Aquatic habitat has been extensively altered throughout the Laurentian Great Lakes to increase navigation connectivity. In particular, the St. Marys River, a Great Lakes connecting channel, lost >50% of its historic rapids habitat over the past century. In 2016, the natural flow was restored to the Little Rapids area of the St. Marys River. The goal of our study was to evaluate physical and ecological responses to the restoration of the Little Rapids area. Extensive habitat and biological data were collected prior to restoration (2013 and 2014), and after restoration (2017 and 2018). Measured parameters included total suspended solids, current velocity, benthic macroinvertebrates, and larval, juvenile, and adult fishes. Total suspended solids stayed low (<4 mg/L) following restoration, with the exception of a single construction‐related event. Pre‐restoration data indicated that all measured velocities were below the target flow rate of 0.24 m/s, whereas 70% of the measured habitat was above the target flow post‐restoration. Abundance and richness of benthic macroinvertebrates were reduced following restoration (>90% reduction). We observed a 45% increase in richness of larval fish 2 years after restoration and a 131% increase in catch per unit effort. For adult fishes, the proportion of individuals with a preference for fast‐moving waters increased from 1.5 to 45% in the restored area, and from 7 to 15% upstream of the restored area; a similar response was observed for lithophilic spawners. The physical and biological conditions of the Little Rapids improved and resembled conditions typical of rapids habitat extent in other areas of the river and other systems.
Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12213524","usgsCitation":"Gao, F., Anderson, M., and Hively, W.D., 2020, Detecting cover crop end-of-season using VENµS and sentinel-2 satellite imagery: Remote Sensing, v. 12, no. 21, 22 p., https://doi.org/10.3390/rs12213524.","productDescription":"22 p.","ipdsId":"IP-123386","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":454959,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12213524","text":"Publisher Index Page"},{"id":380903,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"21","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Gao, Feng 0000-0002-1865-2846","orcid":"https://orcid.org/0000-0002-1865-2846","contributorId":70671,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":805911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Martha","contributorId":210925,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","affiliations":[],"preferred":false,"id":805912,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":805913,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201829,"text":"tm4A3 - 2020 - Statistical methods in water resources","interactions":[{"subject":{"id":47512,"text":"twri04A3 - 2002 - Statistical methods in water resources","indexId":"twri04A3","publicationYear":"2002","noYear":false,"displayTitle":"Statistical Methods in Water Resources","title":"Statistical methods in water resources"},"predicate":"SUPERSEDED_BY","object":{"id":70201829,"text":"tm4A3 - 2020 - Statistical methods in water resources","indexId":"tm4A3","publicationYear":"2020","noYear":false,"title":"Statistical methods in water resources"},"id":1}],"lastModifiedDate":"2024-08-13T14:02:36.434133","indexId":"tm4A3","displayToPublicDate":"2020-10-27T09:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-A3","displayTitle":"Statistical Methods in Water Resources","title":"Statistical methods in water resources","docAbstract":"This text began as a collection of class notes for a course on applied statistical methods for hydrologists taught at the U.S. Geological Survey (USGS) National Training Center. Course material was formalized and organized into a textbook, first published in 1992 by Elsevier as part of their Studies in Environmental Science series. In 2002, the work was made available online as a USGS report.
The text has now been updated as a USGS Techniques and Methods Report. It is intended to be a text in applied statistics for hydrology, environmental science, environmental engineering, geology, or biology that addresses distinctive features of environmental data. For example, water resources data tend to have many variables with a lower bound of zero, tend to be more skewed than data from many other disciplines, commonly contain censored data (less than values), and assumptions that the data are normally distributed are not appropriate. Computer-intensive methods (bootstrapping and permutation tests) now improve upon and replace the dependence on t-intervals, t-tests, and analysis of variance. A new chapter on sampling design addresses questions such as “How many observations do I need?” The chapter also presents distribution-free methods to help plan sampling efforts. The trends chapter has been updated to include the WRTDS (Weighted Regressions on Time, Discharge, and Season) method for analysis of water-quality data. This new version contains updated graphics and updated guidance on the use of statistical techniques. The text utilizes R, a programming language and open-source software environment, for all exercises and most graphics, and the R code used to generate figures and examples is provided for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4A3","usgsCitation":"Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3, 458 p., https://doi.org/10.3133/tm4a3. [Supersedes USGS Techniques of Water-Resources Investigations, book 4, chap. A3, version 1.1.]","productDescription":"Report: xxii, 458 p.; Data Release","numberOfPages":"484","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-089727","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":418371,"rank":5,"type":{"id":12,"text":"Errata"},"url":"https://pubs.usgs.gov/tm/04/a03/Errata_Sheet.pdf","text":"Errata Sheet","size":"136 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Errata Sheet"},{"id":379731,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://store.usgs.gov/product/533012","text":"Print Version Available"},{"id":374999,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JWL6XR","text":"USGS data release","linkHelpText":"Statistical Methods in Water Resources - Supporting Materials"},{"id":375013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/a03/tm4a3.pdf","text":"Report","size":"9.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-A3"},{"id":375000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/a03/coverthb.jpg"}],"publicComments":"Techniques and Methods 4-A3 supersedes Techniques of Water-Resources Investigations, book 4, chapter A3, version 1.1.","contact":"Chief, Analysis and Prediction Branch
Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Dr., Mail Stop 415
Reston, VA 20192
This report provides an overview of existing hydrologic information describing the quality, quantity, and extent of the major surface-water and groundwater resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma. Hydrologic information is provided for five major river systems (Cimarron River, North Canadian River, Canadian River, Washita River, and North Fork Red River), two reservoirs (Foss Reservoir and Canton Lake), and eight aquifers consisting of the alluvial aquifers associated with each of the five major river systems and three major bedrock aquifers (Ogallala aquifer, Elk City aquifer, and Rush Springs aquifer).
Types of information provided about rivers and reservoirs for the Cheyenne and Arapaho Tribal jurisdictional area include diversion sites and amounts of water allocated and diverted for permitted uses in 2015; treated wastewater discharge sites and amounts discharged in 2015; and characteristics describing water-quality field properties, major ions, nutrients, and selected trace elements. Major ions, nutrients, and selected trace elements are compared to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water. Additionally, statistics are provided describing daily, monthly, and annual streamflow characteristics at 12 U.S. Geological Survey streamgages. Streamflow statistics include the magnitudes and frequencies of floods, base-flow characteristics, and long-term streamflow trends.
Types of information provided about the aquifers include amounts of water allocated and pumped for permitted uses in 2015; characteristics of groundwater describing water-quality field properties, major ions, nitrate (measured as nitrogen), and selected trace elements with comparisons to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water; groundwater levels and long-term changes in water levels; and ranges of hydraulic conductivity, aquifer recharge, specific yield, transmissivity, and well yields from reports and groundwater-flow models.
Surface water is used primarily for irrigation and mining and other nonconsumptive uses in the Cheyenne and Arapaho Tribal jurisdictional area, except from the Washita and North Fork Red Rivers, where water is treated for use as a public-water supply. Large concentrations of dissolved solids are the primary limiting factor affecting the use of surface water. Median concentrations of dissolved solids in surface water range from less than 1,000 milligrams per liter (mg/L) in samples from the North Canadian River to greater than 9,000 mg/L in samples from the Cimarron River. Large dissolved solids concentrations are correlated with hard water. Median hardness as calcium carbonate concentrations in surface water ranges from 427 mg/L in samples from Canton Lake to 1,000 mg/L in samples from the Washita River.
In 2015, groundwater was used at more than twice the rate of surface water in the Cheyenne and Arapaho Tribal jurisdictional area. Although the alluvial aquifers are considered reliably good sources of water in the Cheyenne and Arapaho Tribal jurisdictional area, concentrations of nitrate (measured as nitrogen) exceed the maximum contaminant level of 10 mg/L established by the U.S. Environmental Protection Agency for finished drinking water in parts of all of the alluvial aquifers. Water from the three major bedrock aquifers is used for irrigation, mining, public-water supply, and other uses; however, large concentrations of dissolved solids, nitrate (measured as nitrogen), and naturally occurring trace elements such as arsenic and uranium may limit the use of groundwater as a source of public-water supply in some areas. As of 2015, the depletion of groundwater from the major aquifers in west-central Oklahoma is a minor concern to the Oklahoma Water Resources Board. Groundwater levels and other hydrologic information show that recharge rates exceed the rates of water pumped from aquifers, except in areas that may be affected locally by groundwater depletions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205105","collaboration":"Prepared in cooperation with the Cheyenne and Arapaho Tribes of Oklahoma and the Bureau of Indian Affairs","usgsCitation":"Becker, C.J., and Varonka, M.S., 2020, Water resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma, with an analysis of data gaps through 2015 (ver. 1.1, January 2021): U.S. Geological Survey Scientific Investigations Report 2020–5105, 158 p., 1 app., https://doi.org/10.3133/sir20205105..","productDescription":"xi, 158 p.","numberOfPages":"175","onlineOnly":"Y","ipdsId":"IP-109610","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":382059,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5105/versionHist.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5105 Version History"},{"id":379749,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5105/sir20205105.pdf","text":"Report","size":"36.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5105"},{"id":379748,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5105/coverthb2.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Cheyenne and Arapaho Tribal Jurisdictional Area","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.3595,35.1163],[-99.4067,35.1161],[-99.409,35.1148],[-99.4129,35.1134],[-99.4163,35.1134],[-99.4196,35.1143],[-99.4219,35.1138],[-99.4258,35.1129],[-99.4281,35.1111],[-99.4337,35.1097],[-99.4393,35.111],[-99.4432,35.1124],[-99.4449,35.116],[-99.7833,35.1161],[-99.7839,35.1151],[-99.7852,35.028],[-99.8897,35.0277],[-100.0008,35.0295],[-100.0014,35.1818],[-100.0015,35.2034],[-100.0013,35.2689],[-100.0012,35.293],[-100.0009,35.4223],[-100.0014,35.4558],[-100.0011,35.6197],[-100.001,35.64],[-100.0015,35.8008],[-100.0015,35.8782],[-100.0015,35.9478],[-100.002,36.0539],[-100.0025,36.1891],[-100.003,36.3134],[-100.0031,36.3348],[-100.0038,36.4998],[-100.0044,36.5849],[-100.0045,36.5917],[-99.6193,36.5916],[-99.605,36.5917],[-99.6043,36.506],[-99.6038,36.3051],[-99.6034,36.2457],[-99.5954,36.2457],[-99.5976,36.1639],[-99.382,36.1645],[-98.9565,36.1587],[-98.7878,36.1613],[-98.7428,36.1625],[-98.7251,36.1634],[-98.6362,36.1636],[-98.634,36.1636],[-98.3177,36.1645],[-98.2122,36.1656],[-98.1057,36.1658],[-97.6759,36.1663],[-97.6765,36.0715],[-97.6763,35.984],[-97.6761,35.8973],[-97.6757,35.7253],[-97.6753,35.5506],[-97.6719,35.5506],[-97.6729,35.4639],[-97.6733,35.3763],[-97.6729,35.335],[-97.6836,35.3351],[-97.6898,35.3338],[-97.6948,35.3339],[-97.7016,35.3353],[-97.7033,35.3353],[-97.7073,35.334],[-97.7118,35.3313],[-97.7181,35.3287],[-97.7231,35.3278],[-97.7288,35.3274],[-97.7367,35.328],[-97.7423,35.3298],[-97.749,35.3326],[-97.7546,35.3354],[-97.7635,35.3414],[-97.7759,35.3434],[-97.7917,35.3408],[-97.829,35.3348],[-97.838,35.3354],[-97.8464,35.3368],[-97.8565,35.341],[-97.8582,35.3428],[-97.8614,35.3551],[-97.8636,35.3588],[-97.8669,35.3615],[-97.8703,35.3629],[-97.8754,35.3625],[-97.8811,35.3608],[-97.8845,35.3572],[-97.8885,35.3545],[-97.8936,35.3513],[-97.9009,35.3514],[-97.9082,35.3528],[-97.9105,35.3533],[-97.915,35.3556],[-97.925,35.3612],[-97.9351,35.3649],[-97.9368,35.3672],[-97.9378,35.3744],[-97.9395,35.3763],[-97.9429,35.3772],[-97.9474,35.3777],[-97.9491,35.3764],[-97.9502,35.3745],[-97.9492,35.3668],[-97.951,35.3596],[-97.9527,35.3555],[-97.955,35.3528],[-97.9562,35.3519],[-97.9663,35.3525],[-97.9697,35.3529],[-97.9736,35.3543],[-97.9843,35.3599],[-97.9994,35.365],[-98.0184,35.3765],[-98.0985,35.3767],[-98.305,35.3744],[-98.3051,35.5437],[-98.3085,35.541],[-98.3119,35.5401],[-98.3142,35.5406],[-98.3193,35.5429],[-98.3193,35.5438],[-98.3187,35.5479],[-98.3181,35.5502],[-98.6199,35.552],[-98.6209,35.4639],[-98.6216,35.2038],[-98.616,35.2038],[-98.6177,35.0994],[-98.621,35.0981],[-98.6255,35.1035],[-98.6294,35.1167],[-98.6355,35.1231],[-98.6406,35.1231],[-98.6429,35.1145],[-98.6451,35.1113],[-98.6496,35.1141],[-98.6513,35.1177],[-98.649,35.1195],[-98.6485,35.1213],[-98.6485,35.1231],[-98.6513,35.125],[-98.6575,35.1236],[-98.6665,35.1209],[-98.6738,35.1187],[-98.6778,35.1082],[-98.6822,35.1101],[-98.6856,35.1078],[-98.6985,35.1115],[-98.7042,35.111],[-98.7109,35.1065],[-98.7132,35.1065],[-98.721,35.1138],[-98.7255,35.1115],[-98.7317,35.1129],[-98.7351,35.1029],[-98.7379,35.102],[-98.7401,35.107],[-98.748,35.1166],[-98.8244,35.1176],[-98.9312,35.1168],[-98.9807,35.1173],[-99.0425,35.1168],[-99.2555,35.1161],[-99.3595,35.1163]]]},\"properties\":{\"name\":\"Beckham\",\"state\":\"OK\"}}]}","edition":"Version 1.0: October 27, 2020; Version 1.1: January 11, 2021","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The utilization, preservation, and conservation of the Nation’s resources requires well-informed management decisions. The North Atlantic and Appalachian Region (NAAR) of the U.S. Geological Survey (USGS) supports science-based decision making for Federal, State, and local policymakers to meet the challenges of today and into the future. The science centers in the NAAR have well-deserved reputations as world leaders in delivering unbiased science. We help protect the lives and property of our families, friends, neighbors, and the Nation by providing the data and scientific interpretation that decision makers need to make informed choices on a myriad of topics. Many of our jobs include inherent risk. When others are moving themselves and their families to higher ground during storms, NAAR employees can be found heading toward high water to ensure that accurate streamflow and storm-tide data continue to be collected and delivered to the public and first responders.
In March 2020, the world changed, and the NAAR staff adapted to it. Despite the challenges, the NAAR has had an incredibly productive year. I am not just citing publications (with our labs and field offices closed in the spring, centers increased annual publications by 10 to 40 percent compared with 2019) or partnerships (new science initiatives and partnerships are up significantly as well). Leaders at the center level created the right environments for their teams to be safe but still meet and exceed their program goals. Our vast data collection networks were maintained and enhanced. Our laboratories met holding times and quality-control objectives. When folks asked for help, our staff provided. Some solutions were not perfect at first, but they just kept trying. What started as a short-term inconvenience may now have become the new normal, but in quickly adapting, the NAAR staff showed dedication and wisdom, made the region a little safer, and just might change the world. This general information product highlights just a few of the many accomplishments of the NAAR staff during these challenging times and offers a taste of all the great work being done by the USGS community.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip207","isbn":"978-1-4113-4381-8","usgsCitation":"U.S. Geological Survey, 2020, Meeting the challenge—U.S. Geological Survey North Atlantic and Appalachian Region fiscal year 2020 in review: U.S. Geological Survey General Information Product 207, 20 p., https://doi.org/10.3133/gip207.","productDescription":"20 p.","numberOfPages":"20","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123417","costCenters":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"links":[{"id":379535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/207/gip207.pdf","text":"Report","size":"5.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 207"},{"id":379534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/207/coverthb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Kentucky, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia","otherGeospatial":"Appalachian region, North Atlantic region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.76171875,\n 36.56260003738545\n ],\n [\n -73.7841796875,\n 40.34654412118006\n ],\n [\n -70.6640625,\n 41.178653972331674\n ],\n [\n -69.873046875,\n 41.73852846935917\n ],\n [\n -70.7080078125,\n 42.68243539838623\n ],\n [\n -69.3896484375,\n 43.929549935614595\n ],\n [\n -67.5,\n 44.465151013519616\n ],\n [\n -66.70898437499999,\n 45.1510532655634\n ],\n [\n -67.67578124999999,\n 45.85941212790755\n ],\n [\n -67.939453125,\n 47.21956811231547\n ],\n [\n -69.2578125,\n 47.54687159892238\n ],\n [\n -70.57617187499999,\n 45.583289756006316\n ],\n [\n -71.71875,\n 45.058001435398275\n ],\n [\n -74.970703125,\n 45.058001435398275\n ],\n [\n -76.201171875,\n 44.213709909702054\n ],\n [\n -76.9482421875,\n 43.35713822211053\n ],\n [\n -78.31054687499999,\n 43.45291889355465\n ],\n [\n -78.837890625,\n 43.197167282501276\n ],\n [\n -78.8818359375,\n 42.71473218539458\n ],\n [\n -80.5078125,\n 41.86956082699455\n ],\n [\n -80.419921875,\n 40.17887331434696\n ],\n [\n -82.44140625,\n 38.44498466889473\n ],\n [\n -82.9248046875,\n 38.61687046392973\n ],\n [\n -84.6826171875,\n 39.13006024213511\n ],\n [\n -86.17675781249999,\n 38.03078569382294\n ],\n [\n -87.8466796875,\n 37.92686760148135\n ],\n [\n -88.3740234375,\n 37.54457732085582\n ],\n [\n -89.1650390625,\n 37.33522435930639\n ],\n [\n -89.4287109375,\n 36.59788913307022\n ],\n [\n -75.76171875,\n 36.56260003738545\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Regional Director
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12201 Sunrise Valley Drive
Reston, VA 20192
The city of Summerset is a growing community in west South Dakota. The Sun Valley Estates subdivision in the north part of the city was developed on unconsolidated deposits surrounded by steep terrain. During years with greater than normal precipitation, particularly in 2019, groundwater levels increased in the unconsolidated deposits and caused damage to stormwater systems, sewer infrastructure, and houses with basements. The U.S. Geological Survey, in cooperation with the City of Summerset, completed a study of the hydrogeology and groundwater flow in the alluvial aquifer part of the unconsolidated deposits in north Summerset to understand the groundwater system in the area and to provide hydrogeologic information in support of future development planning.
The study area included most of the Sun Valley Estates subdivision in the north part of the city of Summerset in the east Black Hills of west South Dakota. About 0.7 square mile of water-bearing alluvial deposits is included in the study area. Precipitation in the study area from 2017 to 2019 was compared to the monthly normal values at a nearby climate site. The largest departure from normal was in May 2019 with precipitation exceeding the monthly normal by about 5 inches (in.). All months in 2019, except March, exceeded the monthly normal precipitation. Cumulative departure from normal precipitation in 2019 increased from about 4 in. greater than normal in January to about 18 in. greater than normal in December.
The geologic setting of the study area is characterized by the surrounding Black Hills. Unconsolidated Quaternary-age deposits overlie consolidated to partially consolidated Mesozoic-age and Paleozoic-age shales, sandstones, and limestones. Surficial deposits of alluvium and other unconsolidated deposits are the primary surficial geologic units in the study area and form the components of the alluvium hydrogeologic unit of the study area. Results from previous studies of alluvium along nearby Rapid Creek estimated hydraulic conductivity to range from 89 to 2,292 feet per day (ft/d), transmissivity to range from 1,001 to 32,083 feet squared per day, and storage coefficients to range from 0.0002 to 0.16. Hydraulic conductivity and transmissivity generally decreased downstream along Rapid Creek (west to east). Slug tests were completed August 16, 2019, at two observation wells completed in the alluvial aquifer in the Sun Valley Estates subdivision to determine hydraulic conductivity. Hydraulic conductivity estimated from AQTESOLV curve-fitting analysis using the Bouwer-Rice method for all slug-in and slug-out trials from two observation wells in the study ranged from 0.20 to 0.26 ft/d for well 441318103220001 (SunValley1 well) and from 0.54 to 14 ft/d for well 441319103215701 (SunValley2 well). The mean, median, and standard deviation of all trials at both wells were 4.3 ft/d, 0.8 ft/d, and 5.6 ft/d, respectively.
The extent of the alluvial aquifer was determined by geologic maps and lithologic logs. Alluvial deposits in the study area extend to about 1 mile in the north–south direction and about 1.5 miles in the southeast–northwest direction. The direction of groundwater flow was estimated using water-level records and topographic maps. The resulting potentiometric map indicated that groundwater in the alluvial aquifer under the Sun Valley Estates subdivision originates from higher elevations of the west part of the area of interest and from streams in the southeast part. Recharge and evapotranspiration estimates were results from a Soil-Water Balance model that calculated a matrix of recharge for 2019 with values ranging from 0 to 11.4 in. and an annual mean value of 5.1 in. across the study area. Soil-Water Balance-estimated potential evapotranspiration for 2019 ranged from 28.90 to 28.75 in. and the estimated annual mean was 28.86 in. across the study area. Estimated groundwater budget components for the alluvial aquifer in the area of interest included inflows and outflows. Total estimated groundwater budget components for inflows for 2019 were about 66 percent from recharge, 33 percent from streamflow, and 1 percent from inflow from adjacent aquifers. Total estimated outflows were about 99-percent evapotranspiration and less than 1-percent outflow to adjacent aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205097","issn":"2328-0328","usgsCitation":"Eldridge, W.G., and Anderson, T.M., 2020, Hydrogeology and groundwater flow in alluvial deposits, north Summerset, South Dakota: U.S. Geological Survey Scientific Investigations Report 2020–5097, 31 p., https://doi.org/10.3133/sir20205097.","productDescription":"Report: vii, 31 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-116994","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":379700,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5097/coverthb.jpg"},{"id":379703,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","description":"USGS data release","linkHelpText":"USGS Water Data for the Nation"},{"id":379702,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TKVMXU","text":"USGS data release","description":"USGS data release","linkHelpText":"Soil-Water Balance model for alluvial deposits in Summerset, South Dakota"},{"id":379701,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5097/sir20205097.pdf","text":"Report","size":"5.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5097"}],"country":"United States","state":"South Dakota","city":"Sommerset","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.37310791015625,\n 44.15856343854312\n ],\n [\n -103.28109741210938,\n 44.15856343854312\n ],\n [\n -103.28109741210938,\n 44.203866109361435\n ],\n [\n -103.37310791015625,\n 44.203866109361435\n ],\n [\n -103.37310791015625,\n 44.15856343854312\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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Excessive nutrient inputs from tributary streams and rivers contribute to harmful algal blooms and coastal ecosystem degradation worldwide. However, the role that small tributaries play in coastal nutrient dynamics remains unknown because most monitoring and regulatory efforts focus only on the largest tributaries. We combined a 6-d sampling effort with discharge modeling to characterize nutrient inputs from nearly all watersheds draining to the world’s fifth largest lake. We found that streams are particularly likely to promote eutrophication in coastal ecosystems because they deliver water with higher concentrations of nutrients that are readily available to algae. Thus, our findings indicate that efforts to control nutrient loading could be enhanced by looking beyond the largest tributaries to include smaller streams.
Lava that erupted during the 2014–2015 Holuhraun eruption in Iceland flowed into a proglacial river system, resulting in aqueous cooling of the lava and an ephemeral hydrothermal system. We carried out a monitoring study of this system from 2015 to 2018 to document the cooling of the lava over this time, using thermocouple measurements and data-logging sensors. The heat loss rate from advection through this hydrothermal system in August 2015 was ~5.5 × 108 W; since eruption, aqueous cooling likely accounted for ~1% of the total heat loss from the lava. This estimate excludes steam losses from fumaroles as well as any groundwater that was not released to the surface, and thus is a lower bound. Near the terminus of the flow, advection of heat by flowing water may have locally accounted for tens of percent of the total cooling of that part of the flow. Our data quantify the importance of water cooling for this lava flow and can be compared with models to better understand lava–water interactions more generally. We also provide detailed methods for simple, low-cost monitoring of similar instances in the future.
As climate warming and precipitation increase at high latitudes, permafrost terrains across the circumpolar north are poised for intensified geomorphic activity and sediment mobilization that are expected to persist for millennia. In previously glaciated permafrost terrain, ice-rich deposits are associated with large stores of reactive mineral substrate. Over geological timescales, chemical weathering moderates atmospheric CO2 levels, raising the prospect that mass wasting driven by terrain consolidation following thaw (thermokarst) may enhance weathering of permafrost sediments and thus climate feedbacks. The nature of these feedbacks depends upon the mineral composition of sediments (weathering sources) and the balance between atmospheric exchange of CO2 vs. fluvial export of carbonate alkalinity (Σ[
The U.S. Geological Survey (USGS) has been monitoring streamflow in South Carolina since the late 1800s. From the beginning, the USGS streamgage network in South Carolina has been dynamic, with streamgages being added or removed depending on their purpose and the availability of funding from Federal, State, and local partners. Streamflow monitoring is important for acquiring real-time data during flood events, but also for collecting long-term data that can be used to compute the magnitude and frequency of floods and to frame flood events in a historical perspective. These data are also critical for being able to develop regional regression equations that can be used to estimate flood characteristics at ungaged locations, which is important for infrastructure planning and design. The historical flooding that occurred in South Carolina in 2015, 2016, and 2018 highlighted the importance of collecting these data. Therefore, the USGS, in cooperation with the South Carolina Department of Transportation, evaluated the USGS streamgage network in South Carolina for the purpose of helping guide decisions concerning future streamgage location selection, both spatially and in terms of the range of drainage basin characteristics that are typically important in flood-frequency analyses. The results of this evaluation are presented in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201104","collaboration":"Prepared in cooperation with the South Carolina Department of Transportation","usgsCitation":"Feaster, T.D., and Kolb, K.R., 2020, Evaluation of the U.S. Geological Survey streamgage network in South Carolina, 2017: U.S. Geological Survey Open-File Report 2020–1104, 15 p., https://doi.org/10.3133/ofr20201104.","productDescription":"Report: vii, 15 p.; 1 Plate: 40.00 x 40.00 inches; Appendixes 1-3; Data Release","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-116207","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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The “National Field Manual for the Collection of Water-Quality Data” (NFM) provides guidelines and procedures for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation’s surface water and groundwater resources. This chapter, NFM A6.2, provides guidance and protocols for the measurement of dissolved oxygen, which include the scientific basis of the measurement, selection and maintenance of equipment, calibration, troubleshooting, and procedures for measurement and reporting. It updates and supersedes USGS Techniques of Water-Resources Investigations, book 9, chapter A6.2, version 3.0, by Stewart A, Rounds, Franceska D. Wilde, and George F. Ritz. Dissolved oxygen is routinely measured when water samples are collected, is often continually measured at USGS streamgages, and is a parameter regularly measured during laboratory and field experiments. The field method for measuring dissolved oxygen described in this chapter is applicable to most natural waters.
Before 2017, the USGS NFM chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0—U.S. Geological Survey, 2018) at https://doi.org/10.3133/tm9A0. The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.er.usgs.gov. Comments, questions, and suggestions related to the NFM can be addressed to nfm@usgs.gov.
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Insecticides in streams are increasingly a global concern, yet information on safe concentrations for aquatic ecosystems is sparse. In a 30-day mesocosm experiment exposing native benthic aquatic invertebrates to the common insecticide fipronil and four degradates, fipronil compounds caused altered emergence and trophic cascades. Effect concentrations eliciting a 50% response (EC50) were developed for fipronil and its sulfide, sulfone, and desulfinyl degradates; taxa were insensitive to fipronil amide. Hazard concentrations for 5% of affected species derived from up to 15 mesocosm EC50 values were used to convert fipronil compound concentrations in field samples to the sum of toxic units (∑TUFipronils). Mean ∑TUFipronils exceeded 1 (indicating toxicity) in 16% of streams sampled from five regional studies. The Species at Risk invertebrate metric was negatively associated with ∑TUFipronils in four of five regions sampled. This ecological risk assessment indicates that low concentrations of fipronil compounds degrade stream communities in multiple regions of the United States.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.abc1299","usgsCitation":"Miller, J., Schmidt, T., Van Metre, P.C., Mahler, B., Sandstrom, M.W., Nowell, L.H., Carlisle, D.M., and Moran, P.W., 2020, Common insecticide disrupts aquatic communities: A mesocosm-to-field ecological risk assessment of fipronil and its degradates in U.S. streams: Science Advances, v. 6, no. 43, https://doi.org/10.1126/sciadv.abc1299.","productDescription":"eabc1299, 12 p.","startPage":"eabc1299","ipdsId":"IP-114600","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":454979,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.abc1299","text":"Publisher Index Page"},{"id":436746,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XR80GW","text":"USGS data release","linkHelpText":"Data set for an ecological risk assessment of Firpronil compounds in US streams"},{"id":414623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"43","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Janet L.","contributorId":239985,"corporation":false,"usgs":false,"family":"Miller","given":"Janet L.","affiliations":[{"id":48080,"text":"Colorado State University, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":867352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":867353,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Metre, Peter C. 0000-0001-7564-9814","orcid":"https://orcid.org/0000-0001-7564-9814","contributorId":211144,"corporation":false,"usgs":true,"family":"Van Metre","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":867354,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahler, Barbara 0000-0002-9150-9552 bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867355,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":867356,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nowell, Lisa H. 0000-0001-5417-7264 lhnowell@usgs.gov","orcid":"https://orcid.org/0000-0001-5417-7264","contributorId":490,"corporation":false,"usgs":true,"family":"Nowell","given":"Lisa","email":"lhnowell@usgs.gov","middleInitial":"H.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867357,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":867358,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moran, Patrick W. 0000-0002-2002-3539 pwmoran@usgs.gov","orcid":"https://orcid.org/0000-0002-2002-3539","contributorId":489,"corporation":false,"usgs":true,"family":"Moran","given":"Patrick","email":"pwmoran@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867359,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70219095,"text":"70219095 - 2020 - Diverse cataclysmic floods from Pleistocene glacial Lake Missoula","interactions":[],"lastModifiedDate":"2021-04-27T11:52:46.725403","indexId":"70219095","displayToPublicDate":"2020-10-23T07:32:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7779,"text":"GSA Special Volume on Pleistocene megafloods","active":true,"publicationSubtype":{"id":10}},"title":"Diverse cataclysmic floods from Pleistocene glacial Lake Missoula","docAbstract":"In late Wisconsin time, the Purcell Trench lobe of the Cordilleran ice sheet dammed the Clark Fork of the Columbia River in western Montana, creating glacial Lake Missoula. During part of this epoch, the Okanogan lobe also dammed the Columbia River downstream, creating glacial Lake Columbia in northeast Washington. Repeated failure of the Purcell Trench ice dam released glacial Lake Missoula, causing dozens of catastrophic floods in eastern Washington that can be distinguished by the geologic record they left behind. These floods removed tens of meters of pale loess from dark basalt substrate, forming scars along flowpaths visible from space.
Different positions of the Okanogan lobe are required for modeled Missoula floods to inundate the diverse channels that show field evidence for flooding, as shown by accurate dam-break flood modeling using a roughly 185 m digital terrain model of existing topography (with control points dynamically varied using automatic mesh refinement). The maximum extent of the Okanogan lobe, which blocked inundation of the upper Grand Coulee and the Columbia River valley, is required to flood all channels in the Telford scablands and to produce highest flood stages in Pasco Basin. Alternatively, the Columbia River valley must have been open and the upper Grand Coulee blocked to nearly match evidence for high water on Pangborn bar near Wenatchee, Washington, and to flood Quincy Basin from the west. Finally, if the Columbia River valley and upper Grand Coulee were both open, Quincy Basin would have flooded from the northeast.
In all these scenarios, the discrepancy between modeled flood stages and field evidence for maximum flood stages increases in all channels downstream, from Spokane to Umatilla Basin. The pattern of discrepancies indicates that bulking of floods by loess increased flow volume across the scablands, but this alone does not explain low modeled flow stages along the Columbia River valley near Wenatchee. This latter discrepancy between modeled flood stages and field data requires either additional bulking of flow by sediment along the Columbia reach downstream of glacial Lake Columbia, or coincident dam failures of glacial Lake Columbia and glacial Lake Missoula.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.2548(17)","usgsCitation":"Denlinger, R.P., George, D.L., Cannon, C.M., O'Connor, J., and Waitt, R.B., 2020, Diverse cataclysmic floods from Pleistocene glacial Lake Missoula: GSA Special Volume on Pleistocene megafloods, v. 548, 18 p., https://doi.org/10.1130/2021.2548(17).","productDescription":"18 p.","ipdsId":"IP-101636","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":384572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Washington","otherGeospatial":"Lake Missoula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.92675781249999,\n 46.08847179577592\n ],\n [\n -113.3349609375,\n 46.08847179577592\n ],\n [\n -113.3349609375,\n 48.22467264956519\n ],\n [\n -119.92675781249999,\n 48.22467264956519\n ],\n [\n -119.92675781249999,\n 46.08847179577592\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"548","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Denlinger, Roger P. 0000-0003-0930-0635 roger@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-0635","contributorId":2679,"corporation":false,"usgs":true,"family":"Denlinger","given":"Roger","email":"roger@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":812746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":812747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cannon, Charles M. 0000-0003-4136-2350 ccannon@usgs.gov","orcid":"https://orcid.org/0000-0003-4136-2350","contributorId":247680,"corporation":false,"usgs":true,"family":"Cannon","given":"Charles","email":"ccannon@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":812748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":812749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Waitt, Richard B. 0000-0002-6392-5604 waitt@usgs.gov","orcid":"https://orcid.org/0000-0002-6392-5604","contributorId":2343,"corporation":false,"usgs":true,"family":"Waitt","given":"Richard","email":"waitt@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":812750,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215976,"text":"70215976 - 2020 - Geomorphic and sedimentary effects of modern climate change: Current and anticipated future conditions in the western United States","interactions":[],"lastModifiedDate":"2020-12-14T16:49:17.200792","indexId":"70215976","displayToPublicDate":"2020-10-23T07:02:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3283,"text":"Reviews of Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Geomorphic and sedimentary effects of modern climate change: Current and anticipated future conditions in the western United States","docAbstract":"Hydroclimatic changes associated with global warming over the past 50 years have been documented widely, but physical landscape responses are poorly understood thus far. Detecting sedimentary and geomorphic signals of modern climate change presents challenges owing to short record lengths, difficulty resolving signals in stochastic natural systems, influences of land use and tectonic activity, long‐lasting effects of individual extreme events, and variable connectivity in sediment‐routing systems. We review existing literature to investigate the nature and extent of sedimentary and geomorphic responses to modern climate change, focusing on the western United States, a region with generally high relief and high sediment yield likely to be sensitive to climatic forcing. Based on fundamental geomorphic theory and empirical evidence from other regions, we anticipate climate‐driven changes to slope stability, watershed sediment yields, fluvial morphology, and aeolian sediment mobilization in the western U.S. We find evidence for recent climate‐driven changes to slope stability and increased aeolian dune and dust activity, whereas changes in sediment yields and fluvial morphology have been linked more commonly to non‐climatic drivers thus far. Detecting effects of climate change will require better understanding how landscape response scales with disturbance, how lag times and hysteresis operate within sedimentary systems, and how to distinguish the relative influence and feedbacks of superimposed disturbances. The ability to constrain geomorphic and sedimentary response to rapidly progressing climate change has widespread implications for human health and safety, infrastructure, water security, economics, and ecosystem resilience.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019RG000692","usgsCitation":"East, A.E., and Sankey, J.B., 2020, Geomorphic and sedimentary effects of modern climate change: Current and anticipated future conditions in the western United States: Reviews of Geophysics, v. 58, no. 4, e2019RG000692, 59 p., https://doi.org/10.1029/2019RG000692.","productDescription":"e2019RG000692, 59 p.","ipdsId":"IP-115204","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":454985,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019rg000692","text":"Publisher Index Page"},{"id":380009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"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 -125.5078125,\n 31.052933985705163\n ],\n [\n -103.6669921875,\n 31.052933985705163\n ],\n [\n -103.6669921875,\n 48.951366470947725\n ],\n [\n -125.5078125,\n 48.951366470947725\n ],\n [\n -125.5078125,\n 31.052933985705163\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-12-03","publicationStatus":"PW","contributors":{"authors":[{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":803644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":803645,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215422,"text":"sir20205092 - 2020 - Estimating flood magnitude and frequency on gaged and ungaged streams in Maine","interactions":[{"subject":{"id":15070,"text":"ofr75292 - 1975 - A technique for estimating the magnitude and frequency of floods in Maine","indexId":"ofr75292","publicationYear":"1975","noYear":false,"title":"A technique for estimating the magnitude and frequency of floods in Maine"},"predicate":"SUPERSEDED_BY","object":{"id":70215422,"text":"sir20205092 - 2020 - Estimating flood magnitude and frequency on gaged and ungaged streams in Maine","indexId":"sir20205092","publicationYear":"2020","noYear":false,"title":"Estimating flood magnitude and frequency on gaged and ungaged streams in Maine"},"id":1},{"subject":{"id":27717,"text":"wri994008 - 1999 - Estimating the magnitude of peak flows for streams in Maine for selected recurrence intervals","indexId":"wri994008","publicationYear":"1999","noYear":false,"displayTitle":"Estimating the Magnitude of Peak Flows for Streams in Maine for Selected Recurrence Intervals","title":"Estimating the magnitude of peak flows for streams in Maine for selected recurrence intervals"},"predicate":"SUPERSEDED_BY","object":{"id":70215422,"text":"sir20205092 - 2020 - Estimating flood magnitude and frequency on gaged and ungaged streams in Maine","indexId":"sir20205092","publicationYear":"2020","noYear":false,"title":"Estimating flood magnitude and frequency on gaged and ungaged streams in Maine"},"id":2}],"lastModifiedDate":"2020-10-22T19:40:56.715355","indexId":"sir20205092","displayToPublicDate":"2020-10-22T14:15:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5092","displayTitle":"Estimating Flood Magnitude and Frequency on Gaged and Ungaged Streams in Maine","title":"Estimating flood magnitude and frequency on gaged and ungaged streams in Maine","docAbstract":"Accurate estimates of flood frequency and magnitude on rivers and streams in Maine are a key component of effective flood risk management, flood mitigation, and flood recovery programs for the State. Flood-frequency estimates are published here for 148 streamgages in and adjacent to Maine. Equations are provided for users to compute flood-frequency estimates at any location on a stream that does not have a streamgage. Estimates and equations are presented for peak flows with annual exceedance probabilities (AEPs) of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent. AEPs correspond to flood recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. New estimates use a regional skew coefficient of 0.02 with a standard error of prediction of 0.30 developed specifically for Maine as a part of this work.
Equations are designed for use at ungaged sites without substantial flow regulation or urbanization in Maine, with drainage areas between 0.26 and 5,680 square miles. The equations were developed from streamflows and basin characteristics at 124 unregulated streamgages using generalized least-squares regression techniques. Explanatory variables used in the equations for computing peak flows are drainage area, percentage of area in the basin that contains wetlands, and basin mean 24-hour rainfall intensities. The average standard error of prediction (ASEP) for these equations ranges from −31.5 to 45.9 percent for the 50-percent AEP and from −34.2 to 52.0 percent for the 0.2-percent AEP. Equations that use only drainage area are provided for use in cases where lower accuracy is acceptable. The ASEP for estimating peak flows with these simpler equations ranges from −40 to 66 percent for the 50-percent AEP and from −44 to 79 percent for the 0.2-percent AEP.
Final peak flows at unregulated streamgages are computed as weighted averages between the at-station peak flows and peak flows computed at those same sites using the regression equations. Peak flow estimates and equations presented here are accessible in the U.S. Geological Survey StreamStats application. StreamStats is a web application that computes selected basin characteristics and estimates of peak flows and other available streamflow statistics for user-selected streams in Maine.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205092","collaboration":"Prepared in cooperation with the Maine Department of Transportation","usgsCitation":"Lombard, P.J., and Hodgkins, G.A., 2020, Estimating flood magnitude and frequency on gaged and ungaged streams in Maine: U.S. Geological Survey Scientific Investigations Report 2020–5092, 56 p., https://doi.org/10.3133/sir20205092.","productDescription":"Report: vii, 56 p.; 2 Tables; Data Release","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109858","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":379515,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5092/sir20205092_table1.4.csv","text":"Table 1.4","size":"49.3 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Peak flows for selected annual exceedance probabilities for selected streamgages in and near Maine"},{"id":379514,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5092/sir20205092_table1.4.xlsx","text":"Table 1.4","size":"56.7 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Peak flows for selected annual exceedance probabilities for selected streamgages in and near Maine"},{"id":379512,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5092/coverthb.jpg"},{"id":379513,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5092/sir20205092.pdf","text":"Report","size":"3.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5092"},{"id":379516,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RQPPK2","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Results of peak-flow frequency analysis and regionalization for selected streamgages in or near Maine, based on data through water 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
In 2004, the U.S. Geological Survey (USGS) began sampling 14 wadable streams in urban or urbanizing watersheds near Milwaukee, Wisconsin. The overall goal of the study is to assess the health of the aquatic communities in the Milwaukee Metropolitan Sewerage District planning area to inform current and future watershed management. In addition to collection of biological data on aquatic communities, physical and chemical data were also collected to evaluate effects of potential environmental stressors on the aquatic communities. This fact sheet summarizes the primary results of the study from 2004 to 2013. Detailed information is described in Scudder Eikenberry and others (2020a), and all data are available in Scudder Eikenberry and others (2020b; https://doi.org/10.5066/P9FWMODL).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203051","issn":"2327-6932","usgsCitation":"Scudder Eikenberry, B., Nott, M.A., Stewart, J.S., Sullivan, D.J., Alvarez, D.A., Bell, A.H., and Fitzpatrick, F.A., 2020, Physical and chemical stressors on algal, invertebrate, and fish communities in 14 Milwaukee area streams, 2004–2013: U.S. Geological Survey Fact Sheet 2020-3051, 6 p., https://doi.org/10.3133/fs20203051.","productDescription":"Report: 6 p.; Data Release","onlineOnly":"Y","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":379614,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FWMODL","text":"USGS data release","description":"USGS data release","linkHelpText":"Aquatic community and environmental data for 14 rivers and streams in the Milwaukee Metropolitan Sewerage District Planning Area, 2004-13"},{"id":379615,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205035","text":"SIR 2020-5035—","description":"SIR 2020-5035","linkHelpText":"Ecological Status of Aquatic Communities in Selected Streams in the Milwaukee Metropolitan Sewerage District Planning Area of Wisconsin, 2004–13"},{"id":379612,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3051/coverthb.jpg"},{"id":379613,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3051/fs20203051.pdf","text":"Report","size":"8.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3051"}],"country":"United States","state":"Wisconsin","city":"Milwaukee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.1982421875,\n 42.88401467044253\n ],\n [\n -87.84530639648436,\n 42.88401467044253\n ],\n [\n -87.84530639648436,\n 43.313188139196406\n ],\n [\n -88.1982421875,\n 43.313188139196406\n ],\n [\n -88.1982421875,\n 42.88401467044253\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
This study developed a spatially explicit framework to support the conservation of Western Brook Lamprey Lampetra richardsoni and Pacific Lamprey Entosphenus tridentatus in the Umpqua River basin, Oregon. This framework identified locations within the river network likely to support “potential burrowing habitat” for lamprey larvae based on geomorphic conditions and evaluated the overlap of potential burrowing habitat with water temperatures suitable for the nonnative, piscivorous Smallmouth Bass Micropterus dolomieu. The study also documented reach‐scale factors that create heterogeneity in potential burrowing habitat to guide on‐the‐ground habitat restoration. Based on criteria for mean annual suspended sediment loads and channel slope, 18% of the Umpqua River network was classified as potential burrowing habitat. Existing mean August water temperatures of ≥20°C were suitable for Smallmouth Bass for 32% of the potential burrowing habitat. This percentage increased to 41% of the potential burrowing habitat using projected mean August water temperatures for year 2040, suggesting that water temperatures in the future will facilitate upstream expansion of Smallmouth Bass into the potential burrowing habitat. At finer spatial scales, potential burrowing habitat was influenced by channel features, such as large wood, pools, and local channel slope and width. These results provide an initial template for identifying locations in river networks likely to have potential burrowing habitat, considering the overlap between threats and lamprey habitats, and planning conservation actions to support native lampreys.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10487","usgsCitation":"Jones, K., Dunham, J.B., O'Connor, J., Keith, M.K., Mangano, J.F., Coates, K., and Mackie, T., 2020, River network and reach‐scale controls on habitat for lamprey larvae in the Umpqua River Basin, Oregon: North American Journal of Fisheries Management, v. 40, no. 6, p. 1400-1416, https://doi.org/10.1002/nafm.10487.","productDescription":"17 p.","startPage":"1400","endPage":"1416","ipdsId":"IP-109592","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":454987,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10487","text":"Publisher Index Page"},{"id":436747,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CXSCK4","text":"USGS data release","linkHelpText":"Geomorphic and larval lamprey surveys in tributaries of the Umpqua River, Oregon"},{"id":379761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Umpqua River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.1290283203125,\n 42.40317854182803\n ],\n [\n -121.3275146484375,\n 42.40317854182803\n ],\n [\n -121.3275146484375,\n 44.08758502824516\n ],\n [\n -124.1290283203125,\n 44.08758502824516\n ],\n [\n -124.1290283203125,\n 42.40317854182803\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-10-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Krista 0000-0002-0301-4497","orcid":"https://orcid.org/0000-0002-0301-4497","contributorId":205206,"corporation":false,"usgs":true,"family":"Jones","given":"Krista","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802963,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":802964,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":802965,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keith, Mackenzie K. 0000-0002-7239-0576 mkeith@usgs.gov","orcid":"https://orcid.org/0000-0002-7239-0576","contributorId":196963,"corporation":false,"usgs":true,"family":"Keith","given":"Mackenzie","email":"mkeith@usgs.gov","middleInitial":"K.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802966,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mangano, Joseph F. 0000-0003-4213-8406 jmangano@usgs.gov","orcid":"https://orcid.org/0000-0003-4213-8406","contributorId":4722,"corporation":false,"usgs":true,"family":"Mangano","given":"Joseph","email":"jmangano@usgs.gov","middleInitial":"F.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802967,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coates, Kelly","contributorId":244008,"corporation":false,"usgs":false,"family":"Coates","given":"Kelly","affiliations":[],"preferred":false,"id":803016,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mackie, Travis","contributorId":244009,"corporation":false,"usgs":false,"family":"Mackie","given":"Travis","email":"","affiliations":[],"preferred":false,"id":803017,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224320,"text":"70224320 - 2020 - U.S. Geological Survey (USGS) Water-Use Data and Research (WUDR) program overview and status as of October 22, 2020","interactions":[],"lastModifiedDate":"2022-09-05T13:15:35.616524","indexId":"70224320","displayToPublicDate":"2020-10-22T08:10:08","publicationYear":"2020","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"displayTitle":"U.S. Geological Survey (USGS) Water-Use Data and Research (WUDR) Program Overview and Status as of October 22, 2020","title":"U.S. Geological Survey (USGS) Water-Use Data and Research (WUDR) program overview and status as of October 22, 2020","docAbstract":"The USGS Water-Use Data and Research Program (WUDR) is an appropriated program that began in Federal fiscal year 2015 and is authorized under the SECURE Water Act (Sec. 9508 (c)). WUDR provides financial assistance through cooperative agreements to State water resource agencies.\nThe WUDR Program has two main goals:\nTo improve the availability, quality, compatibility, and delivery of water-use data that are collected and/or estimated by States to support national water-use assessments; and\nTo integrate the water-use data into USGS databases in electronic or machine-readable formats.","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Shaffer, K., 2020, U.S. Geological Survey (USGS) Water-Use Data and Research (WUDR) program overview and status as of October 22, 2020, 9 p.","productDescription":"9 p.","ipdsId":"IP-124105","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":389592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389591,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://water.usgs.gov/wausp/wudr-files/WUDR-Overview-20201022.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shaffer, Kimberly 0000-0001-9386-7671 kshaffer@usgs.gov","orcid":"https://orcid.org/0000-0001-9386-7671","contributorId":206648,"corporation":false,"usgs":true,"family":"Shaffer","given":"Kimberly","email":"kshaffer@usgs.gov","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823746,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70215522,"text":"sir20205066 - 2020 - Variable-density groundwater flow and contaminant transport, Operable Unit 1, Naval Base Kitsap, Keyport, Washington","interactions":[],"lastModifiedDate":"2020-10-23T17:59:27.675906","indexId":"sir20205066","displayToPublicDate":"2020-10-21T15:42:09","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":"2020-5066","displayTitle":"Variable-Density Groundwater Flow and Contaminant Transport, Operable Unit 1, Naval Base Kitsap, Keyport, Washington","title":"Variable-density groundwater flow and contaminant transport, Operable Unit 1, Naval Base Kitsap, Keyport, Washington","docAbstract":"Chlorinated volatile organic compounds (CVOCs) have migrated to groundwater beneath a former 9-acre landfill at Operable Unit 1 (OU-1) on Naval Base Kitsap, which was active from the 1930s through 1973 on the Keyport Peninsula, in Kitsap County, Washington. Biodegradation of CVOCs at OU-1 limits the mass of dissolved-phase CVOCs in groundwater that discharges to surface water, but contaminant concentrations up to 630 milligrams per liter persist in localized areas, likely from the dissolution of residual, non-aqueous phase liquids. Variable-density groundwater-flow and contaminant-transport models were developed using the SEAWAT-Version 4 computer program to simulate the direction and rate of groundwater flow in a 5.9 square-mile (mi2) - area surrounding the Keyport Peninsula, to estimate the CVOC mass in groundwater and the rate of mass loading, and to assess possible remedial activities at OU-1.
The study area is underlain by Quaternary deposits consisting of alternating glacial and interglacial sediments ranging from 500 to 1,500 feet (ft) thick. A hydrogeologic model delineated a sequence of 10 units including a relatively thin package (less than 100 ft) of recent sediments (Vashon Stade and younger) beneath the Keyport Peninsula that are underlain by the much thicker (more than 300 ft) Clover Park Aquitard, which overlies a confined, sea-level aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205066","collaboration":"Prepared in cooperation with the Department of the Navy, Naval Facilities Engineering Command, Northwest","usgsCitation":"Yager, R.M., Welch, W.B., Headman, A., and Dinicola, R.S., 2020, Variable-density groundwater flow and contaminant transport, Operable Unit 1, Naval Base Kitsap, Keyport, Washington: U.S. Geological Survey Scientific Investigations Report 2020–5066, 58 p., https://doi.org/10.3133/sir20205066.","productDescription":"x, 62 p.","onlineOnly":"Y","ipdsId":"IP-112628","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":379666,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95WQ7TM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil water balance (SWB) model of Keyport, Washington"},{"id":379617,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5066/sir20205066.pdf","text":"Report","size":"10.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5066"},{"id":379667,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YNPPNL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005, MODFLOW-NWT, and SEAWAT V.4 models used to simulate variable-density groundwater flow and contaminant transport at Naval Base Kitsap, Keyport, Washington"},{"id":379616,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5066/coverthb2.jpg"}],"country":"United States","state":"Washington","city":"Keyport","otherGeospatial":"Naval Base Kitsap","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.65,\n 47.6666\n ],\n [\n -122.60833,\n 47.6666\n ],\n [\n -122.60833,\n 47.71666\n ],\n [\n -122.65,\n 47.71666\n ],\n [\n -122.65,\n 47.6666\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Declining water levels and reduced natural discharge at springs, seeps, and phreatophyte areas primarily are the result of decades of groundwater development in the Death Valley regional flow system, in Nevada and California. A calibrated groundwater-flow model was used to simulate potential future effects of groundwater pumping on water levels and natural groundwater discharge in the study area. Effects of climate change on future groundwater pumping were not considered and were beyond the scope of the study. Four groundwater-pumping scenarios were developed by stakeholders to predict and compare (1) the extent of regional water-level declines; (2) drawdown at Devils Hole; and (3) reductions in natural discharge at select discharge areas, including the Amargosa Wild and Scenic River, the Ash Meadows discharge area, the Furnace Creek area, and Stump Spring. Scenarios were simulated from 1913 to 2120, with historical pumping occurring from 1913 to 2010, historical 2010 pumping rates projected from 2010 to 2020, and scenario pumping beginning in 2020. Pumping scenarios included a base case and scenarios A, B, and C. The base case projected 2010 pumping rates from 2010 to 2120, and scenarios A, B, and C projected base case pumping plus additional pumping at various locations from 2020 to 2120. By 2020, historical (1913–2020) pumping resulted in the propagation of simulated drawdown of 1 foot (ft) or more westward from Pahrump Valley to areas north of Shoshone in the Pahrump to Death Valley South (PDVS) groundwater basin and the merging of simulated 1-ft drawdown contours between the Alkali Flat–Furnace Creek Ranch (AFFCR) and Ash Meadows groundwater basins. In the base case scenario, extent and magnitude of simulated drawdown continued to increase in the Ash Meadows and AFFCR groundwater basins from 2020 to 2120. In the base case, the magnitude of simulated drawdown continued to increase in western Pahrump Valley from 2020 to 2120, whereas simulated water levels rose in eastern Pahrump Valley from 2020 to 2070 and then stabilized from 2070 to 2120. Scenarios A and B primarily affected the PDVS and AFFCR groundwater basins by increasing the magnitude of drawdown in 2120, compared to the base case. In scenario C, drawdown propagated throughout a high-transmissivity part of the carbonate aquifer known as the megachannel, greatly affecting water levels in the Ash Meadows discharge area. Scenario C resulted in an additional 10–100 ft of drawdown (compared to the base case) throughout the southeastern part of the Ash Meadows groundwater basin by 2120. Simulated drawdowns in Devils Hole in 2120 were 3.2, 3.4, 3.8, and 25.4 ft for the base case and scenarios A, B, and C, respectively. The federally mandated minimum water level for Devils Hole is 2.7 ft below a reference point. In 2020, the simulated water level in Devils Hole was above the minimum water level, at 1.7 ft below the reference. Simulated water levels in Devils Hole fell below the federally mandated water level by 2078, 2073, 2058, and 2025 for the base case and scenarios A, B, and C, respectively, assuming a hypothetical recharge scenario of constant natural recharge. Simulated reductions in predevelopment (natural) discharge at select discharge areas ranged from 3 to 38 percent by 2120 for all scenarios. Amargosa Wild and Scenic River was the least affected discharge area with simulated capture rates ranging from 3 to 4 percent of predevelopment discharge by 2120. Ash Meadows discharge area was greatly affected by groundwater pumping in scenario C with a simulated capture rate of 38 percent, compared to simulated capture rates of 8, 8, and 9 percent for the base case, scenario A, and scenario B, respectively, in 2120. Simulated capture rates in the Furnace Creek area ranged from 10 to 11 percent for all scenarios in 2120. Simulated capture rates at Stump Spring ranged from 32 to 36 percent for all scenarios in 2120.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205103","collaboration":"Prepared in cooperation with the Bureau of Land Management; National Park Service; Nevada Division of Wildlife; Nye County, Nevada; and U.S. Fish and Wildlife Service","usgsCitation":"Nelson, N.C., and Jackson, T.R., 2020, Simulated effects of pumping in the Death Valley Regional Groundwater Flow System, Nevada and California—Selected management scenarios projected to 2120: U.S. Geological Survey Scientific Investigations Report 2020–5103, 30 p., https://doi.org/10.3133/sir20205103.","productDescription":"Report: vii, 30 p.; Data Releases","onlineOnly":"Y","ipdsId":"IP-112177","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":379438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5103/coverthb.jpg"},{"id":379439,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5103/sir20205103.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5103"},{"id":379440,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OBUPXU","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 models used to simulate effects of pumping in the Death Valley Regional Groundwater Flow System, Nevada and California—Selected management scenarios projected to 2120"},{"id":379476,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75H7FH3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Update to the groundwater withdrawals database for the Death Valley regional groundwater flow system, Nevada and California, 1913 -2010"},{"id":379477,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HIYVG2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 model and supplementary data used to characterize groundwater flow and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Death Valley Regional Groundwater Flow System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.3779296875,\n 33.62376800118811\n ],\n [\n -114.08203125,\n 33.62376800118811\n ],\n [\n -114.08203125,\n 38.62545397209084\n ],\n [\n -117.3779296875,\n 38.62545397209084\n ],\n [\n -117.3779296875,\n 33.62376800118811\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
Biological assemblages are commonly used for assessing stream health, but there is increased interest among the freshwater research community in incorporating measures of stream function, such as metabolism, to strengthen stream-health assessments. Presently, there is limited information about the relationships between stream metabolism and biological assemblages, along with the measurement period required to relate metabolism with stream biota. Our study assessed which environmental factors explained stream metabolism and to what degree stream metabolism and minimum dissolved oxygen (DOmin) were related to invertebrate and fish metrics in streams distributed across several regions of the United States. Furthermore, we evaluated the number of metabolism monitoring days required for maximizing the ability to detect relationships between stream metabolism and biological assemblage metrics. We sampled 17 sites distributed among reference, agricultural, and urban areas for stream metabolism, nutrients, habitat, and biological assemblages (invertebrates and fishes). Overall, sites were heterotrophic with gross primary production (GPP) and ecosystem respiration (ER) related primarily to days since last high flow, canopy cover, maximum water temperature, and total phosphorus. DOmin was related to days since last high flow, canopy cover, and maximum water temperature. We were unable to determine a clear statistical relationship between invertebrate metrics (invertebrate richness; Ephemeroptera, Plecoptera, and Trichoptera richness; and scraper-taxa richness) and GPP, ER, or DOmin. In contrast, we found that 2 fish-assemblage metrics were associated with stream metabolism and DOmin. A fish multimetric index (FMMI) was negatively correlated with GPP (r = −0.5, p = 0.048) and positively correlated with DOmin (r = 0.47, p = 0.06). Percentage of omnivorous fish taxa was positively correlated with GPP (r = 0.72, p = 0.001) and ER (r = 0.55, p = 0.02) and negatively correlated with DOmin (r = −0.67, p = 0.003). The lack of detected relationships for most of the biological-assemblage metrics with stream metabolism may be partially due to 1 or more factors, including high variability, low sample size, limited range in metabolism values, assemblage metrics used, and geographic distribution of sites. Comparing stream-metabolism measurement periods (in days) to biological-assemblage metrics indicated that optimum correlations occurred at 2 d for DOmin, 3 d for GPP, and 14 d for ER. Although our study found limited relationships of stream metabolism and DOmin with biological assemblages, future studies should consider a larger sample size (≥30), 14-d or longer metabolism measurement period, and assessment of other taxa-specific or assemblage metrics.
Margaritifera monodonta, or the Spectaclecase Mussel, is a federally endangered freshwater mussel species that has experienced a 55% reduction in range and is currently concentrated in 3 rivers in the Midwest region of the United States (Gasconade and Meramec rivers, Missouri, and St Croix River, Wisconsin). The detection of new populations by traditional survey methods has been limited because these mussels tend to occur under large rocks and boulders. Environmental DNA (eDNA) technology has been used to detect invasive and rare species, but its use for detection of rare, benthic-dwelling species in large flowing systems has been limited. Here, we propose using eDNA to assess known populations of M. monodonta. We designed a M. monodonta-specific quantitative polymerase chain reaction (qPCR) assay and tested it using water samples from multiple M. monodonta housing tanks, water samples from 2 known mussel beds on the St Croix River, and water samples from 3 known mussel beds on the Mississippi River. We observed higher overall eDNA detection rates on the St Croix River (30.2%) compared to the upper Mississippi River (0.60%). We also observed higher eDNA detection rates (73.3–93.1%) in 2018 for samples collected during the larval release period in May compared to samples collected in August after the reproductive period had ended (55.6–70.8%) on the St Croix River. We tested samples collected at 3 distances downstream from the 2 mussel beds found in the St Croix River, but we did not observe a substantial effect of distance on our detection rates. However, we did observe greater detection rates for samples collected near the bottom compared to at the surface. Our results indicate that this novel qPCR assay can successfully detect M. monodonta eDNA and could be used to rapidly screen locations to guide intensive physical searches for populations in riverine systems.
","language":"English","publisher":"The University of Chicago Press-Society for Freshwater Science","doi":"10.1086/711673","usgsCitation":"Lor, Y., Schreier, T.M., Waller, D.L., and Merkes, C.M., 2020, Using environmental DNA (eDNA) to detect the endangered Spectaclecase Mussel (Margaritifera monodonta): Freshwater Science, v. 39, no. 4, p. 837-847, https://doi.org/10.1086/711673.","productDescription":"11 p.","startPage":"837","endPage":"847","ipdsId":"IP-111712","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":455012,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/711673","text":"Publisher Index Page"},{"id":436750,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F0COLN","text":"USGS data release","linkHelpText":"Transformation methods for glochidia of the Spectaclecase mussel Cumberlandia monodonta: Data"},{"id":381160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin, Missouri","otherGeospatial":"Gasconade River, Meramec River, St. Croix River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.6534423828125,\n 38.68122173079789\n ],\n [\n -92.021484375,\n 38.33734763569314\n ],\n [\n -92.098388671875,\n 37.87051721701939\n ],\n [\n -91.9281005859375,\n 37.88352498087131\n ],\n [\n -91.505126953125,\n 38.61687046392973\n ],\n [\n -91.6534423828125,\n 38.68122173079789\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.4669189453125,\n 38.56212645363985\n ],\n [\n -90.63514709472656,\n 38.54923944473397\n ],\n [\n -90.74569702148436,\n 38.487994609214795\n ],\n [\n -90.73127746582031,\n 38.44014444555175\n ],\n [\n -90.61454772949219,\n 38.44821130413263\n ],\n [\n -90.46005249023438,\n 38.52775596312173\n ],\n [\n -90.4669189453125,\n 38.56212645363985\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.054443359375,\n 46.28622391806706\n ],\n [\n -92.98828125,\n 45.82879925192134\n ],\n [\n -92.8564453125,\n 45.251688256117646\n ],\n [\n -92.867431640625,\n 44.5435052132082\n ],\n [\n -92.28515625,\n 44.42593442145313\n ],\n [\n -92.054443359375,\n 46.28622391806706\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lor, Yer 0000-0002-5738-2412","orcid":"https://orcid.org/0000-0002-5738-2412","contributorId":210011,"corporation":false,"usgs":true,"family":"Lor","given":"Yer","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":806610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schreier, Theresa M. 0000-0001-7722-6292 tschreier@usgs.gov","orcid":"https://orcid.org/0000-0001-7722-6292","contributorId":3344,"corporation":false,"usgs":true,"family":"Schreier","given":"Theresa","email":"tschreier@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":806611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":806612,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merkes, Christopher M. 0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":806613,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216551,"text":"70216551 - 2020 - Warming of alpine tundra enhances belowground production and shifts community towards resource acquisition traits","interactions":[],"lastModifiedDate":"2020-11-25T17:18:23.314417","indexId":"70216551","displayToPublicDate":"2020-10-20T07:33:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Warming of alpine tundra enhances belowground production and shifts community towards resource acquisition traits","docAbstract":"Climate warming is expected to stimulate plant growth in high‐elevation and high‐latitude ecosystems, significantly increasing aboveground net primary production (ANPP). However, the effects of simultaneous changes in temperature, snowmelt timing, and summer water availability on total net primary production (NPP)—and elucidation of both above‐ and belowground responses—remain an important area in need of further study. In particular, measures of belowground net primary productivity (BNPP) are required to understand whether ANPP changes reflect changes in allocation or are indicative of a whole plant NPP response. Further, plant functional traits provide a key way to scale from the individual plant to the community level and provide insight into drivers of NPP responses to environmental change. We used infrared heaters to warm an alpine plant community at Niwot Ridge, Colorado, and applied supplemental water to compensate for soil water loss induced by warming. We measured ANPP, BNPP, and leaf and root functional traits across treatments after 5 yr of continuous warming. Community‐level ANPP and total NPP (ANPP + BNPP) did not respond to heating or watering, but BNPP increased in response to heating. Heating decreased community‐level leaf dry matter content and increased total root length, indicating a shift in strategy from resource conservation to acquisition in response to warming. Water use efficiency (WUE) decreased with heating, suggesting alleviation of moisture constraints that may have enabled the plant community to increase productivity. Heating may have decreased WUE by melting snow earlier and creating more days early in the growing season with adequate soil moisture, but stimulated dry mass investment in roots as soils dried down later in the growing season. Overall, this study highlights how ANPP and BNPP responses to climate change can diverge, and encourages a closer examination of belowground processes, especially in alpine systems, where the majority of NPP occurs belowground.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3270","usgsCitation":"Yang, Y., Klein, J.A., Winkler, D.E., Peng, A., Lazarus, B., Germino, M., Suding, K., Smith, J., and Kueppers, L.M., 2020, Warming of alpine tundra enhances belowground production and shifts community towards resource acquisition traits: Ecosphere, v. 11, no. 10, e03270, 15 p., https://doi.org/10.1002/ecs2.3270.","productDescription":"e03270, 15 p.","ipdsId":"IP-114058","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455014,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3270","text":"Publisher Index Page"},{"id":380791,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Niwot Ridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.62324523925781,\n 40.04732864506094\n ],\n [\n -105.56548118591309,\n 40.04732864506094\n ],\n [\n -105.56548118591309,\n 40.07189770843059\n ],\n [\n -105.62324523925781,\n 40.07189770843059\n ],\n [\n -105.62324523925781,\n 40.04732864506094\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Yang, Yan","contributorId":245243,"corporation":false,"usgs":false,"family":"Yang","given":"Yan","affiliations":[],"preferred":false,"id":805674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klein, Julia A.","contributorId":76873,"corporation":false,"usgs":true,"family":"Klein","given":"Julia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":805675,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805589,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peng, Ahui","contributorId":245244,"corporation":false,"usgs":false,"family":"Peng","given":"Ahui","email":"","affiliations":[],"preferred":false,"id":805676,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lazarus, Brynne E. 0000-0002-6352-486X","orcid":"https://orcid.org/0000-0002-6352-486X","contributorId":242732,"corporation":false,"usgs":true,"family":"Lazarus","given":"Brynne E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":805591,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Germino, Matthew 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":218007,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":805592,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Suding, Katherine","contributorId":167086,"corporation":false,"usgs":false,"family":"Suding","given":"Katherine","email":"","affiliations":[{"id":6709,"text":"University of Colorado, Denver","active":true,"usgs":false}],"preferred":false,"id":805677,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Smith, Jane G.","contributorId":245245,"corporation":false,"usgs":false,"family":"Smith","given":"Jane G.","affiliations":[],"preferred":false,"id":805678,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kueppers, Lara M.","contributorId":89778,"corporation":false,"usgs":false,"family":"Kueppers","given":"Lara","email":"","middleInitial":"M.","affiliations":[{"id":6670,"text":"Lawrence Berkeley National Laboratory, Berkeley, CA","active":true,"usgs":false},{"id":16805,"text":"University of California, Merced","active":true,"usgs":false}],"preferred":false,"id":805679,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215554,"text":"70215554 - 2020 - Modeling three-dimensional flow over spur-and-groove morphology","interactions":[],"lastModifiedDate":"2020-11-30T16:06:18.953016","indexId":"70215554","displayToPublicDate":"2020-10-19T08:24:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1338,"text":"Coral Reefs","active":true,"publicationSubtype":{"id":10}},"title":"Modeling three-dimensional flow over spur-and-groove morphology","docAbstract":"Spur-and-groove (SAG) morphology characterizes the fore reef of many coral reefs worldwide. Although the existence and geometrical properties of SAG have been well documented, an understanding of the hydrodynamics over them is limited. Here, the three-dimensional flow patterns over SAG formations, and a sensitivity of those patterns to waves, currents, and SAG geometry were characterized using the physics-based Delft3D-FLOW and SWAN models. Shore-normal shoaling waves over SAG formations were shown to drive two circulation cells: a cell on the lower fore reef with offshore flow over the spurs and onshore flow over the grooves, except near the seabed where velocities were always onshore, and a cell on the upper fore reef with offshore surface velocities and onshore bottom currents, which result in depth-averaged onshore and offshore flow over the spurs and grooves, respectively. The mechanism driving this flow results from the net of the radiation stress gradients and pressure gradient, which is balanced by the Reynolds stress gradients and bottom friction that differ over the spur and over the groove. Waves were the primary driver of variations in modelled flow over SAG, with the flow strength increasing for increasing wave heights and periods. Spur height, SAG wavelength, and the water depth at peak spur height were the dominant influences on the hydrodynamics, with spur heights directly proportional to the strength of SAG circulation cells. SAG formations with shorter SAG wavelengths only presented one circulation cell on the shallower portion of the reef, as opposed to the two circulation cells for longer SAG wavelengths. SAG formations with peak spur heights occurring in shallower water had stronger circulation than those with peak spur heights occurring in deeper water. These hydrodynamic patterns also likely affect coral and reef development through sediment and nutrient fluxes.