A two-dimensional hydraulic model and digital flood‑inundation maps were developed for a 30-mile reach of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois. The flood-inundation maps, which can be accessed through the U.S. Geological Survey (USGS) Flood Inundation Mapping Science web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Wabash River at Mount Carmel, Ill (USGS station number 03377500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) at http://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS AHPS site MCRI2). The NWS AHPS forecasts peak stage information that may be used with the maps developed in this study to show predicted areas of flood inundation.
Flood elevations were computed for the Wabash River reach by means of a two-dimensional, finite-volume numerical modeling application for river hydraulics. The hydraulic model was calibrated by using global positioning system measurements of water-surface elevation and the current stage-discharge relation at both USGS streamgage 03377500, Wabash River at Mount Carmel, Ill., and USGS streamgage 03378500, Wabash River at New Harmony, Indiana. The calibrated hydraulic model was then used to compute 27 water-surface elevations for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from less than the action stage (9 ft) to the highest stage (35 ft) of the current stage-discharge rating curve. The simulated water‑surface elevations were then combined with a geographic information system digital elevation model, derived from light detection and ranging data, to delineate the area flooded at each water level.
The availability of these maps, along with information on the internet regarding current stage from the USGS streamgage at Mount Carmel, Ill., and forecasted stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175140","collaboration":"Prepared in cooperation with the Indiana Department of Transportation; Illinois Department of Transportation","usgsCitation":"Boldt, J.A., 2018, Development of a hydraulic model and flood-inundation maps for the Wabash River near the Interstate 64 Bridge near Grayville, Illinois: U.S. Geological Survey Scientific Investigations Report 2017–5140, 13 p., https://doi.org/10.3133/sir20175140.","productDescription":"vi, 13 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087699","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":355963,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78P5ZCD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and model for the flood-inundation study of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois"},{"id":350289,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/sir20175117","text":"Scientific Investigations Report 2017–5117","linkHelpText":"- River Meander Modeling of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois"},{"id":350288,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5140/sir20175140.pdf","text":"Report","size":"3.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5140"},{"id":350287,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5140/coverthb.jpg"}],"country":"United States","state":"Illinois","city":"Grayville","otherGeospatial":"Wabash River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.01456451416016,\n 38.153727245014004\n ],\n [\n -87.77870178222656,\n 38.153727245014004\n ],\n [\n -87.77870178222656,\n 38.338694087313534\n ],\n [\n -88.01456451416016,\n 38.338694087313534\n ],\n [\n -88.01456451416016,\n 38.153727245014004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
Natural river channels continually evolve and change shape over time. As a result, channel evolution or migration can cause problems for bridge structures that are fixed in the flood plain. A once-stable bridge structure that was uninfluenced by a river’s shape could be encroached upon by a migrating river channel. The potential effect of the actively meandering Wabash River on the Interstate 64 Bridge at the border with Indiana near Grayville, Illinois, was studied using a river migration model called RVR Meander. RVR Meander is a toolbox that can be used to model river channel meander migration with physically based bank erosion methods. This study assesses the Wabash River meandering processes through predictive modeling of natural meandering over the next 100 years, climate change effects through increased river flows, and bank protection measures near the Interstate 64 Bridge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175117","collaboration":"Prepared in cooperation with the Indiana Department of Transportation; Illinois Department of Transportation","usgsCitation":"Lant, J.G., and Boldt, J.A., 2018, River meander modeling of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois: U.S. Geological Survey Scientific Investigations Report 2017–5117, 12 p., https://doi.org/10.3133/sir20175117.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087393","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":355972,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70G3HWF","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial output data from the RVR Meander model of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois"},{"id":350284,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5117/coverthb.jpg"},{"id":350286,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/sir20175140","text":"Scientific Investigation Report 2017–5140","linkHelpText":"- Development of a Hydraulic Model and Flood-Inundation Maps for the Wabash River near the Interstate 64 Bridge near Grayville, Illinois"},{"id":350285,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5117/sir20175117.pdf","text":"Report","size":"4.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5117"}],"country":"United States","state":"Illinois","city":"Grayville","otherGeospatial":"Wabash River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.01456451416016,\n 38.0833\n ],\n [\n -87.8,\n 38.0833\n ],\n [\n -87.8,\n 38.338694087313534\n ],\n [\n -88.01456451416016,\n 38.338694087313534\n ],\n [\n -88.01456451416016,\n 38.0833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
Groundwater carbon (C) supply to lakes and streams is important to understanding the role of inland waters in global and regional cycles and in the functioning of aquatic ecosystems. We provide new estimates of the size and discharge of the groundwater C pool using data from a broad survey of groundwater C, information on the depth distribution of groundwater, and data on groundwater age. About 0.25 × 106 km3 of the 8 × 106km3 of groundwater resource is within 100 m of the surface and 4.2 × 106 km3 is above 2000 m. Ages show an average groundwater turnover time of 10 yr at 25 m, 350 yr at 100 m, increasing to about 100 000 yr at 600 m. Global groundwater discharge is 16 000 km3 yr−1; >16% of precipitation passes through groundwater. Groundwater dissolved organic C (DOC) can be high in shallow groundwater but stabilizes at ~2–4 mg L−1 at 100 m. Average groundwater dissolved inorganic C (DIC) is ~30–43 mg L−1. Groundwater C content to 2000 m is ~145 Pg, about the same as all marine sediments and about one-sixth that of the surface ocean. Groundwater C discharge to continental waters is 0.68 Pg yr−1, or 3.4 times that estimated from river base-flow and submarine groundwater discharge. This discharge is 68 times previous estimates, implying a total C flux from land of 3.6 Pg yr−1; 80% of discharge occurs from above 40 m and 99% from the upper 100 m.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/20442041.2017.1412918","usgsCitation":"Downing, J.A., and Striegl, R.G., 2018, Size, age, renewal, and discharge of groundwater carbon: Inland Waters, v. 8, no. 1, p. 122-127, https://doi.org/10.1080/20442041.2017.1412918.","productDescription":"6 p.","startPage":"122","endPage":"127","ipdsId":"IP-076067","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":469095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/20442041.2017.1412918","text":"Publisher Index Page"},{"id":356201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-15","publicationStatus":"PW","scienceBaseUri":"5b6fc4bae4b0f5d57878eac2","contributors":{"authors":[{"text":"Downing, John A.","contributorId":169033,"corporation":false,"usgs":false,"family":"Downing","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":741405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":741404,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70195392,"text":"70195392 - 2018 - Estimating restorable wetland water storage at landscape scales","interactions":[],"lastModifiedDate":"2020-09-01T14:25:35.772943","indexId":"70195392","displayToPublicDate":"2018-01-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Estimating restorable wetland water storage at landscape scales","docAbstract":"Globally, hydrologic modifications such as ditching and subsurface drainage have significantly reduced wetland water storage capacity (i.e., volume of surface water a wetland can retain) and consequent wetland functions. While wetland area has been well documented across many landscapes and used to guide restoration efforts, few studies have directly quantified the associated wetland storage capacity. Here, we present a novel raster-based approach to quantify both contemporary and potential (i.e., restorable) storage capacities of individual depressional basins across landscapes. We demonstrate the utility of this method by applying it to the Delmarva Peninsula, a region punctuated by both depressional wetlands and drainage ditches. Across the entire peninsula, we estimated that restoration (i.e., plugging ditches) could increase storage capacity by 80%. Focusing on an individual watershed, we found that over 59% of restorable storage capacity occurs within 20 m of the drainage network, and that 93% occurs within 1 m elevation of the drainage network. Our demonstration highlights widespread ditching in this landscape, spatial patterns of both contemporary and potential storage capacities, and clear opportunities for hydrologic restoration. In Delmarva and more broadly, our novel approach can inform targeted landscape-scale conservation and restoration efforts to optimize hydrologically mediated wetland functions.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11405","usgsCitation":"Jones, C.N., Evenson, G.R., McLaughlin, D.L., Vanderhoof, M.K., Lang, M.W., McCarty, G.W., Golden, H.E., Lane, C., and Alexander, L., 2018, Estimating restorable wetland water storage at landscape scales: Hydrological Processes, v. 32, no. 2, p. 305-313, https://doi.org/10.1002/hyp.11405.","productDescription":"9 p.","startPage":"305","endPage":"313","ipdsId":"IP-088286","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":461079,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5907502","text":"External Repository"},{"id":351516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-13","publicationStatus":"PW","scienceBaseUri":"5afee751e4b0da30c1bfc22a","contributors":{"authors":[{"text":"Jones, Charles Nathan","contributorId":202421,"corporation":false,"usgs":false,"family":"Jones","given":"Charles","email":"","middleInitial":"Nathan","affiliations":[{"id":36428,"text":"The National Socio-Environmental Synthesis Center, University of Maryland","active":true,"usgs":false}],"preferred":false,"id":728374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evenson, Grey R.","contributorId":202422,"corporation":false,"usgs":false,"family":"Evenson","given":"Grey","email":"","middleInitial":"R.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":728375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McLaughlin, Daniel L.","contributorId":156435,"corporation":false,"usgs":false,"family":"McLaughlin","given":"Daniel","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":728376,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":728373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lang, Megan W.","contributorId":196284,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":728377,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCarty, Greg W.","contributorId":143675,"corporation":false,"usgs":false,"family":"McCarty","given":"Greg","email":"","middleInitial":"W.","affiliations":[{"id":15298,"text":"USDA-ARS Hydrology and Remote Sensing Laboratory, Bldg 007, BARC-W, 10300 Baltimore Avenue, Beltsville, Maryland 20705, United States","active":true,"usgs":false}],"preferred":false,"id":728378,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Golden, Heather E.","contributorId":202423,"corporation":false,"usgs":false,"family":"Golden","given":"Heather","email":"","middleInitial":"E.","affiliations":[{"id":36429,"text":"USEPA ORD","active":true,"usgs":false}],"preferred":false,"id":728379,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lane, Charles R.","contributorId":138991,"corporation":false,"usgs":false,"family":"Lane","given":"Charles R.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":728380,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Alexander, Laurie C.","contributorId":138989,"corporation":false,"usgs":false,"family":"Alexander","given":"Laurie C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":728381,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70195316,"text":"70195316 - 2018 - Beyond the edge: Linking agricultural landscapes, stream networks, and best management practices","interactions":[],"lastModifiedDate":"2018-02-08T14:48:57","indexId":"70195316","displayToPublicDate":"2018-01-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Beyond the edge: Linking agricultural landscapes, stream networks, and best management practices","docAbstract":"Despite much research and investment into understanding and managing nutrients across agricultural landscapes, nutrient runoff to freshwater ecosystems is still a major concern. We argue there is currently a disconnect between the management of watershed surfaces (agricultural landscape) and river networks (riverine landscape). These landscapes are commonly managed separately, but there is limited cohesiveness between agricultural landscape-focused research and river science, despite similar end goals. Interdisciplinary research into stream networks that drain agricultural landscapes is expanding but is fraught with problems. Conceptual frameworks are useful tools to order phenomena, reveal patterns and processes, and in interdisciplinary river science, enable the joining of multiple areas of understanding into a single conceptual–empirical structure. We present a framework for the interdisciplinary study and management of agricultural and riverine landscapes. The framework includes components of an ecosystems approach to the study of catchment–stream networks, resilience thinking, and strategic adaptive management. Application of the framework is illustrated through a study of the Fox Basin in Wisconsin, USA. To fully realize the goal of nutrient reduction in the basin, we suggest that greater emphasis is needed on where best management practices (BMPs) are used within the spatial context of the combined watershed–stream network system, including BMPs within the river channel. Targeted placement of BMPs throughout the riverine landscape would increase the overall buffering capacity of the system to nutrient runoff and thus its resilience to current and future disturbances.
","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, & Soil Science Society of America","doi":"10.2134/jeq2017.08.0319","usgsCitation":"Kreiling, R.M., Thoms, M.C., and Richardson, W.B., 2018, Beyond the edge: Linking agricultural landscapes, stream networks, and best management practices: Journal of Environmental Quality, v. 47, p. 42-53, https://doi.org/10.2134/jeq2017.08.0319.","productDescription":"12 p.","startPage":"42","endPage":"53","ipdsId":"IP-088966","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":351379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a7d6ffde4b00f54eb2441a3","contributors":{"authors":[{"text":"Kreiling, Rebecca M. 0000-0002-9295-4156 rkreiling@usgs.gov","orcid":"https://orcid.org/0000-0002-9295-4156","contributorId":4234,"corporation":false,"usgs":true,"family":"Kreiling","given":"Rebecca","email":"rkreiling@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":727805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thoms, Martin C. 0000-0002-8074-0476","orcid":"https://orcid.org/0000-0002-8074-0476","contributorId":145710,"corporation":false,"usgs":false,"family":"Thoms","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":16205,"text":"Riverine Landscapes Research Laboratory, University of New England, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":727806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richardson, William B. 0000-0002-7471-4394 wrichardson@usgs.gov","orcid":"https://orcid.org/0000-0002-7471-4394","contributorId":3277,"corporation":false,"usgs":true,"family":"Richardson","given":"William","email":"wrichardson@usgs.gov","middleInitial":"B.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":727807,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70194647,"text":"ofr20171159 - 2018 - Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system","interactions":[],"lastModifiedDate":"2018-01-25T15:19:19","indexId":"ofr20171159","displayToPublicDate":"2018-01-12T13:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1159","title":"Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system","docAbstract":"The U.S. Geological Survey, in cooperation with DuPage County Stormwater Management Department, is testing a near real-time streamflow simulation system that assists in the management and operation of reservoirs and other flood-control structures in the Salt Creek and West Branch DuPage River drainage basins in DuPage County, Illinois. As part of this effort, the U.S. Geological Survey maintains a database of hourly meteorological and hydrologic data for use in this near real-time streamflow simulation system. Among these data are next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data, which are retrieved from the North Central River Forecasting Center of the National Weather Service. The DuPage County streamflow simulation system uses these quantitative precipitation forecast data to create streamflow predictions for the two simulated drainage basins. This report discusses in detail how these data are processed for inclusion in the Watershed Data Management files used in the streamflow simulation system for the Salt Creek and West Branch DuPage River drainage basins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171159","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Bera, Maitreyee, and Ortel, T.W., 2018, Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system: \nU.S. Geological Survey Open-File Report 2017–1159, 16 p., https://doi.org/10.3133/ofr20171159.","productDescription":"iv, 15 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087229","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":350409,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1159/coverthb.jpg"},{"id":350410,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1159/ofr20171159.pdf","text":"Report","size":"3.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1159"}],"country":"United States","state":"Illinois","county":"DuPage County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-88.2634,41.9876],[-88.1473,41.9883],[-88.0342,41.9925],[-87.9175,41.9938],[-87.9188,41.9076],[-87.9178,41.8185],[-87.9142,41.7318],[-87.9139,41.7172],[-87.9438,41.7017],[-87.9482,41.694],[-87.9674,41.6879],[-87.9883,41.6877],[-88.0013,41.6874],[-88.0308,41.6868],[-88.0317,41.7295],[-88.1499,41.7272],[-88.2625,41.7251],[-88.2628,41.811],[-88.2632,41.8623],[-88.2631,41.9],[-88.2634,41.9876]]]},\"properties\":{\"name\":\"Dupage\",\"state\":\"IL\"}}]}","contact":"Director, Illinois-Iowa Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801
Flow-proportional composite water samples were collected in water years 2013 and 2014 by the U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, from the Blackstone River at Millville, Massachusetts (U.S. Geological Survey station 01111230), about 0.5 mile from the border with Rhode Island. Samples were collected in order to better understand the dynamics of selected nutrient and metal constituents, assist with planning, guide activities to meet water-quality goals, and provide real-time water-quality information to the public. An automated system collected the samples at 14-day intervals to determine total and dissolved nitrogen and phosphorus concentrations, to provide accurate monthly nutrient concentration data, and to calculate monthly load estimates. Concentrations of dissolved trace metals and total aluminum were determined from 4-day composite water samples that were collected twice monthly by the automated system. Results from 4-day composites provide stakeholders with information to evaluate trace metals on the basis of chronic 4-day exposure criteria for aquatic life, and the potential to use the biotic ligand model to evaluate copper concentrations. Nutrient, trace metal, suspended sediment, dissolved organic carbon, and chlorophyll a concentrations were determined from discrete samples collected at the Millville station and from across the stream transect at the upstream railroad bridge, and these concentrations served as a means to evaluate the representativeness of the Millville point location.
Analytical results from samples collected with the automated flow-proportional sampling system provided the means to calculate monthly and annual loading data. Total nitrogen and total phosphorus loads in water year (WY) 2013 were about 447,000 and 36,000 kilograms (kg), respectively. In WY 2014, annual loads of total nitrogen and total phosphorus were about 342,000 and 21,000 kg, respectively. Total nitrogen and total phosphorus loads from WYs 2013 and 2014 were about 56 and 65 percent lower than those reported for WYs 2008 and 2009. The higher loads in 2008 and 2009 may be explained by the higher than average flows in WY 2009 and by facility upgrades made by wastewater treatment facilities in the basin.
Median loads were determined from composite samples collected with the automated system between October 2012 and October 2014. Median dissolved cadmium and chromium 4-day loads were 0.55 and 0.84 kg, respectively. Dissolved copper and total lead median 4-day loads were 8.02 and 1.42 kg, respectively. The dissolved nickel median 4-day load was 5.45 kg, and the dissolved zinc median 4-day load was 36 kg. Median total aluminum 4-day loads were about 197 kg.
Spearman’s rank correlation analyses were used with discrete sample concentrations and continuous records of temperature, specific conductance, turbidity, and chlorophyll a to identify correlations between variables that could be used to develop regression equations for estimating real-time concentrations of constituents. Correlation coefficients were generated for flow, precipitation, antecedent precipitation, physical parameters, and chemical constituents. A 95-percent confidence limit for each value of Spearman’s rho was calculated, and multiple linear regression analysis using ordinary least squares regression techniques was used to develop regression equations for concentrations of total phosphorus, total nitrogen, suspended sediment concentration, total copper, and total aluminum. Although the correlations are based on the limited amount of data collected as part of this study, the potential to monitor water-quality changes in real time may be of value to resource managers and decision makers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175094","isbn":"ISBN 978-1-4113-4181-4","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Sorenson, J.R., Granato, G.E., and Smith, K.P., 2018, Nutrient and metal loads estimated by using discrete, automated, and continuous water-quality monitoring techniques for the Blackstone River at the Massachusetts-Rhode Island State line, water years 2013–14: U.S. Geological Survey Scientific Investigations Report 2017–5094, 41 p., https://doi.org/10.3133/sir20175094.","productDescription":"Report: ix, 41 p.; 4 Tables","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-079789","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":350359,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5094/sir20175094.pdf","text":"Report","size":"4.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5094"},{"id":350366,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table6.csv","text":"Table 6","size":"19.4 csv","linkHelpText":"- Concentrations of nutrients, trace metals, and suspended sediment in manually collected samples from the upstream railroad bridge and from the collection point at the Blackstone River at Millville, Massachusetts, station (01111230) during water years 2013 and 2014."},{"id":350368,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table7.csv","text":"Table 7","size":"13.1 KB csv","linkHelpText":"- Loads of nutrients based on 14-day nutrient composite samples, and loads of dissolved trace metals and total aluminum based on 4-day metal composite samples collected by the automated sampling system from the point location at the Blackstone River at Millville, Massachusetts, station (01111230) during water years 2013 and 2014."},{"id":350361,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table4.xlsx","text":"Table 4 (Microsoft Excel)","size":"48.5 KB"},{"id":350358,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5094/coverthb.jpg"},{"id":350360,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table4.csv","text":"Table 4","size":"15 KB csv","linkHelpText":"- Concentrations of nutrients, trace metals, and suspended sediment in sample pairs collected from the upstream railroad bridge and from the point location at the Blackstone River at Millville, Massachusetts, station (01111230)."},{"id":350367,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table7.xlsx","text":"Table 7 (Microsoft Excel)","size":"44 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":350364,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table5.csv","text":"Table 5","size":"13.7 KB csv","linkHelpText":"- Concentrations of nutrients, total aluminum, and dissolved trace metals in 14-day nutrient composite samples and 4-day metal composite samples collected by using the automated sampling system from the point location at the Blackstone River at Millville, Massachusetts, station (01111230) during water years 2013 and 2014."},{"id":350365,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table6.xlsx","text":"Table 6 (Microsoft Excel)","size":"46.7 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":350363,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table5.xlsx","text":"Table 5 (Microsoft Excel)","size":"39.6 KB","linkFileType":{"id":3,"text":"xlsx"}}],"country":"United States","state":"Massachusetts, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.9167,\n 41.8333\n ],\n [\n -71.3333,\n 41.8333\n ],\n [\n -71.3333,\n 42.3333\n ],\n [\n -71.9167,\n 42.3333\n ],\n [\n -71.9167,\n 41.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
","tableOfContents":"
Agricultural activities can affect water quality and the health of aquatic ecosystems; many water-quality issues originate with the movement of water, agricultural chemicals, and eroded soil from agricultural areas to streams and groundwater. Most agricultural activities are designed to sustain or increase crop production, while some are designed to protect soil and water resources. Numerous soil- and water-protection practices are designed to reduce the volume and velocity of runoff and increase infiltration. This report presents a conceptual framework that combines generalized concepts on the movement of water, the environmental behavior of chemicals and eroded soil, and the designed functions of various agricultural activities, as they relate to hydrology, to create attainable expectations for the protection of—with the goal of improving—water quality through changes in an agricultural activity.
The framework presented uses two types of decision trees to guide decision making toward attainable expectations regarding the effectiveness of changing agricultural activities to protect and improve water quality in streams. One decision tree organizes decision making by considering the hydrologic setting and chemical behaviors, largely at the field scale. This decision tree can help determine which agricultural activities could effectively protect and improve water quality in a stream from the movement of chemicals, or sediment, from a field. The second decision tree is a chemical fate accounting tree. This decision tree helps set attainable expectations for the permanent removal of sediment, elements, and organic chemicals—such as herbicides and insecticides—through trapping or conservation tillage practices. Collectively, this conceptual framework consolidates diverse hydrologic settings, chemicals, and agricultural activities into a single, broad context that can be used to set attainable expectations for agricultural activities. This framework also enables better decision making for future agricultural activities as a means to reduce current, and prevent new, water-quality issues.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175095","usgsCitation":"Capel, P.D., Wolock, D.M., Coupe, R.H., and Roth, J.L., 2018, A conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities: U.S. Geological Survey Scientific Investigations Report 2017–5095, 35 p., https://doi.org/10.3133/sir20175095.","productDescription":"Report: viii, 35 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-071052","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":350408,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75T3HN9","text":"USGS data release","description":"USGS data release","linkHelpText":"Data set used to develop a conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities"},{"id":349840,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5095/coverthb.jpg"},{"id":349841,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5095/sir20175095.pdf","text":"Report","size":"2.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5095"}],"contact":"National Water-Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
This report documents several components of the U.S. Geological Survey National Hydrologic Model of the conterminous United States for use with the Precipitation-Runoff Modeling System (PRMS). It provides descriptions of the (1) National Hydrologic Model, (2) Geospatial Fabric for National Hydrologic Modeling, (3) PRMS hydrologic simulation code, (4) parameters and estimation methods used to compute spatially and temporally distributed default values as required by PRMS, (5) National Hydrologic Model Parameter Database, and (6) model extraction tool named Bandit. The National Hydrologic Model Parameter Database contains values for all PRMS parameters used in the National Hydrologic Model. The methods and national datasets used to estimate all the PRMS parameters are described. Some parameter values are derived from characteristics of topography, land cover, soils, geology, and hydrography using traditional Geographic Information System methods. Other parameters are set to long-established default values and computation of initial values. Additionally, methods (statistical, sensitivity, calibration, and algebraic) were developed to compute parameter values on the basis of a variety of nationally-consistent datasets. Values in the National Hydrologic Model Parameter Database can periodically be updated on the basis of new parameter estimation methods and as additional national datasets become available. A companion ScienceBase resource provides a set of static parameter values as well as images of spatially-distributed parameters associated with PRMS states and fluxes for each Hydrologic Response Unit across the conterminuous United States.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Surface water in Book 6: Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6B9","usgsCitation":"Regan, R.S., Markstrom, S.L., Hay, L.E., Viger, R.J., Norton, P.A., Driscoll, J.M., LaFontaine, J.H., 2018, Description of the National Hydrologic Model for use with the Precipitation-Runoff Modeling System (PRMS): U.S. Geological Survey Techniques and Methods, book 6, chap B9, 38 p., https://doi.org/10.3133/tm6B9.","productDescription":"vii, 38 p.","onlineOnly":"Y","ipdsId":"IP-084916","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":438059,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TYOJKN","text":"USGS data release","linkHelpText":"National Hydrologic Model v1.0 water budget components aggregated to 10 and 12-digit Hydrologic Unit Code boundaries"},{"id":350326,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/b09/coverthb.jpg"},{"id":350327,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/b09/tm6b9.pdf","text":"Report","size":"5.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-B9"}],"publicComments":"This report is Chapter 9 of Section B: Surface Water in Book 6 Modeling Techniques.","contact":"Director, Integrated Modeling and Prediction Division
U.S. Geological Survey
Mail Stop 415
12201 Sunrise Valley Drive
Reston, VA 20192
The analysis described in this report is part of a longterm project monitoring the biological communities, habitat, and water quality of the Fountain Creek Basin. Biology, habitat, and water-quality data have been collected at 10 sites since 2003. These data include annual samples of aquatic invertebrate communities, fish communities, water quality, and quantitative riverine habitat. This report examines trends in biological communities from 2003 to 2016 and explores relationships between biological communities and abiotic variables (antecedent streamflow, physical habitat, and water quality). Six biological metrics (three invertebrate and three fish) and four individual fish species were used to examine trends in these data and how streamflow, habitat, and (or) water quality may explain these trends. The analysis of 79 trends shows that the majority of significant trends decreased over the trend period. Overall, 19 trends before adjustments for streamflow in the fish (12) and invertebrate (7) metrics were all decreasing except for the metric Invertebrate Species Richness at the most upstream site in Monument Creek. Seven of these trends were explained by streamflow and four trends were revealed that were originally masked by variability in antecedent streamflow. Only two sites (Jimmy Camp Creek at Fountain, CO and Fountain Creek near Pinon, CO) had no trends in the fish or invertebrate metrics. Ten of the streamflow-adjusted trends were explained by habitat, one was explained by water quality, and five were not explained by any of the variables that were tested. Overall, from 2003 to 2016, all the fish metric trends were decreasing with an average decline of 40 percent, and invertebrate metrics decreased on average by 9.5 percent. A potential peak streamflow threshold was identified above which there is severely limited production of age-0 flathead chub (Platygobio gracilis).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175162","collaboration":"Prepared in cooperation with the City of Colorado Springs, Water Resources Engineering Division, Public Works Department and Colorado Springs Utilities","usgsCitation":"Roberts, J.J., Bruce, J.F., and Zuellig, R.E., 2018, Changes in biological communities of the Fountain Creek Basin, Colorado, 2003–2016, in relation to antecedent streamflow, water quality, and habitat: U.S. Geological Survey Scientific Investigations Report 2017–5162, 20 p., https://doi.org/10.3133/sir20175162.","productDescription":"Report: vi, 20 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-088880","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":350235,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/f747493V","text":"USGS data release","linkHelpText":"Datasets of ecological communities (invertebrates and fish), streamflow, habitat, and water quality to examine the presence of trends in ecological communities from the Fountain Creek Basin, Colorado, USA, 2003-2016"},{"id":438060,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F747493V","text":"USGS data release","linkHelpText":"Datasets of ecological communities (invertebrates and fish), streamflow, habitat, and water quality to examine the presence of trends in ecological communities from the Fountain Creek basin, Colorado, USA, 2003-2016."},{"id":350234,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5162/sir20175162.pdf","text":"Report","size":"5.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5162"},{"id":350233,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5162/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Fountain Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105,\n 38.25\n ],\n [\n -104.5,\n 38.25\n ],\n [\n -104.5,\n 39\n ],\n [\n -105,\n 39\n ],\n [\n -105,\n 38.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
Groundwater provides more than 40 percent of California’s drinking water. To protect this vital resource, the State of California created the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Program’s Priority Basin Project assesses the quality of groundwater resources used for drinking-water supply and increases public access to groundwater-quality information. Many households and small communities in the Madera– Chowchilla and Kings subbasins of the San Joaquin Valley rely on private domestic wells for their drinking-water supplies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171162","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Fram, M.S. and Shelton, J.L., 2018, Groundwater Quality in the Shallow Aquifers of the Madera–Chowchilla and Kings Subbasins, San Joaquin Valley, California: U.S. Geological Survey Open-File Report 2017–1162, 4 p., https://doi.org/10.3133/ofr20171162.","productDescription":"4 p.","ipdsId":"IP-089766","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":350380,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1162/ofr20171162.pdf","text":"Report","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1162"},{"id":350379,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1162/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Madera– Chowchilla Subbasin, Kings Subbasin, San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.5,\n 36.25\n ],\n [\n -119.25,\n 36.25\n ],\n [\n -119.25,\n 37.25\n ],\n [\n -120.5,\n 37.25\n ],\n [\n -120.5,\n 36.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"California Water Science Center
California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Lake St. Croix is a naturally impounded, riverine lake that makes up the last 40 kilometers of the St. Croix River. Substantial land-use changes during the past 150 years, including increased agriculture and urban development, have reduced Lake St. Croix water-quality and increased nutrient loads delivered to Lake St. Croix. A recent (2012–13) total maximum daily load phosphorus-reduction plan set the goal to reduce total phosphorus loads to Lake St. Croix by 20 percent by 2020 and reduce Lake St. Croix algal bloom frequencies. The U.S. Geological Survey, in cooperation with the National Park Service, developed a two-dimensional, carbon-based, laterally averaged, hydrodynamic and water-quality model, CE–QUAL–W2, that addresses the interaction between nutrient cycling, primary production, and trophic dynamics to predict responses in the distribution of water temperature, oxygen, and chlorophyll a. Distribution is evaluated in the context of habitat for lake sturgeon, including a combination of temperature and dissolved oxygen conditions termed oxy-thermal habitat.
The Lake St. Croix CE–QUAL–W2 model successfully reproduced temperature and dissolved oxygen in the lake longitudinally (from upstream to downstream), vertically, and temporally over the seasons. The simulated water temperature profiles closely matched the measured water temperature profiles throughout the year, including the prediction of thermocline transition depths (often within 1 meter), the absolute temperature of the thermocline transitions (often within 1.0 degree Celsius), and profiles without a strong thermocline transition. Simulated dissolved oxygen profiles matched the trajectories of the measured dissolved oxygen concentrations at multiple depths over time, and the simulated concentrations matched the depth and slope of the measured concentrations.
Additionally, trends in the measured water-quality data were captured by the model simulation, gaining some potential insights into the underlying mechanisms of critical Lake St. Croix metabolic processes. The CE–QUAL–W2 model tracked nitrate plus nitrite, total nitrogen, and total phosphorus throughout the year. Inflow nutrient contributions (loads), largely dominated by upstream St. Croix River loads, were the most important controls on Lake St. Croix water quality. Close to 60 percent of total phosphorus to the lake was from phosphorus derived from organic matter, and about 89 percent of phosphorus to Lake St. Croix was delivered by St. Croix River inflows. The Lake St. Croix CE–QUAL–W2 model offered potential mechanisms for the effect of external and internal loadings on the biotic response regarding the modeled algal community types of diatoms, green algae, and blue-green algae. The model also suggested the seasonal dominance of blue-green algae in all four pools of the lake.
A sensitivity analysis was completed to test the total maximum daily load phosphorus-reduction scenario responses of total phosphorus and chlorophyll a. The modeling indicates that phosphorus reductions would result in similar Lake St. Croix reduced concentrations, although chlorophyll a concentrations did not decrease in the same proportional amounts as the total phosphorus concentrations had decreased. The smaller than expected reduction in algal growth rates highlighted that although inflow phosphorus loads are important, other constituents also can affect the algal response of the lake, such as changes in light penetration and the breakdown of organic matter releasing nutrients.
The available habitat suitable for lake sturgeon was evaluated using the modeling results to determine the total volume of good-growth habitat, optimal growth habitat, and lethal temperature habitat. Overall, with the calibrated model, the fish habitat volume in general contained a large proportion of good-growth habitat and a sustained period of optimal growth habitat in the summer. Only brief periods of lethal oxy-thermal habitat were present in Lake St. Croix during the model simulation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175157","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Smith, E.A., Kiesling, R.L., Ziegeweid, J.R., Elliott, S.M., and Magdalene, Suzanne, 2018, Simulation of hydrodynamics, water quality, and lake sturgeon habitat volumes in Lake St. Croix, Wisconsin and Minnesota, 2013: U.S. Geological Survey Scientific Investigations Report 2017–5157, 60 p., https://doi.org/10.3133/sir20175157.","productDescription":"Report: ix, 60 p.; Data Release","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-075804","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":350333,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7319V2J","text":"USGS data release","description":"USGS data release","linkHelpText":"CE–QUAL–W2 water-quality model and supporting LOADEST models for Lake St. Croix, Wisconsin and Minnesota, 2013"},{"id":350332,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5157/sir20175157.pdf","text":"Report","size":"3.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5157"},{"id":350331,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5157/coverthb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"Lake St. Croix","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.81936645507811,\n 44.74136858658327\n ],\n [\n -92.73216247558594,\n 44.74136858658327\n ],\n [\n -92.73216247558594,\n 45.07352060670971\n ],\n [\n -92.81936645507811,\n 45.07352060670971\n ],\n [\n -92.81936645507811,\n 44.74136858658327\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Liming techniques are being explored as a means to accelerate the recovery of aquatic biota from decades of acid deposition in many regions. The preservation or restoration of native sportfish populations has typically been the impetus for liming programs, and as such, less attention has been given to its effects on other biological assemblages such as macroinvertebrates. Furthermore, the differing effects of various lime application strategies such as in-stream and watershed applications are not well understood. In 2012, a program was initiated using in-stream and aerial (whole-watershed) liming to improve water quality and Brook Trout (Salvelinus fontinalis) recruitment in three acidified tributaries of a high-elevation Adirondack lake in New York State. Concurrently, macroinvertebrates were sampled annually between 2013 and 2016 at 3 treated sites and 3 untreated reference sites to assess the effects of each liming technique on this community. Despite improvements in water chemistry in all three limed streams, our results generally suggest that neither liming technique succeeded in improving the condition of macroinvertebrate communities. The watershed application caused an immediate and unsustained decrease in the density of macroinvertebrates and increase in the proportion of sensitive taxa. These changes were driven primarily by a one-year 71 percent reduction of the acid-tolerant Leuctra stoneflies and likely represent an initial chemistry shock from the lime application rather than a recovery response. The in-stream applications appeared to reduce the density of macroinvertebrates, particularly in one stream where undissolved lime covered the natural substrate. The close proximity of our study sites to the in-stream application points (50 and 1230 m) may partly explain these negative effects. Our results are consistent with prior studies of in-stream liming which indicate that this technique often fails to restore macroinvertebrate communities to a pre-acidification condition, especially at distances <1.5 km downstream of the lime application point. The inability of either liming technique to improve the condition of macroinvertebrate communities may be partly explained by the persistence of acidic episodes in all three streams. This suggests that in order to be effective, liming programs should attempt to eliminate even temporary episodes of unsuitable water chemistry rather than just meeting minimal criteria the majority of the time. Because watershed liming produced a more stable water chemistry regime than in-stream liming, this technique may have greater future potential to eliminate toxic episodes and accelerate the recovery of acid-impacted macroinvertebrate communities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2017.11.048","usgsCitation":"George, S.D., Baldigo, B.P., Lawrence, G.B., and Fuller, R.L., 2018, Effects of watershed and in-stream liming on macroinvertebrate communities in acidified tributaries to an Adirondack lake: Ecological Indicators, v. 85, no. February 2018, p. 1058-1067, https://doi.org/10.1016/j.ecolind.2017.11.048.","productDescription":"10 p.","startPage":"1058","endPage":"1067","ipdsId":"IP-080307","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":350330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Honnedaga Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.88006591796874,\n 43.50174856516506\n ],\n [\n -74.76848602294922,\n 43.50174856516506\n ],\n [\n -74.76848602294922,\n 43.55203173091177\n ],\n [\n -74.88006591796874,\n 43.55203173091177\n ],\n [\n -74.88006591796874,\n 43.50174856516506\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"February 2018","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fad1e4b06e28e9c22727","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":724815,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":724816,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":724818,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fuller, Randall L.","contributorId":196969,"corporation":false,"usgs":false,"family":"Fuller","given":"Randall","email":"","middleInitial":"L.","affiliations":[{"id":35994,"text":"Colgate University, Hamilton, NY","active":true,"usgs":false}],"preferred":false,"id":724817,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194820,"text":"ds1073 - 2018 - Chemical concentrations in water and suspended sediment, Green River to Lower Duwamish Waterway near Seattle, Washington, 2016–17","interactions":[],"lastModifiedDate":"2018-06-06T14:11:46","indexId":"ds1073","displayToPublicDate":"2018-01-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1073","title":"Chemical concentrations in water and suspended sediment, Green River to Lower Duwamish Waterway near Seattle, Washington, 2016–17","docAbstract":"From August 2016 to March 2017, the U.S. Geological Survey (USGS) collected representative samples of filtered and unfiltered water and suspended sediment (including the colloidal fraction) at USGS streamgage 12113390 (Duwamish River at Golf Course, at Tukwila, Washington) during 13 periods of differing flow conditions. Samples were analyzed by Washington-State-accredited laboratories for a large suite of compounds, including metals, dioxins/furans, semivolatile compounds including polycyclic aromatic hydrocarbons, butyltins, the 209 polychlorinated biphenyl (PCB) congeners, and total and dissolved organic carbon. Concurrent with the chemistry sampling, water-quality field parameters were measured, and representative water samples were collected and analyzed for river suspended-sediment concentration and particle-size distribution. The results provide new data that can be used to estimate sediment and chemical loads transported by the Green River to the Lower Duwamish Waterway.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1073","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Conn, K.E., Black, R.W., Peterson, N.T., Senter, C.A., and Chapman, E.A., 2018, Chemical concentrations in water and\nsuspended sediment, Green River to Lower Duwamish Waterway near Seattle, Washington, 2016–17: U.S. Geological\nSurvey Data Series 1073, 17 p., https://doi.org/10.3133/ds1073.","productDescription":"Report: v, 17 p.; Appendix","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-091233","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":350280,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1073/ds1073_appendixa.xlsx","text":"Appendix A","size":"259 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 1073 Appendix A"},{"id":350278,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1073/coverthb.jpg"},{"id":350279,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1073/ds1073.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1073"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Green River, Lower Duwamish Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.35507965087889,\n 47.50931654292719\n ],\n [\n -122.29499816894531,\n 47.50931654292719\n ],\n [\n -122.29499816894531,\n 47.572124991940015\n ],\n [\n -122.35507965087889,\n 47.572124991940015\n ],\n [\n -122.35507965087889,\n 47.50931654292719\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Thaw lakes are ubiquitous on arctic coastal plains (ACPs). While many thaw lakes have steep banks, stable water levels, and static surface areas, others only partially fill their basins and vary in area over the summer. These partially drained lakes (PDLs) are hydrologically connected to the wetlands immediately surrounding them. Heat and nutrient availability limit aquatic productivity on ACPs, and we hypothesized that shallow shorelines and greater hydrologic connectivity with the landscape should result in greater nutrient concentrations and biogeochemical cycling in PDLs. We tested this by monitoring water chemistry in lakes with varying levels of seasonal drainage in sandy and silty peaty lowland sites on the ACP of Alaska. One highly drained lake (N1) was significantly warmer than minimally drained lakes (minDLs) related to earlier ice off, reaching temperatures as high as 16 °C in June when minDLs still contained ice. Ammonia, total dissolved phosphorus, and dissolved organic carbon and nitrogen concentrations were higher in lakes with greater drainage, and concentrations in N1 rivaled those in the small, biologically productive ponds. Many PDLs displayed a midsummer decrease in nutrients consistent with assimilation by the aquatic ecosystem, and a late‐summer increase most likely related to runoff from drained lake margins following precipitation. N1 exported kilograms of ammonium and total dissolved phosphorus to the stream network over the summer. Given increased warming and drying in the arctic, the proportion of PDLs may be changing, which in turn may affect nutrient and organic matter availability in arctic lakes and export to downstream environments.
","language":"English","publisher":"Wiley","doi":"10.1002/2017JG004187","usgsCitation":"Koch, J.C., Fondell, T.F., Schmutz, J.A., and Laske, S.M., 2018, Nutrient dynamics in partially drained arctic thaw lakes: Journal of Geophysical Research: Biogeosciences, v. 123, no. 2, p. 440-452, https://doi.org/10.1002/2017JG004187.","productDescription":"13 p.","startPage":"440","endPage":"452","ipdsId":"IP-085121","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":469103,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017jg004187","text":"Publisher Index Page"},{"id":438061,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BC3XHJ","text":"USGS data release","linkHelpText":"Arctic Coastal Plain Seasonal Lake Drainage, Water Temperature, and Solute and Nutrient Concentrations, 2011 - 2014"},{"id":356006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"2","noUsgsAuthors":false,"publicationDate":"2018-02-17","publicationStatus":"PW","scienceBaseUri":"5b6fc4cbe4b0f5d57878eacc","contributors":{"authors":[{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":741071,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fondell, Tom F. tfondell@usgs.gov","contributorId":3563,"corporation":false,"usgs":true,"family":"Fondell","given":"Tom","email":"tfondell@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":741072,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmutz, Joel A. 0000-0002-6516-0836 jschmutz@usgs.gov","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":1805,"corporation":false,"usgs":true,"family":"Schmutz","given":"Joel","email":"jschmutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":741073,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laske, Sarah M. 0000-0002-6096-0420 slaske@usgs.gov","orcid":"https://orcid.org/0000-0002-6096-0420","contributorId":204872,"corporation":false,"usgs":true,"family":"Laske","given":"Sarah","email":"slaske@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":741074,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202537,"text":"70202537 - 2018 - Comparison of time nonlocal transport models for characterizing non-Fickian transport: From mathematical interpretation to laboratory application","interactions":[],"lastModifiedDate":"2019-03-07T16:38:39","indexId":"70202537","displayToPublicDate":"2018-01-01T16:38:26","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of time nonlocal transport models for characterizing non-Fickian transport: From mathematical interpretation to laboratory application","docAbstract":"Non-Fickian diffusion has been increasingly documented in hydrology and modeled by promising time nonlocal transport models. While previous studies showed that most of the time nonlocal models are identical with correlated parameters, fundamental challenges remain in real-world applications regarding model selection and parameter definition. This study compared three popular time nonlocal transport models, including the multi-rate mass transfer (MRMT) model, the continuous time random walk (CTRW) framework, and the tempered time fractional advection–dispersion equation (tt-fADE), by focusing on their physical interpretation and feasibility in capturing non-Fickian transport. Mathematical comparison showed that these models have both related parameters defining the memory function and other basic-transport parameters (i.e., velocity v and dispersion coefficient D) with different hydrogeologic interpretations. Laboratory column transport experiments and field tracer tests were then conducted, providing data for model applicability evaluation. Laboratory and field experiments exhibited breakthrough curves with non-Fickian characteristics, which were better represented by the tt-fADE and CTRW models than the traditional advection–dispersion equation. The best-fit velocity and dispersion coefficient, however, differ significantly between the tt-fADE and CTRW. Fitting exercises further revealed that the observed late-time breakthrough curves were heavier than the MRMT solutions with no more than two mass-exchange rates and lighter than the MRMT solutions with power-law distributed mass-exchange rates. Therefore, the time nonlocal models, where some parameters are correlated and exchangeable and the others have different values, differ mainly in their quantification of pre-asymptotic transport dynamics. In all models tested above, the tt-fADE model is attractive, considering its small fitting error and the reasonable velocity close to the measured flow rate.
","language":"English","publisher":"MDPI","doi":"10.3390/w10060778","usgsCitation":"Lu, B., Zhang, Y., Zheng, C., Green, C.T., O’Neill, C., Sun, H., and Qian, J., 2018, Comparison of time nonlocal transport models for characterizing non-Fickian transport: From mathematical interpretation to laboratory application: Water, v. 10, no. 6, p. 1-28, https://doi.org/10.3390/w10060778.","productDescription":"Article 778; 28 p.","startPage":"1","endPage":"28","ipdsId":"IP-086405","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":469107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w10060778","text":"Publisher Index Page"},{"id":361861,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Lu, Bingqing","contributorId":214039,"corporation":false,"usgs":false,"family":"Lu","given":"Bingqing","email":"","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":758998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Yong","contributorId":214040,"corporation":false,"usgs":false,"family":"Zhang","given":"Yong","email":"","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":758999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zheng, Chunmiao","contributorId":214041,"corporation":false,"usgs":false,"family":"Zheng","given":"Chunmiao","email":"","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":759000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Green, Christopher T. 0000-0002-6480-8194 ctgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":1343,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"ctgreen@usgs.gov","middleInitial":"T.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":758997,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Neill, Charles","contributorId":214042,"corporation":false,"usgs":false,"family":"O’Neill","given":"Charles","email":"","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":759001,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sun, Hong-Guang 0000-0002-8422-3871","orcid":"https://orcid.org/0000-0002-8422-3871","contributorId":176581,"corporation":false,"usgs":false,"family":"Sun","given":"Hong-Guang","email":"","affiliations":[],"preferred":false,"id":759002,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Qian, Jiazhong","contributorId":214043,"corporation":false,"usgs":false,"family":"Qian","given":"Jiazhong","email":"","affiliations":[{"id":38964,"text":"Hefei University of Technology, Hefei","active":true,"usgs":false}],"preferred":false,"id":759003,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227978,"text":"70227978 - 2018 - Photographs of wading bird depredation events to monitor invasion extent of Asian Swamp Eel (Monopterus albus)","interactions":[],"lastModifiedDate":"2022-02-03T22:23:44.371866","indexId":"70227978","displayToPublicDate":"2018-01-01T16:05:53","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3444,"text":"Southeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Photographs of wading bird depredation events to monitor invasion extent of Asian Swamp Eel (Monopterus albus)","title":"Photographs of wading bird depredation events to monitor invasion extent of Asian Swamp Eel (Monopterus albus)","docAbstract":"Several anecdotes exist of wading birds depredating invasive Monopterus albus (Asian Swamp Eel) in waterways of the conterminous US. We present photographic evidence of 4 different wading bird species depredating adult Asian Swamp Eels in Georgia and Florida herein. Photographs taken by wildlife enthusiasts could provide a means for early detection of the Asian Swamp Eel and other aquatic species that are challenging to detect in waterways.
","language":"English","publisher":"Eagle Hill Publications","doi":"10.1656/058.017.0408","usgsCitation":"Taylor, A.T., Long, J.M., and von Scmeling, H., 2018, Photographs of wading bird depredation events to monitor invasion extent of Asian Swamp Eel (Monopterus albus): Southeastern Naturalist, v. 17, no. 4, p. N72-N76, https://doi.org/10.1656/058.017.0408.","productDescription":"5 p.","startPage":"N72","endPage":"N76","ipdsId":"IP-096090","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia","otherGeospatial":"Chattahoochee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.078125,\n 33.25706340236547\n ],\n [\n -85.078125,\n 33.30298618122413\n ],\n [\n -85.330810546875,\n 33.119150226768866\n ],\n [\n -85.25390625,\n 32.85190345738802\n ],\n [\n -85.10009765625,\n 32.37068286611427\n ],\n [\n -85.242919921875,\n 32.0639555946604\n ],\n [\n -85.220947265625,\n 31.62532121329918\n ],\n [\n -85.20996093749999,\n 31.50362930577303\n ],\n [\n -85.177001953125,\n 31.156408414557\n ],\n [\n -84.990234375,\n 30.89279747750818\n ],\n [\n -84.88037109375,\n 30.62845887475364\n ],\n [\n -84.6826171875,\n 30.817346256492073\n ],\n [\n -84.891357421875,\n 31.175209828310845\n ],\n [\n -84.990234375,\n 31.774877618507386\n ],\n [\n -84.825439453125,\n 32.41706632846282\n ],\n [\n -85.05615234375,\n 32.79651010951669\n ],\n [\n -85.078125,\n 33.25706340236547\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Andrew T.","contributorId":274252,"corporation":false,"usgs":false,"family":"Taylor","given":"Andrew","email":"","middleInitial":"T.","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":832842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832841,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"von Scmeling, H.","contributorId":274253,"corporation":false,"usgs":false,"family":"von Scmeling","given":"H.","email":"","affiliations":[{"id":56584,"text":"Chattahoochee Nature Center","active":true,"usgs":false}],"preferred":false,"id":832843,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199960,"text":"70199960 - 2018 - High resolution water body mapping for SWAT evaporative modelling in the Upper Oconee watershed of Georgia, USA","interactions":[],"lastModifiedDate":"2018-10-05T14:44:36","indexId":"70199960","displayToPublicDate":"2018-01-01T14:44:30","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"High resolution water body mapping for SWAT evaporative modelling in the Upper Oconee watershed of Georgia, USA","docAbstract":"Technological improvements in remote sensing and geographic information systems have demonstrated the abundance of artificially constructed water bodies across the landscape. Although research has shown the ubiquity of small ponds globally, and in the southeastern United States in particular, their cumulative impact in terms of evaporative alteration is less well quantified. The objectives of this study are to examine the hydrologic and evaporative importance of small artificial water bodies in the Upper Oconee watershed in the northern Georgia Piedmont, USA, by mapping their locations and modelling these small reservoirs using the Soil Water Assessment Tool. Comparative Soil Water Assessment Tool models were run with and without the inclusion of small reservoir surface area and volume. The models used meteorological inputs from 1990–2013 to represent years with drought, high precipitation, and moderate precipitation for both the calibration and evaluation periods. Statistical comparison of streamflow indicated that the calibration methodology produced results where the default model simulation without reservoirs fit observed flows more closely than the modified model with small reservoirs included (e.g., Nash–Sutcliffe efficiency of 0.72 vs. 0.64, r2 of 0.73 vs. 0.66, and percent bias of 11.4 vs. 21.6). In addition, Penman–Monteith, Hargreaves, and Priestley–Taylor evapotranspiration equations were used to estimate actual evaporation from 2,219 small water bodies identified throughout the 1,936.8 km2 watershed. Depending on the evaporation equation used, water bodies evaporated an average of 0.03–0.036 km3/year for the period 2003–2013. Using Penman–Monteith further, if the reservoirs were not considered and average actual evapotranspiration rates from the rest of the basin were applied, only 0.016 km3 of water would have left the basin as a result of evapotranspiration. This finding suggests construction of small reservoirs increased evaporation by an average of 0.017 km3 per year (approximately 46,500 m3/day). As the construction of small reservoirs continues and high resolution image data used to map these water bodies becomes increasingly available, watershed models that evolve to address the cumulative impacts of small water bodies on evaporation and other hydrologic processes will have greater potential to benefit the water resource management community.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11398","usgsCitation":"Ignatius, A., and Jones, J., 2018, High resolution water body mapping for SWAT evaporative modelling in the Upper Oconee watershed of Georgia, USA: Hydrological Processes, v. 32, no. 1, p. 51-65, https://doi.org/10.1002/hyp.11398.","productDescription":"15 p.","startPage":"51","endPage":"65","ipdsId":"IP-073606","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469108,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.11398","text":"Publisher Index Page"},{"id":358190,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Upper Oconee watershed","volume":"32","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-18","publicationStatus":"PW","scienceBaseUri":"5bc0304de4b0fc368eb539ec","contributors":{"authors":[{"text":"Ignatius, Amber R. 0000-0002-2636-836X","orcid":"https://orcid.org/0000-0002-2636-836X","contributorId":193407,"corporation":false,"usgs":false,"family":"Ignatius","given":"Amber R.","affiliations":[],"preferred":false,"id":747475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":747474,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70199946,"text":"70199946 - 2018 - Advancements in hydrochemistry mapping: methods and application to groundwater arsenic and iron concentrations in Varanasi, Uttar Pradesh, India","interactions":[],"lastModifiedDate":"2018-10-05T14:30:40","indexId":"70199946","displayToPublicDate":"2018-01-01T14:30:35","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3478,"text":"Stochastic Environmental Research and Risk Assessment","active":true,"publicationSubtype":{"id":10}},"title":"Advancements in hydrochemistry mapping: methods and application to groundwater arsenic and iron concentrations in Varanasi, Uttar Pradesh, India","docAbstract":"The area east of Varanasi is one of numerous places along the watershed of the Ganges River with groundwater concentrations of arsenic surpassing the maximum value of 10 parts per billion (ppb) recommended by the World Health Organization in drinking water. Here we apply geostatistics and compositional data analysis for the mapping of arsenic and iron to help in understanding the conditions leading to the occurrence of elevated level of arsenic in groundwater. The methodology allows for displaying concentrations of arsenic and iron as maps consistent with the limited information from 95 water wells across an area of approximately 210 km2; visualization of the uncertainty associated with the sampling; and summary of the findings in the form of probability maps. For thousands of years, Varanasi has been on the erosional side in a meander of the river that is free of arsenic values above 10 ppb. Maps reveal two anomalies of high arsenic concentrations on the depositional side of the valley, which has started seeing urban development. The methodology using geostatistics combined with compositional data analysis is completely general, so this study could be used as a prototype for hydrochemistry mapping in other areas.
","language":"English","publisher":"Springer","doi":"10.1007/s00477-017-1390-3","usgsCitation":"Olea, R., Raju, N.J., Egozcue, J.J., Pawlowsky-Glahn, V., and Singh, S., 2018, Advancements in hydrochemistry mapping: methods and application to groundwater arsenic and iron concentrations in Varanasi, Uttar Pradesh, India: Stochastic Environmental Research and Risk Assessment, v. 32, no. 1, p. 241-259, https://doi.org/10.1007/s00477-017-1390-3.","productDescription":"19 p.","startPage":"241","endPage":"259","ipdsId":"IP-102331","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":469109,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10256/14599","text":"External Repository"},{"id":358185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","state":"Uttar Pradesh","city":"Varanasi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 82.94094085693358,\n 25.22171429348812\n ],\n [\n 83.16032409667969,\n 25.22171429348812\n ],\n [\n 83.16032409667969,\n 25.353644304321104\n ],\n [\n 82.94094085693358,\n 25.353644304321104\n ],\n [\n 82.94094085693358,\n 25.22171429348812\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-13","publicationStatus":"PW","scienceBaseUri":"5bc0304de4b0fc368eb539ee","contributors":{"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":47873,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":747416,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Raju, N. Janardhana","contributorId":208504,"corporation":false,"usgs":false,"family":"Raju","given":"N.","email":"","middleInitial":"Janardhana","affiliations":[],"preferred":false,"id":747476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Egozcue, Juan J.","contributorId":208010,"corporation":false,"usgs":false,"family":"Egozcue","given":"Juan","email":"","middleInitial":"J.","affiliations":[{"id":37677,"text":"Dept. Civil and Environmental Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain","active":true,"usgs":false}],"preferred":false,"id":747477,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pawlowsky-Glahn, Vera","contributorId":208011,"corporation":false,"usgs":false,"family":"Pawlowsky-Glahn","given":"Vera","email":"","affiliations":[{"id":37678,"text":"Dept. Informatics, Applied Matematics and Statistics, Universitat de Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":747478,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singh, Shubhra","contributorId":208505,"corporation":false,"usgs":false,"family":"Singh","given":"Shubhra","email":"","affiliations":[],"preferred":false,"id":747479,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70199759,"text":"70199759 - 2018 - Characterizing aquatic habitats for long‐term monitoring of a fourth‐order, regulated river in the Pacific Northwest, USA","interactions":[],"lastModifiedDate":"2018-09-27T13:53:21","indexId":"70199759","displayToPublicDate":"2018-01-01T13:53:15","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing aquatic habitats for long‐term monitoring of a fourth‐order, regulated river in the Pacific Northwest, USA","docAbstract":"A pragmatic approach to the long‐term monitoring of rivers leverages available information with targeted field investigations to address key uncertainties relevant to management decisions. An over‐arching management issue for many rivers is how reservoir operation affects the amount and location of in‐channel sediment and the resulting distribution of aquatic habitats. We integrate remotely acquired and field‐survey morphologic data for the Cedar River, Washington, to constitute the current status of aquatic habitats and benchmarks for long‐term monitoring that will inform streamflow management. Four key habitats (river edge, side channels, riffles, and pools) are feasible to monitor with high‐resolution aerial imagery, a longitudinal profile of the river, and a side channel inventory, but full characterization of the functional differences within these habitats requires additional information. Habitat use information such as redd surveys will continue to be important for long‐term monitoring where it cannot be inferred reliably from physical habitat characteristics.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3230","usgsCitation":"Konrad, C.P., Burton, K., Little, R., Gendaszek, A.S., Munn, M.D., and Anderson, S.W., 2018, Characterizing aquatic habitats for long‐term monitoring of a fourth‐order, regulated river in the Pacific Northwest, USA: River Research and Applications, v. 34, no. 1, p. 24-33, https://doi.org/10.1002/rra.3230.","productDescription":"10 p.","startPage":"24","endPage":"33","ipdsId":"IP-084622","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":469110,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.3230","text":"Publisher Index Page"},{"id":357837,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124,\n 46\n ],\n [\n -120,\n 46\n ],\n [\n -120,\n 49\n ],\n [\n -124,\n 49\n ],\n [\n -124,\n 46\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-30","publicationStatus":"PW","scienceBaseUri":"5bc0304de4b0fc368eb539f0","contributors":{"authors":[{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746509,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burton, K.","contributorId":208244,"corporation":false,"usgs":false,"family":"Burton","given":"K.","email":"","affiliations":[],"preferred":false,"id":746516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Little, R.","contributorId":208245,"corporation":false,"usgs":false,"family":"Little","given":"R.","email":"","affiliations":[],"preferred":false,"id":746517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gendaszek, Andrew S. 0000-0002-2373-8986 agendasz@usgs.gov","orcid":"https://orcid.org/0000-0002-2373-8986","contributorId":3509,"corporation":false,"usgs":true,"family":"Gendaszek","given":"Andrew","email":"agendasz@usgs.gov","middleInitial":"S.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Munn, Mark D. 0000-0002-7154-7252 mdmunn@usgs.gov","orcid":"https://orcid.org/0000-0002-7154-7252","contributorId":976,"corporation":false,"usgs":true,"family":"Munn","given":"Mark","email":"mdmunn@usgs.gov","middleInitial":"D.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746519,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":107001,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":false,"id":746520,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70198749,"text":"70198749 - 2018 - Quantifying uncertainty and tradeoffs in resilience assessments","interactions":[],"lastModifiedDate":"2018-08-24T12:20:22","indexId":"70198749","displayToPublicDate":"2018-01-01T09:32:03","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying uncertainty and tradeoffs in resilience assessments","docAbstract":"Several frameworks have been developed to assess the resilience of social-ecological systems, but most require substantial data inputs, time, and technical expertise. Stakeholders and practitioners often lack the resources for such intensive efforts. Furthermore, most end with problem framing and fail to explicitly address trade-offs and uncertainty. To remedy this gap, we developed a rapid survey assessment that compares the relative resilience of social-ecological systems with respect to a number of resilience properties. This approach generates large amounts of information relative to stakeholder inputs. We targeted four stakeholder categories: government (policy, regulation, management), end users (farmers, ranchers, landowners, industry), agency/public science (research, university, extension), and NGOs (environmental, citizen, social justice) in four North American watersheds, to assess social-ecological resilience through surveys. Conceptually, social-ecological systems are comprised of components ranging from strictly human to strictly ecological, but that relate directly or indirectly to one another. They have soft boundaries and several important dimensions or axes that together describe the nature of social-ecological interactions, e.g., variability, diversity, modularity, slow variables, feedbacks, capital, innovation, redundancy, and ecosystem services. There is no absolute measure of resilience, so our design takes advantage of cross-watershed comparisons and therefore focuses on relative resilience. Our approach quantifies and compares the relative resilience across watershed systems and potential trade-offs among different aspects of the social-ecological system, e.g., between social, economic, and ecological contributions. This approach permits explicit assessment of several types of uncertainty (e.g., self-assigned uncertainty for stakeholders; uncertainty across respondents, watersheds, and subsystems), and subjectivity in perceptions of resilience among key actors and decision makers and provides an efficient way to develop the mental models that inform our stakeholders and stakeholder categories.
","language":"English","publisher":"Ecology and Society","doi":"10.5751/ES-09920-230103","usgsCitation":"Allen, C.R., Birge, H.E., Angeler, D.G., Arnold, C.A., Chaffin, B.C., DeCaro, D.A., Garmestani, A.S., and Gunderson, L., 2018, Quantifying uncertainty and tradeoffs in resilience assessments: Ecology and Society, v. 1, no. 3, Article 3; 23 p., https://doi.org/10.5751/ES-09920-230103.","productDescription":"Article 3; 23 p.","ipdsId":"IP-089079","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469113,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/es-09920-230103","text":"Publisher Index Page"},{"id":356614,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a317e4b0702d0e84302a","contributors":{"authors":[{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":742844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Birge, Hannah E.","contributorId":166737,"corporation":false,"usgs":false,"family":"Birge","given":"Hannah","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":743039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Angeler, David G.","contributorId":205240,"corporation":false,"usgs":false,"family":"Angeler","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":37065,"text":"Swedish University of Agricultural Sciences, Uppsala, Sweden","active":true,"usgs":false}],"preferred":false,"id":743040,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arnold, Craig Anthony","contributorId":189230,"corporation":false,"usgs":false,"family":"Arnold","given":"Craig","email":"","middleInitial":"Anthony","affiliations":[],"preferred":false,"id":743041,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chaffin, Brian C.","contributorId":189131,"corporation":false,"usgs":false,"family":"Chaffin","given":"Brian","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":743042,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeCaro, Daniel A.","contributorId":198374,"corporation":false,"usgs":false,"family":"DeCaro","given":"Daniel","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":743043,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Garmestani, Ahjond S.","contributorId":77285,"corporation":false,"usgs":true,"family":"Garmestani","given":"Ahjond","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":743044,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gunderson, Lance","contributorId":30797,"corporation":false,"usgs":true,"family":"Gunderson","given":"Lance","affiliations":[],"preferred":false,"id":743045,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70250484,"text":"70250484 - 2018 - Data quality from a community-based, water-quality monitoring project in the Yukon River basin","interactions":[],"lastModifiedDate":"2023-12-13T12:51:43.374094","indexId":"70250484","displayToPublicDate":"2018-01-01T06:43:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17109,"text":"Citizen Science: Theory and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Data quality from a community-based, water-quality monitoring project in the Yukon River basin","docAbstract":"This paper examines the quality of data collected by the Indigenous Observation Network, a community-based water-quality project in the Yukon River Basin of Alaska and Canada. The Indigenous Observation Network relies on community technicians to collect surface-water samples from as many as fifty locations to achieve their goals of monitoring the quality of the Yukon River and major tributaries in the basin and maintaining a long-term record of baseline data against which future changes can be measured. This paper addresses concerns about the accuracy, precision, and reliability of data collected by non-professionals. The Indigenous Observation Network data are examined in the context of a standard data life cycle: plan, collect, assure, and describe; as compared to professional scientific activities. Field and laboratory protocols and procedures of the Indigenous Observation Network are compared to those utilized by professional scientists. The data of the Indigenous Observation Network are statistically compared to those collected by professional scientists through a retrospective analysis of a set of water-quality parameters reported by all three projects over a number of years. No statistical differences were found among the three projects for pH, Calcium, Magnesium, or Alkalinity, although statistically significant differences were found for Sodium, Chloride, Sulfate, and Potassium concentrations. The statistical differences found were small and likely not significant in terms of interpreting the data for a variety of uses. Our results suggest that Indigenous Observation Network data are of high quality, and with consistent protocols and participant training, community based monitoring projects can collect data that are accurate, precise, and reliable.
","language":"English","publisher":"Citizen Science Association","doi":"10.5334/cstp.123","usgsCitation":"Herman-Mercer, N.M., Antweiler, R.C., Wilson, N.J., Mutter, E., Toohey, R.C., and Schuster, P.F., 2018, Data quality from a community-based, water-quality monitoring project in the Yukon River basin: Citizen Science: Theory and Practice, v. 3, no. 2, p. 1-13, https://doi.org/10.5334/cstp.123.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-088123","costCenters":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":469114,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5334/cstp.123","text":"Publisher Index Page"},{"id":423510,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia, Yukon Territory","otherGeospatial":"Yukon River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -133.38911936519082,\n 58.884699983594345\n ],\n [\n -131.80584712800214,\n 59.244500831247194\n ],\n [\n -128.0559184925799,\n 61.39245886503679\n ],\n [\n -130.37691440466298,\n 63.19862022039152\n ],\n [\n -132.90057689608258,\n 64.7273916812401\n ],\n [\n -136.07655344004274,\n 65.17321405136767\n ],\n [\n -136.38167602557422,\n 67.43445698306925\n ],\n [\n -137.1936261054335,\n 67.81571786351273\n ],\n [\n -139.80155493577553,\n 68.99525360147322\n ],\n [\n -142.18709101817953,\n 69.29294508385559\n ],\n [\n -144.357592273753,\n 68.64436591416057\n ],\n [\n -147.32751559325345,\n 68.36230477407085\n ],\n [\n -149.53547156519656,\n 68.0192473460342\n ],\n [\n -153.75763621292555,\n 67.50111931667189\n ],\n [\n -154.56797469736006,\n 67.3184970818009\n ],\n [\n -156.62421370889334,\n 67.24829968525793\n ],\n [\n -159.5851219416737,\n 66.37380394117832\n ],\n [\n -159.08643398198825,\n 64.97955849472734\n ],\n [\n -160.6760988121657,\n 63.54873962024479\n ],\n [\n -162.13991487369796,\n 62.67155198550202\n ],\n [\n -164.87240737476762,\n 62.85520700787475\n ],\n [\n -165.3288784756021,\n 62.493231527850924\n ],\n [\n -163.58419744437603,\n 61.47402386933206\n ],\n [\n -162.57824390444924,\n 60.07374325046584\n ],\n [\n -162.15289796638802,\n 60.01562417244955\n ],\n [\n -160.93231343636953,\n 60.855674765094506\n ],\n [\n -159.8606420727593,\n 61.24005967489518\n ],\n [\n -157.64851738119302,\n 61.452428109156784\n ],\n [\n -155.9371677503363,\n 61.767555251525295\n ],\n [\n -153.67894487429524,\n 61.998213663696106\n ],\n [\n -151.28172214877722,\n 62.857154574512975\n ],\n [\n -148.5219190010917,\n 63.54958472068245\n ],\n [\n -145.8471505146883,\n 63.44715968730057\n ],\n [\n -143.94486164777743,\n 62.9635954088607\n ],\n [\n -141.98726198811795,\n 61.74665287257059\n ],\n [\n -139.7042739197659,\n 61.032418827416535\n ],\n [\n -137.29442553480158,\n 60.28833067431336\n ],\n [\n -134.9034939422285,\n 59.575660364740656\n ],\n [\n -133.38911936519082,\n 58.884699983594345\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"2","noUsgsAuthors":false,"publicationDate":"2018-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Herman-Mercer, Nicole M. 0000-0001-5933-4978 nhmercer@usgs.gov","orcid":"https://orcid.org/0000-0001-5933-4978","contributorId":3927,"corporation":false,"usgs":true,"family":"Herman-Mercer","given":"Nicole","email":"nhmercer@usgs.gov","middleInitial":"M.","affiliations":[{"id":438,"text":"National Research Program - 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Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":890107,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195967,"text":"70195967 - 2018 - Type and amount of organic amendments affect enhanced biogenic methane production from coal and microbial community structure","interactions":[],"lastModifiedDate":"2018-03-09T15:27:28","indexId":"70195967","displayToPublicDate":"2018-01-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1709,"text":"Fuel","active":true,"publicationSubtype":{"id":10}},"title":"Type and amount of organic amendments affect enhanced biogenic methane production from coal and microbial community structure","docAbstract":"Slow rates of coal-to-methane conversion limit biogenic methane production from coalbeds. This study demonstrates that rates of coal-to-methane conversion can be increased by the addition of small amounts of organic amendments. Algae, cyanobacteria, yeast cells, and granulated yeast extract were tested at two concentrations (0.1 and 0.5 g/L), and similar increases in total methane produced and methane production rates were observed for all amendments at a given concentration. In 0.1 g/L amended systems, the amount of carbon converted to methane minus the amount produced in coal only systems exceeded the amount of carbon added in the form of amendment, suggesting enhanced coal-to-methane conversion through amendment addition. The amount of methane produced in the 0.5 g/L amended systems did not exceed the amount of carbon added. While the archaeal communities did not vary significantly, the bacterial populations appeared to be strongly influenced by the presence of coal when 0.1 g/L of amendment was added; at an amendment concentration of 0.5 g/L the bacterial community composition appeared to be affected most strongly by the amendment type. Overall, the results suggest that small amounts of amendment are not only sufficient but possibly advantageous if faster in situcoal-to-methane production is to be promoted.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fuel.2017.09.074","usgsCitation":"Davis, K.J., Lu, S., Barnhart, E.P., Parker, A., Fields, M.W., and Gerlach, R., 2018, Type and amount of organic amendments affect enhanced biogenic methane production from coal and microbial community structure: Fuel, v. 211, p. 600-608, https://doi.org/10.1016/j.fuel.2017.09.074.","productDescription":"9 p.","startPage":"600","endPage":"608","ipdsId":"IP-088405","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":469117,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1549227","text":"Publisher Index Page"},{"id":352384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"211","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee753e4b0da30c1bfc257","contributors":{"authors":[{"text":"Davis, Katherine J.","contributorId":203246,"corporation":false,"usgs":false,"family":"Davis","given":"Katherine","email":"","middleInitial":"J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":730721,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Shipeng","contributorId":203234,"corporation":false,"usgs":false,"family":"Lu","given":"Shipeng","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":730722,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393 epbarnhart@usgs.gov","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":5385,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","email":"epbarnhart@usgs.gov","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730720,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parker, Albert E.","contributorId":203235,"corporation":false,"usgs":false,"family":"Parker","given":"Albert E.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":730723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fields, Matthew W.","contributorId":172391,"corporation":false,"usgs":false,"family":"Fields","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":730724,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gerlach, Robin","contributorId":203247,"corporation":false,"usgs":false,"family":"Gerlach","given":"Robin","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":730725,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196877,"text":"70196877 - 2018 - Behavior and reproductive ecology of the Sicklefin Redhorse: An imperiled southern Appalachian Mountain fish","interactions":[],"lastModifiedDate":"2018-05-08T13:15:45","indexId":"70196877","displayToPublicDate":"2018-01-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Behavior and reproductive ecology of the Sicklefin Redhorse: An imperiled southern Appalachian Mountain fish","docAbstract":"Many nongame fishes are poorly understood but are essential to maintaining healthy aquatic ecosystems globally. The undescribed Sicklefin Redhorse Moxostoma sp. is a rare, imperiled, nongame fish endemic to two southern Appalachian Mountain river basins. Little is known of its behavior and ecology, but this information is urgently needed for conservation planning. We assessed the spatial and temporal bounds of spawning migration, quantified seasonal weekly movement patterns, and characterized seasonal and spawning behavior using radiotelemetry and weir sampling in the Hiwassee River basin, North Carolina–Georgia, during 2006 and 2007. Hiwassee River tributaries were occupied predominantly during the fish's spawning season, lower reaches of the tributaries and the Hiwassee River were primarily occupied during the postspawning season (i.e., summer and fall), and lower lotic reaches of Hiwassee River (upstream from Hiwassee Lake) were occupied during winter. Adults occupied Hiwassee Lake only as a movement corridor during spawning migrations. Both sexes conducted upstream spawning migrations simultaneously, but males occupied spawning tributaries longer than females. Sicklefin Redhorse exhibited interannual spawning‐area and tributary fidelity. Cold water temperatures associated with hypolimnetic releases from reservoirs and meteorological conditions influenced spawning migration distance and timing. During 2007, decreased discharges during the spawning season were associated with decreases in migration distance and spawning tributary occupancy duration. Foraging was the dominant behavior observed annually, followed by reproductive behaviors (courting and spawning) during the spawning season. No agonistic reproductive behavior was observed, but females exhibited a repetitious postspawning digging behavior that may be unique in the family Catostomidae. Our findings suggest that protection and restoration of river continuity, natural flow regimes, seasonally appropriate water temperatures, and geographic range expansion are critical components to include in Sicklefin Redhorse conservation planning. Fisheries and ecosystem managers can use our findings to justify sensitive management decisions that conserve and restore critical streams and rivers occupied by this imperiled species.
","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10010","usgsCitation":"Favrot, S.D., and Kwak, T.J., 2018, Behavior and reproductive ecology of the Sicklefin Redhorse: An imperiled southern Appalachian Mountain fish: Transactions of the American Fisheries Society, v. 147, no. 1, p. 204-222, https://doi.org/10.1002/tafs.10010.","productDescription":"19 p.","startPage":"204","endPage":"222","ipdsId":"IP-091271","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Hiwassee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.22393798828125,\n 34.84254924386249\n ],\n [\n -83.69316101074219,\n 34.84254924386249\n ],\n [\n -83.69316101074219,\n 35.184471743812225\n ],\n [\n -84.22393798828125,\n 35.184471743812225\n ],\n [\n -84.22393798828125,\n 34.84254924386249\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"147","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-26","publicationStatus":"PW","scienceBaseUri":"5afee752e4b0da30c1bfc238","contributors":{"authors":[{"text":"Favrot, Scott D.","contributorId":171445,"corporation":false,"usgs":false,"family":"Favrot","given":"Scott","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":734892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734890,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196641,"text":"70196641 - 2018 - Walleye recruitment success is less resilient to warming water temperatures in lakes with abundant largemouth bass populations","interactions":[],"lastModifiedDate":"2018-04-23T15:01:24","indexId":"70196641","displayToPublicDate":"2018-01-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Walleye recruitment success is less resilient to warming water temperatures in lakes with abundant largemouth bass populations","docAbstract":"Lakes respond heterogeneously to climate, with implications for fisheries management. We analyzed walleye (Sander vitreus) recruitment to age-0 in 359 lakes in Wisconsin, USA, to (i) quantify the relationship between annual water temperature degree days (DD) and walleye recruitment success and (ii) identify the influence of lake characteristics — area, conductivity, largemouth bass (Micropterus salmoides) catch rates, and mean DD — on this relationship. The relationship between walleye recruitment and annual DD varied among lakes and was not distinguishable from zero overall (posterior mean = −0.11, 90% CI = −0.34, 0.15). DD effects on recruitment were negative in 198 lakes (55%) and positive in 161 (45%). The effect of annual DD was most negative in lakes with high largemouth bass densities, and, on average, the probability of recruitment was highest in large lakes with low largemouth bass densities. Conductivity and mean DD influenced neither recruitment nor the effect of annual DD. Walleye recruitment was most resilient to warming in lakes with few largemouth bass, suggesting that the effects of climate change depend on lake-specific food-web and habitat contexts.
","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2016-0249","usgsCitation":"Hansen, G.J., Midway, S.R., and Wagner, T., 2018, Walleye recruitment success is less resilient to warming water temperatures in lakes with abundant largemouth bass populations: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 1, p. 106-115, https://doi.org/10.1139/cjfas-2016-0249.","productDescription":"10 p.","startPage":"106","endPage":"115","ipdsId":"IP-076918","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":461091,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2016-0249","text":"External Repository"},{"id":353666,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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