuals and species. Among long‐distance migrants, sea ducks have particularly complex migratory patterns, which often include distinct post‐breeding molt sites as well as breeding, staging and wintering locations. Using a large set of individual tracking data (n = 476 individuals) from five species of sea ducks in eastern North America, we evaluated multi‐species habitat suitability and partitioning across the breeding, post‐breeding migration and molt, wintering and pre‐breeding migration seasons. During breeding, species generally occupied distinct habitat areas, with the highest levels of multi‐species overlap occurring in the Barrenlands west of Hudson Bay. Species generally preferred flatter areas closer to lakes with lower maximum temperatures relative to average conditions, but varied in distance to shore, elevation and precipitation. During non‐breeding, species overlapped extensively during winter but diverged during migration. All species preferred shallow‐water, nearshore habitats with high productivity, but varied in their relationships to salinity, temperature and bottom slope. Sea ducks selected most strongly for preferred habitats during post‐breeding migration, with high partitioning among species; however, both selection and partitioning were weaker during pre‐breeding migration. The addition of tidal current velocity, aquatic vegetation presence and bottom substrate improved non‐breeding habitat models where available. Our results highlight the utility of multi‐species, annual‐cycle habitat assessments in identifying key habitat features and periods of vulnerability in order to optimize conservation strategies for migratory wildlife.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.05003","usgsCitation":"Lamb, J.S., Paton, P.W., Osenkowski, J.E., Badzinski, S.S., Berlin, A., Bowman, T.D., Dwyer, C., Fara, L., Gilliland, S.G., Kenow, K.P., Lepage, C., Mallory, M.L., Olsen, G., Perry, M., Petrie, S.A., Savard, J.L., Savoy, L., Schummer, M.L., Spiegel, C.S., and McWilliams, S.R., 2020, Assessing year‐round habitat use by migratory sea ducks in a multi‐species context reveals seasonal variation in habitat selection and partitioning: Ecography, v. 43, no. 12, p. 1842-1858, https://doi.org/10.1111/ecog.05003.","productDescription":"17 p.","startPage":"1842","endPage":"1858","onlineOnly":"Y","ipdsId":"IP-115137","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455607,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.05003","text":"Publisher Index 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