Wastewater disposal associated with rapid population growth and development on Cape Cod, Massachusetts, during the past several decades has resulted in widespread contamination of groundwater with nitrogen. As a result, water quality in many of the streams, lakes, and coastal embayments on Cape Cod is impaired by excess nitrogen. To reduce nitrogen loads to these impaired water bodies, watershed-based planning is currently [2019] underway following a regional strategy, the section 208 areawide water-quality management plan update for Cape Cod. In the updated plan, traditional (sewering) and alternative wastewater management options are under consideration for restoring water quality in impaired surface-water bodies. Permeable reactive barriers, which are reactive zones emplaced below the water table for passive treatment of groundwater contaminants, are one of the alternatives being considered by Cape Cod towns as a potentially cost-effective technology for the removal of nitrogen from groundwater. However, the effectiveness of permeable reactive barriers depends on local conditions, and site-specific hydrologic and water-quality data are needed to inform the decision to install a permeable reactive barrier in a given location. These data are not available in most locations on Cape Cod; consequently, site assessments are needed before selecting this treatment option.
To address this need, the U.S. Environmental Protection Agency, U.S. Geological Survey, and Cape Cod Commission formed a technical team in 2015 to develop and evaluate a hydrologic site-assessment approach for permeable reactive barrier installation. The approach developed by the technical team includes a preliminary regional assessment followed by a phased onsite investigation. The approach was intended to provide the hydrologic data needed to make informed decisions on site suitability and to support installation and monitoring should the site be deemed appropriate for a permeable reactive barrier. The factors that were evaluated to characterize local hydrologic conditions and inform site selection included groundwater flow directions and rates, depth to the water table, hydraulic conductivity and degree of heterogeneity of the aquifer, spatial distribution and concentration of nitrate and oxidation-reduction-sensitive constituents, thickness and depth of the treatment zone, distance to downgradient water bodies, and access for drilling and permeable reactive barrier installation. The approach was demonstrated on Cape Cod by conducting a preliminary assessment of 27 sites, from which 5 sites were selected for onsite investigations. Results indicated that the site-assessment approach was successful for screening sites and characterizing the geologic, hydrologic, and water-quality conditions at the sites selected for onsite investigations. Overall, the phased assessment evaluated in this study provided an efficient means of obtaining the hydrologic information needed to determine if a site was suitable for permeable reactive barrier installation on Cape Cod for the passive treatment of nitrogen in groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195047","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Barbaro, J.R., Belaval, M., Truslow, D.B., LeBlanc, D.R., Cambareri, T.C., and Michaud, S.C., 2019, Hydrologic site assessment for passive treatment of groundwater nitrogen with permeable reactive barriers, Cape Cod, Massachusetts: U.S. Geological Survey Scientific Investigations Report 2019–5047, 39 p., https://doi.org/10.3133/sir20195047.","productDescription":"viii, 39 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-104222","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":365261,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5047/coverthb.jpg"},{"id":365262,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5047/sir20195047.pdf","text":"Report","size":"2.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5047"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.24932861328125,\n 42.06356771883277\n ],\n [\n -70.2081298828125,\n 42.02481360781777\n ],\n [\n -70.1806640625,\n 42.01665183556825\n ],\n [\n -70.16693115234375,\n 42.032974332441405\n ],\n [\n -70.18890380859375,\n 42.04317376494972\n ],\n [\n -70.16143798828125,\n 42.06356771883277\n ],\n [\n -70.11199951171875,\n 42.04113400940807\n ],\n [\n -70.07904052734375,\n 41.92475971933975\n ],\n [\n -70.0653076171875,\n 41.89818843043047\n ],\n [\n -70.0543212890625,\n 41.920672548686824\n ],\n [\n -70.02960205078125,\n 41.92271616673922\n ],\n [\n -70.0213623046875,\n 41.887965758804484\n ],\n [\n -70.00762939453125,\n 41.81021999190292\n ],\n [\n -70.07080078125,\n 41.777456667491066\n ],\n [\n -70.125732421875,\n 41.76106872528616\n ],\n [\n -70.16693115234375,\n 41.75492216766298\n ],\n [\n -70.20263671875,\n 41.748775021355044\n ],\n [\n -70.257568359375,\n 41.7180304600481\n ],\n [\n -70.279541015625,\n 41.72828028223453\n ],\n [\n -70.345458984375,\n 41.74262728637672\n ],\n [\n -70.44158935546875,\n 41.75287318430239\n ],\n [\n -70.49102783203125,\n 41.77336007442076\n ],\n [\n -70.55145263671875,\n 41.775408403663285\n ],\n [\n -70.587158203125,\n 41.748775021355044\n ],\n [\n -70.6201171875,\n 41.73647896274239\n ],\n [\n -70.65582275390625,\n 41.68316883525891\n ],\n [\n -70.6475830078125,\n 41.64213096472801\n ],\n [\n -70.653076171875,\n 41.60312076451184\n ],\n [\n -70.64208984375,\n 41.57436130598913\n ],\n [\n -70.78765869140625,\n 41.47771800887871\n ],\n [\n -70.94146728515625,\n 41.42625319507269\n ],\n [\n -70.94970703125,\n 41.409775832009565\n ],\n [\n -70.86181640625,\n 41.41801503608024\n ],\n [\n -70.784912109375,\n 41.4509614012039\n ],\n [\n -70.653076171875,\n 41.51680395810118\n ],\n [\n -70.6201171875,\n 41.541477666790286\n ],\n [\n -70.5487060546875,\n 41.53531012183376\n ],\n [\n -70.48828125,\n 41.54764462357737\n ],\n [\n -70.4443359375,\n 41.588742636696765\n ],\n [\n -70.43060302734375,\n 41.60517452129933\n ],\n [\n -70.39764404296875,\n 41.60722821271717\n ],\n [\n -70.367431640625,\n 41.61749568924243\n ],\n [\n -70.3125,\n 41.6257084937525\n ],\n [\n -70.26580810546875,\n 41.60928183876483\n ],\n [\n -70.24108886718749,\n 41.6257084937525\n ],\n [\n -70.17791748046875,\n 41.65239288426814\n ],\n [\n -70.13946533203124,\n 41.65034063112266\n ],\n [\n -70.06805419921875,\n 41.66470503009207\n ],\n [\n -70.015869140625,\n 41.668808555620586\n ],\n [\n -69.98016357421875,\n 41.65239288426814\n ],\n [\n -70.0103759765625,\n 41.566141964768384\n ],\n [\n -70.0103759765625,\n 41.539421883822854\n ],\n [\n -69.9884033203125,\n 41.54764462357737\n ],\n [\n -69.98016357421875,\n 41.59490508367679\n ],\n [\n -69.92523193359375,\n 41.68316883525891\n ],\n [\n -69.93072509765625,\n 41.81431422987254\n ],\n [\n -69.97467041015625,\n 41.94927724511655\n ],\n [\n -70.048828125,\n 42.039094188385945\n ],\n [\n -70.13397216796875,\n 42.07783959017503\n ],\n [\n -70.21087646484375,\n 42.08803181932636\n ],\n [\n -70.24932861328125,\n 42.06356771883277\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Road, Suite 2
Pembroke, NH 03275-3718
The USGS Water Mission Area’s Sediment Science Program provides leadership, training, and methods development in fluvial sediment science for the USGS and its external partners. Overarching objectives of the USGS Sediment Science Program (which includes the Federal Interagency Sedimentation Project) include: 1) developing and promoting innovative sediment monitoring techniques that result in cost effective, accurate, and high resolution fluvial sediment data for the Nation; 2) advancing sediment science through collaboration with external agencies to ensure USGS science and leadership directions are aligned with external agency and public needs; and 3) providing technical support to sediment data collectors and scientists, in an effort to improve quality assurance/quality control practices and efficiencies in field data collection and analysis.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2019","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD 2019 Conference","conferenceDate":"June 24-28, 2019","conferenceLocation":"Reno, NV","language":"English","publisher":"Federal Interagency Sedimentation Conference (FISC) and Federal Interagency Hydrologic Modeling Conference (FIHMC)","usgsCitation":"Wood, M.S., and Straub, T.D., 2019, Strategic directions of the USGS water mission area’s fluvial sediment science program, in Proceedings of SEDHYD 2019, v. 3, Reno, NV, June 24-28, 2019, 6 p.","productDescription":"6 p.","ipdsId":"IP-105708","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":366863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":366861,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2019/#sedhyd-2019-proceedings"}],"volume":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Molly S. 0000-0002-5184-8306 mswood@usgs.gov","orcid":"https://orcid.org/0000-0002-5184-8306","contributorId":788,"corporation":false,"usgs":true,"family":"Wood","given":"Molly","email":"mswood@usgs.gov","middleInitial":"S.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":768879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Straub, Timothy D. 0000-0002-5896-0851","orcid":"https://orcid.org/0000-0002-5896-0851","contributorId":215662,"corporation":false,"usgs":true,"family":"Straub","given":"Timothy","email":"","middleInitial":"D.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768880,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203189,"text":"70203189 - 2019 - Sediment monitoring to support modeling a reservoir sediment flush on a sand-bed river in Northern Nebraska","interactions":[],"lastModifiedDate":"2022-01-12T15:29:09.638114","indexId":"70203189","displayToPublicDate":"2019-07-01T11:38:01","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Sediment monitoring to support modeling a reservoir sediment flush on a sand-bed river in Northern Nebraska","docAbstract":"The U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers (USACE), monitored a sediment flush event from Spencer Dam located on the Niobrara River near Spencer, Nebraska, during the fall of 2014. Data collected during the flush was used to validate a one-dimensional sediment transport model developed by the USACE. The USACE surveyed 26 cross sections within the reservoir and as far as 1 kilometer (km) upstream from the reservoir pool to about 10 km downstream from the dam before and after the flushing event to measure erosion and deposition. They also collected surficial sediment samples from sandbars within the reservoir. The USGS assisted USACE in its model validation efforts by collecting sediment data before, during and after the flush using both traditional sampling techniques and a continuous laser-diffraction particle-size analyzer. From the context of longitudinal volumetric change, the model replicated erosion in the upper half of the reservoir within four percent of that observed by survey data and it replicated deposition downstream of the dam within 5 percent. However, the model underpredicted the erosion of the accumulated delta sediments in the reservoir by 43 percent. The timing and magnitude of suspended sediment concentrations produced by the model compared reasonably well to the discrete suspended-sediment sample results. These results indicate cross-sectional survey data and discrete sediment data may be adequate for developing sediment flush models for reservoirs in similar well-sorted sand-bed streams.\n\nThe USGS installed a continuous particle-size analyzer immediately downstream from the dam. Although the particle-size analyzer was successful in providing a large dataset during the flushing event, based on discrete point samples, it overestimated the amount of fine particles and underrepresented the amount of coarse material. It also required a significant amount of maintenance during the flushing event because of the large sediment load and the rapid bed aggradation. The maintenance issues with the particle-size analyzer along with uncertainty in the correlation to discrete suspended-sediment samples reduced its value for model validation. However, these issues may have been specific to the flushing event at Spencer Dam, which involved a sand-bed dominated stream and a wide channel. It is foreseeable that other sediment flush models developed for different streams with dissimilar sediment gradations may benefit from similar continuous sediment data, but adequate planning and evaluation should be performed.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2019","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD 2019 Conference","conferenceDate":"June 24-28, 2019","conferenceLocation":"Reno, NV","language":"English","publisher":"Federal Interagency Sedimentation Conference (FISC) and Federal Interagency Hydrologic Modeling Conference (FIHMC)","usgsCitation":"Schaepe, N.J., and Boyd, P.M., 2019, Sediment monitoring to support modeling a reservoir sediment flush on a sand-bed river in Northern Nebraska, in Proceedings of SEDHYD 2019, v. 2, Reno, NV, June 24-28, 2019, 14 p.","productDescription":"14 p.","ipdsId":"IP-105260","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":368654,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2019/#sedhyd-2019-proceedings"},{"id":368655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","city":"Spencer","otherGeospatial":"Niobara River, Spencer Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.68452072143555,\n 42.79514872764227\n ],\n [\n -98.61997604370117,\n 42.79514872764227\n ],\n [\n -98.61997604370117,\n 42.81391436163743\n ],\n [\n -98.68452072143555,\n 42.81391436163743\n ],\n [\n -98.68452072143555,\n 42.79514872764227\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schaepe, Nathaniel J. 0000-0003-1776-7411 nschaepe@usgs.gov","orcid":"https://orcid.org/0000-0003-1776-7411","contributorId":2377,"corporation":false,"usgs":true,"family":"Schaepe","given":"Nathaniel","email":"nschaepe@usgs.gov","middleInitial":"J.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":761566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyd, Paul M","contributorId":215066,"corporation":false,"usgs":false,"family":"Boyd","given":"Paul","email":"","middleInitial":"M","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":761567,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203958,"text":"70203958 - 2019 - Near-field remote sensing of Alaskan Rivers","interactions":[],"lastModifiedDate":"2019-10-17T11:29:25","indexId":"70203958","displayToPublicDate":"2019-07-01T11:24:13","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Near-field remote sensing of Alaskan Rivers","docAbstract":"The U.S. Geological Survey (USGS) Geomorphology and Sediment Transport Laboratory (GSTL), in collaboration with the U.S. Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL), acquired remotely sensed data from several Alaskan rivers in 2017 and 2018 with the goal of developing a methodology for measuring streamflow from a helicopter. CRREL operates a custom airborne lidar system that can be deployed in a helicopter-based pod (HeliPod). Data were collected with the HeliPod near existing USGS streamflow information stations on the Knik, Matanuska, Chena, and Salcha Rivers in both 2017 and 2018. Sites on the Tanana and Snow Rivers were added in 2018. In 2018, the HeliPod was modified to accommodate both a thermal infrared and a visible camera. The cameras were integrated with the flight management software to simultaneously acquire imagery with lidar. The Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) in the HeliPod were used to compute trajectories with precise position and orientation information needed for image orthorectification. The HeliPod sensors provide data for measuring river channel characteristics. Lidar can map the elevation of the water surface and thus be used to measure water-surface slopes and return intensity can be used to delineate the extent of the wetted river channel. Various approaches are currently being evaluated to estimate surface flow velocity from visible and thermal image time series. In this paper, we examine and compare water-surface elevation returns and slopes derived from the HeliPod lidar and found good agreement with measurements made using conventional field-based techniques.","conferenceTitle":"Federal Interagency Sedimentation and Hydrologic Modeling Conference (SEDHYD 2019)","conferenceDate":"June 24-28, 2019","conferenceLocation":"Reno, Nevada","language":"English","publisher":"Federal Interagency Sedimentation and Hydrologic Modeling Conference (SEDHYD 2019)","usgsCitation":"Kinzel, P.J., Legleiter, C.J., Nelson, J.M., Conaway, J., LeWinter, A., Gadomski, P., and Filiano, D., 2019, Near-field remote sensing of Alaskan Rivers, Federal Interagency Sedimentation and Hydrologic Modeling Conference (SEDHYD 2019), Reno, Nevada, June 24-28, 2019, 10 p.","productDescription":"10 p.","ipdsId":"IP-106032","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":368383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364998,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2019/openconf/modules/request.php?module=oc_program&action=program.php&p=program"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.3251953125,\n 59.085738569819505\n ],\n [\n -144.3603515625,\n 59.085738569819505\n ],\n [\n -144.3603515625,\n 66.47820814385636\n ],\n [\n -153.3251953125,\n 66.47820814385636\n ],\n [\n -153.3251953125,\n 59.085738569819505\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":764968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":764969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":764970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conaway, Jeff 0000-0002-3036-592X","orcid":"https://orcid.org/0000-0002-3036-592X","contributorId":214226,"corporation":false,"usgs":true,"family":"Conaway","given":"Jeff","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":764971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LeWinter, Adam","contributorId":192072,"corporation":false,"usgs":false,"family":"LeWinter","given":"Adam","affiliations":[],"preferred":false,"id":764972,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gadomski, Peter","contributorId":216532,"corporation":false,"usgs":false,"family":"Gadomski","given":"Peter","email":"","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":764973,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Filiano, Dominic","contributorId":216533,"corporation":false,"usgs":false,"family":"Filiano","given":"Dominic","email":"","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":764974,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70216496,"text":"70216496 - 2019 - Measurement of sounds emitted by certain high-resolution geophysical survey systems","interactions":[],"lastModifiedDate":"2020-11-23T17:15:21.846135","indexId":"70216496","displayToPublicDate":"2019-07-01T11:12:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1941,"text":"IEEE Journal of Oceanic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Measurement of sounds emitted by certain high-resolution geophysical survey systems","docAbstract":"Scientific questions regarding the impact of anthropomorphic noise in the marine environment have resulted in an increasing number of regulatory requirements and precautionary mitigation strategies to reduce the risks associated with high-resolution marine geophysical surveys performed in waters subjected to government jurisdiction. An example of regulatory frameworks includes the Marine Mammal Protection Act in the United States and the Marine Strategy Framework Directive 2008/56/EC in the European Union. Regulatory compliance often requires an assessment of the potential ecological risks before initiating a marine geophysical survey. However, the acoustic source data needed to estimate the risk associated with the operation of a given high-resolution survey system are frequently lacking. A comprehensive measurement program was performed to quantify the characteristics of sounds radiated by a variety of commercial marine geophysical survey systems, including boomers, sparkers, airguns, chirp sub-bottom profilers, sidescan sonars, and swath-bathymetric sonars [Crocker and Fratantonio, “Characteristics of high-frequency sounds emitted during high-resolution marine geophysical surveys,” Naval Undersea Warfare Center, Newport, RI, USA, NUWC-NPT Tech. Rep. 12, 203, 2016]. Calibrated acoustic source data, including source levels, source spectra, and beam patterns, were acquired for a total of 18 different marine geophysical survey systems. The data support modeling to estimate the potential ecological impacts resulting from the operation of certain high-resolution marine geophysical survey systems.
","language":"English","publisher":"IEEE","doi":"10.1109/JOE.2018.2829958","usgsCitation":"Crocker, S.E., Fratantonio, F.D., Hart, P.E., Foster, D.S., O’Brien, T.F., and Labak, S., 2019, Measurement of sounds emitted by certain high-resolution geophysical survey systems: IEEE Journal of Oceanic Engineering, v. 44, no. 3, p. 796-813, https://doi.org/10.1109/JOE.2018.2829958.","productDescription":"18 p.","startPage":"796","endPage":"813","ipdsId":"IP-085103","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467490,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/joe.2018.2829958","text":"Publisher Index Page"},{"id":380704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Crocker, Steven E","contributorId":245144,"corporation":false,"usgs":false,"family":"Crocker","given":"Steven","email":"","middleInitial":"E","affiliations":[{"id":49092,"text":"Naval Undersea Warfare Center,","active":true,"usgs":false}],"preferred":false,"id":805438,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fratantonio, Frank D","contributorId":245145,"corporation":false,"usgs":false,"family":"Fratantonio","given":"Frank","email":"","middleInitial":"D","affiliations":[{"id":49092,"text":"Naval Undersea Warfare Center,","active":true,"usgs":false}],"preferred":false,"id":805439,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Patrick E. 0000-0002-5080-1426 hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5080-1426","contributorId":2879,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick","email":"hart@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":805440,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foster, David S. 0000-0003-1205-0884 dfoster@usgs.gov","orcid":"https://orcid.org/0000-0003-1205-0884","contributorId":1320,"corporation":false,"usgs":true,"family":"Foster","given":"David","email":"dfoster@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":805441,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Brien, Thomas F. 0000-0003-0906-8450 tobrien@usgs.gov","orcid":"https://orcid.org/0000-0003-0906-8450","contributorId":4151,"corporation":false,"usgs":true,"family":"O’Brien","given":"Thomas","email":"tobrien@usgs.gov","middleInitial":"F.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":805442,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Labak, Stanley","contributorId":245146,"corporation":false,"usgs":false,"family":"Labak","given":"Stanley","email":"","affiliations":[{"id":49093,"text":"Bureau of Ocean Energy Management,","active":true,"usgs":false}],"preferred":false,"id":805443,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227424,"text":"70227424 - 2019 - Characterization of hydrology and sediment transport following drought and wildfire in Cache Creek, California","interactions":[],"lastModifiedDate":"2022-01-14T16:47:03.777945","indexId":"70227424","displayToPublicDate":"2019-07-01T10:39:51","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Characterization of hydrology and sediment transport following drought and wildfire in Cache Creek, California","docAbstract":"The worst drought in California in over 1,200 years occurred between 2012-2017 (Griffin, 2014), depleting surface water and groundwater supply and drying out the soils past wilting point. In the summer of 2015, the Jerusalem and Rocky fires burned roughly 40,000 acres within the Cache Creek watershed. To fully characterize the post-fire effects in the Cache Creek watershed, an hourly model of streamflow and sediment transport was developed using the Hydrological Simulation Program – FORTRAN (HSPF). This model requires air temperature, precipitation, and potential evapotranspiration as climate inputs. Hourly station data are sparse in the area and may not capture the variability of elevation and local climatology patterns within the watershed. \n\nA technique used previously to spatially-interpolate daily-climate station data has improved the characterization of local and regional climate patterns on a daily scale in areas with sparse data (Flint et al., 2014). This technique was extended to hourly observed data to produce spatially-varying climate inputs for the Cache Creek hydrologic model to run as a continuous multi-year simulation with hourly time steps. Monthly PRISM grids were used in a two-step scaling method with climate Gradient and Inverse Distance Squared (GIDS) maps (Nalder and Wein, 1998) to develop daily grids, then the daily grids were used to scale hourly climate GIDS maps. This method captures the temporal variability at each climate station yet preserves the regional monthly spatial structure of the PRISM data.\n\nHydrologic calibration used data from water year 2015, and validation used the same parameters for water year 2016. The model was run through water year 2017 to characterize the effects of wildfire on hydrology and sediment transport. For final simulations, the model was run at an hourly time step from June 2014 through September 2017 to ensure a model initiation period of 4 months prior to the target simulation period used for analysis. Sediment parameters were initially set using the existing Sacramento River Basin model for this sub-watershed area and then iteratively adjusted in the calibration process. To simulate a fire across the landscape, sediment parameters for water years 2016-17 were further modified for burned sub-basins to represent post-fire vegetation and soils in 2016, then partial recovery in 2017. \n\nResults were inconclusive for drought and wildfire effects on runoff. Modeled peak flows generally underpredicted observed peak flows; however, the modeled storm volumes were only slightly under or over the observed storm volumes. Sediment transport was sensitive to the watershed disturbances and R^2 values for daily mean suspended concentrations (SSC) and sediment discharge were 0.70 and 0.75, respectively. Simulated hourly values correlated less strongly with observed instantaneous SSC and sediment discharge (R^2 values of 0.56 and 0.46, respectively).","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2019","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD 2019 Conference","conferenceDate":"June 24-28, 2019","conferenceLocation":"Reno, NV","language":"English","publisher":"Federal Interagency Sedimentation Conference (FISC) and Federal Interagency Hydrologic Modeling Conference (FIHMC)","usgsCitation":"Stern, M.A., Flint, L.E., and Flint, A.L., 2019, Characterization of hydrology and sediment transport following drought and wildfire in Cache Creek, California, in Proceedings of SEDHYD 2019, v. 5, Reno, NV, June 24-28, 2019, 8 p.","productDescription":"8 p.","ipdsId":"IP-107472","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":394386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":394372,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2019/#sedhyd-2019-proceedings"}],"country":"United States","state":"California","otherGeospatial":"Cache Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.76397705078124,\n 38.792626957868904\n ],\n [\n -122.36297607421874,\n 38.792626957868904\n ],\n [\n -122.36297607421874,\n 39.17478791493289\n ],\n [\n -122.76397705078124,\n 39.17478791493289\n ],\n [\n -122.76397705078124,\n 38.792626957868904\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":830820,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208842,"text":"70208842 - 2019 - Southern California and range‐wide raccoon gastrointestinal helminth database","interactions":[],"lastModifiedDate":"2020-03-03T09:14:41","indexId":"70208842","displayToPublicDate":"2019-07-01T09:13:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Southern California and range‐wide raccoon gastrointestinal helminth database","docAbstract":"Local and global measurements of parasite prevalence and abundance are critical for understanding the dynamics that underlie the diversity, distribution, and evolution of infectious diseases. Here, we present a data set of gut helminths found in (1) raccoons throughout their range, based on primary literature from 1925–2017 and (2) raccoons in Santa Barbara County, California, USA surveyed from 2012 to 2015. The range‐wide data set has 1,256 parasite entries from 217 literature sources across three continents and 32 states in the USA. This data set includes a list of all recorded raccoon gut helminths (n = 100) and their presence and prevalence in surveyed raccoon populations. The Santa Barbara data set includes gut helminth data from 182 raccoons from one Southern California County. In addition to the presence and abundance data for 13 parasite species, this data set includes measurements of 7,465 individual raccoon roundworms (Baylisascaris procyonis). For both range‐wide and Santa Barbara data sets, we include information on parasite site of infection in host, sampling method, and sample size. We also provide geographic coordinates for infected raccoon populations (range‐wide database) and individuals (Santa Barbara). In the associated metadata, we include sampling methods and summary figures for both the range‐wide and Santa Barbara raccoon gut helminth records. There are no copyright or proprietary restrictions for research and/or teaching purposes. S. B. Weinstein and J. C. Van Wert contributed equally to this manuscript and are shared first authors.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2807","usgsCitation":"Weinstein, S.B., Van Wert, J.C., Kinsella, M., Tkach, V.V., and Lafferty, K.D., 2019, Southern California and range‐wide raccoon gastrointestinal helminth database: Ecology, v. 100, no. 9, e02807, https://doi.org/10.1002/ecy.2807.","productDescription":"e02807","ipdsId":"IP-108629","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":467492,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.2807","text":"Publisher Index Page"},{"id":372840,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Barbara County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.750732421875,\n 34.225429015241396\n ],\n [\n -118.377685546875,\n 34.225429015241396\n ],\n [\n -118.377685546875,\n 34.88593094075317\n ],\n [\n -120.750732421875,\n 34.88593094075317\n ],\n [\n -120.750732421875,\n 34.225429015241396\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"100","issue":"9","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Weinstein, Sara B.","contributorId":141028,"corporation":false,"usgs":false,"family":"Weinstein","given":"Sara","email":"","middleInitial":"B.","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":783611,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Wert, Jacey C.","contributorId":221858,"corporation":false,"usgs":false,"family":"Van Wert","given":"Jacey","email":"","middleInitial":"C.","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":783612,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kinsella, Mike","contributorId":221859,"corporation":false,"usgs":false,"family":"Kinsella","given":"Mike","email":"","affiliations":[{"id":40444,"text":"Helm West Laboratory, Missoula, MT","active":true,"usgs":false}],"preferred":false,"id":783613,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tkach, Vasyl V.","contributorId":190351,"corporation":false,"usgs":false,"family":"Tkach","given":"Vasyl","email":"","middleInitial":"V.","affiliations":[{"id":52695,"text":"Department of Biology, University of North Dakota, Grand Forks, ND 58201, USA","active":true,"usgs":false}],"preferred":false,"id":783614,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783610,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204519,"text":"70204519 - 2019 - Evaluation of environmental DNA surveys for identifying occupancy and spatial distribution of Pacific Lamprey (Entosphenus tridentatus) and Lampetra spp. in a Washington coast watershed","interactions":[],"lastModifiedDate":"2019-08-01T09:06:53","indexId":"70204519","displayToPublicDate":"2019-07-01T09:04:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5840,"text":"Environmental DNA","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of environmental DNA surveys for identifying occupancy and spatial distribution of Pacific Lamprey (Entosphenus tridentatus) and Lampetra spp. in a Washington coast watershed","docAbstract":"Surveys of environmental DNA (eDNA) have become an important and multifaceted tool for monitoring and identifying distributions and occupancy of aquatic species. This tool is attractive because it is powerful, easy to apply, and provides an alternative to traditional field survey methods. However, validating eDNA survey methods against traditional field survey methods is warranted prior to their application. We used eDNA and electrofishing to survey 10 sites in 3 tributaries of the Chehalis River, Washington, to infer distribution and occupancy of Entosphenus tridentatus and Lampetra spp. Both methods produced similar detection rates for E. tridentatus, and Lampetra spp. were detected at slightly greater frequency with eDNA in the Black River and Skookumchuck River. Within each of the three tributaries, eDNA concentration was negatively related to sample distance from the Chehalis River mainstem for E. tridentatus but not for Lampetra spp., which indicates E. tridentatus and Lampetra spp. may be distributed differently within tributaries. Application of lamprey eDNA data to a multiscale occupancy model indicated high probability of detecting eDNA in water samples and quantitative PCR (qPCR) assays. Broad distribution and high detection of E. tridentatus and Lampetra spp. suggest robust populations inhabit the Chehalis River basin. Our findings suggest eDNA surveys may be comparable to electrofishing for informing lamprey occupancy and distributions. Such sampling is efficient and cost‐effective and we anticipate that eDNA surveys will become a valuable tool in addressing key research and monitoring needs for conservation and restoration of lampreys in general.
Under the Council-Selected Restoration Component of the RESTORE Act, the Council develops Funded Priority Lists (FPLs) that describe the projects and programs it will fund. Projects and programs funded through this component must be in furtherance of the goals and objectives of the Council’s Comprehensive Plan and address at least one of the restoration criteria identified in the RESTORE Act. The Initial FPL, finalized in December of 2015, had a strong focus on watershed and estuary restoration and foundational cross-Gulf projects. Approved as a Gulf-wide investment in the 2015 Initial FPL, The Council Monitoring and Assessment Program (CMAP) is administered jointly by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS). Funded activities include the development of basic, foundational components for Gulf-wide monitoring to measure beneficial impacts of investments in Gulf restoration by the Council. The program, in coordination with the Gulf of Mexico Alliance (GOMA) and through collaboration with the Gulf States, Federal and local partners, academia, non-governmental organizations, and business and industry, has leveraged existing resources, capacities, and expertise and build on existing monitoring data and programs.
Model predictions of severe storm impacts provide coastal residents, emergency managers, and partner organizations valuable predictive information for planning and response to extreme storm events. The foundation of this work is a USGS-developed numerical model to forecast storm-induced coastal water levels and expected coastal change, including dune erosion, overwash, and inundation. The model is operated in three modes: generalized scenarios, real-time storms, and an operational forecast, with each mode requiring slightly different water level inputs. To evaluate and improve the accuracy of the models, we collect data on water levels and coastal change. In particular, observations before, after, and during storm conditions are used to test the different model applications. Forecast validation for Hurricanes Matthew (2016) and Irma (2017) illustrate three cases with demonstrated forecast skill and three cases with poor skill, and reveal elements of the modeling and/or testing approach which require improvement.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Coastal Sediments 2019: Proceedings of the 9th international conference ","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2019","conferenceDate":"May 27-31, 2019","conferenceLocation":"Tampa/St. Petersburg, FL","language":"English","publisher":"World Scientific","doi":"10.1142/9789811204487_0122","usgsCitation":"Doran, K.S., Stockdon, H.F., Joseph Long, and Plant, N.G., 2019, Forecasts of coastal change hazards, in Coastal Sediments 2019: Proceedings of the 9th international conference , Tampa/St. Petersburg, FL, May 27-31, 2019, p. 1400-1409, https://doi.org/10.1142/9789811204487_0122.","productDescription":"10 p.","startPage":"1400","endPage":"1409","ipdsId":"IP-105870","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":369944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Doran, Kara S. 0000-0001-8050-5727 kdoran@usgs.gov","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":148059,"corporation":false,"usgs":true,"family":"Doran","given":"Kara","email":"kdoran@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":758393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stockdon, Hilary F. 0000-0003-0791-4676 hstockdon@usgs.gov","orcid":"https://orcid.org/0000-0003-0791-4676","contributorId":2153,"corporation":false,"usgs":true,"family":"Stockdon","given":"Hilary","email":"hstockdon@usgs.gov","middleInitial":"F.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":758394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Joseph Long","contributorId":213744,"corporation":false,"usgs":false,"family":"Joseph Long","affiliations":[{"id":38846,"text":"UNC Wilmington","active":true,"usgs":false}],"preferred":false,"id":758395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":758396,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205847,"text":"70205847 - 2019 - Consistency counts: Modeling the effects of a change in protocol on Breeding Bird Survey counts","interactions":[],"lastModifiedDate":"2019-10-21T14:40:37","indexId":"70205847","displayToPublicDate":"2019-06-29T12:59:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Consistency counts: Modeling the effects of a change in protocol on Breeding Bird Survey counts","docAbstract":"Analysis of North American Breeding Bird Survey (BBS) data requires controls for factors that influence detectability of birds along survey routes. Identifying factors that influence the counting process and incorporating them into analyses is a primary means of limiting bias in estimates of population change. Twedt (2015) implemented an alternative counting protocol on operational and non-random BBS survey routes in the southeastern United States. Observers on selected routes employed a time-distance protocol in which they recorded birds in 1-minute intervals and in 2 distance categories. We hypothesized that processing and recording observations using this time-distance protocol could cause observers to count fewer birds relative to observers using the standard protocol. We used a hierarchical log-linear model with a categorical covariate associated with protocol (standard vs time-distance) to assess whether use of the time-distance protocol had a measurable effect on counting birds along BBS routes. We applied this model to BBS data from portions of eight states in which the time-distance protocol was implemented and estimated a protocol effect for 167 bird species. We documented a significant overall effect of the time-distance protocol on observers’ counts of birds. On average, the effect of the time-distance protocol was a 10% decline in count, and 80% of species had lower counts when the time-distance protocol was used on a survey route. However, because the time-distance protocol was only used on a small portion of the operational BBS routes and for a limited time, including the covariate for the time-distance protocol data had insignificant effects on analysis of population change. Although the covariate controlled for the effects of the time-distance protocol in BBS data, the results emphasize the importance of standardization as well as a need to track and, if necessary, control in analyses for changes in counting procedures along BBS routes.","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duz009","usgsCitation":"Sauer, J.R., Link, W.A., Ziolkowski, D., Pardieck, K.L., and Twedt, D.J., 2019, Consistency counts: Modeling the effects of a change in protocol on Breeding Bird Survey counts: The Condor, v. 121, no. 2, duz009, 12 p., https://doi.org/10.1093/condor/duz009.","productDescription":"duz009, 12 p.","ipdsId":"IP-081193","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467495,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duz009","text":"Publisher Index Page"},{"id":368104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"121","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":772603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Link, William A. 0000-0002-9913-0256 wlink@usgs.gov","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":146920,"corporation":false,"usgs":true,"family":"Link","given":"William","email":"wlink@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":773533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ziolkowski, David 0000-0002-2500-4417 dziolkowski@usgs.gov","orcid":"https://orcid.org/0000-0002-2500-4417","contributorId":195409,"corporation":false,"usgs":true,"family":"Ziolkowski","given":"David","email":"dziolkowski@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":773534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":773535,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Twedt, Daniel J. 0000-0003-1223-5045 dtwedt@usgs.gov","orcid":"https://orcid.org/0000-0003-1223-5045","contributorId":398,"corporation":false,"usgs":true,"family":"Twedt","given":"Daniel","email":"dtwedt@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":773536,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203473,"text":"ofr20191056 - 2019 - Optimization of salt marsh management at the Chincoteague National Wildlife Refuge, Virginia, through use of structured decision making","interactions":[],"lastModifiedDate":"2024-03-04T18:44:10.106398","indexId":"ofr20191056","displayToPublicDate":"2019-06-28T13:45:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1056","displayTitle":"Optimization of Salt Marsh Management at the Chincoteague National Wildlife Refuge, Virginia, Through Use of Structured Decision Making","title":"Optimization of salt marsh management at the Chincoteague National Wildlife Refuge, Virginia, through use of structured decision making","docAbstract":"Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing salt marsh management decisions at the Chincoteague National Wildlife Refuge in Virginia. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of 12 salt marsh management units within the refuge and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that would be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per salt marsh management unit, that would maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to approximately $2.5 million, but that further expenditures may yield diminishing return on investment. For multiple salt marsh management units, a scenario incorporating managing grazing practices within the marsh was selected to maximize benefits while constraining total costs for the refuge at less than $2.5 million. Thin-layer deposition was predicted to increase the total management benefit substantially, but at considerable total costs ($2.5 million to $83 million). The prototype presented here provides a framework for decision making at the Chincoteague National Wildlife Refuge that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191056","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., and Holcomb, K.S., 2019, Optimization of salt marsh management at the Chincoteague National Wildlife Refuge, Virginia, through use of structured decision making: U.S. Geological Survey Open-File Report 2019–1056, 29 p., https://doi.org/10.3133/ofr20191056.","productDescription":"vi, 29 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101219","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":364633,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1056/coverthb.jpg"},{"id":364634,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1056/ofr20191056.pdf","text":"Report","size":"2.60 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1056"}],"country":"United States","state":"Virginia","otherGeospatial":"Chincoteague Island, Chincoteague National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.38440704345703,\n 37.91278405007035\n ],\n [\n -75.3830337524414,\n 37.916169765380076\n ],\n [\n -75.37445068359375,\n 37.917117737741826\n ],\n [\n -75.37273406982422,\n 37.916576040745376\n ],\n [\n -75.36895751953125,\n 37.91468006984207\n ],\n [\n -75.36449432373047,\n 37.91562806140234\n ],\n [\n -75.35608291625977,\n 37.91562806140234\n ],\n [\n -75.35076141357422,\n 37.91684688974227\n ],\n [\n -75.34818649291992,\n 37.920367835943516\n ],\n [\n -75.34629821777344,\n 37.924565666890146\n ],\n [\n -75.34406661987303,\n 37.9276800317787\n ],\n [\n -75.34046173095703,\n 37.936074619194514\n ],\n [\n -75.33823013305664,\n 37.93945926228315\n ],\n [\n -75.34475326538086,\n 37.94094845586459\n ],\n [\n -75.34612655639648,\n 37.947040297194114\n ],\n [\n -75.34114837646484,\n 37.953673061162604\n ],\n [\n -75.33102035522461,\n 37.958681077955525\n ],\n [\n -75.32638549804688,\n 37.958004338883114\n ],\n [\n -75.32655715942383,\n 37.96720745598712\n ],\n [\n -75.31213760375975,\n 37.98141588591056\n ],\n [\n -75.31848907470703,\n 37.983310135428795\n ],\n [\n -75.3219223022461,\n 37.98019812825676\n ],\n [\n -75.32432556152342,\n 37.98182180063798\n ],\n [\n -75.32827377319335,\n 37.982904228934125\n ],\n [\n -75.33102035522461,\n 37.98141588591056\n ],\n [\n -75.33187866210936,\n 37.97153793552019\n ],\n [\n -75.33308029174805,\n 37.968966744104264\n ],\n [\n -75.33565521240234,\n 37.96829009981737\n ],\n [\n -75.34097671508789,\n 37.9630120603577\n ],\n [\n -75.34664154052734,\n 37.959493156611366\n ],\n [\n -75.35642623901367,\n 37.953266990781884\n ],\n [\n -75.36518096923828,\n 37.94568659832042\n ],\n [\n -75.36792755126953,\n 37.942166864531906\n ],\n [\n -75.36672592163085,\n 37.94798787156644\n ],\n [\n -75.3691291809082,\n 37.94988298364895\n ],\n [\n -75.37359237670898,\n 37.94825860485678\n ],\n [\n -75.37445068359375,\n 37.944874367025136\n ],\n [\n -75.37273406982422,\n 37.94162535206254\n ],\n [\n -75.37256240844727,\n 37.93851157792925\n ],\n [\n -75.377197265625,\n 37.935262281661146\n ],\n [\n -75.38389205932617,\n 37.932554425046405\n ],\n [\n -75.39127349853516,\n 37.92402402474885\n ],\n [\n -75.39505004882812,\n 37.916305190751174\n ],\n [\n -75.40895462036133,\n 37.90492859043573\n ],\n [\n -75.40672302246094,\n 37.89937508713545\n ],\n [\n -75.40157318115234,\n 37.898020510564386\n ],\n [\n -75.39573669433594,\n 37.89896871678171\n ],\n [\n -75.39281845092773,\n 37.899781455245545\n ],\n [\n -75.38818359375,\n 37.89910417381559\n ],\n [\n -75.38320541381836,\n 37.90072963877702\n ],\n [\n -75.38114547729492,\n 37.90398046100267\n ],\n [\n -75.3823471069336,\n 37.90858554667319\n ],\n [\n -75.38440704345703,\n 37.91115885137137\n ],\n [\n -75.38440704345703,\n 37.91278405007035\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
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Dissolved selenium is a contaminant of concern in the lower Gunnison River Basin, Colorado. Selenium is naturally present in the Cretaceous Mancos Shale and is leached to groundwater and surface water by irrigation. The groundwater on the east side of the Uncompahgre River in Delta and Montrose Counties is one of the primary sources of selenium concentration and load to surface water in the lower Gunnison River Basin. Although little information about the contribution of groundwater to surface water has been historically available, groundwater has often been implicated as an appreciable source of selenium to surface water. From 2012 to 2016, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, the Colorado Water Conservation Board, and the Gunnison Basin Selenium Management Program, established a 30-well groundwater-monitoring network on irrigated land to characterize the hydrology and groundwater quality of the shallow groundwater system on the east side of the Uncompahgre River in the lower Gunnison River Basin. The installation of the 30-well network and the data collected allowed for the development of a conceptual model of selenium mobilization and transport in the shallow groundwater system. Monitoring wells were completed in surficial deposits and in weathered Mancos Shale, which generally exhibited unconfined and confined conditions, respectively. Groundwater-quality monitoring provides information on the distribution of selenium and the geochemical processes controlling selenium concentrations in shallow groundwater. Monitoring wells were sampled between August 2013 and March 2015 to understand groundwater quality, seasonality, sources of recharge, and groundwater age. Concentrations of dissolved selenium ranged from below the limit of detection to 4,100 micrograms per liter (µg/L), with a median concentration of 14 µg/L. Concentrations showed a high degree of spatial variability and no seasonal difference. Similarly, no seasonal pattern was observed in specific conductance values of groundwater despite the considerably lower specific conductance value of irrigation water.
Reduction-oxidation processes are important controls on selenium mobility. Nitrate derived from geologic material was a primary control on reduction-oxidation conditions in groundwater and inhibited selenium reduction to less mobile forms. Nitrate was reduced by denitrification in groundwater, but it was not reduced to the extent necessary to allow for selenium reduction. Groundwater ages were determined for groundwater samples from eight wells and ranged from 6 to 20 years old. Isotopic data indicate groundwater was recharged by irrigation water; no information collected supported an older, deeper source of recharge to the shallow groundwater system. Data on water level in all wells showed response to irrigation practices, but the response was delayed in some wells, which may be an indication of distance from recharge source.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195029","collaboration":"Prepared in cooperation with the Bureau of Reclamation, the Colorado Water Conservation Board, and the Gunnison Basin Selenium Management Program","usgsCitation":"Thomas, J.C., McMahon, P.B., and Arnold, L.R., 2019, Groundwater quality and hydrology with emphasis on selenium mobilization and transport in the lower Gunnison River Basin, Colorado, 2012–16: U.S. Geological Survey Scientific Investigations Report 2019–5029, 69 p., https://doi.org/10.3133/sir20195029.","productDescription":"viii, 69 p.","onlineOnly":"Y","ipdsId":"IP-084069","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":365132,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5029/coverthb.jpg"},{"id":365133,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5029/sir20195029.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5029"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.80584716796875,\n 39.01064750994083\n ],\n [\n -109.11895751953125,\n 38.8782049970615\n ],\n [\n -108.6328125,\n 38.10214399750345\n ],\n [\n -108.69598388671875,\n 37.77288579232439\n ],\n [\n -107.87750244140625,\n 37.309014074275915\n ],\n [\n -107.4462890625,\n 37.31338308990806\n ],\n [\n -107.1441650390625,\n 37.727280276860036\n ],\n [\n -107.18536376953125,\n 38.07620357665235\n ],\n [\n -107.26776123046875,\n 38.50304202775689\n ],\n [\n -107.50671386718749,\n 38.9380483825641\n ],\n [\n -107.6495361328125,\n 39.115144700901475\n ],\n [\n -108.80584716796875,\n 39.01064750994083\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
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Infections from antibiotic resistant microorganisms are considered to be one of the greatest global public health challenges that result in huge annual economic losses. While genes that impart resistance to antibiotics (AbR) existed long before the discovery and use of antibiotics, anthropogenic uses of antibiotics in agriculture, domesticated animals, and humans are known to influence the prevalence of these genes in pathogenic microorganisms. It is critical to understand the role that natural and anthropogenic processes have on the occurrence and distribution of antibiotic resistance in microbial populations to minimize health risks associated with exposures. As part of this research, 15 antibiotic resistance genes were analyzed in coastal sediments and soils along the eastern seaboard of the USA using presence/absence quantitative and digital polymerase chain reaction assays. Samples (53 soil and 192 sediment samples including 54 replicates) were collected from a variety of coastal settings where human and wildlife exposure is likely. At least one of the antibiotic resistance genes was detected in 76.4% of the samples. Samples that contained at least five or more antibiotic resistance genes (5.7%) where typically hydrologically down gradient of watersheds influenced by combined sewer outfalls (CSO). The most frequently detected antibiotic resistance target genes were found in 33.2%, 34.4%, and 42.2% of samples (target genes blaSHV, tetO, and aadA2, respectively). These data provide unique insight into potential exposure of AbR genes over a large geographical region of the eastern seaboard of the USA.
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tjreilly@usgs.gov","orcid":"https://orcid.org/0000-0002-2939-3050","contributorId":1858,"corporation":false,"usgs":true,"family":"Reilly","given":"Timothy","email":"tjreilly@usgs.gov","middleInitial":"J.","affiliations":[{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":769599,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jones, Daniel K. 0000-0003-0724-8001 dkjones@usgs.gov","orcid":"https://orcid.org/0000-0003-0724-8001","contributorId":4959,"corporation":false,"usgs":true,"family":"Jones","given":"Daniel","email":"dkjones@usgs.gov","middleInitial":"K.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":769600,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70204015,"text":"sir20195040 - 2019 - Capacity and area of Grand Lake O’ the Cherokees, northeastern Oklahoma, 2009","interactions":[],"lastModifiedDate":"2019-06-28T09:37:04","indexId":"sir20195040","displayToPublicDate":"2019-06-27T19:35:22","publicationYear":"2019","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":"2019-5040","displayTitle":"Capacity and Area of Grand Lake O’ the Cherokees, Northeastern Oklahoma, 2009","title":"Capacity and area of Grand Lake O’ the Cherokees, northeastern Oklahoma, 2009","docAbstract":"In February 2017, the Grand River Dam Authority filed to relicense the Pensacola Hydroelectric Project with the Federal Energy Regulatory Commission. The predominant feature of the Pensacola Hydroelectric Project is Pensacola Dam, which impounds Grand Lake O’ the Cherokees (locally called Grand Lake) in northeastern Oklahoma. Identification of information gaps and assessment of project effects on stakeholders are central aspects of the Federal Energy Regulatory Commission relicensing process. Due to the natural changes to the reservoir over time, new capacity and area tables are needed periodically. The most recent complete capacity and area table was produced in 1940. Capacity and area tables identify the relations between the elevation of the water surface and the volume of water that can be impounded at each water surface elevation. This report (1) presents an updated capacity and area table for Grand Lake O’ the Cherokees for 2009, (2) describes the methods used to calculate the updated capacity and area values presented in the table, and (3) compares the updated capacity table to historical capacity tables produced from a survey in 1940 and from a hydrographic survey of the lake by the Oklahoma Water Resources Board in 2009.
The new capacity values computed for Grand Lake O’ the Cherokees indicate that capacity at conservation pool elevation has decreased about 157,000 acre-feet or 10 percent since 1940 and capacity at top of dam elevation has decreased about 200,000 acre-feet or 8 percent since 1940. This difference in the capacities could be attributed to the advancements of technologies; the techniques used for surveying lakes have changed from the 1940 survey to the 2009 survey. Another possible reason for loss in capacity could be as time progresses, lakes like Grand Lake O’ the Cherokees slowly impound sediment carried by the rivers that feed into the lakes, thus diminishing the amount of water that the lake holds. The most recent survey used measured water depths and Global Position System collected electronically, but the methods used to collect data in 1940 are unknown. Due to the advancement of technology, the 2009 survey is likely more precise than the 1940 survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195040","collaboration":"Prepared in cooperation with the Grand River Dam Authority","usgsCitation":"Hunter, S.L., and Labriola, L.G., 2019, Capacity and area of Grand Lake O’ the Cherokees, northeastern Oklahoma, 2009: U.S. Geological Survey Scientific Investigations Report 2019–5040, 18 p., https://doi.org/10.3133/sir20195040.","productDescription":"Report: vi, 18 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-104220","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":365105,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VDBFWJ","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Data release for capacity and area of Grand Lake O’ the Cherokees, northeastern Oklahoma, 2009"},{"id":365103,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5040/coverthb.jpg"},{"id":365104,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5040/sir20195040.pdf","text":"Report","size":"12.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5040"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Grand Lake O’ the Cherokees","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.79141235351561,\n 36.832370801556834\n ],\n [\n -94.81475830078124,\n 36.771892444961026\n ],\n [\n -94.80926513671875,\n 36.705861603381145\n ],\n [\n -94.86007690429688,\n 36.66952043455806\n ],\n [\n -94.910888671875,\n 36.691547435472636\n ],\n [\n -95.08941650390625,\n 36.485348924361425\n ],\n [\n -95.0592041015625,\n 36.45000844447082\n ],\n [\n -94.94522094726562,\n 36.48093224547937\n ],\n [\n -94.89715576171875,\n 36.45000844447082\n ],\n [\n -94.85733032226562,\n 36.465471886798134\n ],\n [\n -94.89715576171875,\n 36.518465989675875\n ],\n [\n -94.87518310546875,\n 36.53612263184686\n ],\n [\n -94.85183715820312,\n 36.516258626036624\n ],\n [\n -94.79141235351561,\n 36.52839834681223\n ],\n [\n -94.72686767578125,\n 36.54163950596125\n ],\n [\n -94.7515869140625,\n 36.59127365634205\n ],\n [\n -94.73648071289061,\n 36.62103883480288\n ],\n [\n -94.63485717773438,\n 36.64638529597495\n ],\n [\n -94.69253540039062,\n 36.76639204454785\n ],\n [\n -94.70489501953125,\n 36.82247761166621\n ],\n [\n -94.79141235351561,\n 36.832370801556834\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma Water Science Center
U.S. Geological Survey
202 NW 66th Street, Building 7
Oklahoma City, OK 73116
The Stochastic Empirical Loading and Dilution Model (SELDM) was developed by the U.S. Geological Survey (USGS) in cooperation with the Federal Highway Administration to simulate stormwater quality. To assess the effects of runoff, SELDM uses a stochastic mass-balance approach to estimate combinations of pre-storm streamflow, stormflow, highway runoff, event mean concentrations (EMCs) and stormwater constituent loads from a site of interest. In addition, SELDM can be used to assess the effects of stormwater Best Management Practices (BMPs), which are designed to mitigate the adverse effects of runoff into a waterbody.
Adverse effects of stormwater on receiving waters are one of the greatest unsolved water-quality problems Nationwide. State DOTs, municipalities, Federal facilities, and private property owners who manage impervious surfaces need information about the potential magnitude of their contributions and the potential effectiveness of methods to mitigate the adverse effects of runoff. Because the efficacy of at-site controls are limited, information about the potential effectiveness of alternative strategies is needed.
The USGS, in cooperation with the Oregon Department of Transportation (ODOT), conducted a study to research methods in which SELDM can be used to enhance the efficiency of ODOT’s stormwater program, support the development of a stormwater banking program, and meet environmental goals. Results can be used to develop a strategic, systems-level approach to stormwater management by considering entire watersheds instead of individual road crossings. Two watersheds, Bear Creek and Mill Creek, in western Oregon were selected for analysis. Within each watershed, seven road crossings were selected for demonstrating the utility of SELDM in nested basins.
Precipitation statistics, pre-storm streamflow, runoff coefficients, and hydrograph recession factors were calculated for each location and used in SELDM to simulate flow, water-quality concentrations, and constituent loads in the upstream basin, from the highway (or developed area), and downstream from the road crossing. Three water-quality constituents were selected for modeling: suspended-sediment concentration (SSC), total phosphorus (TP), and total copper (TCu). Using water-quality transport curves, the relations between streamflow and SSC and between streamflow and TP were simulated. Concentrations of TCu were simulated by configuring a linear relation between SSC and TCu. A generic BMP was simulated using the median treatment statistics for flow reductions, hydrograph extensions, concentration reductions, and minimum irreducible concentrations from nine BMP categories with data from the 2012 International BMP database.
Five simulation scenarios were modeled for demonstrative purposes. These simulations were used to evaluate potential effects of different watershed properties, water-quality inputs, and stormwater mitigation measures. Instream EMCs were compared to hypothetical water-quality criteria for suspended sediment, total phosphorus, and total copper to demonstrate the concept of water-quality risk analysis. For all five scenarios, it was assumed that highway runoff concentrations were independent of location or average annual daily traffic. These five scenarios are as follows:
• Simulation Scenario 1—Natural Conditions (hereafter Simulation Scenario 1) represents conditions in an undeveloped watershed. This scenario demonstrates that the strategic placement of a hypothetical road crossing within a watershed could be used to avoid exceeding water-quality standards of TP and SSC, but that no location choice results in meeting TCu standards. Implementation of BMP had the most pronounced effects on downstream water-quality constituent EMCs at road crossings with the highest ratio of highway catchment area to upstream drainage area, but the largest effect of BMP treatment on mean annual load is based on highway catchment area alone.
• Simulation Scenario 2—Current Conditions (hereafter Simulation Scenario 2) represents current watershed conditions, where all developed area upstream from the road crossing was modeled as a highway and combined with the undeveloped part of the upstream drainage area (scenario 2A) and where the output from scenario 2A is used for the upstream area (developed area and the undeveloped area), and where the road crossing is added as usual (scenario 2B). Scenario 2 results indicate that attaining water-quality standards is more difficult with upstream developed areas. Specific road-crossing sites can be selected to achieve the fewest water-quality exceedances per year, but water-quality targets are not met without BMP implementation, and in some instances are not achievable even with BMP implementation. Results from this scenario also serve to quantify the upper limit of constituent reduction if funding were available to implement BMPs to large areas of development, and to quantify how much area would need BMP implementation to achieve water-quality targets.
• Simulation Scenario 3—Alternative Road Layouts (hereafter Simulation Scenario 3) was designed to assess the sensitivity of SELDM to various road layouts. In this scenario, different highway configurations were superimposed at one road crossing. Results indicate that downstream waterquality constituent EMCs did not exhibit much variation, but annual water-quality constituent loads varied considerably.
• Simulation Scenario 4—Varying Road Width (hereafter Simulation Scenario 4) was designed to assess the sensitivity of SELDM to road width. Similar to scenario 3, the results indicate little variation in downstream water-quality constituent EMCs, but annual water-quality constituent loads increased in proportion to road width.
• Simulation scenario 5—Changes to Impervious Area (hereafter Simulation Scenario 5) was designed to investigate the effects of changing amounts of imperviousness upstream from the road crossing. Results indicate that the downstream water-quality constituent EMCs are highly correlated with the percentage of impervious area upstream.
Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Water samples from 50 domestic wells located <1 km (proximal) and >1 km (distal) from shale-gas wells in upland areas of the Marcellus Shale region were analyzed for chemical, isotopic, and groundwater-age tracers. Uplands were targeted because natural mixing with brine and hydrocarbons from deep formations is less common in those areas compared to valleys. CH4-isotope, predrill CH4-concentration, and other data indicate that one proximal sample (5% of proximal samples) contains thermogenic CH4 (2.6 mg/L) from a relatively shallow source (Catskill/Lock Haven Formations) that appears to have been mobilized by shale-gas production activities. Another proximal sample contains five other volatile hydrocarbons (0.03–0.4 μg/L), including benzene, more hydrocarbons than in any other sample. Modeled groundwater-age distributions, calibrated to 3H, SF6, and 14C concentrations, indicate that water in that sample recharged prior to shale-gas development, suggesting that land-surface releases associated with shale-gas production were not the source of those hydrocarbons, although subsurface leakage from a nearby gas well directly into the groundwater cannot be ruled out. Age distributions in the samples span ∼20 to >10000 years and have implications for relating occurrences of hydrocarbons in groundwater to land-surface releases associated with recent shale-gas production and for the time required to flush contaminants from the system.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.9b01440","usgsCitation":"McMahon, P.B., Lindsey, B.D., Conlon, M.D., Hunt, A.G., Belitz, K., Jurgens, B., and Varela, B.A., 2019, Hydrocarbons in upland groundwater, Marcellus Shale Region, Northeastern Pennsylvania and Southern New York, USA: Environmental Science & Technology, v. 53, no. 14, p. 8027-8035, https://doi.org/10.1021/acs.est.9b01440.","productDescription":"9 p.","startPage":"8027","endPage":"8035","ipdsId":"IP-104959","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"links":[{"id":437401,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93M7JCD","text":"USGS data release","linkHelpText":"Data Release for Hydrocarbons in Upland Groundwater, Marcellus Shale Region, Northeastern Pennsylvania and Southern New York, USA"},{"id":365631,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Pennsylvania","volume":" 53","issue":"14","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766203,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":175346,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce","email":"blindsey@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766204,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conlon, Matthew D. 0000-0001-8266-9610 mconlon@usgs.gov","orcid":"https://orcid.org/0000-0001-8266-9610","contributorId":201291,"corporation":false,"usgs":true,"family":"Conlon","given":"Matthew","email":"mconlon@usgs.gov","middleInitial":"D.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766205,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hunt, Andrew G. 0000-0002-3810-8610 ahunt@usgs.gov","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":1582,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew","email":"ahunt@usgs.gov","middleInitial":"G.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":766206,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766207,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203409,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766208,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Varela, Brian A. 0000-0001-9849-6742 bvarela@usgs.gov","orcid":"https://orcid.org/0000-0001-9849-6742","contributorId":178091,"corporation":false,"usgs":true,"family":"Varela","given":"Brian","email":"bvarela@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":766209,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70204037,"text":"70204037 - 2019 - Morphology and genesis of giant seafloor depressions on the southeasterncontinental shelf of the Korean Peninsula","interactions":[],"lastModifiedDate":"2019-06-28T09:25:09","indexId":"70204037","displayToPublicDate":"2019-06-27T14:31:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Morphology and genesis of giant seafloor depressions on the southeasterncontinental shelf of the Korean Peninsula","docAbstract":"We identify and describe five giant seafloor depressions from the southeastern continental shelf of the Korean Peninsula using multibeam bathymetry, sub-bottom profiler, and multi-channel seismic reflection data, supplemented by piston cores. Multibeam bathymetry data from the shelf show four crescent-shaped depressions (SD1 to SD4) and one near-circular depression (SD5) within a group of NW-SE trending depressions, the largest covering an area of about 7 km2 on the seafloor. The depressions reach up to ~4.5 km in width and ~2 km in length and have asymmetric cross-sections. Some have depths as large as 40 m below the surrounding seafloor with walls as steep as 45°. The depressions are confined to water depths between 130 and 170 m and bounded on the north by a large submarine channel that was plausibly formed by fluvial or tidal processes during the Last Glacial Maximum (LGM) sea-level lowstand. Multi-channel seismic and sub-bottom profiler data reveal truncated depression walls and the presence of sediment drift deposits within the depressions, indicating that both erosion and deposition are active processes. Flaser and lenticular bedding in the cored drift deposits along with variable grain size (ranging between ~2.6 phi and ~4.3 phi) are diagnostic features of the bottom currents influenced by tidal forces. Depressions SD1 to SD4 lack evidence of fluid or gas escape. In contrast, many features of depression SD5 are characteristic of gas escapes and blowouts, including acoustic anomalies, a 20-m-high carbonate mound or carbonate-encrusted mound, and mud dikes and mud patches in cores. Based on the SD5 example, we think it is likely that the other crescent-shaped seafloor depressions formed originally as pockmarks by gas/fluid venting, and have since become inactive. The pockmarks represent zones of weakened sediment that were eroded, expanded, and merged by bottom currents to form larger seafloor depressions. Modern currents are strong enough to transport shelf sediments, and these currents were probably much stronger at lower sea levels when the Korea Strait was a more restricted passage between the East China Sea and East Sea.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2019.105966","usgsCitation":"Cukur, D., Kong, G., Chun, J., Kang, M., Um, I., Kwon, T., Jordan, S.E., and Kim, K., 2019, Morphology and genesis of giant seafloor depressions on the southeasterncontinental shelf of the Korean Peninsula: Marine Geology, v. 415, 105966, 13 p., https://doi.org/10.1016/j.margeo.2019.105966.","productDescription":"105966, 13 p.","ipdsId":"IP-106382","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":365123,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"North Korea, South Korea","otherGeospatial":"Korea Strait","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 127.869873046875,\n 33.284619968887675\n ],\n [\n 130.97900390625,\n 33.284619968887675\n ],\n [\n 130.97900390625,\n 37.18657859524883\n ],\n [\n 127.869873046875,\n 37.18657859524883\n ],\n [\n 127.869873046875,\n 33.284619968887675\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"415","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cukur, Deniz","contributorId":216636,"corporation":false,"usgs":false,"family":"Cukur","given":"Deniz","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kong, Gee-Soo","contributorId":216637,"corporation":false,"usgs":false,"family":"Kong","given":"Gee-Soo","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chun, Jong-Hwa","contributorId":216638,"corporation":false,"usgs":false,"family":"Chun","given":"Jong-Hwa","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kang, Moo-Hee","contributorId":216639,"corporation":false,"usgs":false,"family":"Kang","given":"Moo-Hee","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765224,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Um, In-Kwon","contributorId":216640,"corporation":false,"usgs":false,"family":"Um","given":"In-Kwon","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765225,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kwon, Taekhyun","contributorId":216641,"corporation":false,"usgs":false,"family":"Kwon","given":"Taekhyun","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765226,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jordan, Samuel E. 0000-0001-6074-3330","orcid":"https://orcid.org/0000-0001-6074-3330","contributorId":216635,"corporation":false,"usgs":true,"family":"Jordan","given":"Samuel","email":"","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":765220,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kim, Kyong-O","contributorId":216642,"corporation":false,"usgs":false,"family":"Kim","given":"Kyong-O","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765227,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70203662,"text":"sir20195050 - 2019 - Flood-inundation maps for the Iowa River at the Meskwaki Settlement in Iowa, 2019","interactions":[],"lastModifiedDate":"2019-06-27T12:31:59","indexId":"sir20195050","displayToPublicDate":"2019-06-27T11:30:00","publicationYear":"2019","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":"2019-5050","displayTitle":"Flood-Inundation Maps for the Iowa River at the Meskwaki Settlement in Iowa, 2019","title":"Flood-inundation maps for the Iowa River at the Meskwaki Settlement in Iowa, 2019","docAbstract":"Digital flood-inundation maps for a 9.3-mile reach of the Iowa River along the Meskwaki Settlement, Iowa, were created by the U.S. Geological Survey (USGS) in cooperation with the Sac and Fox Tribe of the Mississippi in Iowa. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 05451770 on the Iowa River at County Highway E49 near Tama, Iowa. Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site.
Flood profiles were computed for the stream reach by means of a calibrated one-dimensional and two-dimensional step-backwater hydraulic model. The model was calibrated by using the current stage-discharge relation at the USGS streamgage 05451770 on the Iowa River at County Highway E49 near Tama, Iowa, and stage and discharge data from historic flooding events that were recorded at the streamgage.
The hydraulic model was then used to compute eight water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from the NWS “action stage” of 11 feet (ft) to 18 ft, the stage exceeding the estimated 0.2-percent annual exceedance probability (500-year recurrence interval) flood, as determined at the USGS streamgage 05451770. The simulated water-surface profiles were then combined with a geographic information system digital elevation model to delineate the area flooded at each flood stage (water level).
In addition, potential modifications to hydraulic structures within the flood plain were modeled so any effects from the potential modifications could be evaluated. Four comparison points, which were along the flood plain, showed little to no change (less than 0.1 ft) in flood elevation from the existing conditions within the flood plain for the 11- to 16-ft stages as referenced to the USGS streamgage 05451770. There were greater changes (more than 0.1 ft) in flood elevation for the 2 comparison points that were closest to the modified hydraulic structure for the 2 highest modeled stages of 17 and 18 ft.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195050","collaboration":"Prepared in cooperation with the Sac and Fox Tribe of the Mississippi in Iowa","usgsCitation":"Cigrand, C.V., 2019, Flood-inundation maps for the Iowa River at the Meskwaki Settlement in Iowa, 2019: U.S. Geological Survey Scientific Investigations Report 2019–5050, 12 p., https://doi.org/10.3133/sir20195050.","productDescription":"12 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103795","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":365048,"rank":3,"type":{"id":30,"text":"Data Release"},"url":" https://doi.org/10.5066/P912FO3L ","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets for the flood-inundation study for the Iowa River at the Meskwaki Settlement in Iowa, 2019"},{"id":365046,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5050/coverthb.jpg"},{"id":365047,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5050/sir20195050.pdf","text":"Report","size":"26.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5050"}],"country":"United States","state":"Iowa","otherGeospatial":"Meskwaki Settlement","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -92.7175,41.916666666666664 ], [ -92.7175,42.034166666666664 ], [ -92.55,42.034166666666664 ], [ -92.55,41.916666666666664 ], [ -92.7175,41.916666666666664 ] ] ] } } ] }","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
We present a petrologic and mineral physics database as part of the U.S. Geological Survey National Crustal Model (NCM). Each of 209 geologic units, 134 of which are currently part of the geologic framework within the NCM, was assigned a mineralogical composition according to generalized classifications with some refinement for specific geologic formations. This report is concerned with the petrology and mineral physics of each geologic unit within the NCM, which control the physical behavior of the solid mineral matrix within the rock.
This mineral physics database builds on the work of Abers and Hacker to include 13 minerals specific to continental rock types. We explored the effect of this database on zero-porosity anharmonic P- and S-wave rock velocities and density relative to a well-used empirical study of relations between wavespeeds and density by Brocher. We found that empirical relations between P-wave velocity and S-wave velocity or density do well on average but can differ from mineral physics calculations by up to 15 percent in S-wave velocity and almost 40 percent in density. This is consistent with Brocher’s study where he obtained similar results for in situ measurements and laboratory rock specimens.
Additionally, the substantial presence of quartz in many rocks plays a major role in crustal seismic velocities and density due to quartz’s α–β phase transition, which can interfere with these empirical relationships. With increasing depth, quartz P-wave velocity can suddenly jump by 15 percent accompanied by little change in S-wave velocity and a modest decrease in density. Empirical relations based on observed P-wave velocity where P-wave velocity is positively correlated with S-wave velocity and density would then significantly overestimate both S-wave velocity and density.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191035","usgsCitation":"Sowers, T., and Boyd, O.S., 2019, Petrologic and mineral physics database for use with the U.S. Geological Survey National Crustal Model: U.S. Geological Survey Open-File Report 2019–1035, 17 p.,https://doi.org/10.3133/ofr20191035.","productDescription":"17 p.","onlineOnly":"Y","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":437403,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FK25WM","text":"USGS data release","linkHelpText":"MinVel"},{"id":364257,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HN170G","text":"USGS data release","linkHelpText":"Petrologic and Mineral Physics Database for use with the USGS National Crustal Model - Data Release"},{"id":365082,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://github.com/usgs/MinVel","text":"MinVel ","linkHelpText":"software that supports this report is available in the GitHub repository."},{"id":364255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1035/coverthb.jpg"},{"id":364256,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1035/ofr20191035.pdf","text":"Report","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1035"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
Information concerning the availability, use, and quality of water in East Carroll Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, 39.63 million gallons per day (Mgal/d) of water were withdrawn in East Carroll Parish: 32.43 Mgal/d from groundwater sources and 7.20 Mgal/d from surface-water sources. Withdrawals for agricultural use—composed of general irrigation, rice irrigation, and livestock—accounted for 97 percent (38.55 Mgal/d) of the total water withdrawn. Other categories of use included public supply and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 1980 at 47.96 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193014","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2019, Water resources of East Carroll Parish, Louisiana: U.S. Geological Survey Fact Sheet 2019–3014, 6 p., https://doi.org/10.3133/fs20193014.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-081702","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":365084,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3014/fs20193014.pdf","text":"Report","size":"929 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3014"},{"id":365083,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3014/coverthb.jpg"},{"id":365085,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"}],"country":"United States","state":"Louisiana","county":"East Carroll 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U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816