The Everglades Depth Estimation Network (EDEN), with over 240 real-time gaging stations, provides hydrologic data for freshwater and tidal areas of the Everglades. These data are used to generate daily water-level and water-depth maps of the Everglades that are used to assess biotic responses to hydrologic change resulting from the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. The generation of EDEN daily water-level and water-depth maps is dependent on high quality real-time data from water-level stations. Real-time data are automatically checked for outliers by assigning minimum and maximum thresholds for each station. Small errors in the real-time data, such as gradual drift of malfunctioning pressure transducers, are more difficult to immediately identify with visual inspection of time-series plots and may only be identified during on-site inspections of the stations. Correcting these small errors in the data often is time consuming and water-level data may not be finalized for several months. To provide daily water-level and water-depth maps on a near real-time basis, EDEN needed an automated process to identify errors in water-level data and to provide estimates for missing or erroneous water-level data.
The Automated Data Assurance and Management (ADAM) software uses inferential sensor technology often used in industrial applications. Rather than installing a redundant sensor to measure a process, such as an additional water-level station, inferential sensors, or virtual sensors, were developed for each station that make accurate estimates of the process measured by the hard sensor (water-level gaging station). The inferential sensors in the ADAM software are empirical models that use inputs from one or more proximal stations. The advantage of ADAM is that it provides a redundant signal to the sensor in the field without the environmental threats associated with field conditions at stations (flood or hurricane, for example). In the event that a station does malfunction, ADAM provides an accurate estimate for the period of missing data. The ADAM software also is used in the quality assurance and quality control of the data. The virtual signals are compared to the real-time data, and if the difference between the two signals exceeds a certain tolerance, corrective action to the data and (or) the gaging station can be taken. The ADAM software is automated so that, each morning, the real-time EDEN data are compared to the inferential sensor signals and digital reports highlighting potential erroneous real-time data are generated for appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165094","collaboration":"Greater Everglades Priority Ecosystems Science","usgsCitation":"Petkewich, M.D., Daamen, R.C., Roehl, E.A., and Conrads, P.A., 2016, Using inferential sensors for quality control of Everglades Depth Estimation Network water-level data: U.S. Geological Survey Scientific Investigations Report 2016–5094, 25 p., https://dx.doi.org/10.3133/sir20165094.","productDescription":"v, 25 p.","onlineOnly":"Y","ipdsId":"IP-066447","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":329015,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20161116","text":"Open-File Report 2016–1116","description":"Open-File Report 2016–1116","linkHelpText":"- User’s Manual for the Automated Data Assurance and Management Application Developed for Quality Control of Everglades Depth Estimation Network Water-Level Data"},{"id":329013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5094/coverthb.jpg"},{"id":329014,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5094/sir20165094.pdf","text":"Report","size":"14.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5094"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.36749267578125,\n 25.916055815010868\n ],\n [\n -81.3482666015625,\n 26.25893609446839\n ],\n [\n -80.88958740234375,\n 26.261399213411483\n ],\n [\n -80.88134765625,\n 26.152972606566966\n ],\n [\n -80.84564208984375,\n 26.09625490696853\n ],\n [\n -80.82916259765625,\n 26.335268473041793\n ],\n [\n -80.53802490234375,\n 26.337729970839195\n ],\n [\n -80.4583740234375,\n 26.477948705162902\n ],\n [\n -80.45562744140625,\n 26.681821407205977\n ],\n [\n -80.35400390625,\n 26.681821407205977\n ],\n [\n -80.34576416015625,\n 26.64745870265938\n ],\n [\n -80.2716064453125,\n 26.5958952526538\n ],\n [\n -80.22491455078125,\n 26.519735305660795\n ],\n [\n -80.2166748046875,\n 26.423849388805877\n ],\n [\n -80.233154296875,\n 26.369724677109726\n ],\n [\n -80.2880859375,\n 26.35988109485539\n ],\n [\n -80.299072265625,\n 26.194876675795218\n ],\n [\n -80.3594970703125,\n 26.123384198664976\n ],\n [\n -80.4364013671875,\n 26.14804172600284\n ],\n [\n -80.43090820312499,\n 25.94816628853973\n ],\n [\n -80.48309326171875,\n 25.8962911755466\n ],\n [\n -80.4913330078125,\n 25.668760323272966\n ],\n [\n -80.5572509765625,\n 25.57465306409282\n ],\n [\n -80.4803466796875,\n 25.29437116258816\n ],\n [\n -80.75225830078125,\n 25.227304826281653\n ],\n [\n -81.36749267578125,\n 25.916055815010868\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
Stephenson Center, Suite 129
Gracern Road
Columbia, SC 29210
https://www2.usgs.gov/water/southatlantic/
The generation of Everglades Depth Estimation Network (EDEN) daily water-level and water-depth maps is dependent on high quality real-time data from over 240 water-level stations. To increase the accuracy of the daily water-surface maps, the Automated Data Assurance and Management (ADAM) tool was created by the U.S. Geological Survey as part of Greater Everglades Priority Ecosystems Science. The ADAM tool is used to provide accurate quality-assurance review of the real-time data from the EDEN network and allows estimation or replacement of missing or erroneous data. This user’s manual describes how to install and operate the ADAM software. File structure and operation of the ADAM software is explained using examples.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161116","collaboration":"Greater Everglades Priority Ecosystems Science","usgsCitation":"Petkewich, M.D., Daamen, R.C., Roehl, E.A., and Conrads, P.A., 2016, User’s manual for the Automated Data Assurance and Management application developed for quality control of Everglades Depth Estimation Network water-level data: U.S. Geological Survey Open-File Report 2016–1116, 28 p., https://dx.doi.org/10.3133/ofr20161116.","productDescription":"Report: vi, 28 p.; Companion File","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-076311","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":329016,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20165094","text":"Scientific Investigations Report 2016–5094","description":"Scientific Investigations Report 2016–5094","linkHelpText":"- Using Inferential Sensors for Quality Control of Everglades Depth Estimation Network Water-Level Data"},{"id":328990,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1116/ofr20161116.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1116"},{"id":328989,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1116/coverthb.jpg"},{"id":329002,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2016/1116/downloads","text":"Executable files for Automated Data Assurance and Management application","description":"OFR 2016-1116"}],"contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
Stephenson Center, Suite 129
Gracern Road
Columbia, SC 29210
https://www2.usgs.gov/water/southatlantic
The Souris River Basin is a 61,000 square kilometer basin in the provinces of Saskatchewan and Manitoba and the state of North Dakota. Record setting rains in May and June of 2011 led to record flooding with peak annual streamflow values (762 cubic meters per second [m3/s]) more than twice that of any previously recorded peak streamflow and more than five times the estimated 100 year postregulation streamflow (142 m3/s) at the U.S. Geological Survey (USGS) streamflow-gaging station above Minot, North Dakota. Upstream from Minot, N. Dak., the Souris River is regulated by three reservoirs in Saskatchewan (Rafferty, Boundary, and Alameda) and Lake Darling in North Dakota. During the 2011 flood, the city of Minot, N. Dak., experienced devastating damages with more than 4,000 homes flooded and 11,000 evacuated. As a result, the Souris River Basin Task Force recommended the U.S. Geological Survey (in cooperation with the North Dakota State Water Commission) develop a model for estimating the probabilities of future flooding and drought. The model that was developed took on four parts: (1) looking at past climate, (2) predicting future climate, (3) developing a streamflow model in response to certain climatic variables, and (4) combining future climate estimates with the streamflow model to predict future streamflow events. By taking into consideration historical climate record and trends in basin response to various climatic conditions, it was determined flood risk will remain high in the Souris River Basin until the wet climate state ends.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163073","collaboration":"Prepared in cooperation with the North Dakota State Water Commission","usgsCitation":"Nustad, R.A., Kolars, K.A., Vecchia, A.V., and Ryberg, K.R., 2016, 2011 Souris River flood—Will it happen again?: U.S. Geological Survey Fact Sheet 2016–3073, 4 p., https://dx.doi.org/10.3133/fs20163073.","productDescription":"4 p.","startPage":"1","endPage":"4","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-078012","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":328874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3073/fs20163073.pdf","text":"Fact Sheet","size":"994 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3073"},{"id":328873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3073/coverthb.jpg"}],"country":"Canada, United States","otherGeospatial":"Souris River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.81201171875,\n 48.545705491847464\n ],\n [\n -95.03173828125,\n 49.53946900793534\n ],\n [\n -95.73486328124999,\n 50.387507803003146\n ],\n [\n -96.78955078125,\n 51.2206474303833\n ],\n [\n -97.36083984375,\n 52.03897658307622\n ],\n [\n -98.72314453125,\n 52.77618568896171\n ],\n [\n -100.08544921874999,\n 53.173119202640635\n ],\n [\n -101.6455078125,\n 53.4357192066942\n ],\n [\n -102.94189453125,\n 53.63161060657857\n ],\n [\n -104.52392578125,\n 53.8265967429941\n ],\n [\n -105.75439453125,\n 53.77468884583577\n ],\n [\n -107.29248046875,\n 52.908902047770255\n ],\n [\n -107.841796875,\n 52.22779941887071\n ],\n [\n -108.67675781249999,\n 51.2206474303833\n ],\n [\n -108.896484375,\n 49.85215166776998\n ],\n [\n -109.0283203125,\n 48.83579746243093\n ],\n [\n -108.5009765625,\n 47.916342040161155\n ],\n [\n -107.22656249999999,\n 46.22545288226939\n ],\n [\n -105.27099609375,\n 44.88701247981298\n ],\n [\n -103.55712890625,\n 44.66865287227321\n ],\n [\n -102.7880859375,\n 44.26093725039923\n ],\n [\n -101.53564453124999,\n 43.91372326852401\n ],\n [\n -99.90966796875,\n 43.99281450048989\n ],\n [\n -97.66845703124999,\n 44.653024159812\n ],\n [\n -96.767578125,\n 45.44471679159555\n ],\n [\n -96.064453125,\n 46.210249600187225\n ],\n [\n -95.51513671875,\n 47.42808726171425\n ],\n [\n -94.81201171875,\n 48.545705491847464\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, North Dakota Water Science Center
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A hydrogeologic framework was constructed to represent the altitudes and thicknesses of hydrogeologic units within the Ozark Plateaus aquifer system as part of a regional groundwater-flow model supported by the U.S. Geological Survey Water Availability and Use Science Program. The Ozark Plateaus aquifer system study area is nearly 70,000 square miles and includes parts of Arkansas, Kansas, Missouri, and Oklahoma. Nine hydrogeologic units were selected for delineation within the aquifer system and include the Western Interior Plains confining system, the Springfield Plateau aquifer, the Ozark confining unit, the Ozark aquifer, which was divided into the upper, middle, and lower Ozark aquifers to better capture the spatial variation in the hydrologic properties, the St. Francois confining unit, the St. Francois aquifer, and the basement confining unit. Geophysical and well-cutting logs, along with lithologic descriptions by well drillers, were compiled and interpreted to create hydrologic altitudes for each unit. The final compiled dataset included more than 23,000 individual altitude points (excluding synthetic points) representing the nine hydrogeologic units within the Ozark Plateaus aquifer system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165130","collaboration":"Water Availability and Use Science Program","usgsCitation":"Westerman, D.A., Gillip, J.A., Richards, J.M., Hays, P.D., Clark, B.R., 2016, Altitudes and thicknesses of hydrogeologic units of the Ozark Plateaus aquifer system in Arkansas, Kansas, Missouri, and Oklahoma: U.S. Geological Survey Scientific Investigations Report 2016–5130, 32 p., https://dx.doi.org/10.3133/sir20165130. 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U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
Cyanobacterial harmful algal blooms (CyanoHABs) are increasingly a global concern because CyanoHABs pose a threat to human and aquatic ecosystem health and cause economic damages. Despite advances in scientific understanding of cyanobacteria and associated compounds, many unanswered questions remain about occurrence, environmental triggers for toxicity, and the ability to predict the timing, duration, and toxicity of CyanoHABs. U.S. Geological Survey (USGS) scientists are leading a diverse range of studies to address CyanoHAB issues in water bodies throughout the United States, using a combination of traditional methods and emerging technologies, and in collaboration with numerous partners. By providing practical applications of cutting edge CyanoHAB research, USGS studies have advanced scientific understanding, enabling the development of approaches to help protect ecological and human health.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161174","usgsCitation":"Graham, J.L., Dubrovsky, N.M., and Eberts, S.M., 2017, Cyanobacterial harmful algal blooms and U.S. Geological Survey science capabilities (ver 1.1, December 2017): U.S. Geological Survey Open-File Report 2016–1174, 12 p., https://doi.org/10.3133/ofr20161174.\n","productDescription":"12 p.","startPage":"1","endPage":"12","numberOfPages":"12","onlineOnly":"N","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":350093,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1174/coverthb2.jpg"},{"id":350078,"rank":2,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2016/1174/versionHist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016–1174 Version 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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
This is the eighth annual report highlighting U.S. Geological Survey (USGS) science and decision-support activities conducted for the Wyoming Landscape Conservation Initiative (WLCI). The activities address specific management needs identified by WLCI partner agencies. In 2015, USGS scientists continued 24 WLCI projects in 5 categories: (1) acquiring and analyzing resource-condition data to form a foundation for understanding and monitoring landscape conditions and projecting changes; (2) using new technologies to improve the scope and accuracy of landscape-scale monitoring and assessments, and applying them to monitor indicators of ecosystem conditions and the effectiveness of on-the-ground habitat projects; (3) conducting research to elucidate the mechanisms that drive wildlife and habitat responses to changing land uses; (4) managing and making accessible the large number of databases, maps, and other products being developed; and (5) coordinating efforts among WLCI partners, helping them to use USGS-developed decision-support tools, and integrating WLCI outcomes with future habitat enhancement and research projects. Of the 24 projects, 21 were ongoing, including those that entered new phases or more in-depth lines of inquiry, 2 were new, and 1 was completed.
A highlight of 2015 was the WLCI science conference sponsored by the USGS, Bureau of Land Management, and National Park Service in coordination with the Wyoming chapter of The Wildlife Society. Of 260 participants, 41 were USGS professionals representing 13 USGS science centers, field offices, and Cooperative Wildlife Research Units. Major themes of USGS presentations included using new technologies for developing more efficient research protocols for modeling and monitoring natural resources, researching effects of energy development and other land uses on wildlife species and habitats of concern, and modeling species distributions, population trends, habitat use, and effects of land-use changes. There was also a special session on the effectiveness of Wyoming’s Sage-Grouse Executive Order. Combined, USGS presentations provided WLCI partners with a wealth of information and conservation tools.
The project completed in 2015 yielded an index of important agricultural lands in the WLCI region. The index improves upon existing measures of agricultural productivity and provides planners and managers with additional values to consider when making decisions about land use and conservation actions. The two new projects include an analysis of satellite imagery to quantify sagebrush productivity and mortality, and an evaluation of how groundwater and small streams interact in the upper Green River Basin. Initiated in response to concern among WLCI partners that large areas of sagebrush appear to have died recently, the sagebrush study objectives are to assess effects of these mortality events on overall sagebrush ecosystem productivity, evaluate the feasibility of using satellite imagery to detect patterns in sagebrush mortality over time, and identify factors driving these mortality events. The groundwater-streamflow interaction study is being conducted by hydrologists and fish ecologists to better understand how groundwater-streamflow interactions are affected by energy-resource development and how native fish communities are affected by these factors. Expected outcomes of both new projects will provide WLCI partners with additional information and decision-support tools.
Highlights of ongoing science foundation activities included simulations of nine alternative build-out scenarios for oil and gas development and an associated online fact sheet that explains how the simulations were conducted, with an applied example for the Atlantic Rim. Also completed in 2015 was an update of the USGS online inventory of mineral resources data, and publication of a USGS uranium resource survey for the WLCI region. Combined, the outcomes of this work provide decisionmakers and managers with important baseline information for existing and (or) future planning and monitoring efforts.
Terrestrial monitoring activities in 2015 emphasized the use of satellite data in combination with other technologies and field data to monitor, assess, and (or) forecast distribution patterns and (or) trends in sagebrush ecosystems, seasonal and migration stopover habitats used by mule deer and elk, and semi-arid aspen woodlands. Several professional papers detailing new monitoring models and results have been published. Combined, this and related work will help managers understand distribution patterns and trends among priority habitats, identify areas in need of restoration or conservation, and monitor the effectiveness of habitat-management actions.
Aquatic monitoring activities entailed not only the new groundwater-streamflow interaction study already mentioned, but also continued monitoring with streamgages paired with nearby wells in the Green River Basin to assess groundwater effects on streamflow and surface water temperatures. A map that portrays groundwater levels and general direction of flow in the Green River Basin was published as well. Overall, outcomes of USGS hydrological research and monitoring will inform WLCI partners about water resources in the WLCI region and help to explain fish-community responses to energy-resource development.
In 2015, USGS terrestrial wildlife ecologists continued to make crucial strides towards better understanding wildlife species responses to energy-resource development and other land-use changes. This body of research includes six taxa that require or heavily depend on sagebrush habitats: sage-grouse, pygmy rabbits, 3 songbird species, and mule deer. Native fish communities are also being evaluated. Approaches include modeling and mapping wildlife species distributions, abundances, and trends; using satellite and other technologies to track wildlife seasonal movements; conducting successive phases of research that build on the knowledge gained through prior phases to reveal the specific factors or thresholds that drive population- or individual-level responses to changes; and conducting population viability analyses. Additionally, wildlife habitat association models for pygmy rabbit and sage-grouse were combined with the oil and gas build-out scenarios to project species responses to alternative energy development scenarios. Outcomes of the wildlife response research are helping decisionmakers and managers identify specific factors that contribute to species population trends, the potential for spatial overlap between important wildlife habitats and proposed energy-resource development, locations of priority habitats for restoration and conservation, and more.
Data and WLCI Web site management highlights of 2015 included not only ongoing software upgrades, but also an update of the datasets displayed in two of the online products developed for the WLCI effort: (1) a map of 15,532 oil and natural gas well pad scars and other features associated with oil and gas extraction, and (2) a map of oil and gas, oil shale, uranium, and solar energy production, both for southwestern Wyoming. In addition, a map viewer was developed for a previously published map of coal and wind production in relation to sage-grouse distribution and core management areas in southwestern Wyoming. Combined, these maps place valuable decision-support tools in the hands of WLCI partners.
The USGS coordination efforts on behalf of the WLCI in 2015 included significant work on planning and executing the WLCI science conference. They also included ongoing efforts to support Local Project Development Teams and the WLCI Coordination Team (CT) with developing conservation priorities and strategies, identifying priority areas for future conservation actions, supporting the evaluation and ranking of conservation projects, and evaluating the ways in which proposed habitat projects relate to WLCI priorities. In 2015, the USGS also assisted the WLCI CT with updating the WLCI Conservation Action Plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161141","usgsCitation":"Bowen, Z.H., Aldridge, C.L., Anderson, P.J., Assal, T.J., Bartos, T.T., Chalfoun, A.D., Chong, G.W., Dematatis, M.K., Eddy-Miller, C.A., Garman, S.L., Germaine, S.S., Homer, C.G., Huber, C.C., Kauffman, M.J., Manier, D.J., Melcher, C.P., Miller, K.A., Norkin, Tamar, Sanders, L.E., Walters, A.W., Wilson, A.B., and Wyckoff, T.B., 2016, U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2015 annual report: U.S. Geological Survey Open-File Report 2016–1141, 59 p., https://dx.doi.org/10.3133/ofr20161141.","productDescription":"viii, 59 p.","onlineOnly":"Y","ipdsId":"IP-075182","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":329020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1141/coverthb.jpg"},{"id":329021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1141/ofr20161141.pdf","text":"Report","size":"36.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1141"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.07177734375,\n 43.40504748787035\n ],\n [\n -111.0498046875,\n 41\n ],\n [\n -105.99609375,\n 41\n ],\n [\n -105.99609375,\n 41.80407814427234\n ],\n [\n -105.35888671875,\n 41.82045509614034\n ],\n [\n -105.380859375,\n 42.47209690919285\n ],\n [\n -106.5673828125,\n 42.52069952914966\n ],\n [\n -107.20458984375,\n 42.48830197960227\n ],\n [\n -107.70996093749999,\n 42.53689200787315\n ],\n [\n -108.5009765625,\n 42.779275360241904\n ],\n [\n -108.80859375,\n 42.98857645832184\n ],\n [\n -109.22607421875,\n 43.229195113965005\n ],\n [\n -109.3798828125,\n 43.42100882994726\n ],\n [\n -109.79736328125,\n 43.5326204268101\n ],\n [\n -110.41259765625,\n 43.56447158721811\n ],\n [\n -111.07177734375,\n 43.40504748787035\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"
Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
The city of Sioux Falls is the fastest growing community in South Dakota. In response to this continued growth and planning for future development, Sioux Falls requires a sustainable supply of municipal water. Planning and managing sustainable groundwater supplies requires a thorough understanding of local groundwater resources. The Big Sioux aquifer consists of glacial outwash sands and gravels and is hydraulically connected to the Big Sioux River, which provided about 90 percent of the city’s source-water production in 2015. Managing sustainable groundwater supplies also requires an understanding of groundwater availability. An effective mechanism to inform water management decisions is the development and utilization of a groundwater-flow model. A groundwater-flow model provides a quantitative framework for synthesizing field information and conceptualizing hydrogeologic processes. These groundwater-flow models can support decision making processes by mapping and characterizing the aquifer. Accordingly, the city of Sioux Falls partnered with the U.S. Geological Survey to construct a groundwater-flow model. Model inputs will include data from advanced geophysical techniques, specifically airborne electromagnetic methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163075","collaboration":"Prepared in cooperation with the City of Sioux Falls","usgsCitation":"Valder, J.F., Delzer, G.C., Carter, J.M., Smith, B.D., and Smith, D.V., 2016, Construction of a groundwater-flow model for the Big Sioux aquifer using airborne electromagnetic methods, Sioux Falls, South Dakota: U.S. Geological Survey Fact Sheet 2016–3075, 4 p., https://dx.doi.org/10.3133/fs20163075.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-079092","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":328947,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3075/fs20163075.pdf","text":"Fact Sheet","size":"4.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3075"},{"id":328946,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3075/coverthb.jpg"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.79023742675781,\n 43.56098868633536\n ],\n [\n -96.79023742675781,\n 43.823133635349556\n ],\n [\n -96.69136047363281,\n 43.823133635349556\n ],\n [\n -96.69136047363281,\n 43.56098868633536\n ],\n [\n -96.79023742675781,\n 43.56098868633536\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Dakota Water Science Center
U.S. Geological Survey
1608 Mountain View Road
Rapid City, South Dakota 57702
Potential sources of analytical bias and error associated with laboratory analyses for selected trace elements where concentrations were greater in filtered samples than in paired unfiltered samples were evaluated by U.S. Geological Survey (USGS) Water Quality Specialists in collaboration with the USGS National Water Quality Laboratory (NWQL) and the Branch of Quality Systems (BQS).
Causes for trace-element concentrations in filtered samples to exceed those in associated unfiltered samples have been attributed to variability in analytical measurements, analytical bias, sample contamination either in the field or laboratory, and (or) sample-matrix chemistry. These issues have not only been attributed to data generated by the USGS NWQL but have been observed in data generated by other laboratories. This study continues the evaluation of potential analytical bias and error resulting from matrix chemistry and instrument variability by evaluating the performance of seven selected trace elements in paired filtered and unfiltered surface-water and groundwater samples collected from 23 sampling sites of varying chemistries from six States, matrix spike recoveries, and standard reference materials.
Filtered and unfiltered samples have been routinely analyzed on separate inductively coupled plasma-mass spectrometry instruments. Unfiltered samples are treated with hydrochloric acid (HCl) during an in-bottle digestion procedure; filtered samples are not routinely treated with HCl as part of the laboratory analytical procedure. To evaluate the influence of HCl on different sample matrices, an aliquot of the filtered samples was treated with HCl. The addition of HCl did little to differentiate the analytical results between filtered samples treated with HCl from those samples left untreated; however, there was a small, but noticeable, decrease in the number of instances where a particular trace-element concentration was greater in a filtered sample than in the associated unfiltered sample for all trace elements except selenium. Accounting for the small dilution effect (2 percent) from the addition of HCl, as required for the in-bottle digestion procedure for unfiltered samples, may be one step toward decreasing the number of instances where trace-element concentrations are greater in filtered samples than in paired unfiltered samples.
The laboratory analyses of arsenic, cadmium, lead, and zinc did not appear to be influenced by instrument biases. These trace elements showed similar results on both instruments used to analyze filtered and unfiltered samples. The results for aluminum and molybdenum tended to be higher on the instrument designated to analyze unfiltered samples; the results for selenium tended to be lower. The matrices used to prepare calibration standards were different for the two instruments. The instrument designated for the analysis of unfiltered samples was calibrated using standards prepared in a nitric:hydrochloric acid (HNO3:HCl) matrix. The instrument designated for the analysis of filtered samples was calibrated using standards prepared in a matrix acidified only with HNO3. Matrix chemistry may have influenced the responses of aluminum, molybdenum, and selenium on the two instruments. The best analytical practice is to calibrate instruments using calibration standards prepared in matrices that reasonably match those of the samples being analyzed.
Filtered and unfiltered samples were spiked over a range of trace-element concentrations from less than 1 to 58 times ambient concentrations. The greater the magnitude of the trace-element spike concentration relative to the ambient concentration, the greater the likelihood spike recoveries will be within data control guidelines (80–120 percent). Greater variability in spike recoveries occurred when trace elements were spiked at concentrations less than 10 times the ambient concentration. Spike recoveries that were considerably lower than 90 percent often were associated with spiked concentrations substantially lower than what was present in the ambient sample. Because the main purpose of spiking natural water samples with known quantities of a particular analyte is to assess possible matrix effects on analytical results, the results of this study stress the importance of spiking samples at concentrations that are reasonably close to what is expected but sufficiently high to exceed analytical variability. Generally, differences in spike recovery results between paired filtered and unfiltered samples were minimal when samples were analyzed on the same instrument.
Analytical results for trace-element concentrations in ambient filtered and unfiltered samples greater than 10 and 40 μg/L, respectively, were within the data-quality objective for precision of ±25 percent. Ambient trace-element concentrations in filtered samples greater than the long-term method detection limits but less than 10 μg/L failed to meet the data-quality objective for precision for at least one trace element in about 54 percent of the samples. Similarly, trace-element concentrations in unfiltered samples greater than the long-term method detection limits but less than 40 μg/L failed to meet this data-quality objective for at least one trace-element analysis in about 58 percent of the samples. Although, aluminum and zinc were particularly problematic, limited re-analyses of filtered and unfiltered samples appeared to improve otherwise failed analytical precision.
The evaluation of analytical bias using standard reference materials indicate a slight low bias for results for arsenic, cadmium, selenium, and zinc. Aluminum and molybdenum show signs of high bias. There was no observed bias, as determined using the standard reference materials, during the analysis of lead.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165135","usgsCitation":"Paul, A.P., Garbarino, J.R., Olsen, L.D., Rosen, M.R., Mebane, C.A., and Struzeski, T.M., 2016, Potential sources of analytical bias and error in selected trace-element data quality analyses: U.S. Geological Survey Scientific Investigations Report 2016–5135, 58 p., https://dx.doi.org/10.3133/sir20165135.","productDescription":"vi, 58 p.","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-074836","costCenters":[{"id":5066,"text":"Office of the Director USGS","active":true,"usgs":true}],"links":[{"id":329081,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5135/sir20165135.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5138"},{"id":329080,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5135/coverthb.jpg"}],"contact":"Chief, Water Science Field Team
U.S. Geological Survey
520 North Park Avenue
Tucson, Arizona 85721
http://az.water.usgs.gov/
Assessments of streamflow (discharge) parameters, water quality, physical habitat, and biological communities were completed between May and September 2014 as part of a monitoring program in the Big Wood River watershed of south-central Idaho. The sampling was conducted by the U.S. Geological Survey in cooperation with Blaine County, Trout Unlimited, the Nature Conservancy, and the Wood River Land Trust to help identify the status of aquatic resources at selected locations in the watershed. Information in this report provides a basis with which to evaluate and monitor the long-term health of the Big Wood River and its major tributaries. Sampling sites were co-located with existing U.S. Geological Survey streamgaging stations: three on the main stem Big Wood River and four on the North Fork Big Wood River (North Fork), Warm Springs Creek (Warm Sp), Trail Creek (Trail Ck), and East Fork Big Wood River (East Fork) tributaries.
The analytical results and quality-assurance information for water quality, physical habitat, and biological community samples collected at study sites during 2 weeks in September 2014 are summarized. Water-quality data include concentrations of major nutrients, suspended sediment, dissolved oxygen, and fecal-coliform bacteria. To assess the potential effects of nutrient enrichment on algal growth, concentrations of periphyton biomass (chlorophyll-a and ash free dry weight) in riffle habitats were determined at each site. Physical habitat parameters include stream channel morphology, habitat volume, instream structure, substrate composition, and riparian vegetative cover. Biological data include taxa richness, abundance, and stream-health indicator metrics for macroinvertebrate and fish communities. Statistical summaries of the water-quality, habitat, and biological data are provided along with discussion of how these findings relate to the health of aquatic resources in the Big Wood River watershed.
Seasonal discharge patterns using statistical summaries of daily discharge from selected sites are reported for water years 2012–15. Results showed that annual average daily mean discharge increased from the Big Wood River near Ketchum, ID (BW Ketchum) downstream to the Big Wood River at Hailey, ID (BW Hailey), but decreased by nearly 50 percent from BW Hailey downstream to Big Wood River at Stanton Crossing near Bellevue, ID (BW Stanton). Annual variability in daily mean discharge among main-stem sites was highest at BW Stanton, suggesting that this part of the river may be subject to some level of flow alteration.
Hydrologic alterations resulting in flow reduction can contribute to higher water temperature, especially during the summer months when conditions are often most stressful to fish and other stream organisms. Daily water temperature and water temperature trends from June to September 2014 are reported for select tributary and main-stem sites on the Big Wood River and can be used to assess the potential for biological impairment based on aquatic life temperature criteria for cold-water streams. The State of Idaho maximum temperature criteria for protection of cold-water aquatic life of 22 °C was exceeded at Warm Sp and BW Stanton during summer 2014, but at none of the other main-stem or tributary sites. The 13 °C critical temperature criterion for salmonid spawning was exceeded in early July 2014 at BW Ketchum and BW Hailey near the end of the rainbow trout critical spawning and rearing period. Temperature exceedances were most frequent at BW Stanton, where exceedances for rainbow trout and brown trout occurred from May through early July 2014 during most of the critical spawning and rearing period.
Water quality and habitat availability did not seem to be limiting for fish or other aquatic organisms at most sites in the Big Wood River watershed. Water quality assessments in September 2014 determined no exceedances of total maximum daily load target levels. The availability and quality of habitat was limited at BW Stanton, where shallow-water habitat conditions prevailed.
Macroinvertebrate community diversity was high at all sites except for BW Stanton, where low community diversity was attributed to low species richness and high abundances of a few tolerant taxa. Presence of low species diversity and high macroinvertebrate tolerance values at BW Stanton indicates that benthic community condition and stream health were reduced at that location.
Fish surveys done in September 2014 did not indicate any significant reductions in native fish communities in the Big Wood River or its tributaries. Native rainbow trout (Oncorhynchus mykiss) and Wood River sculpin (Cottus leiopomus) were the dominant fish species in the drainage and were found at all tributary and main-stem sites. Non-native brown (Salmo trutta) and brook trout (Salvelinus fontinalis) were limited to lower drainage sites on the Big Wood River (BW Hailey and BW Stanton), and occurred in relatively low numbers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165128","collaboration":"Prepared in cooperation with Blaine County, Trout Unlimited, The Nature Conservancy, and the Wood River Land Trust","usgsCitation":"MacCoy, D.E., and Short, T.M., 2016, Aquatic biological communities and associated habitats at selected sites in the Big Wood River watershed, south-central Idaho, 2014: U.S. Geological Survey Scientific Investigations Report 2016–5128, 37 p., https://dx.doi.org/10.3133/sir20165128.","productDescription":"vi, 37 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-061251","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":329075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5128/sir20165128.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5128"},{"id":329074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5128/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Big Wood River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.58465576171875,\n 43.2622061978402\n ],\n [\n -114.58465576171875,\n 43.81471121600004\n ],\n [\n -114.01473999023438,\n 43.81471121600004\n ],\n [\n -114.01473999023438,\n 43.2622061978402\n ],\n [\n -114.58465576171875,\n 43.2622061978402\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
http://id.water.usgs.gov
Warm Mineral Springs, located in southern Sarasota County, Florida, is a warm, highly mineralized, inland spring. Since 1946, a bathing spa has been in operation at the spring, attracting vacationers and health enthusiasts. During the winter months, the warm water attracts manatees to the adjoining spring run and provides vital habitat for these mammals. Well-preserved late Pleistocene to early Holocene-age human and animal bones, artifacts, and plant remains have been found in and around the spring, and indicate the surrounding sinkhole formed more than 12,000 years ago. The spring is a multiuse resource of hydrologic importance, ecological and archeological significance, and economic value to the community.
The pool of Warm Mineral Springs has a circular shape that reflects its origin as a sinkhole. The pool measures about 240 feet in diameter at the surface and has a maximum depth of about 205 feet. The sinkhole developed in the sand, clay, and dolostone of the Arcadia Formation of the Miocene-age to Oligocene-age Hawthorn Group. Underlying the Hawthorn Group are Oligocene-age to Eocene-age limestones and dolostones, including the Suwannee Limestone, Ocala Limestone, and Avon Park Formation. Mineralized groundwater, under artesian pressure in the underlying aquifers, fills the remnant sink, and the overflow discharges into Warm Mineral Springs Creek, to Salt Creek, and subsequently into the Myakka River. Aquifers described in the vicinity of Warm Mineral Springs include the surficial aquifer system, the intermediate aquifer system within the Hawthorn Group, and the Upper Floridan aquifer in the Suwannee Limestone, Ocala Limestone, and Avon Park Formation. The Hawthorn Group acts as an upper confining unit of the Upper Floridan aquifer.
Groundwater flow paths are inferred from the configuration of the potentiometric surface of the Upper Floridan aquifer for September 2010. Groundwater flow models indicate the downward flow of water into the Upper Floridan aquifer in inland areas, and upward flow toward the surface in coastal areas, such as at Warm Mineral Springs. Warm Mineral Springs is located in a discharge area. Changes in water use in the region have affected the potentiometric surface of the Upper Floridan aquifer. Historical increase in groundwater withdrawals resulted in a 10- to 20-foot regional decline in the potentiometric surface of the Upper Floridan aquifer by May 1975 relative to predevelopment levels and remained at approximately that level in May 2007 in the area of Warm Mineral Springs. Discharge measurements at Warm Mineral Springs (1942–2014) decreased from about 11–12 cubic feet per second in the 1940s to about 6–9 cubic feet per second in the 1970s and remained at about that level for the remainder of the period of record. Similarity of changes in regional water use and discharge at Warm Mineral Springs indicates that basin-scale changes to the groundwater system have affected discharge at Warm Mineral Springs. Water temperature had no significant trend in temperature over the period of record, 1943–2015, and outliers were identified in the data that might indicate inconsistencies in measurement methods or locations.
Within the regional groundwater basin, Warm Mineral Springs is influenced by deep Upper Floridan aquifer flow paths that discharge toward the coast. Associated with these flow paths, the groundwater temperatures increase with depth and toward the coast. Multiple lines of evidence indicate that a source of warm groundwater to Warm Mineral Springs is likely the permeable zone of the Avon Park Formation within the Upper Floridan aquifer at a depth of about 1,400 to 1,600 feet, or deeper sources. The permeable zone contains saline groundwater with water temperatures of at least 95 degrees Fahrenheit.
The water quality of Warm Mineral Springs, when compared with other springs in Florida had the highest temperature and the greatest mineralized content. Warm Mineral Springs water is characterized by a slight-green color, with varying water clarity, low dissolved oxygen (indicative of deep groundwater), and a hydrogen sulfide odor. Water-quality samples detected ammonium-nitrogen and nitrates, but at low concentrations. The drinking water standard for nitrate adopted by the U.S. Environmental Protection Agency is 10 milligrams per liter, measured as nitrogen. Water samples collected at spring vents by divers on April 29, 2015, had concentrations of 0.9 milligram per liter nitrate-nitrogen at vent A and 0.04–0.05 milligram per liter at vents B, C, and D. Typically, the water clarity is highest in the morning (about 30 feet Secchi depth) and often decreases throughout the day.
Analysis of existing data provided some insight into the hydrologic processes affecting Warm Mineral Springs; however, data have been sparsely and discontinuously collected since the 1940s. Continuous monitoring of hydrologic characteristics such as discharge, water temperature, specific conductance, and water-quality indicators, such as nitrate and turbidity (water clarity), would be valuable for monitoring and development of models of spring discharge and water quality. In addition, water samples could be analyzed for isotopic tracers, such as strontium, and the results used to identify and quantify the sources of groundwater that discharge at Warm Mineral Springs. Groundwater flow/transport models could be used to evaluate the sensitivity of the quality and quantity of water flowing from Warm Mineral Springs to changes in climate, aquifer levels, and water use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161166","collaboration":"Prepared in cooperation with the City of North Port and Sarasota County ","usgsCitation":"Metz, P.A., 2016, Discharge, water temperature, and water quality of Warm Mineral Springs, Sarasota County, Florida: A retrospective analysis: U.S. Geological Survey Open-File Report 2016–1166, 31 p., https://dx.doi.org/10.3133/ofr20161166.","productDescription":"vi, 31 p.","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-066216","costCenters":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"links":[{"id":328877,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1166/coverthb2.jpg"},{"id":328878,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1166/ofr20161166.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1166"}],"country":"United States","state":"Florida","county":"Sarasota County","otherGeospatial":"Warm Mineral Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.26177334785461,\n 27.058867814427533\n ],\n [\n -82.26177334785461,\n 27.060654482992454\n ],\n [\n -82.25942373275757,\n 27.060654482992454\n ],\n [\n -82.25942373275757,\n 27.058867814427533\n ],\n [\n -82.26177334785461,\n 27.058867814427533\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
(813) 498-5000
Or visit the Caribbean-Florida Water Science Center Web page at
http://fl.water.usgs.gov
This study provides an independent estimate of the areal and volumetric extent of groundwater contaminant plumes which are affected by waste disposal in the 100-K and 100-N Areas (study area) along the Columbia River Corridor of the Hanford Site. The Hanford Natural Resource Trustee Council requested that the U.S. Geological Survey perform this interpolation to assess the accuracy of delineations previously conducted by the U.S. Department of Energy and its contractors, in order to assure that the Natural Resource Damage Assessment could rely on these analyses. This study is based on previously existing chemical (or radionuclide) sampling and analysis data downloaded from publicly available Hanford Site Internet sources, geostatistically selected and interpreted as representative of current (from 2009 through part of 2012) but average conditions for groundwater contamination in the study area. The study is limited in scope to five contaminants—hexavalent chromium, tritium, nitrate, strontium-90, and carbon-14, all detected at concentrations greater than regulatory limits in the past.
All recent analytical concentrations (or activities) for each contaminant, adjusted for radioactive decay, non-detections, and co-located wells, were converted to log-normal distributions and these transformed values were averaged for each well location. The log-normally linearized well averages were spatially interpolated on a 50 × 50-meter (m) grid extending across the combined 100-N and 100-K Areas study area but limited to avoid unrepresentative extrapolation, using the minimum curvature geostatistical interpolation method provided by SURFER®data analysis software. Plume extents were interpreted by interpolating the log-normally transformed data, again using SURFER®, along lines of equal contaminant concentration at an appropriate established regulatory concentration . Total areas for each plume were calculated as an indicator of relative environmental damage. These plume extents are shown graphically and in tabular form for comparison to previous estimates. Plume data also were interpolated to a finer grid (10 × 10 m) for some processing, particularly to estimate volumes of contaminated groundwater. However, hydrogeologic transport modeling was not considered for the interpolation. The compilation of plume extents for each contaminant also allowed estimates of overlap of the plumes or areas with more than one contaminant above regulatory standards.
A mapping of saturated aquifer thickness also was derived across the 100-K and 100–N study area, based on the vertical difference between the groundwater level (water table) at the top and the altitude of the top of the Ringold Upper Mud geologic unit, considered the bottom of the uppermost unconfined aquifer. Saturated thickness was calculated for each cell in the finer (10 × 10 m) grid. The summation of the cells’ saturated thickness values within each polygon of plume regulatory exceedance provided an estimate of the total volume of contaminated aquifer, and the results also were checked using a SURFER® volumetric integration procedure. The total volume of contaminated groundwater in each plume was derived by multiplying the aquifer saturated thickness volume by a locally representative value of porosity (0.3).
Estimates of the uncertainty of the plume delineation also are presented. “Upper limit” plume delineations were calculated for each contaminant using the same procedure as the “average” plume extent except with values at each well that are set at a 95-percent upper confidence limit around the log-normally transformed mean concentrations, based on the standard error for the distribution of the mean value in that well; “lower limit” plumes are calculated at a 5-percent confidence limit around the geometric mean. These upper- and lower-limit estimates are considered unrealistic because the statistics were increased or decreased at each well simultaneously and were not adjusted for correlation among the well distributions (i.e., it is not realistic that all wells would be high simultaneously). Sources of the variability in the distributions used in the upper- and lower-extent maps include time varying concentrations and analytical errors.
The plume delineations developed in this study are similar to the previous plume descriptions developed by U.S. Department of Energy and its contractors. The differences are primarily due to data selection and interpolation methodology. The differences in delineated plumes are not sufficient to result in the Hanford Natural Resource Trustee Council adjusting its understandings of contaminant impact or remediation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161161","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Hanford Natural Resource Trustee Council","usgsCitation":"Johnson, K.H., 2016, Groundwater contaminant plume maps and volumes, 100-K and 100–N Areas, Hanford Site, Washington: U.S. Geological Survey Open-File Report 2016–1161, 64 p., https://dx.doi.org/10.3133/ofr20161161.","productDescription":"Report: vi, 64 p.; Appendix A","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-071546","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":329028,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1161/coverthb.jpg"},{"id":329029,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1161/ofr20161161.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1161"},{"id":329030,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1161/ofr20161161_appendixa.xlsx","text":"Appendix A","size":"2.1 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1161 Appendix A"}],"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 -119.83886718750001,\n 46.25204849722291\n ],\n [\n -119.83886718750001,\n 46.82637528602131\n ],\n [\n -119.19616699218749,\n 46.82637528602131\n ],\n [\n -119.19616699218749,\n 46.25204849722291\n ],\n [\n -119.83886718750001,\n 46.25204849722291\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
http://wa.water.usgs.gov
Three different geophysical sensor types were used to characterize the underwater pressure waves and ground velocities generated by the underwater firing of seismic water guns. These studies evaluated the use of water guns as a tool to alter the movement of Asian carp. Asian carp are aquatic invasive species that threaten to move into the Great Lakes Basin from the Mississippi River Basin. Previous studies have identified a threshold of approximately 5 pounds per square inch (lb/in2) for behavioral modification and for structural limitation of a water gun barrier.
Two studies were completed during August 2014 and May 2015 in a backwater pond connected to the Illinois River at a sand and gravel quarry near Morris, Illinois. The August 2014 study evaluated the performance of two 80-cubic-inch (in3) water guns. Data from the 80-in3 water guns showed that the pressure field had the highest pressures and greatest extent of the 5-lb/in2 target value at a depth of 5 feet (ft). The maximum recorded pressure was 13.7 lb/in2, approximately 25 ft from the guns. The produced pressure field took the shape of a north-south-oriented elongated sphere with the 5-lb/in2 target value extending across the entire study area at a depth of 5 ft. Ground velocities were consistent over time, at 0.0067 inches per second (in/s) in the transverse direction, 0.031 in/s in the longitudinal direction, and 0.013 in/s in the vertical direction.
The May 2015 study evaluated the performance of one and two 100-in3 water guns. Data from the 100-in3 water guns, fired both individually and simultaneously, showed that the pressure field had the highest pressures and greatest extent of the 5-lb/in2 target value at a depth of 5 ft. The maximum pressure was 57.4 lb/in2, recorded at the underwater blast sensor closest to the water guns (at a horizontal distance of approximately 3 ft), as two guns fired simultaneously. Pressures and extent of the 5-lb/in2 target value decrease above and below this 5-ft depth, producing a relatively north-south-oriented pressure field shaped like an elongated sphere.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165098","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency as part of the Great Lakes Restoration Initiative","usgsCitation":"Koebel, C.M., and Egly, R.M., 2016, Water pressure and ground vibrations induced by water guns at a backwater pond on the Illinois River near Morris, Illinois: U.S. Geological Survey Scientific Investigations Report 2016–5098, 29 p., https://dx.doi.org/10.3133/sir20165098.","productDescription":"Report: v, 29 p.; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-072620","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":328926,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2016/5098/sir20165098_morris_seismic_data.zip","text":"Seismic Data","size":"890.7 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5098 Seismic Data"},{"id":328934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5098/coverthb.jpg"},{"id":328935,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5098/sir20165098.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5098"}],"country":"United States","state":"Illinois","city":"Morris","otherGeospatial":"Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.50388526916504,\n 41.32726186584144\n ],\n [\n -88.50388526916504,\n 41.334738165428035\n ],\n [\n -88.46534729003906,\n 41.334738165428035\n ],\n [\n -88.46534729003906,\n 41.32726186584144\n ],\n [\n -88.50388526916504,\n 41.32726186584144\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director , Illinois-Iowa Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801
http://il.water.usgs.gov
Scientists and technical support staff from the U.S. Geological Survey measured suspended-sediment concentrations, currents, pressure, and water temperature in two tidal creeks, Reedy Creek and Dinner Creek, in Barnegat Bay, New Jersey, from August 11, 2014, to July 10, 2015 as part of the Estuarine Physical Response to Storms project (GS2–2D). The oceanographic and water-quality data quantify suspended-sediment transport in Reedy Creek and Dinner Creek, which are part of a tidal marsh wetland complex in the Edwin B. Forsythe National Wildlife Refuge. All deployed instruments were removed between January 7, 2015, and April 14, 2015, to avoid damage by ice.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161149","usgsCitation":"Suttles, S.E., Ganju, N.K, Montgomery, E.T., Dickhudt, P.J., Borden, Jonathan., Brosnahan, S.M., and Martini, M.A., 2016, Summary of oceanographic and water-quality measurements in Barnegat Bay, New Jersey, 2014–15: U.S. Geological Survey Open-File Report 2016–1149, 22 p., https://dx.doi.org/10.3133/ofr20161149.","productDescription":"Report: vii, 22 p.; Appendix: 1-2","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076598","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":328800,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1149/appendix/downloads/adcptransects","text":"Appendix 2 ","description":"OFR 2016-1149","linkHelpText":"- Acoustic Doppler Current Profiler Transect Data, August 2014 and May 2015 for Reedy and Dinner Creeks"},{"id":328792,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1149/coverthb.jpg"},{"id":328793,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1149/ofr20161149.pdf","text":"Report","size":"5.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1149"},{"id":328794,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1149/appendix/downloads/watersamples/ofr20161149_appendix1.zip","text":"Appendix 1","size":"40.4 KB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2016-1149","linkHelpText":"- Water Sample Data, August 2014 to January 2015 and April to July 2015"}],"country":"United States","state":"New Jersey","otherGeospatial":"Barnegat Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.55184936523438,\n 39.36190883564925\n ],\n [\n -74.55184936523438,\n 40.08752795413118\n ],\n [\n -74.02450561523438,\n 40.08752795413118\n ],\n [\n -74.02450561523438,\n 39.36190883564925\n ],\n [\n -74.55184936523438,\n 39.36190883564925\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543
http://woodshole.er.usgs.gov/
MODPATH is a particle-tracking post-processing program designed to work with MODFLOW, the U.S. Geological Survey (USGS) finite-difference groundwater flow model. MODPATH version 7 is the fourth major release since its original publication. Previous versions were documented in USGS Open-File Reports 89–381 and 94–464 and in USGS Techniques and Methods 6–A41.
MODPATH version 7 works with MODFLOW-2005 and MODFLOW–USG. Support for unstructured grids in MODFLOW–USG is limited to smoothed, rectangular-based quadtree and quadpatch grids.
A software distribution package containing the computer program and supporting documentation, such as input instructions, output file descriptions, and example problems, is available from the USGS over the Internet (http://water.usgs.gov/ogw/modpath/).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161086","usgsCitation":"Pollock, D.W., 2016, User guide for MODPATH Version 7—A particle-tracking model for MODFLOW: U.S. Geological Survey Open-File Report 2016–1086, 35 p., https://dx.doi.org/10.3133/ofr20161086. ","productDescription":"v, 35 p.","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-073899","costCenters":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"links":[{"id":325509,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr94464","text":"Open-File Report 1994-464","description":"ofr 2016-1086","linkHelpText":"- User's guide for MODPATH/MODPATH-PLOT, Version 3; a particle tracking post-processing package for MODFLOW, the U.S. Geological Survey finite-difference ground-water flow model"},{"id":325510,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/tm6A41","text":"Techniques and Methods 6-A41","linkFileType":{"id":5,"text":"html"},"description":"ofr 2016-1086","linkHelpText":"- User guide for MODPATH Version 6 - A particle-tracking model for MODFLOW"},{"id":325508,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr89381","text":"Open-File Report 1989-381","description":"ofr 2016-1086","linkHelpText":"- Documentation of computer programs to compute and display pathlines using results from the U.S. Geological Survey modular three-dimensional finite-difference ground-water flow model"},{"id":325506,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1086/coverthb.jpg"},{"id":325507,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1086/ofr20161086.pdf","text":"Report","size":"3.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"ofr 2016-1086"},{"id":328711,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://water.usgs.gov/ogw/modpath/","text":"MODPATH Version 7","description":"ofr 2016-1086","linkHelpText":"- MODPATH: A Particle-Tracking Model for MODFLOW"}],"contact":"Office of Groundwater
U.S. Geological Survey
Mail Stop 411
12201 Sunrise Valley Drive
Reston, VA 20192
http://water.usgs.gov/ogw/
Namskaket Marsh and its tidal creek system are potential receptors for a treated wastewater plume originating from a septage treatment facility in the northwest part of Orleans, Massachusetts, on Cape Cod. From 1989 to 2011, the U.S. Geological Survey, in cooperation with State and local partners, conducted a series of studies in the Namskaket Marsh area to characterize the potential effects of the plume on the marsh and its tidal creek system. Studies included characterizing the baseline vegetation and salinity distribution in the marsh, monitoring the movement of the wastewater plume downgradient of the septage treatment facility, and sampling nutrient concentrations in the tidal creek system during a baseline period prior to the arrival of the plume at the marsh boundary. The Inner Namskaket Marsh baseline vegetation survey in 1995 found it to be dominated by Phragmites australis (common reed, 44 percent of vegetative cover), Spartina patens (salt marsh hay, 17 percent), and Spartina alterniflora (cordgrass, 9 percent). Phragmites occurrence was correlated with shallow pore-water salinity in the marsh peat and was largely confined to areas with salinities less than 4 parts per thousand. Baseline, ebb-tide nutrient concentrations at the tidal creek sampling stations during 1994–96 showed strong seasonal variations for ammonium, likely associated with the seasonal cycle of growth and senescence for the dominant salt marsh grasses (S. alterniflora and S. patens). The seasonal cycle for nitrate was generally less pronounced.
\nThe movement of the wastewater plume has been monitored from its source at the septage treatment facility to areas immediately adjacent to and beneath the most inland part of the marsh. In late 1994, the plume was first detected by borehole geophysical logging in observation wells along the Cape Cod Rail Trail (rail trail), 600 feet northwest of the infiltration beds, at an elevation of 47 to 53 feet below the National Geodetic Vertical Datum of 1929 (NGVD 29). At the rail trail, the plume was largely confined below a 3- to 8-foot-thick silt/clay layer detected by borehole geophysical logging and confirmed by lithologic samples. By early 1998, a second plume segment was detected above this silt/clay layer at the rail trail, near the plume’s southwest boundary. Groundwater sampling in 2003–4 at additional stations southwest of the main plume, as well as beneath Namskaket Marsh, defined the extent of this shallow plume segment in glacial sands underlying the marsh.
\nThe tidal creek sampling stations established in the 1990s were resampled in 2003–4 and 2010–11 to evaluate potential effects of the treated wastewater plume on creek water quality. The annual medians of the 2011 biweekly nitrate and total dissolved nitrogen concentrations were determined for each station and compared to the annual medians of biweekly samples for the baseline years 1994, 1995, and 1996. At all stations, the 2011 median nitrate concentrations were within the range of medians for the 3 baseline years. A similar result was obtained for total dissolved nitrogen. We conclude that the 2011 creek samples, collected approximately 8 years after the shallow plume segment was first detected beneath the marsh, do not show evidence of elevated nitrate or total dissolved nitrogen concentrations attributable to discharge of either the shallow or deep segments of the treated wastewater plume.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165122","collaboration":"Prepared in cooperation with the Orleans, Brewster, and Eastham Groundwater Protection District","usgsCitation":"Weiskel, P.K., Barbaro, J.R., and DeSimone, L.A., 2016, Environmental conditions in the Namskaket Marsh area, Orleans, Massachusetts—A summary of studies by the U.S. Geological Survey, 1989–2011: U.S. Geological Survey Scientific Investigations Report 2016–5122, 29 p., https://dx.doi.org/10.3133/sir20165122.","productDescription":"viii, 29 p.","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-072329","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":328802,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5122/sir20165122.pdf","text":"Report","size":"1.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5122"},{"id":328801,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5122/coverthb.jpg"}],"country":"United States","state":"Massachusetts","city":"Orleans","otherGeospatial":"Namskaket Marsh Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.03174781799316,\n 41.768623062506876\n ],\n [\n -70.03174781799316,\n 41.79998325207397\n ],\n [\n -69.99750137329102,\n 41.79998325207397\n ],\n [\n -69.99750137329102,\n 41.768623062506876\n ],\n [\n -70.03174781799316,\n 41.768623062506876\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Or Visit our Web site at:
http://newengland.water.usgs.gov
The Northern Arizona Regional Groundwater Flow Model was used to estimate the hydrologic changes, including water-level change and groundwater discharge to streams and springs, that may result from future changes in groundwater withdrawals in and near the Coconino Plateau Water Advisory Council study area, Coconino and Navajo Counties, Arizona. Three future groundwater withdrawal scenarios for tribal and nontribal uses were developed by the Coconino Plateau Water Advisory Council and were simulated for the period representing the years from 2006 through 2105. Scenario 1 assumes no major changes in groundwater use except for increased demand based on population projections. Scenario 2 assumes that a pipeline will provide a source of surface water from Lake Powell to areas near Cameron and Moenkopi that would replace local groundwater withdrawals. Scenario 3 assumes that the pipeline extends to the Flagstaff and Williams areas, and would replace groundwater demands for water in the area.
The Coconino Plateau Water Advisory Council withdrawal scenarios primarily influence water levels and groundwater discharge in the Coconino Plateau basin, near the western margin of the Little Colorado River Plateau basin, and the Verde Valley subbasin. Simulated effects of the withdrawal scenarios are superimposed on effects of previous variations in groundwater withdrawals and artificial and incidental recharge. Pre-scenario variations include changes in water-levels in wells; groundwater storage; discharge to streams and springs; and evapotranspiration by plants that use groundwater. Future variations in groundwater discharge and water-levels in wells will continue to occur as a result of both the past and any future changes.
Water-level variations resulting from post-2005 stresses, including groundwater withdrawals and incidental and artificial recharge, in the area of the withdrawal scenarios are primarily localized and superimposed on the regional changes caused by variations in stresses that occurred since the beginning of the initial stresses in the early 1900s through 2005. Withdrawal scenario 1 produced a broad region on the Coconino Plateau where water-levels declined 3–5 feet by 2105, and local areas with water-level declines of 100 feet or more where groundwater withdrawals are concentrated, near the City of Flagstaff Woody Mountain and Lake Mary well fields, and the towns of Tusayan, Williams, and Moenkopi. Water-level rises of 100 feet or more were simulated at areas of incidental recharge near wastewater treatment facilities near Flagstaff, Tusayan, Grand Canyon South Rim, Williams, and Munds Park.
Simulated water-level change from 2006 through 2105 for scenarios 2 and 3 is mostly different from water-level change simulated for scenario 1 at the local level. For scenarios 2 and 3, water levels near Cameron in 2105 where 1–3 feet higher than simulated for scenario 1. Water levels at Moenkopi are more than 100 feet higher due to the elimination of a proposed withdrawal well that was simulated in scenario 1. Scenario 3 eliminates more groundwater withdrawals in the Flagstaff and Williams areas, simulates 1–3 feet less water-level decline than scenario 1 across much of the Coconino Plateau, and water levels that are as much as 50 feet higher than simulated by scenario 1 near withdrawal wells in the Williams and Flagstaff areas.
Scenario 1 simulated the most change in groundwater discharge for the Little Colorado River below Cameron and for Oak Creek above Page Springs where declines in discharge of about 1.3 and 0.9 cubic feet per second (ft3/s), respectively, were simulated. Other simulated changes in discharge through 2105 in scenario 1 are losses of less than 0.4 ft3/s at the Upper Verde River, losses of less than 0.3 ft3/s at Havasu Creek and at Colorado River below Havasu Creek, losses of less than 0.1 ft3/s at Clear Creek, and increases in flow at the south rim springs and Chevelon Creek of less than 0.1 and 0.3 ft3/s, respectively. Simulated changes in discharge for scenarios 2 and 3 are less than for scenario 1 because of lower rates of groundwater withdrawal. Scenario 3 resulted in greater groundwater discharge than scenarios 1 and 2 at all major groundwater discharge features from 2006 through 2105 except for Clear and Chevelon Creeks, where the same groundwater discharge was simulated by each of the three scenarios.
Changes in groundwater discharge are expected to occur after 2105 to all major surface features that discharge from the Redwall-Muav and Coconino aquifers because change in aquifer storage was occurring at the end of the simulation in 2105. The accuracy of simulated changes resulting from the Coconino Plateau Water Advisory Council groundwater withdrawal scenarios is dependent on the persistence of several hydrologic assumptions that are inherent in the Northern Arizona Regional Groundwater Flow Model including, but not limited to, the reasonably accurate simulation of (1) transmissivity distributions, (2) distributions of vertical hydraulic properties, (3) distributions of spatial rates of withdrawal and incidental recharge, (4) aquifer extents, and (5) hydrologic barriers and conduits.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165115","collaboration":"Prepared in cooperation with the Arizona Department of Water Resources and Yavapai County","usgsCitation":"Pool, D.R., 2016, Simulation of groundwater withdrawal scenarios for the Redwall-Muav and Coconino aquifer systems of northern and central Arizona: U.S. Geological Survey Scientific Investigations Report 2016–5115, 38 p., https://dx.doi.org/10.3133/sir20165115.","productDescription":"vi, 38 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-072545","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":328682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5115/coverthb.jpg"},{"id":328683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5115/sir20165115.pdf","text":"Report","size":"7.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5115"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.5384521484375,\n 34.511083202999714\n ],\n [\n -112.5384521484375,\n 36.9806150652861\n ],\n [\n -110.50048828124999,\n 36.9806150652861\n ],\n [\n -110.50048828124999,\n 34.511083202999714\n ],\n [\n -112.5384521484375,\n 34.511083202999714\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
http://az.water.usgs.gov/
Although biotic responses to contemporary climate change are spatially pervasive and often reflect synergies between climate and other ecological disturbances, the relative importance of climatic factors versus habitat extent for species persistence remains poorly understood. To address this shortcoming, we performed surveys for American pikas (Ochotona princeps) at > 910 locations in 3 geographic regions of western North America during 2014 and 2015, complementing earlier modern (1994–2013) and historical (1898–1990) surveys. We sought to compare extirpation rates and the relative importance of climatic factors versus habitat area for pikas in a mainland-versus-islands framework. In each region, we found widespread evidence of distributional loss—local extirpations, upslope retractions, and encounter of only old sign. Locally comprehensive surveys suggest extirpation of O. princeps from 5 of 9 new sites from the hydrographic Great Basin and from 11 of 29 sites in northeastern California. Although American pikas were recorded as recently as 2011 in Zion National Park and in 2012 from Cedar Breaks National Monument in Utah, O. princeps now appears extirpated from all reported localities in both park units. Multiple logistic regressions for each region suggested that both temperature-related and water-balance-related variables estimated from DAYMET strongly explained pika persistence at sites in the Great Basin and in Utah but not in the Sierra-Cascade “mainland” portion of northeastern California. Conversely, talus-habitat area did not predict American pika persistence in the Great Basin or Utah but strongly predicted persistence in the Sierra-Cascade mainland. These results not only add new areas to our understanding of long-term trend of the American pika’s distribution, but also can inform decisions regarding allocation of conservation effort and management actions. Burgeoning research on species such as O. princeps has collectively demonstrated the heterogeneity and nuance with which climate can act on the distribution of mountain-dwelling mammals.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/jmammal/gyw128","usgsCitation":"Beever, E.A., Perrine, J.D., Rickman, T., Flores, M., Clark, J.P., Waters, C., Weber, S.S., Yardley, B., Thoma, D.P., Chesley-Preston, T.L., Goehring, K.E., Magnuson, M., Nordensten, N., Nelson, M., and Collins, G.H., 2016, Pika (Ochotona princeps) losses from two isolated regions reflect temperature and water balance, but reflect habitat area in a mainland region: Journal of Mammalogy, v. 97, no. 6, p. 1495-1511, https://doi.org/10.1093/jmammal/gyw128.","productDescription":"17 p.","startPage":"1495","endPage":"1511","ipdsId":"IP-061599","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":462075,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyw128","text":"Publisher Index Page"},{"id":328851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"6","noUsgsAuthors":false,"publicationDate":"2016-08-25","publicationStatus":"PW","scienceBaseUri":"57f7c63ce4b0bc0bec09c866","chorus":{"doi":"10.1093/jmammal/gyw128","url":"http://dx.doi.org/10.1093/jmammal/gyw128","publisher":"Oxford University Press (OUP)","authors":"Beever Erik A., Perrine John D., Rickman Tom, Flores Mary, Clark John P., Waters Cassie, Weber Shana S., Yardley Braden, Thoma David, Chesley-Preston Tara, Goehring Kenneth E., Magnuson Michael, Nordensten Nancy, Nelson Melissa, Collins Gail H.","journalName":"Journal of Mammalogy","publicationDate":"8/25/2016"},"contributors":{"authors":[{"text":"Beever, Erik A. 0000-0002-9369-486X ebeever@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-486X","contributorId":2934,"corporation":false,"usgs":true,"family":"Beever","given":"Erik","email":"ebeever@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":649297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perrine, John D.","contributorId":174797,"corporation":false,"usgs":false,"family":"Perrine","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":649298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rickman, Tom","contributorId":174798,"corporation":false,"usgs":false,"family":"Rickman","given":"Tom","email":"","affiliations":[],"preferred":false,"id":649299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flores, Mary","contributorId":174799,"corporation":false,"usgs":false,"family":"Flores","given":"Mary","email":"","affiliations":[],"preferred":false,"id":649300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, John P.","contributorId":174800,"corporation":false,"usgs":false,"family":"Clark","given":"John","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":649301,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waters, Cassie","contributorId":174801,"corporation":false,"usgs":false,"family":"Waters","given":"Cassie","email":"","affiliations":[],"preferred":false,"id":649302,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weber, Shana S.","contributorId":174802,"corporation":false,"usgs":false,"family":"Weber","given":"Shana","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":649303,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yardley, Braden","contributorId":174803,"corporation":false,"usgs":false,"family":"Yardley","given":"Braden","email":"","affiliations":[],"preferred":false,"id":649304,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thoma, David P.","contributorId":45975,"corporation":false,"usgs":true,"family":"Thoma","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":649305,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Chesley-Preston, Tara L. tchesley-preston@usgs.gov","contributorId":5557,"corporation":false,"usgs":true,"family":"Chesley-Preston","given":"Tara","email":"tchesley-preston@usgs.gov","middleInitial":"L.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":649306,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Goehring, Kenneth E.","contributorId":174804,"corporation":false,"usgs":false,"family":"Goehring","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":649307,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Magnuson, Michael","contributorId":174806,"corporation":false,"usgs":false,"family":"Magnuson","given":"Michael","email":"","affiliations":[],"preferred":false,"id":649308,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Nordensten, Nancy","contributorId":174807,"corporation":false,"usgs":false,"family":"Nordensten","given":"Nancy","email":"","affiliations":[],"preferred":false,"id":649309,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Nelson, Melissa","contributorId":174808,"corporation":false,"usgs":false,"family":"Nelson","given":"Melissa","email":"","affiliations":[],"preferred":false,"id":649310,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Collins, Gail H.","contributorId":59170,"corporation":false,"usgs":false,"family":"Collins","given":"Gail","email":"","middleInitial":"H.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":649311,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70175519,"text":"sir20165116 - 2016 - Simulating groundwater flow in karst aquifers with distributed parameter models—Comparison of porous-equivalent media and hybrid flow approaches","interactions":[],"lastModifiedDate":"2016-09-22T15:54:17","indexId":"sir20165116","displayToPublicDate":"2016-09-22T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5116","title":"Simulating groundwater flow in karst aquifers with distributed parameter models—Comparison of porous-equivalent media and hybrid flow approaches","docAbstract":"Understanding karst aquifers, for purposes of their management and protection, poses unique challenges. Karst aquifers are characterized by groundwater flow through conduits (tertiary porosity), and (or) layers with interconnected pores (secondary porosity) and through intergranular porosity (primary or matrix porosity). Since the late 1960s, advances have been made in the development of numerical computer codes and the use of mathematical model applications towards the understanding of dual (primary [matrix] and secondary [fractures and conduits]) porosity groundwater flow processes, as well as characterization and management of karst aquifers. The Floridan aquifer system (FAS) in Florida and parts of Alabama, Georgia, and South Carolina is composed of a thick sequence of predominantly carbonate rocks. Karst features are present over much of its area, especially in Florida where more than 30 first-magnitude springs occur, numerous sinkholes and submerged conduits have been mapped, and numerous circular lakes within sinkhole depressions are present. Different types of mathematical models have been applied for simulation of the FAS. Most of these models are distributed parameter models based on the assumption that, like a sponge, water flows through connected pores within the aquifer system and can be simulated with the same mathematical methods applied to flow through sand and gravel aquifers; these models are usually referred to as porous-equivalent media models. The partial differential equation solved for groundwater flow is the potential flow equation of fluid mechanics, which is used when flow is dominated by potential energy and has been applied for many fluid problems in which kinetic energy terms are dropped from the differential equation solved. In many groundwater model codes (basic MODFLOW), it is assumed that the water has a constant temperature and density and that flow is laminar, such that kinetic energy has minimal impact on flow. Some models have been developed that incorporate the submerged conduits as a one-dimensional pipe network within the aquifer rather than as discrete, extremely transmissive features in a porous-equivalent medium; these submerged conduit models are usually referred to as hybrid models and may include the capability to simulate both laminar and turbulent flow in the one-dimensional pipe network. Comparisons of the application of a porous-equivalent media model with and without turbulence (MODFLOW-Conduit Flow Process mode 2 and basic MODFLOW, respectively) and a hybrid (MODFLOW-Conduit Flow Process mode 1) model to the Woodville Karst Plain near Tallahassee, Florida, indicated that for annual, monthly, or seasonal average hydrologic conditions, all methods met calibration criteria (matched observed groundwater levels and average flows). Thus, the increased effort required, such as the collection of data on conduit location, to develop a hybrid model and its increased computational burden, is not necessary for simulation of average hydrologic conditions (non-laminar flow effects on simulated head and spring discharge were minimal). However, simulation of a large storm event in the Woodville Karst Plain with daily stress periods indicated that turbulence is important for matching daily springflow hydrographs. Thus, if matching streamflow hydrographs over a storm event is required, the simulation of non-laminar flow and the location of conduits are required. The main challenge in application of the methods and approaches for developing hybrid models relates to the difficulty of mapping conduit networks or having high-quality datasets to calibrate these models. Additionally, hybrid models have long simulation times, which can preclude the use of parameter estimation for calibration. Simulation of contaminant transport that does not account for preferential flow through conduits or extremely permeable zones in any approach is ill-advised. Simulation results in other karst aquifers or other parts of the FAS may differ from the comparison demonstrated herein.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165116","collaboration":"A product of the Water Use and Availability Science Program","usgsCitation":"Kuniansky, E.L., 2016, Simulating groundwater flow in karst aquifers with distributed parameter models—Comparison of porous-equivalent media and hybrid flow approaches: U.S. Geological Survey Scientific Investigations Report 2016–5116, 14 p., https://dx.doi.org/10.3133/sir20165116.","productDescription":"Report: v, 14 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-071317","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":328727,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5116/sir20165116.pdf","text":"Report","size":"3.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5116"},{"id":328833,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7PK0D87","text":"USGS data release - MODFLOW and MODFLOW Conduit Flow Process data sets for simulation experiments of the Woodville Karst Plain, near Tallahassee, Florida with three different approaches and different stress periods","description":"SIR 2016–5116 Data Release"},{"id":328726,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5116/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Woodville Karst Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.4903564453125,\n 30.04532159026885\n ],\n [\n -84.4903564453125,\n 30.456368670179007\n ],\n [\n -84.06875610351562,\n 30.456368670179007\n ],\n [\n -84.06875610351562,\n 30.04532159026885\n ],\n [\n -84.4903564453125,\n 30.04532159026885\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Caribbean-Florida Water Science Center-Florida
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Squat lobsters (Galatheoidea and Chirostyloidea), a diverse group of decapod crustaceans, are ubiquitous members of the deep-sea fauna. Within Galatheoidea, the genera Munida and Munidopsis are the most diverse, but accurate estimates of biodiversity are difficult due to morphological complexity and cryptic diversity. Four species of Munida and nine species of Munidopsis from cold-water coral (CWC) and cold seep communities in the northwestern Atlantic Ocean (NWA) and the Gulf of Mexico (GOM) were collected over eleven years and fifteen research cruises in order to assess faunal associations and estimate squat lobster biodiversity. Identification of the majority of specimens was determined morphologically. Mitochondrial COI sequence data, obtained from material collected during these research cruises, was supplemented with published sequences of congeners from other regions. The phylogenetic analysis of Munida supports three of the four NWA and GOM species (M. microphthalma, M. sanctipauli, and M. valida) as closely related taxa. The fourth species, Munida iris, is basal to most other species of Munida, and is closely related to M. rutllanti, a species found in the northeastern Atlantic Ocean (NEA). The majority of the nine species of Munidopsis included in our analyses were collected from chemosynthetic cold seep sites from the GOM. While seep taxa were scattered throughout the phylogenetic tree, four of these species (Munidopsis livida, M. similis, M. bermudezi, and M. species A) from the NWA and the GOM were part of a large eighteen-species clade that included species collected from Pacific Ocean chemosynthetic habitats, such as hydrothermal vents and whale falls. Shinkaia crosnieri was the sister taxon to the chemosynthetic clade, and M. livida was the most basal member of this clade. Munidopsis sp. B, an undescribed species with representative individuals collected from two GOM chemosynthetic sites, exhibited the largest genetic distance from other northern Atlantic species. Generally, intraspecific diversity was lower and patterns of haplotype diversity more simple in species of Munidopsis relative to Munida. This study puts two genera of NWA and GOM squat lobsters into a population genetic and phylogenetic context with regard to biogeography and habitat to enhance understanding of the history and evolutionary trajectories of these morphologically and ecologically diverse groups.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2016.08.014","usgsCitation":"Coykendall, D.K., Nizinski, M.S., and Morrison, C.L., 2016, A phylogenetic perspective on diversity of Galatheoidea (Munida, Munidopsis) from cold-water coral and cold seep communities in the western North Atlantic Ocean: Deep-Sea Research Part II: Topical Studies in Oceanography, v. 137, p. 258-272, https://doi.org/10.1016/j.dsr2.2016.08.014.","productDescription":"15 p.","startPage":"258","endPage":"272","ipdsId":"IP-073301","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":470560,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr2.2016.08.014","text":"Publisher Index Page"},{"id":328826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Atlantic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.181640625,\n 43.58039085560784\n ],\n [\n -64.3359375,\n 42.35854391749705\n ],\n [\n -64.072265625,\n 41.244772343082076\n ],\n [\n -65.478515625,\n 39.027718840211605\n ],\n [\n -67.8515625,\n 37.09023980307208\n ],\n [\n -70.927734375,\n 31.57853542647338\n ],\n [\n -74.1796875,\n 28.536274512989916\n ],\n [\n -78.662109375,\n 25.878994400196202\n ],\n [\n -80.33203125,\n 24.367113562651262\n ],\n [\n -85.341796875,\n 24.206889622398023\n ],\n [\n -89.56054687499999,\n 23.885837699862005\n ],\n [\n -94.130859375,\n 24.046463999666567\n ],\n [\n -95.80078125,\n 25.24469595130604\n ],\n [\n -97.470703125,\n 26.27371402440643\n ],\n [\n -97.998046875,\n 27.68352808378776\n ],\n [\n -96.591796875,\n 29.99300228455108\n ],\n [\n -93.33984375,\n 31.80289258670676\n ],\n [\n -88.681640625,\n 31.80289258670676\n ],\n [\n -84.111328125,\n 32.24997445586331\n ],\n [\n -80.33203125,\n 33.7243396617476\n ],\n [\n -78.486328125,\n 35.02999636902566\n ],\n [\n -77.431640625,\n 36.38591277287651\n ],\n [\n -75.849609375,\n 38.95940879245423\n ],\n [\n -74.70703125,\n 40.84706035607122\n ],\n [\n -72.94921875,\n 42.22851735620852\n ],\n [\n -70.48828125,\n 43.389081939117496\n ],\n [\n -68.994140625,\n 43.58039085560784\n ],\n [\n -66.181640625,\n 43.58039085560784\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"137","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7c63de4b0bc0bec09c86e","contributors":{"authors":[{"text":"Coykendall, D. Katharine 0000-0002-1148-2397 dcoykendall@usgs.gov","orcid":"https://orcid.org/0000-0002-1148-2397","contributorId":5472,"corporation":false,"usgs":true,"family":"Coykendall","given":"D.","email":"dcoykendall@usgs.gov","middleInitial":"Katharine","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":649227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nizinski, Martha S.","contributorId":174770,"corporation":false,"usgs":false,"family":"Nizinski","given":"Martha","email":"","middleInitial":"S.","affiliations":[{"id":27510,"text":"NMFS National Systematics Laboratory, Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":649229,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morrison, Cheryl L. 0000-0001-9425-691X cmorrison@usgs.gov","orcid":"https://orcid.org/0000-0001-9425-691X","contributorId":146488,"corporation":false,"usgs":true,"family":"Morrison","given":"Cheryl","email":"cmorrison@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":649228,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176532,"text":"70176532 - 2016 - Freshwater polychaetes (Manayunkia speciosa) near the Detroit River, western Lake Erie: Abundance and life‐history characteristics","interactions":[],"lastModifiedDate":"2016-11-16T11:25:11","indexId":"70176532","displayToPublicDate":"2016-09-20T17:40:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Freshwater polychaetes (Manayunkia speciosa) near the Detroit River, western Lake Erie: Abundance and life‐history characteristics","docAbstract":"Freshwater polychaetes are relatively rare and little-studied members of the benthos of lakes and rivers. We studied one polychaete species (Manayunkia speciosa) in Lake Erie near the mouth of the Detroit River. Abundances at one site were determined between 1961 and 2013 and life‐history characteristics at two sites were determined seasonally (March–November) in 2009–2010 and 2012–2013. Life‐history characteristics included abundances, length‐frequency distributions, presence/absence of constructed tubes, sexual maturity, and number and maturation of young of year (YOY) in tubes. Long-term abundances decreased in successive time periods between 1961 and 2003 (mean range = 57,570 to 2583/m2) but few changes occurred between 2003 and 2013 (mean = 5007/m2; range/y = 2355–8216/m2). Seasonal abundances varied substantially between sites and years, but overall, abundances were low in March–April, high in May–August, and low in September–November. Although reproduction was continuous throughout warmer months, en masse recruitment, as revealed by length–frequency distributions, occurred in a brief period late‐June to mid-July, and possibly in early-September. All life history characteristics, including tube construction, were dependent on water temperatures (> 5 °C in spring and < 15 °C in fall). These results generally agree with and complement laboratory studies of M. speciosa in the Pacific Northwest where M. speciosa hosts parasites that cause substantial fish mortalities. Although abundance ofM. speciosa near the mouth of the Detroit River was 33-fold lower in 2013 than it was in 1961, this population has persisted for five decades and, therefore, has the potential to harbor parasites that may cause fish mortalities in the Great Lakes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2016.07.006","usgsCitation":"Schloesser, D.W., Malakauskas, D.M., and Malakauskas, S.J., 2016, Freshwater polychaetes (Manayunkia speciosa) near the Detroit River, western Lake Erie: Abundance and life‐history characteristics: Journal of Great Lakes Research, v. 42, no. 5, p. 1070-1083, https://doi.org/10.1016/j.jglr.2016.07.006.","productDescription":"14 p.","startPage":"1070","endPage":"1083","ipdsId":"IP-068959","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":328784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Detroit River, Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.26812744140625,\n 41.96919079421467\n ],\n [\n -83.26812744140625,\n 42.10739254393655\n ],\n [\n -83.00308227539062,\n 42.10739254393655\n ],\n [\n -83.00308227539062,\n 41.96919079421467\n ],\n [\n -83.26812744140625,\n 41.96919079421467\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7c63de4b0bc0bec09c874","contributors":{"authors":[{"text":"Schloesser, Donald W. dschloesser@usgs.gov","contributorId":3579,"corporation":false,"usgs":true,"family":"Schloesser","given":"Donald","email":"dschloesser@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":649130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malakauskas, David M.","contributorId":43247,"corporation":false,"usgs":true,"family":"Malakauskas","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":649131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Malakauskas, Sarah J.","contributorId":150991,"corporation":false,"usgs":false,"family":"Malakauskas","given":"Sarah","email":"","middleInitial":"J.","affiliations":[{"id":18158,"text":"Francis Marion Uinversity","active":true,"usgs":false}],"preferred":false,"id":649132,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176535,"text":"70176535 - 2016 - White sucker Catostomus commersonii respond to conspecific and sea lamprey Petromyzon marinus alarm cues but not potential predator cues","interactions":[],"lastModifiedDate":"2016-09-20T16:32:30","indexId":"70176535","displayToPublicDate":"2016-09-20T17:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"White sucker Catostomus commersonii respond to conspecific and sea lamprey Petromyzon marinus alarm cues but not potential predator cues","docAbstract":"Recent studies proposed the use of chemosensory alarm cues to control the distribution of invasive sea lamprey Petromyzon marinus populations in the Laurentian Great Lakes and necessitate the evaluation of sea lamprey chemosensory alarm cues on valuable sympatric species such as white sucker. In two laboratory experiments, 10 replicate groups (10 animals each) of migratory white suckers were exposed to deionized water (control), conspecific whole-body extract, heterospecific whole-body extract (sea lamprey) and two potential predator cues (2-phenylethylamine HCl (PEA HCl) and human saliva) during the day, and exposed to the first four of the above cues at night. White suckers avoided the conspecific and the sea lamprey whole-body extract both during the day and at night to the same extent. Human saliva did not induce avoidance during the day. PEA HCl did not induce avoidance at a higher concentration during the day, or at night at the minimum concentration that was previously shown to induce maximum avoidance by sea lamprey under laboratory conditions. Our findings suggest that human saliva and PEA HCl may be potential species-specific predator cues for sea lamprey.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2016.04.003","usgsCitation":"Jordbro, E.J., Di Rocco, R.T., Imre, I., Johnson, N., and Brown, G.E., 2016, White sucker Catostomus commersonii respond to conspecific and sea lamprey Petromyzon marinus alarm cues but not potential predator cues: Journal of Great Lakes Research, v. 42, no. 4, p. 849-853, https://doi.org/10.1016/j.jglr.2016.04.003.","productDescription":"5 p.","startPage":"849","endPage":"853","ipdsId":"IP-072760","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":328781,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7c63de4b0bc0bec09c87a","contributors":{"authors":[{"text":"Jordbro, Ethan J.","contributorId":174734,"corporation":false,"usgs":false,"family":"Jordbro","given":"Ethan","email":"","middleInitial":"J.","affiliations":[{"id":6585,"text":"Algoma University","active":true,"usgs":false}],"preferred":false,"id":649134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Di Rocco, Richard T.","contributorId":150984,"corporation":false,"usgs":false,"family":"Di Rocco","given":"Richard","email":"","middleInitial":"T.","affiliations":[{"id":6586,"text":"Concordia University","active":true,"usgs":false}],"preferred":false,"id":649135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Imre, Istvan","contributorId":150985,"corporation":false,"usgs":false,"family":"Imre","given":"Istvan","email":"","affiliations":[{"id":6585,"text":"Algoma University","active":true,"usgs":false}],"preferred":false,"id":649136,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":150983,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas S.","email":"njohnson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":649133,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Grant E.","contributorId":173005,"corporation":false,"usgs":false,"family":"Brown","given":"Grant","email":"","middleInitial":"E.","affiliations":[{"id":6586,"text":"Concordia University","active":true,"usgs":false}],"preferred":false,"id":649137,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70176523,"text":"70176523 - 2016 - Observations of nearshore groundwater discharge: Kahekili Beach Park submarine springs, Maui, Hawaii","interactions":[],"lastModifiedDate":"2025-05-13T16:46:33.72118","indexId":"70176523","displayToPublicDate":"2016-09-20T16:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Observations of nearshore groundwater discharge: Kahekili Beach Park submarine springs, Maui, Hawaii","docAbstract":"The study region encompasses the nearshore, coastal waters off west Maui, Hawaii. Here abundant groundwater—that carries with it a strong land-based fingerprint—discharges into the coastal waters and over a coral reef.
Coastal groundwater discharge is a ubiquitous hydrologic feature that has been shown to impact nearshore ecosystems and material budgets. A unique combined geochemical tracer and oceanographic time-series study addressed rates and oceanic forcings of submarine groundwater discharge at a submarine spring site off west Maui, Hawaii.
Estimates of submarine groundwater discharge were derived for a primary vent site and surrounding coastal waters off west Maui, Hawaii using an excess 222Rn (t1/2 = 3.8 d) mass balance model. Such estimates were complemented with a novel thoron (220Rn,t1/2 = 56 s) groundwater discharge tracer application, as well as oceanographic time series and thermal infrared imagery analyses. In combination, this suite of techniques provides new insight into the connectivity of the coastal aquifer with the near-shore ocean and examines the physical drivers of submarine groundwater discharge. Lastly, submarine groundwater discharge derived constituent concentrations were tabulated and compared to surrounding seawater concentrations. Such work has implications for the management of coastal aquifers and downstream nearshore ecosystems that respond to sustained constituent loadings via this submarine route.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2015.12.056","usgsCitation":"Swarzenski, P.W., Dulai, H., Kroeger, K., Smith, C.G., Dimova, N., Storlazzi, C., Prouty, N., Gingerich, S.B., and Glenn, C.R., 2016, Observations of nearshore groundwater discharge: Kahekili Beach Park submarine springs, Maui, Hawaii: Journal of Hydrology: Regional Studies, v. 11, p. 147-165, https://doi.org/10.1016/j.ejrh.2015.12.056.","productDescription":"19 p.","startPage":"147","endPage":"165","ipdsId":"IP-068143","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":328777,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":470561,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2015.12.056","text":"Publisher Index Page"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.69799804687497,\n 20.92604896920106\n ],\n [\n -156.69799804687497,\n 20.94685150573486\n ],\n [\n -156.6826343536377,\n 20.94685150573486\n ],\n [\n -156.6826343536377,\n 20.92604896920106\n ],\n [\n -156.69799804687497,\n 20.92604896920106\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7c63de4b0bc0bec09c87c","contributors":{"authors":[{"text":"Swarzenski, Peter W. 0000-0003-0116-0578 pswarzen@usgs.gov","orcid":"https://orcid.org/0000-0003-0116-0578","contributorId":1070,"corporation":false,"usgs":true,"family":"Swarzenski","given":"Peter","email":"pswarzen@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":649120,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dulai, H.","contributorId":174725,"corporation":false,"usgs":false,"family":"Dulai","given":"H.","email":"","affiliations":[],"preferred":false,"id":649121,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroeger, K.D.","contributorId":26060,"corporation":false,"usgs":true,"family":"Kroeger","given":"K.D.","email":"","affiliations":[],"preferred":false,"id":649122,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Christopher G. 0000-0002-8075-4763 cgsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":3410,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher","email":"cgsmith@usgs.gov","middleInitial":"G.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":649123,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dimova, N.","contributorId":66051,"corporation":false,"usgs":true,"family":"Dimova","given":"N.","affiliations":[],"preferred":false,"id":649124,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Storlazzi, C. D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":127154,"corporation":false,"usgs":true,"family":"Storlazzi","given":"C. D.","affiliations":[],"preferred":false,"id":649125,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prouty, N.G.","contributorId":36766,"corporation":false,"usgs":true,"family":"Prouty","given":"N.G.","email":"","affiliations":[],"preferred":false,"id":649126,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gingerich, S. B.","contributorId":83958,"corporation":false,"usgs":true,"family":"Gingerich","given":"S.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":649127,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Glenn, C. R.","contributorId":174726,"corporation":false,"usgs":false,"family":"Glenn","given":"C.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":649128,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70176512,"text":"70176512 - 2016 - Learning and adaptation in waterfowl conservation: By chance or by design?","interactions":[],"lastModifiedDate":"2016-09-28T16:00:27","indexId":"70176512","displayToPublicDate":"2016-09-20T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Learning and adaptation in waterfowl conservation: By chance or by design?","docAbstract":"The most recent revision of the North American Waterfowl Management Plan seeks to increase the adaptive capacity of the management enterprise to cope with accelerating changes in climate, land-use patterns, agency priorities, and the waterfowl and wetlands constituency. Institutional and cultural changes of the magnitude envisioned are necessarily slow, messy processes, involving many actors who at a minimum must agree on the need for change. Waterfowl conservation now finds itself in the transition zone between business as usual and some new mode of operation. There are at least 2 different perspectives of this transition: one focuses on process, accountability, and planning for change; another focuses on solutions generated from an organic process of creativity, information sharing, and risk-taking. Both of these views have something to contribute, but some in the wildlife management enterprise may tend to focus more on the first view. We suggest that ideas from panarchy theory, especially those related to the behaviors of complex adaptive systems, can help waterfowl managers better understand and foster the institutional changes they seek.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.682","usgsCitation":"Johnson, F.A., Case, D.J., and Humburg, D.H., 2016, Learning and adaptation in waterfowl conservation: By chance or by design?: Wildlife Society Bulletin, v. 40, no. 3, p. 423-427, https://doi.org/10.1002/wsb.682.","productDescription":"5 p.","startPage":"423","endPage":"427","ipdsId":"IP-075263","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":498971,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.682","text":"Publisher Index Page"},{"id":328755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-29","publicationStatus":"PW","scienceBaseUri":"57ed5309e4b090825011d501","contributors":{"authors":[{"text":"Johnson, Fred A. 0000-0002-5854-3695 fjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":2773,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","email":"fjohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":649038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Case, David J.","contributorId":140653,"corporation":false,"usgs":false,"family":"Case","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":13543,"text":"DJ Case & Associates","active":true,"usgs":false}],"preferred":false,"id":649039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Humburg, Dale H.","contributorId":174698,"corporation":false,"usgs":false,"family":"Humburg","given":"Dale","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":649040,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176515,"text":"70176515 - 2016 - The persistence and characteristics of Chinook salmon migrations to the Upper Klamath River prior to exclusion by dams","interactions":[],"lastModifiedDate":"2016-09-20T11:03:31","indexId":"70176515","displayToPublicDate":"2016-09-20T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2957,"text":"Oregon Historical Society Quarterly","active":true,"publicationSubtype":{"id":10}},"title":"The persistence and characteristics of Chinook salmon migrations to the Upper Klamath River prior to exclusion by dams","docAbstract":"In this research article, John Hamilton and his co-authors present extensive new research and information gathered since a 2005 publication on the historical evidence of anadromomous fish distribution in the Upper Klamath River watershed. Using historical accounts from early explorers and ethnographers to early-twentieth-century photographs, newspaper accounts, and government reports, the authors provide a more complete record of past salmon migrations. The updated record “substantiate[s] the historical persistence of salmon, their migration characteristics, and the broad population baseline that will be key to future commercial, recreational, and Tribal fisheries in the Klamath River and beyond.” During a time when salmon restoration plans are being considered in the region, the historical record can serve as guidance to once again establish diverse and thriving populations.","language":"English","publisher":"The Oregon Historical Society","usgsCitation":"Hamilton, J.B., Rondorf, D.W., Tinniswood, W., Leary, R.J., Mayer, T., Gavette, C., and Casal, L.A., 2016, The persistence and characteristics of Chinook salmon migrations to the Upper Klamath River prior to exclusion by dams: Oregon Historical Society Quarterly, v. 117, no. 3, p. 326-377.","productDescription":"52 p.","startPage":"326","endPage":"377","ipdsId":"IP-059293","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":328754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":328749,"type":{"id":15,"text":"Index Page"},"url":"https://www.ohs.org/research-and-library/oregon-historical-quarterly/"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath Upper River, Link River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.51953124999999,\n 41.74262728637672\n ],\n [\n -122.51953124999999,\n 43.02071359427862\n ],\n [\n -120.88256835937499,\n 43.02071359427862\n ],\n [\n -120.88256835937499,\n 41.74262728637672\n ],\n [\n -122.51953124999999,\n 41.74262728637672\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"117","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7c63de4b0bc0bec09c880","contributors":{"authors":[{"text":"Hamilton, John B","contributorId":174701,"corporation":false,"usgs":false,"family":"Hamilton","given":"John","email":"","middleInitial":"B","affiliations":[{"id":27499,"text":"U.S. Fish and Wildlife Service, Yreka Fish and Wildlife Office 1829 S. Oregon St., Yreka, CA 96097","active":true,"usgs":false}],"preferred":false,"id":649052,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rondorf, Dennis W. drondorf@usgs.gov","contributorId":2970,"corporation":false,"usgs":true,"family":"Rondorf","given":"Dennis","email":"drondorf@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":649051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tinniswood, William","contributorId":174703,"corporation":false,"usgs":false,"family":"Tinniswood","given":"William","email":"","affiliations":[{"id":27501,"text":"Fish Biologist, Oregon Department of Fish and Wildlife, 1850 Miller Island Road, Klamath Falls, OR 97603","active":true,"usgs":false}],"preferred":false,"id":649054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leary, Ryan J","contributorId":174702,"corporation":false,"usgs":false,"family":"Leary","given":"Ryan","email":"","middleInitial":"J","affiliations":[{"id":27500,"text":"Fisheries Biologist, The Klamath Tribes, 5671 Sprague River Road, Chiloquin, OR 97624","active":true,"usgs":false}],"preferred":false,"id":649053,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mayer, Tim","contributorId":174705,"corporation":false,"usgs":false,"family":"Mayer","given":"Tim","email":"","affiliations":[{"id":27503,"text":"Supervisory Hydrologist, Water Resources Branch, U.S. Fish and Wildlife Service, 911 NE 11th Ave., Portland, OR 97232-4181","active":true,"usgs":false}],"preferred":false,"id":649056,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gavette, Charleen","contributorId":174706,"corporation":false,"usgs":false,"family":"Gavette","given":"Charleen","email":"","affiliations":[{"id":27504,"text":"Geographer/GIS Coordinator, NMFS, 777 Sonoma Ave., Suite 325, Santa Rosa, CA 95405","active":true,"usgs":false}],"preferred":false,"id":649057,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Casal, Lynne A. lcasal@usgs.gov","contributorId":5166,"corporation":false,"usgs":true,"family":"Casal","given":"Lynne","email":"lcasal@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":649058,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}