Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are network‐based models (also known as graphical models) specifically designed to model spatially autocorrelated data based on neighborhood relationships. We identify and discuss six different types of practical ecological inference using CAR and SAR models, including: (1) model selection, (2) spatial regression, (3) estimation of autocorrelation, (4) estimation of other connectivity parameters, (5) spatial prediction, and (6) spatial smoothing. We compare CAR and SAR models, showing their development and connection to partial correlations. Special cases, such as the intrinsic autoregressive model (IAR), are described. Conditional autoregressive and SAR models depend on weight matrices, whose practical development uses neighborhood definition and row‐standardization. Weight matrices can also include ecological covariates and connectivity structures, which we emphasize, but have been rarely used. Trends in harbor seals (Phoca vitulina) in southeastern Alaska from 463 polygons, some with missing data, are used to illustrate the six inference types. We develop a variety of weight matrices and CAR and SAR spatial regression models are fit using maximum likelihood and Bayesian methods. Profile likelihood graphs illustrate inference for covariance parameters. The same data set is used for both prediction and smoothing, and the relative merits of each are discussed. We show the nonstationary variances and correlations of a CAR model and demonstrate the effect of row‐standardization. We include several take‐home messages for CAR and SAR models, including (1) choosing between CAR and IAR models, (2) modeling ecological effects in the covariance matrix, (3) the appeal of spatial smoothing, and (4) how to handle isolated neighbors. We highlight several reasons why ecologists will want to make use of autoregressive models, both directly and in hierarchical models, and not only in explicit spatial settings, but also for more general connectivity models.
Salinity has a major effect on water users in the Colorado River Basin, estimated to cause almost $300 million per year in economic damages. The Colorado River Basin Salinity Control Program implements and manages projects to reduce salinity loads, investing millions of dollars per year in irrigation upgrades, canal projects, and other mitigation strategies. To inform and improve mitigation efforts, there is a need to better understand sources of salinity to streams and how salinity has changed over time. This study explores salinity in the baseflow fraction of streamflow, assessing whether groundwater is a significant contributor of dissolved solids to streams in the Upper Colorado River Basin (UCRB). Chemical hydrograph separation was used to estimate baseflow discharge and baseflow dissolved solids loads at stream gages (n = 69) across the UCRB. On average, it is estimated that 89% of dissolved solids loads originate from the baseflow fraction of streamflow, indicating that subsurface transport processes play a dominant role in delivering dissolved solids to streams in the UCRB. A statistical trend analysis using weighted regressions on time, discharge, and season was used to evaluate changes in baseflow dissolved solids loads in streams (n = 27) from 1986 to 2011. Decreasing trends in baseflow dissolved solids loads were observed at 63% of streams. At the three most downstream sites, Green River at Green River, UT, Colorado River at Cisco, UT, and the San Juan River near Bluff, UT, baseflow dissolved solids loads decreased by a combined 823,000 metric tons (mT), which is approximately 69% of projected basin‐scale decreases in total dissolved solids loads as a result of salinity control efforts. Decreasing trends in baseflow dissolved solids loads suggest that salinity mitigation projects, landscape changes, and/or climate are reducing dissolved solids transported to streams through the subsurface. Notably, the pace and extent of decreases in baseflow dissolved solids loads declined during the most recent decade; average decreasing loads during the 2000s (28,200 mT) were only 54% of average decreasing loads in the 1990s (51,700 mT).
A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016.
","language":"English","publisher":"MDPI","doi":"10.3390/rs9101065","usgsCitation":"Xiong, J., Thenkabail, P.S., James C. Tilton, Gumma, M.K., Teluguntla, P.G., Oliphant, A., Congalton, R., Yadav, K., and Gorelick, N., 2019, Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 Data on Google Earth Engine: Remote Sensing, v. 9, no. 10, Article 1065: 27 p., https://doi.org/10.3390/rs9101065.","productDescription":"Article 1065: 27 p.","ipdsId":"IP-088538","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468137,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9101065","text":"Publisher Index Page"},{"id":362333,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Africa","volume":"9","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Xiong, Jun 0000-0002-2320-0780 jxiong@usgs.gov","orcid":"https://orcid.org/0000-0002-2320-0780","contributorId":5276,"corporation":false,"usgs":true,"family":"Xiong","given":"Jun","email":"jxiong@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"James C. Tilton","contributorId":214483,"corporation":false,"usgs":false,"family":"James C. Tilton","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":760063,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gumma, Murali Krishna 0000-0002-3760-3935","orcid":"https://orcid.org/0000-0002-3760-3935","contributorId":192327,"corporation":false,"usgs":false,"family":"Gumma","given":"Murali","email":"","middleInitial":"Krishna","affiliations":[],"preferred":false,"id":760064,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Teluguntla, Pardhasaradhi G. 0000-0001-8060-9841 pteluguntla@usgs.gov","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":5275,"corporation":false,"usgs":true,"family":"Teluguntla","given":"Pardhasaradhi","email":"pteluguntla@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760065,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760066,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Congalton, Russell G.","contributorId":84646,"corporation":false,"usgs":true,"family":"Congalton","given":"Russell G.","affiliations":[],"preferred":false,"id":760067,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yadav, Kamini","contributorId":214487,"corporation":false,"usgs":false,"family":"Yadav","given":"Kamini","email":"","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":760068,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gorelick, Noel ","contributorId":214496,"corporation":false,"usgs":false,"family":"Gorelick","given":"Noel ","affiliations":[],"preferred":false,"id":760069,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70203515,"text":"70203515 - 2019 - Twenty years (1990–2010) of geodetic monitoring of Galeras volcano (Colombia) from continuous tilt measurements.","interactions":[],"lastModifiedDate":"2019-05-20T08:51:51","indexId":"70203515","displayToPublicDate":"2017-09-15T08:50:49","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Twenty years (1990–2010) of geodetic monitoring of Galeras volcano (Colombia) from continuous tilt measurements.","docAbstract":"Galeras - an andesitic stratovolcano part of the Galeras Volcanic Complex - is one of the most active volcanoes in Colombia. Historic activity is centered on a small-volume cone inside the youngest amphitheater, which breaches the west flank of the volcano. At least 30 confirmed eruption periods have been recorded in the past 480 years, with episodes of unrest ranging from weak fumarolic activity and ash emissions to larger explosive events. The most recent eruption periods, recorded instrumentally since 1988, have been characterized by minor explosive eruptions, and the emplacement of three crater domes and small pyroclastic flow deposits. In this paper, we discuss the evolution of volcanic activity using a 20-year-long record of tilt measurements. In particular, we focus on three episodes of unrest occurred in 1991, 2006 and 2008, when the deformation was clearly associated with shallow magma intrusions, and the emplacement and destruction of crater domes. The depth of the intrusions varied from a few hundred meters (August 2005) to two kilometers (January 2009), while the volume change ranged from 104 m3 (May–October 2009) to 106 m3 (January 2009). A comparison with seismic data indicates that the deformation sources were located within the cloud of hypocenters of the volcano-tectonic events. The lack of a clear correlation between the volume change (and depth) of the sources and the total SO2 flux could indicate that the unrest at Galeras was related to a larger intrusive event with only a small part of the magma erupted in the form of tephra and lava domes.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2017.03.026","usgsCitation":"Narvaez Medina, L., Arcos, D.F., and Battaglia, M., 2019, Twenty years (1990–2010) of geodetic monitoring of Galeras volcano (Colombia) from continuous tilt measurements.: Journal of Volcanology and Geothermal Research, v. 344, p. 232-245, https://doi.org/10.1016/j.jvolgeores.2017.03.026.","productDescription":"14 p.","startPage":"232","endPage":"245","ipdsId":"IP-077748","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":468138,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2017.03.026","text":"Publisher Index Page"},{"id":364000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Colombia","otherGeospatial":"Galeras volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.49137878417967,\n 1.1342642839220822\n ],\n [\n -77.22908020019531,\n 1.1342642839220822\n ],\n [\n -77.22908020019531,\n 1.3100054779424755\n ],\n [\n -77.49137878417967,\n 1.3100054779424755\n ],\n [\n -77.49137878417967,\n 1.1342642839220822\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"344","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Narvaez Medina, Lourdes","contributorId":215678,"corporation":false,"usgs":false,"family":"Narvaez Medina","given":"Lourdes","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762960,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arcos, Dario F","contributorId":215679,"corporation":false,"usgs":false,"family":"Arcos","given":"Dario","email":"","middleInitial":"F","affiliations":[{"id":39304,"text":"Colomban Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762961,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Battaglia, Maurizio 0000-0003-4726-5287 mbattaglia@usgs.gov","orcid":"https://orcid.org/0000-0003-4726-5287","contributorId":204742,"corporation":false,"usgs":true,"family":"Battaglia","given":"Maurizio","email":"mbattaglia@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":762959,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202783,"text":"70202783 - 2019 - MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types","interactions":[],"lastModifiedDate":"2019-03-26T11:03:41","indexId":"70202783","displayToPublicDate":"2017-09-01T11:02:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types","docAbstract":"Innovative, open, and rapid methods to map crop types over large areas are needed for long-term cropland monitoring. We developed two novel and automated decision tree classification approaches to map crop types across the conterminous United States (U.S.) using MODIS 250 m resolution data: 1) generalized, and 2) year-specific classification. The classification approaches use similarities and dissimilarities in crop type phenologyderived from NDVI time-series data for the two approaches. The year-specific approach uses the training samples from one year and classifies crop types for that year only, whereas the generalized classification approach uses above-average, average, and below-average precipitation years for training to produce crop type maps for one or multiple years more robustly. We produced annual crop type maps using the generalized classification approach for 2001–2014 and the year-specific approach for 2008, 2010, 2011 and 2012. The year-specific classification had overall accuracies > 78%, while the generalized classifier had accuracies > 75% for the conterminous U.S. for 2008, 2010, 2011, and 2012. The generalized classifier enables automated and routine crop type mapping without repeated and expensive ground sample collection year after year. The resulting crop type maps for years prior to 2007 are new and especially important for long-term cropland monitoring and food security analysis because no other map products are currently available for 2001–2007.
We evaluated the impact of predation on juvenile steelhead Oncorhynchus mykiss and yearling and subyearling Chinook Salmon O. tshawytscha by piscivorous waterbirds from 11 different breeding colonies in the Columbia River basin during 2012 and 2014. Fish were tagged with both acoustic tags and PIT tags and were tracked via a network of hydrophone arrays to estimate total smolt mortality (1 – survival) at various spatial and temporal scales during out‐migration. Recoveries of PIT tags on bird colonies, coupled with the last known detections of live fish passing hydrophone arrays, were used to estimate the impact of avian predation relative to total smolt mortality. Results indicated that avian predation was a substantial source of steelhead mortality, with predation probability (proportion of available fish consumed by birds) ranging from 0.06 to 0.28 for fish traveling through the lower Snake River and the lower and middle Columbia River. Predation probability estimates ranged from 0.03 to 0.09 for available tagged yearling Chinook Salmon and from 0.01 to 0.05 for subyearlings. Smolt predation by gulls Larusspp. was concentrated near hydroelectric dams, while predation by Caspian terns Hydroprogne caspia was concentrated within reservoirs. No concentrated areas of predation were identified for double‐crested cormorants Phalacrocorax auritus or American white pelicans Pelecanus erythrorhynchos. Comparisons of total smolt mortality relative to mortality from colonial waterbirds indicated that avian predation was one of the greatest sources of mortality for steelhead and yearling Chinook Salmon during out‐migration. In contrast, avian predation on subyearling Chinook Salmon was generally low and constituted a minor component of total mortality. Our results demonstrate that acoustic and PIT tag technologies can be combined to quantify where and when smolt mortality occurs and the fraction of mortality that is due to colonial waterbird predation relative to non‐avian mortality sources.
","language":"English","publisher":"Wiley","doi":"10.1080/00028487.2016.1150881","usgsCitation":"Evans, A.F., Payton, Q., Turecek, A., Cramer, B., Collis, K., Roby, D.D., Loschl, P.J., Sullivan, L., Skalski, Weiland, M., and Dotson, C., 2019, Avian predation on juvenile Salmonids: Spatial and temporal analysis based on acoustic and passive integrated transponder tags: Transactions of the American Fisheries Society, https://doi.org/10.1080/00028487.2016.1150881.","ipdsId":"IP-071908","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490058,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/dataset/Avian_Predation_on_Juvenile_Salmonids_Spatial_and_Temporal_Analysis_Based_on_Acoustic_and_Passive_Integrated_Transponder_Tags/3471605","text":"External Repository"},{"id":364261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, Allen F.","contributorId":171691,"corporation":false,"usgs":false,"family":"Evans","given":"Allen","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":763477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Payton, Quinn","contributorId":149990,"corporation":false,"usgs":false,"family":"Payton","given":"Quinn","email":"","affiliations":[{"id":17879,"text":"Real Time Research, Inc., 231 SW Scalehouse Loop, Suite 101, Bend, OR 97702","active":true,"usgs":false}],"preferred":false,"id":763478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turecek, Aaron aturecek@usgs.gov","contributorId":4940,"corporation":false,"usgs":true,"family":"Turecek","given":"Aaron","email":"aturecek@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":763479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cramer, Bradley D.","contributorId":51562,"corporation":false,"usgs":true,"family":"Cramer","given":"Bradley D.","affiliations":[],"preferred":false,"id":763480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collis, Ken","contributorId":149991,"corporation":false,"usgs":false,"family":"Collis","given":"Ken","email":"","affiliations":[{"id":17879,"text":"Real Time Research, Inc., 231 SW Scalehouse Loop, Suite 101, Bend, OR 97702","active":true,"usgs":false}],"preferred":false,"id":763481,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roby, Daniel D. 0000-0001-9844-0992 droby@usgs.gov","orcid":"https://orcid.org/0000-0001-9844-0992","contributorId":3702,"corporation":false,"usgs":true,"family":"Roby","given":"Daniel","email":"droby@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":763482,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Loschl, Peter J.","contributorId":7195,"corporation":false,"usgs":true,"family":"Loschl","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":763483,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sullivan, Leah","contributorId":215942,"corporation":false,"usgs":false,"family":"Sullivan","given":"Leah","email":"","affiliations":[],"preferred":false,"id":763484,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Skalski, John","contributorId":120021,"corporation":false,"usgs":true,"family":"Skalski","suffix":"John","affiliations":[],"preferred":false,"id":763485,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weiland, Mark","contributorId":215944,"corporation":false,"usgs":false,"family":"Weiland","given":"Mark","email":"","affiliations":[],"preferred":false,"id":763486,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dotson, Curtis","contributorId":215945,"corporation":false,"usgs":false,"family":"Dotson","given":"Curtis","email":"","affiliations":[],"preferred":false,"id":763487,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70203024,"text":"70203024 - 2019 - Organic geochemistry and toxicology of a stream impacted by unconventional oil and gas wastewater disposal operations","interactions":[],"lastModifiedDate":"2019-04-11T16:06:24","indexId":"70203024","displayToPublicDate":"2017-05-09T15:54:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Organic geochemistry and toxicology of a stream impacted by unconventional oil and gas wastewater disposal operations","docAbstract":"Water and sediment extracts samples were analyzed for extractable hydrocarbons by gas chromatography/mass spectrometry (GC/MS) using an Agilent (Agilent Technologies, Palo Alto, CA, USA) 7890 series GC and 5975 electron ionization (EI) mass selective detector (MSD) operated in scan mode. Agilent ChemStation software was used for data acquisition and analysis (version E.02.00.493 on GC/MS computer and version F.01.03.2357 on laptop for data workup). A 30 m x 250 m x 0.25 m HP-5MS column (95% dimethyl 5% diphenyl polydimethylsiloxane) was used for GC/MS under the following conditions: 1.0 L splitless injection, constant flow of 0.7 mL/min, solvent delay of 7.5 min, injector temperature of 280C, interface at 300C, temperature program of 50-150C at 7C/min, 150-230C at 6C/min, and 230-300C at 3C/min with mass scanned from 35-500 Da.","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2017.02.016","usgsCitation":"Orem, W.H., Varonka, M.S., Crosby, L.M., Haase, K.B., Loftin, K.A., Hladik, M., Akob, D.M., Tatu, C., Mumford, A.C., Jaeschke, J.B., Bates, A.L., Schell, T., and Cozzarelli, I.M., 2019, Organic geochemistry and toxicology of a stream impacted by unconventional oil and gas wastewater disposal operations: Applied Geochemistry, v. 80, p. 155-167, https://doi.org/10.1016/j.apgeochem.2017.02.016.","productDescription":"13 p.","startPage":"155","endPage":"167","ipdsId":"IP-075085","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":460617,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2017.02.016","text":"Publisher Index Page"},{"id":362922,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Wolf Creek","volume":"80","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":760827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Varonka, Matthew S. 0000-0003-3620-5262 mvaronka@usgs.gov","orcid":"https://orcid.org/0000-0003-3620-5262","contributorId":4726,"corporation":false,"usgs":true,"family":"Varonka","given":"Matthew","email":"mvaronka@usgs.gov","middleInitial":"S.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":760828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crosby, Lynn M. lcrosby@usgs.gov","contributorId":369,"corporation":false,"usgs":true,"family":"Crosby","given":"Lynn","email":"lcrosby@usgs.gov","middleInitial":"M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":760829,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haase, Karl B. 0000-0002-6897-6494 khaase@usgs.gov","orcid":"https://orcid.org/0000-0002-6897-6494","contributorId":3405,"corporation":false,"usgs":true,"family":"Haase","given":"Karl","email":"khaase@usgs.gov","middleInitial":"B.","affiliations":[{"id":436,"text":"National Research Program - 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Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":760833,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tatu, Calin","contributorId":39081,"corporation":false,"usgs":true,"family":"Tatu","given":"Calin","email":"","affiliations":[],"preferred":false,"id":760834,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mumford, Adam C. 0000-0002-8082-8910 amumford@usgs.gov","orcid":"https://orcid.org/0000-0002-8082-8910","contributorId":197795,"corporation":false,"usgs":true,"family":"Mumford","given":"Adam","email":"amumford@usgs.gov","middleInitial":"C.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":760835,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jaeschke, Jeanne B. 0000-0002-6237-6164 jaeschke@usgs.gov","orcid":"https://orcid.org/0000-0002-6237-6164","contributorId":3876,"corporation":false,"usgs":true,"family":"Jaeschke","given":"Jeanne","email":"jaeschke@usgs.gov","middleInitial":"B.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":760836,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bates, Anne L. 0000-0002-4875-4675 abates@usgs.gov","orcid":"https://orcid.org/0000-0002-4875-4675","contributorId":2789,"corporation":false,"usgs":true,"family":"Bates","given":"Anne","email":"abates@usgs.gov","middleInitial":"L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":760837,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Schell, Tiffani","contributorId":214806,"corporation":false,"usgs":false,"family":"Schell","given":"Tiffani","email":"","affiliations":[],"preferred":false,"id":760838,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":760839,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70204784,"text":"70204784 - 2019 - A 15-year catalog of more than 1 million low-frequency earthquakes: Tracking tremor and slip along the deep San Andreas Fault","interactions":[],"lastModifiedDate":"2019-08-16T06:58:39","indexId":"70204784","displayToPublicDate":"2017-05-01T06:56:43","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"A 15-year catalog of more than 1 million low-frequency earthquakes: Tracking tremor and slip along the deep San Andreas Fault","docAbstract":"Low-frequency earthquakes (LFEs) are small, rapidly recurring slip events that occur on the deep extensions of some major faults. Their collective activation is often observed as a semi-continuous signal known as tectonic (or non-volcanic) tremor. This manuscript presents a catalog of more than 1 million LFEs detected along the central San Andreas Fault from 2001-2016. These events have been detected via a multi-channel matched-filter search, cross-correlating waveform templates representing 88 different LFE families with continuous seismic data. Together, these source locations span nearly 150 km along the central San Andreas Fault, ranging in depth from ~16-30 km. \nThis accumulating catalog has been the source of numerous studies examining the behavior of these LFE sources and the inferred slip behavior of the deep fault. The relatively high temporal and spatial resolution of the catalog has provided new insights into properties such as tremor migration, recurrence, and triggering by static and dynamic stress perturbations. Collectively, these characteristics are inferred to reflect a very weak fault likely under near-lithostatic fluid pressure, yet the physical processes controlling the stuttering rupture observed as tremor and LFE signals remain poorly understood. This paper aims to document the LFE catalog assembly process and associated caveats, while also updating earlier observations and inferred physical constraints. The catalog itself accompanies this manuscript as part of the electronic supplement, with the goal of providing a useful resource for continued future investigations.","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JB014047","usgsCitation":"Shelly, D.R., 2019, A 15-year catalog of more than 1 million low-frequency earthquakes: Tracking tremor and slip along the deep San Andreas Fault: Journal of Geophysical Research, v. 122, no. 5, p. 3739-3753, https://doi.org/10.1002/2017JB014047.","productDescription":"15 p.","startPage":"3739","endPage":"3753","ipdsId":"IP-083653","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":366580,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Andreas Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.95849609375,\n 35.79108281624994\n ],\n [\n -120.95947265624999,\n 35.79108281624994\n ],\n [\n -120.95947265624999,\n 39.9434364619742\n ],\n [\n -124.95849609375,\n 39.9434364619742\n ],\n [\n -124.95849609375,\n 35.79108281624994\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":768470,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70205836,"text":"70205836 - 2019 - Using a process-based model of pre-eruptive seismic patterns to forecast evolving eruptive styles at Sinabung Volcano, Indonesia","interactions":[],"lastModifiedDate":"2021-08-12T15:55:44.445534","indexId":"70205836","displayToPublicDate":"2017-04-09T07:43:17","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Using a process-based model of pre-eruptive seismic patterns to forecast evolving eruptive styles at Sinabung Volcano, Indonesia","docAbstract":"Most volcanoes worldwide are not monitored in real-time; for those that are, patterns of pre-eruptive earthquakes coupled with conceptual models of magma ascent enable short-term forecasting of eruption onset. Basic event locations, characterization of background seismicity, and recognition of changes in earthquake types and energy release are most important to successful eruption forecasting. During renewed activity at Sinabung volcano, Indonesia, this approach was used by the Center for Volcanology and Geological Hazards Mitigation (CVGHM) and the USGS Volcano Disaster Assistance Program to forecast eruption onset, identify changes in eruptive styles and raise or lower alert levels and extend or contract evacuation zones. After > 400 years of quiescence, Sinabung began erupting in August 2010. The volcano was unmonitored at the onset of these eruptions, which were phreatic, but soon after a monitoring network was installed by CVGHM. Increasing swarms of high-frequency volcano tectonic (VT) earthquakes were used to forecast continuing phreatic eruptions. Volcanic activity decreased in mid-September 2010, while additional intrusions at depth (inferred from continued distal VT swarms) continued through September 2013, when explosive phreatic eruptions recurred. Explosive eruptions were forecast based on increases in real-time seismic amplitude measurement (RSAM) and VT seismicity. Seismicity changed markedly in late November and early December 2013 with the occurrence of deep earthquakes and an overall transition from low-frequency (LF) dominated and irregular (in time and magnitude) earthquakes to more regular LF and hybrid seismicity – a transition that accompanied the continued rise, eventual emergence and growth of a lava dome in the summit crater. This lava dome was first observed on 18 December. In late December 2013 to early January 2014, the eruptive style changed again as additional ascending magma deformed the summit and the dome grew beyond the capacity of the summit crater, resulting in the en masse collapse of the lava dome (2 Mm3) on 11 January and the largest pyroclastic flow to date. The collapse was forecast on the basis of a several order of magnitude increase in RSAM, continued strong distal VT seismicity, an increase in proximal seismicity, and large-scale observed deformation of the summit area. Similarly, a later collapse of a second summit lava dome on 1 February 2014 was forecast on the basis of increased distal seismicity. Here, we demonstrate how a process-based volcano seismicity model was used in combination with real-time data to forecast the time and magnitude of eruptions, as well as changes in eruption style.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2017.04.004","usgsCitation":"McCausland, W.A., Gunawan, H., White, R.A., Indrastuti, N., Patria, C., Suparman, Y., Putra, A., Triastuty, H., and Hendrasto, M., 2019, Using a process-based model of pre-eruptive seismic patterns to forecast evolving eruptive styles at Sinabung Volcano, Indonesia: Journal of Volcanology and Geothermal Research, v. 382, p. 253-266, https://doi.org/10.1016/j.jvolgeores.2017.04.004.","productDescription":"14 p.","startPage":"253","endPage":"266","ipdsId":"IP-078320","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":468140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2017.04.004","text":"Publisher Index Page"},{"id":368082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Sinabung Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 96.01226806640625,\n 3.9300201571114752\n ],\n [\n 97.174072265625,\n 3.9300201571114752\n ],\n [\n 97.174072265625,\n 5.019810836520885\n ],\n [\n 96.01226806640625,\n 5.019810836520885\n ],\n [\n 96.01226806640625,\n 3.9300201571114752\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"382","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCausland, Wendy A. 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":204380,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":772552,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gunawan, Hendra","contributorId":194977,"corporation":false,"usgs":false,"family":"Gunawan","given":"Hendra","email":"","affiliations":[],"preferred":false,"id":772553,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Randall A. 0000-0003-4074-8577 rwhite@usgs.gov","orcid":"https://orcid.org/0000-0003-4074-8577","contributorId":1993,"corporation":false,"usgs":true,"family":"White","given":"Randall","email":"rwhite@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":772554,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Indrastuti, Novianti","contributorId":204389,"corporation":false,"usgs":false,"family":"Indrastuti","given":"Novianti","email":"","affiliations":[{"id":36928,"text":"Center for Volcanology and Geological Hazard Mitigation, Bandung, Indonesia","active":true,"usgs":false}],"preferred":false,"id":772555,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Patria, Cahya","contributorId":219559,"corporation":false,"usgs":false,"family":"Patria","given":"Cahya","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":772558,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Suparman, Yasa","contributorId":219560,"corporation":false,"usgs":false,"family":"Suparman","given":"Yasa","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":772559,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Putra, Armen","contributorId":219561,"corporation":false,"usgs":false,"family":"Putra","given":"Armen","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":772560,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Triastuty, Hetty","contributorId":219558,"corporation":false,"usgs":false,"family":"Triastuty","given":"Hetty","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":772557,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hendrasto, Mochammad","contributorId":219557,"corporation":false,"usgs":false,"family":"Hendrasto","given":"Mochammad","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":772556,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70203707,"text":"70203707 - 2019 - U-Pb and Lu-Hf isotope, age, and trace-element data from zircons at four sites in the western Alaska Range and Talkeetna Mountains, Alaska","interactions":[],"lastModifiedDate":"2019-06-06T13:34:41","indexId":"70203707","displayToPublicDate":"2017-04-01T13:30:22","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesNumber":"2017-2","title":"U-Pb and Lu-Hf isotope, age, and trace-element data from zircons at four sites in the western Alaska Range and Talkeetna Mountains, Alaska","docAbstract":"This Division of Geological & Geophysical Surveys (DGGS) Raw Data File presents U-Pb geochronology and Lu-Hf isotopic compositions, age-dating results, and additional trace-elemental composition of zircons from four granitoids sampled during investigations by DGGS geologists in the western Alaska Range and the Talkeetna Mountains. The purpose of the Lu-Hf and U-Pb isotopic study was to better understand the Jurassic through early Tertiary magmatic and tectonic evolution of the Alaska Range and the significance of that magmatism for the formation of metallic mineral deposits. The U-Pb isotopic data also serve to further refine the crystallization age of plutons, which were previously ambiguous in 40Ar/39Ar geochronology studies.","language":"English","publisher":"Alaska Department of Natural Resources","doi":"10.14509/29717","usgsCitation":"Todd, E., Kylander-Clark, A.R., Wypych, A., Twelker, E., and Sicard, K.R., 2019, U-Pb and Lu-Hf isotope, age, and trace-element data from zircons at four sites in the western Alaska Range and Talkeetna Mountains, Alaska, 7 p., https://doi.org/10.14509/29717.","productDescription":"7 p.","ipdsId":"IP-082376","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":468141,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14509/29717","text":"Publisher Index Page"},{"id":364462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198–0001
Transport and fate of dissolved organic carbon (DOC) in rivers are important aspects of the carbon cycle and the critical linkage between terrestrial, aquatic, and marine systems. Recent studies have quantified fluvial export to the marine environment in many systems, but in-stream losses of DOC are poorly constrained. This study compares DOC yields (kg C/ha) between the area-weighted averages of several tributaries within larger watersheds with the DOC yields of the larger watersheds to gain insight on in-stream losses in larger river systems. Four large watersheds, 22 tributaries to those watersheds, and 5 additional main stem locations in Maine were studied during 1 April to 15 November in 2011 through 2013. There were no significant differences in the area-weighted average DOC yield of the tributaries and the larger watersheds indicating little net in-stream loss in the main stems of the larger rivers. It is unlikely that inputs of DOC from un-gauged areas compensated for losses from gauged tributaries based on similarity in DOC yield longitudinally along the main stems of two of the rivers. In addition, wetland abundance, which is associated with higher DOC yield in this environment, did not consistently increase from tributaries to the larger watershed or longitudinally along the main stems. This geographic distribution of wetlands therefore also indicates that it is unlikely that inputs of DOC from un-gauged areas compensated for losses from gauged tributaries. These findings suggest that in-stream losses of DOC in these larger river systems are minimal and that the vast majority of DOC in major rivers in Maine is transported conservatively to the coastal ocean.
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Director of BISON and ITIS
Core Science Systems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, Mailstop 302
Reston, Virginia 20192
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States\"}}]}","edition":"Version 1.1: May 28, 2019","contact":"Chief, EcoScience Synthesis
Director of BISON and ITIS
Core Science Systems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, Mailstop 302
Reston, Virginia 20192
bison@usgs.gov
The “National Field Manual for the Collection of Water-Quality Data” (NFM) provides guidelines and procedures for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation’s surface-water and groundwater resources. This chapter, NFM A6.3, provides guidance and protocols for the measurement of specific conductance of a water sample, which include the scientific basis of the measurement, selection and maintenance of equipment, calibration, troubleshooting, and procedures for measurement and reporting. It updates and supersedes USGS Techniques of Water-Resources Investigations, book 9, chapter A6.3, version 1.2, by D.B. Radtke, J.V. Davis, and F.D. Wilde.
Specific conductance is routinely measured when water samples are collected, is often measured continually at USGS streamgages, and is a parameter regularly measured during laboratory and field experiments. The field method for measuring specific conductance described in this chapter is applicable to most natural waters.
Before 2017, the NFM chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0) at https://doi.org/10.3133/tm9A0. The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.er.usgs.gov. Comments, questions, and suggestions related to the NFM can be addressed to nfm@usgs.gov.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: National field manual for the collection of water-quality data in Book 9: Handbooks for water-resources investigations","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm9A6.3","usgsCitation":"U.S. Geological Survey, 2019, Chapter A6.3. Specific Conductance: U.S. Geological Survey Techniques and Methods 9-A6.3, vi, 15 p., https://doi.org/10.3133/tm9A6.3.","productDescription":"vi, 15 p.","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":361085,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/tm9A0","text":"Techniques and Methods 9-A0","linkHelpText":"General Introduction for the “National Field Manual for the Collection of Water-Quality Data\""},{"id":360655,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/09/a6.3/tm9-a6_3.pdf","text":"Report","size":"1.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 9A63"},{"id":360656,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/09/a6.3//versionHist.txt","size":"2.91 KB","linkFileType":{"id":2,"text":"txt"}},{"id":360654,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/09/a6.3/coverthb.jpg"}],"contact":"Chief, Office of Quality Assurance
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 432
Reston, VA 20192
Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Chlorinated volatile organic compounds have affected groundwater beneath a former 9-acre landfill at Operable Unit 1 (OU 1) of Naval Base Kitsap (NBK) Keyport, in Keyport, Washington. The landfill was the primary disposal area for domestic and industrial waste generated by NBK Keyport from the 1930s through 1973. Naval Facilities Engineering Command Northwest, in conjunction with the Environmental Protection Agency, Washington State Department of Ecology, and the Suquamish Tribe, is charged with collecting necessary data to monitor the contamination left in place and to ensure that the site does not pose a risk to human health or the environment.
To support these efforts, refined information was collected on how groundwater levels throughout OU 1 respond to tidal fluctuations at this nearshore site adjacent to Liberty Bay, an inlet of Puget Sound. The information was analyzed to determine the optimal times during the semidiurnal and the neap-spring tidal cycles to sample groundwater for contaminants associated with fresh groundwater originating from OU 1. The optimal times for sampling are presumed to be when fresh groundwater flowing seaward is least impeded by elevated tides, and those times are related to predicted tide levels by tidal lags, the durations between low tides, and corresponding low groundwater levels. Discrete groundwater-specific conductance data also were collected to determine if a seawater/freshwater interface was present at any of the monitoring wells, and to inform decisions on the depth at which groundwater should be sampled in existing wells.
Groundwater and surface-water levels were monitored at 19 monitoring wells and five adjacent surface-water sites. Specific conductance was monitored in each surface-water site. All time-series data parameters were collected every 15 minutes during a 4-week duration to measure how nearshore groundwater responds to tidal forcing. Time-series data were collected from July 12, 2018, to August 8, 2018, a period that included neap and spring tides. Vertical water-quality profiles were measured once in the screened interval of nine selected monitoring wells. The profiles included measurements at the top, middle, and bottom of each saturated screen interval.
Tidal lag times were determined relative to tidal levels in Liberty Bay (rather than in the more nearby Tide Flats) because the predicted tides for the Poulsbo, Washington Station (National Oceanic and Atmospheric Administration [NOAA] Station 9445719) that are used to schedule groundwater sampling represent open-water conditions in the area; a sill that separates Dogfish Bay from the Tide Flats clearly affects the timing and magnitude of low-low tides in the Tide Flats. Calculated tidal lag times were divided into three general groups: (1) wells where groundwater responded to tidal level changes immediately, (2) wells where groundwater responded to tidal level changes within about 2–5 hours, and (3) wells where groundwater had minimal response to tidal level changes. Groundwater levels in the middle group of wells primarily responded in concert with tidal level changes in the Tide Flats rather than tidal level changes in Liberty Bay.
An intended sampling depth refinement based on an assessment of transient seawater intrusion was not completed because of a failure to collect specific-conductance time-series data in select wells. Instead, discrete specific-conductance data from this and prior studies were evaluated to determine that the midpoint of well screens in OU 1 wells can be assumed to be a reasonably representative of undiluted groundwater. When sampling during spring (rather than neap) tides (as has generally been the standard practice at OU 1), the optimal time to sample the monitoring wells influenced by tides would be to add the tidal lags presented in this report to the time of the predicted low-low tide for Liberty Bay as measured at NOAA Station 9445719 at Poulsbo, Washington. Sampling schedules for the six wells where groundwater levels were only minimally influenced by tide changes should not be constrained by tidal conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191098","collaboration":"Prepared in cooperation with the Department of the Navy, Naval Facilities Engineering Command, Northwest","usgsCitation":"Opatz, C.C., and Dinicola, R.S., 2019, Analysis of groundwater response to tidal fluctuations, Operable Unit 1, Naval Base Kitsap, Keyport, Washington: U.S. Geological Survey Open-File Report 2019-1098, 36 p., https://doi.org/10.3133/ofr20191098.","productDescription":"vi, 36 p.","onlineOnly":"Y","ipdsId":"IP-107656","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":367168,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1098/coverthb.jpg"},{"id":367169,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1098/ofr20191098.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1098"}],"country":"United States","state":"Washington","city":"Keyport","otherGeospatial":"Naval Base Kitsap","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.62941598892212,\n 47.694699930336995\n ],\n [\n -122.62280702590942,\n 47.694699930336995\n ],\n [\n -122.62280702590942,\n 47.69943693711954\n ],\n [\n -122.62941598892212,\n 47.69943693711954\n ],\n [\n -122.62941598892212,\n 47.694699930336995\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402