Groundwater quality in the approximately 7,820-square-kilometer (km2) Monterey-Salinas Shallow Aquifer (MS-SA) study unit was investigated from October 2012 to May 2013 as part of the second phase of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in the central coast region of California in the counties of Santa Cruz, Monterey, and San Luis Obispo. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board in cooperation with the U.S. Geological Survey and the Lawrence Livermore National Laboratory.
The MS-SA study was designed to provide a statistically robust assessment of untreated-groundwater quality in the shallow aquifer systems. The assessment was based on water-quality samples collected by the U.S. Geological Survey from 100 groundwater sites and 70 household tap sites, along with ancillary data such as land use and well-construction information. The shallow aquifer systems were defined by the depth interval of wells associated with domestic supply. The MS-SA study unit consisted of four study areas—Santa Cruz (210 km2), Pajaro Valley (360 km2), Salinas Valley (2,000 km2), and Highlands (5,250 km2).
This study had two primary components: the status assessment and the understanding assessment. The first primary component of this study—the status assessment—assessed the quality of the groundwater resource indicated by data from samples analyzed for volatile organic compounds (VOCs), pesticides, and naturally present inorganic constituents, such as major ions and trace elements. The status assessment is intended to characterize the quality of groundwater resources in the shallow aquifer system of the MS-SA study unit, not the treated drinking water delivered to consumers by water purveyors. As opposed to the public wells, however, water from private wells, which often tap the shallow aquifer, is usually consumed without any treatment. The second component of this study—the understanding assessment—identified the natural and human factors that potentially affect groundwater quality by evaluating land-use characteristics, measures of location, geologic factors, groundwater age, and geochemical conditions of the shallow aquifer. An additional component of this study was a comparison of MS-SA water-quality results to those of the GAMA Monterey Bay and Salinas Valley Groundwater Basins study unit. This study unit covered much of the same areal extent as the MS-SA, but assessed the deeper, public drinking-water aquifer system.
Relative concentrations (sample concentration divided by the benchmark concentration) were used to evaluate concentrations of constituents in groundwater samples relative to water-quality benchmarks for those constituents that have Federal or California benchmarks, such as maximum contaminant levels. For organic and special-interest constituents, relative concentrations were classified as high, greater than 1.0; moderate, greater than 0.1 and less than or equal to 1.0; or low, less than or equal to 0.1. For inorganic constituents, relative concentrations were classified as high, greater than 1.0; moderate, greater than 0.5 and less than or equal to 1.0; or low, less than or equal to 0.5. A relative concentration greater than 1.0 indicates that the concentration was greater than a benchmark. Aquifer-scale proportions were used to quantify regional-scale groundwater quality. The aquifer-scale proportions are the areal percentages of the shallow aquifer system where relative concentrations for a given constituent or class of constituents were high, moderate, or low.
Inorganic constituents were measured at high and moderate relative concentrations more frequently than organic constituents. In the MS-SA study unit, inorganic constituents with benchmarks were detected at high relative concentrations in 51 percent of the study unit. The greatest proportions of high relative concentrations of trace elements and radioactive constituents were in the Highlands and Santa Cruz study areas, whereas high relative concentrations of nutrients were most often detected in the Salinas Valley and Pajaro Valley study areas and salinity indicators were most often detected in the Highlands and Salinas Valley study areas. The trace elements detected at high relative concentrations were arsenic, boron, iron, manganese, molybdenum, selenium, and strontium. The radioactive constituents detected at high relative concentrations were adjusted gross alpha radioactivity and uranium. The nutrient detected at high relative concentrations was nitrate plus nitrite. The salinity indicators detected at high relative concentrations were chloride, sulfate, and total dissolved solids.
Organic constituents (VOCs and pesticides) were not detected at high relative concentrations in any of the study areas. The fumigant 1,2-dichloropropane was detected at moderate relative concentrations. The VOC chloroform and the pesticide simazine were the only organic constituents detected in more than 10 percent of samples. The constituents of special interest NDMA (N-nitrosodimethylamine) and perchlorate were detected at high relative concentrations in the MS-SA study unit.
Selected constituents were evaluated with explanatory factors to identify potential sources or processes that could explain their presence and distribution. Trace elements and radioactive constituents came from natural sources and were not elevated by anthropogenic sources or processes, except for selenium and the radioactive constituent uranium. Arsenic, manganese, iron, selenium, and uranium concentrations were all influenced by oxidation-reduction conditions.
Unlike other trace elements and radioactive constituents, uranium and selenium can be affected by agricultural practices. Uranium and selenium can be released from aquifer sediments as a result of irrigation recharge water interacting with bicarbonate systems.
Nitrate can be strongly affected by anthropogenic sources. Nitrate concentrations were significantly higher in modern groundwater, indicating recent inputs of nitrate to the shallow aquifer system. Nitrate was positively correlated with agricultural land use, indicating that irrigation-return water could be leaching nitrogen fertilizer and naturally present nitrate to elevate nitrate concentrations in shallow groundwater.
The salinity indicators total dissolved solids, chloride, and sulfate all had natural sources in the MS-SA study unit, primarily marine sediments. Concentrations of the constituents were elevated as a result of evaporative concentration of irrigation water or precipitation. Sulfate concentrations were significantly correlated to agricultural land use, indicating that agricultural land-use practices are a contributing source of sulfate to groundwater.
The samples with most of the detections of VOCs were from sites in the Pajaro Valley and northern part of the Salinas Valley. Most of the samples with pesticide detections were from sites in the Salinas Valley study area. The herbicide simazine was positively correlated to the percentage of agricultural land use, and its concentrations were higher in modern groundwater than in pre-modern groundwater.
Perchlorate, similar to nitrate, has natural and anthropogenic sources. Correlations of perchlorate to dissolved oxygen, nitrate, and percentage of agricultural land use indicated that the irrigation-return water could be leaching naturally present perchlorate, as well as perchlorate from historical applications of Chilean nitrate fertilizer, to increase perchlorate concentrations in groundwater.
The quality of the water in the shallow aquifer system from this study was compared with the quality of water in the public drinking-water aquifer in a previous GAMA (MS-PA) study in the same area. The shallow system was more oxic and had more sites with modern groundwater than the public drinking-water aquifer, which was more anoxic and had sites with more pre-modern groundwater. Arsenic and selenium were found at high relative concentrations in a greater proportion of the shallow system. Manganese and iron were found at high relative concentrations in a greater proportion of the public drinking-water aquifer. Uranium was found at higher relative concentrations in a greater proportion of the shallow system. Concentrations of arsenic, iron, manganese, and molybdenum are not likely to change much as groundwater percolates from the shallow system to the public drinking-water aquifer because there are no anthropogenic sources affecting these constituents. Uranium and selenium concentrations in the public drinking-water aquifer could be affected by the higher concentrations in the shallow system because of irrigation-return water, however.
Nitrate and salinity indicators had concentrations that were much higher in the shallow system than the deeper public drinking-water aquifer. High concentrations of these constituents in the shallow system could lead to increased concentrations in the public drinking-water aquifer in parts of the study units because of land-use practices, such as irrigated agriculture.
Organic constituents were detected more frequently in the public drinking-water aquifer than in the shallow system, possibly because more of the sites sampled in the public drinking-water aquifer were in urban areas compared to the sites sampled for the shallow system or because sources of contamination have decreased as a result of changes in use at the land surface.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185057","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Burton, C.A., and Wright, M.T., 2018, Status and understanding of groundwater quality in the Monterey-Salinas Shallow Aquifer study unit, 2012–13: California GAMA Priority Basin Project (ver. 1.1, September 2018): U.S. Geological Survey Scientific Investigations Report 2018–5057, 116 p., https://doi.org/10.3133/sir20185057.","productDescription":"Report: x, 116 p.","numberOfPages":"132","onlineOnly":"Y","ipdsId":"IP-056428","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":357554,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2018/5057/sir20185057_versionhist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2018-5057"},{"id":354600,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5057/coverthb.jpg"},{"id":354601,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5057/sir20185057_v1.1.pdf","text":"Report","size":"38.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5057"}],"country":"United States","state":"California","otherGeospatial":"Monterey-Salinas Shallow Aquifer study unit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.904296875,\n 36.53170884914869\n ],\n [\n -121.761474609375,\n 36.527294814546245\n ],\n [\n -121.58569335937501,\n 36.49638952000399\n ],\n [\n -121.5142822265625,\n 36.40359962073253\n ],\n [\n -121.4813232421875,\n 36.363798554158635\n ],\n [\n -121.33300781249999,\n 36.33282808737917\n ],\n [\n -121.234130859375,\n 36.27085020723902\n ],\n [\n -121.17370605468749,\n 36.19995805932895\n ],\n [\n -121.08581542968751,\n 36.10237644873644\n ],\n [\n -121.01989746093749,\n 36.02688935430189\n ],\n [\n -120.94299316406249,\n 35.96911507577482\n ],\n [\n -120.88256835937499,\n 35.89795019335754\n ],\n [\n -120.80566406250001,\n 35.79999392988527\n ],\n [\n -120.89080810546875,\n 35.66622234103479\n ],\n [\n -120.8551025390625,\n 35.61488368245436\n ],\n [\n -120.6683349609375,\n 35.46961797120201\n ],\n [\n -120.56945800781249,\n 35.37113502280101\n ],\n [\n -120.465087890625,\n 35.37113502280101\n ],\n [\n -120.2838134765625,\n 35.37113502280101\n ],\n [\n -120.12451171875,\n 35.37561413174875\n ],\n [\n -120.0146484375,\n 35.44277092585766\n ],\n [\n -119.94323730468749,\n 35.46514408578589\n ],\n [\n -119.99267578124999,\n 35.53222622770337\n ],\n [\n -120.10253906249999,\n 35.59925232772949\n ],\n [\n -120.2838134765625,\n 35.755428369259626\n ],\n [\n -120.421142578125,\n 35.88905007936091\n ],\n [\n -120.50903320312501,\n 35.97356075349624\n ],\n [\n -120.673828125,\n 36.09349937380574\n ],\n [\n -120.80566406250001,\n 36.22211876039103\n ],\n [\n -120.94299316406249,\n 36.359374956015856\n ],\n [\n -121.06933593749999,\n 36.46988944681576\n ],\n [\n -121.55273437499999,\n 36.83127162140714\n ],\n [\n -121.61315917968749,\n 36.88840804313823\n ],\n [\n -121.72302246093749,\n 36.9806150652861\n ],\n [\n -121.84936523437499,\n 37.020098201368114\n ],\n [\n -121.9976806640625,\n 37.03325468997236\n ],\n [\n -122.06909179687501,\n 37.01132594307015\n ],\n [\n -122.1075439453125,\n 36.96306042436515\n ],\n [\n -122.0855712890625,\n 36.92793899776678\n ],\n [\n -121.9976806640625,\n 36.94989178681327\n ],\n [\n -121.92626953124999,\n 36.96306042436515\n ],\n [\n -121.86584472656251,\n 36.94989178681327\n ],\n [\n -121.8438720703125,\n 36.88401445049676\n ],\n [\n -121.8109130859375,\n 36.80928470205937\n ],\n [\n -121.8109130859375,\n 36.760891249565624\n ],\n [\n -121.83288574218749,\n 36.69044623523481\n ],\n [\n -121.8438720703125,\n 36.641977814705946\n ],\n [\n -121.8878173828125,\n 36.602299135790446\n ],\n [\n -121.9317626953125,\n 36.602299135790446\n ],\n [\n -121.915283203125,\n 36.56260003738545\n ],\n [\n -121.904296875,\n 36.53170884914869\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: September 2018; Version 1.0: May 2018","contact":"Director,
California Water Science Center
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6000 J Street, Placer Hall
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The western slopes of Hawaii's Mauna Kea volcano are mantled by fine-grained soils, the record of volcanic airfall and eolian deposition. Where exposed, strong winds transport this sediment across West Hawaii, affecting tourism and local communities with decreased air and water quality. Operations on US Army's Ke'amuku Maneuver Area (KMA) have the potential to increase dust flux from these deposits. The USGS established 18 ground monitoring sites and sampling locations surrounding KMA. For over 3 years, each station measured vertical and horizontal dust flux, while co-located anemometers measured wind speed and direction. We used these datasets to develop a parsimonious regional model for dust supply and transport to assess whether KMA is a net dust sink or source.
We found that dust transport is most highly correlated with threshold wind speeds of 8 m/s. We used this value as the regional average threshold wind speed for dust entrainment. Using a model that partitions measured horizontal dust flux into inward- and outward-directed components, we estimate that KMA is currently a net dust sink. Geochemical analysis of dust samples illustrates that local organics and carbonate make up 64% of dust mass, the remainder being volcanic silt and fine sand. Measured vertical dust deposition rates of 0.006 mm/yr are similar to 0.004 mm/yr of deposition predicted from taking the divergence of dust across KMA's boundary. These rates are low compared with pre-historic rates of ~0.2–0.3 mm/yr, from radiocarbon dating of buried soils.
KMA's soils record persistent deposition over millennia, at rates that imply episodic dust storms. Such events created a soil-mantled landscape in the middle of a largely Pleistocene rocky landscape. A substantial portion of fine-grained soils in other leeward Hawaiian Island landscapes may have formed from similar eolian deposition, and not direct weathering of parent rock. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.4433","usgsCitation":"Douglas, M.M., Stock, J.D., Bishaw, K., Cerovski-Darriau, C., and Bedford, D., 2018, Dust on a Hawaiian volcano: A regional model using field measurements to estimate transport and deposition: Earth Surface Processes and Landforms, v. 43, no. 13, p. 2794-2807, https://doi.org/10.1002/esp.4433.","productDescription":"14 p.","startPage":"2794","endPage":"2807","ipdsId":"IP-089070","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":495600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Kea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.43611220977434,\n 20.37037201678426\n ],\n [\n -156.43611220977434,\n 18.704375414661897\n ],\n [\n -154.52622863370635,\n 18.704375414661897\n ],\n [\n -154.52622863370635,\n 20.37037201678426\n ],\n [\n -156.43611220977434,\n 20.37037201678426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"13","noUsgsAuthors":false,"publicationDate":"2018-07-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Douglas, Madison M; 0000-0002-0762-4719","orcid":"https://orcid.org/0000-0002-0762-4719","contributorId":361469,"corporation":false,"usgs":false,"family":"Douglas","given":"Madison","middleInitial":"M;","affiliations":[{"id":86294,"text":"Caltech/USGS","active":true,"usgs":false}],"preferred":false,"id":948864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stock, Jonathan D. 0000-0001-8565-3577 jstock@usgs.gov","orcid":"https://orcid.org/0000-0001-8565-3577","contributorId":3648,"corporation":false,"usgs":true,"family":"Stock","given":"Jonathan","email":"jstock@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":948865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bishaw, Kai'ena; II","contributorId":361470,"corporation":false,"usgs":false,"family":"Bishaw","given":"Kai'ena;","suffix":"II","affiliations":[{"id":86297,"text":"Hawaii Cooperative Studies Unit, University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":948866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cerovski-Darriau, Corina 0000-0002-0543-0902","orcid":"https://orcid.org/0000-0002-0543-0902","contributorId":221159,"corporation":false,"usgs":true,"family":"Cerovski-Darriau","given":"Corina","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":948867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedford, David","contributorId":361471,"corporation":false,"usgs":true,"family":"Bedford","given":"David","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":948868,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196565,"text":"tm7C18 - 2018 - User’s guide for MapMark4GUI—A graphical user interface for the MapMark4 R package","interactions":[],"lastModifiedDate":"2018-05-29T16:03:36","indexId":"tm7C18","displayToPublicDate":"2018-05-29T02:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C18","title":"User’s guide for MapMark4GUI—A graphical user interface for the MapMark4 R package","docAbstract":"MapMark4GUI is an R graphical user interface (GUI) developed by the U.S. Geological Survey to support user implementation of the MapMark4 R statistical software package. MapMark4 was developed by the U.S. Geological Survey to implement probability calculations for simulating undiscovered mineral resources in quantitative mineral resource assessments. The GUI provides an easy-to-use tool to input data, run simulations, and format output results for the MapMark4 package. The GUI is written and accessed in the R statistical programming language. This user’s guide includes instructions on installing and running MapMark4GUI and descriptions of the statistical output processes, output files, and test data files.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computers programs in Book 7:Automated data processing and computations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C18","usgsCitation":"Shapiro, Jason, 2018, User’s guide for MapMark4GUI—A graphical user interface for the MapMark4 R package: U.S. Geological Survey Techniques and Methods, book 7, chap. C18, 19 p., https://doi.org/10.3133/tm7c18.","productDescription":"Report: v, 19 p.; Downloadable Software; Read Me","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091540","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":354472,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/tm/07/c18/readme.pdf","text":"Read Me","size":"113 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":354474,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c18/tm7c18_MapMark4Package.zip","text":"MapMark4GUI Package","size":"1.75 MB","linkFileType":{"id":6,"text":"zip"}},{"id":354471,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c18/tm07c18.pdf","text":"Report","size":"1.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7C18"},{"id":354470,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c18/coverthb.jpg"},{"id":354473,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c18/tm7c18_MapMark4GUIRun.R","text":"MapMaprk4GUIRun.R"}],"publicComments":"This report is Chapter 18 of Section C: Computers programs in Book 7:Automated data processing and computations.","contact":"Eastern Mineral and Environmental Resources Science Center
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The Klamath River Basin stretches from the mountains and inland basins of south-central Oregon and northern California to the Pacific Ocean, spanning multiple climatic regions and encompassing a variety of ecosystems. Water quantity and water quality are important topics in the basin, because water is a critical resource for farming and municipal use, power generation, and for the support of wildlife, aquatic ecosystems, and endangered species. Upper Klamath Lake is the largest freshwater lake in Oregon (112 square miles) and is known for its seasonal algal blooms. The Klamath River has dams for hydropower and the upper basin requires irrigation water to support agriculture and grazing. Multiple species of endangered fish inhabit the rivers and lakes, and the marshes are key stops on the Pacific flyway for migrating birds. For these and other reasons, the water resources in this basin have been studied and monitored to support their management distribution.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183031","usgsCitation":"Smith, C.D., Rounds, S.A., and Orzol, L.L., 2018, Klamath River Basin water-quality data: U.S. Geological Survey Fact Sheet 2018-3031, 4 p., https://doi.org/10.3133/fs20183031.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-096068","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":354491,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3031/fs20183031.pdf","text":"Report","size":"621 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018-3031"},{"id":354490,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3031/coverthb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath River Basin","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
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The U.S. Geological Survey, in cooperation with the lower Yakima River Basin Groundwater Management Area (GWMA) group, conducted an intensive groundwater sampling collection effort of collecting nitrate concentration data in drinking water to provide a baseline for future nitrate assessments within the GWMA. About every 6 weeks from April through December 2017, a total of 1,059 samples were collected from 156 wells and 24 surface-water drains. The domestic wells were selected based on known location, completion depth, ability to collect a sample prior to treatment on filtration, and distribution across the GWMA. The drains were pre-selected by the GWMA group, and further assessed based on ability to access sites and obtain a representative sample.
More than 20 percent of samples from the domestic wells and 12.8 percent of drain samples had nitrate concentrations that exceeded the maximum contaminant level (MCL) of 10 milligrams per liter established by the U.S. Environmental Protection Agency. At least one nitrate concentration above the MCL was detected in 26 percent of wells and 33 percent of drains sampled. Nitrate was not detected in 13 percent of all samples collected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1084","collaboration":"Prepared in cooperation with Yakima County, Washington, for the Lower Yakima River Basin Groundwater Management Area","usgsCitation":"Huffman, R.L., 2018, Concentrations of nitrate in drinking water in the lower Yakima River Basin, Groundwater Management Area, Yakima County, Washington, 2017: U.S. Geological Survey Data Series 1084, 18 p., https://doi.org/10.3133/ds1084.","productDescription":"v, 18 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-095600","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":354540,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1084/ds1084.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1084"},{"id":354539,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1084/coverthb.jpg"}],"country":"United States","state":"Washington","county":"Yakima County","otherGeospatial":"Lower Yakima River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.5,\n 46.1667\n ],\n [\n -119.8333,\n 46.1667\n ],\n [\n -119.8333,\n 46.56452573114373\n ],\n [\n -120.5,\n 46.56452573114373\n ],\n [\n -120.5,\n 46.1667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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The U.S. Geological Survey and Natural Resources Canada conducted a study on the net import reliance of each North American country, and the impact of North American trade on the net import reliance of 12 nonfuel mineral commodities that are associated with advanced technology products: cadmium, cobalt, gallium, germanium, graphite, indium, lithium, nickel, rare earth elements, selenium, silver and tellurium. The combined results for North America, using 2014 data, showed greatly reduced net import reliance for nearly all of the commodities evaluated, which is largely the result of pooling the resources of production and recovery in Canada and Mexico of materials that are consumed in the United States. This study highlights the mitigation of potential supply risk for critical materials that results from trade within the North American trade bloc.
Wildlife populations occurring at the edge of their range boundaries are thought to be the most sensitive to climate change due to temperatures being at or near the limit of a species’ thermal envelope. Moose (Alces americanus) are a cold adapted species that are showing population declines in some portions of the southern edge of their range. However, other moose populations are actively expanding southward into thermally stressful areas. The direct effects of temperature on moose have not yet been studied in these southwardly expanding populations and may offer insights into how moose are successfully establishing in areas at the edge of their thermal envelope. We used ambient temperature and GPScollar data from moose to quantify the direct effect of temperature on moose habitat use in Massachusetts, USA, which is one of these southwardly expanding populations. The mean daily temperature in our study area exceeded the reported physiological tolerances of moose in over 90% of daytime and 75% of nighttime locations in summer and in over 80% of daytime and 67% of nighttime locations in winter. Across seasons and times of day, moose preferred regenerating forest, but as ambient air temperatures increased, selection for regenerating forest declined and selection for forested wetlands and coniferous forestincreased. This response indicates moose are altering their behavior to utilize thermal shelters when temperatures are high. We observed higher temperatures and stronger behavioral responses than other studies at the southern edge of moose range. We found habitat for moose in Massachusetts is climatically marginal and loss of habitat, increase in parasites, and further climatic warming may cause population declines in the future.
","language":"English","publisher":"Elsevier ","doi":"10.1016/j.mambio.2018.05.009","usgsCitation":"Zeller, K.A., Wattles, D.W., and DeStefano, S., 2018, Range expansion in unfavorable environments through behavioral responses to microclimatic conditions: Moose (Alces americanus) as the model: Mammalian Biology, v. 93, p. 189-197, https://doi.org/10.1016/j.mambio.2018.05.009.","productDescription":"9 p.","startPage":"189","endPage":"197","ipdsId":"IP-096685","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":356622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a2bbe4b0702d0e842fd1","contributors":{"authors":[{"text":"Zeller, Katherine A.","contributorId":204574,"corporation":false,"usgs":false,"family":"Zeller","given":"Katherine","email":"","middleInitial":"A.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":742886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wattles, David W.","contributorId":204573,"corporation":false,"usgs":false,"family":"Wattles","given":"David","email":"","middleInitial":"W.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":742885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeStefano, Stephen 0000-0003-2472-8373 destef@usgs.gov","orcid":"https://orcid.org/0000-0003-2472-8373","contributorId":166706,"corporation":false,"usgs":true,"family":"DeStefano","given":"Stephen","email":"destef@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":742884,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196717,"text":"sir20185061 - 2018 - Comparability among four invertebrate sampling methods and two multimetric indexes, Fountain Creek Basin, Colorado, 2010–2012","interactions":[],"lastModifiedDate":"2018-05-24T11:13:06","indexId":"sir20185061","displayToPublicDate":"2018-05-24T11:10:00","publicationYear":"2018","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":"2018-5061","title":"Comparability among four invertebrate sampling methods and two multimetric indexes, Fountain Creek Basin, Colorado, 2010–2012","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with Colorado Springs City Engineering and Colorado Springs Utilities, analyzed previously collected invertebrate data to determine the comparability among four sampling methods and two versions (2010 and 2017) of the Colorado Benthic Macroinvertebrate Multimetric Index (MMI). For this study, annual macroinvertebrate samples were collected concurrently (in space and time) at 15 USGS surface-water gaging stations in the Fountain Creek Basin from 2010 to 2012 using four sampling methods. The USGS monitoring project in the basin uses two of the methods and the Colorado Department of Public Health and Environment recommends the other two. These methods belong to two distinct sample types, one that targets single habitats and one that targets multiple habitats. The study results indicate that there are significant differences in MMI values obtained from the single-habitat and multihabitat sample types but methods from each program within each sample type produced comparable values. This study also determined that MMI values calculated by different versions of the Colorado Benthic Macroinvertebrate MMI are indistinguishable. This indicates that the Colorado Department of Public Health and Environment methods are comparable with the USGS monitoring project methods for single-habitat and multihabitat sample types. This report discusses the direct application of the study results to inform the revision of the existing USGS monitoring project in the Fountain Creek Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185061","collaboration":"Prepared in cooperation with Colorado Springs City Engineering and Colorado Springs Utilities","usgsCitation":"Bruce, J.F., Roberts, J.J., and Zuellig, R.E., 2018, Comparability among four invertebrate sampling methods and two multimetric indexes, Fountain Creek Basin, Colorado, 2010–2012: U.S. Geological Survey Scientific Investigations\nReport 2018–5061, 11 p., https://doi.org/10.3133/sir20185061.","productDescription":"Report: vi, 11 p.; Data release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-094808","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":354395,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5061/sir20185061.pdf","text":"Report","size":"908 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5061"},{"id":354396,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VQ320K","text":"USGS data release","description":"USGS data release","linkHelpText":"Multimetric Index macroinvertebrate values from the Fountain Creek Basin, Colorado 2005 to 2016"},{"id":354394,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5061/coverthb.jpg"}],"country":"United States","state":"Colorado","city":"Colorado Springs, Pueblo","otherGeospatial":"Fountain Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.35888671875,\n 38.1151107557172\n ],\n [\n -104.05426025390625,\n 38.1151107557172\n ],\n [\n -104.05426025390625,\n 39.16414104768742\n ],\n [\n -105.35888671875,\n 39.16414104768742\n ],\n [\n -105.35888671875,\n 38.1151107557172\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
To protect the aquatic living resources of Chesapeake Bay, the Chesapeake Bay Program partnership has developed guidance for state water quality standards, which include ambient water quality criteria to protect designated uses (DUs), and associated assessment procedures for dissolved oxygen (DO), water clarity/underwater bay grasses, and chlorophyll-a. For measuring progress toward meeting the respective states' water quality standards, a multimetric attainment indicator approach was developed to estimate combined standards attainment. We applied this approach to three decades of monitoring data of DO, water clarity/underwater bay grasses, and chlorophyll-a data on annually updated moving 3-year periods to track the progress in all 92 management segments of tidal waters in Chesapeake Bay. In 2014–2016, 40% of tidal water segment-DU-criterion combinations in the Bay (n = 291) are estimated to meet thresholds for attainment of their water quality criteria. This index score marks the best 3-year status in the entire record. Since 1985–1987, the indicator has followed a nonlinear trajectory, consistent with impacts from extreme weather events and subsequent recoveries. Over the period of record (1985–2016), the indicator exhibited a positive and statistically significant trend (p < 0.05), indicating that the Bay has been recovering since 1985. Patterns of attainment of individual DUs are variable, but improvements in open water DO, deep channel DO, and water clarity/submerged aquatic vegetation have combined to drive the improvement in the Baywide indicator in 2014–2016 relative to its long-term median. Finally, the improvement in estimated Baywide attainment was statistically linked to the decline of total nitrogen, indicating responsiveness of attainment status to the reduction of nutrient load through various management actions since at least the 1980s.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2018.05.025","usgsCitation":"Zhang, Q., Murphy, R.R., Tian, R., Forsyth, M.K., Trentacoste, E.M., Keisman, J.L., and Tango, P., 2018, Chesapeake Bay's water quality condition has been recovering: Insights from a multimetric indicator assessment of thirty years of tidal monitoring data: Science of the Total Environment, v. 637-638, p. 1617-1625, https://doi.org/10.1016/j.scitotenv.2018.05.025.","productDescription":"9 p.","startPage":"1617","endPage":"1625","ipdsId":"IP-097377","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":468731,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6688177","text":"External Repository"},{"id":395536,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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R.","contributorId":274698,"corporation":false,"usgs":false,"family":"Murphy","given":"Rebecca","email":"","middleInitial":"R.","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":833365,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tian, Richard 0000-0002-9416-8669","orcid":"https://orcid.org/0000-0002-9416-8669","contributorId":261309,"corporation":false,"usgs":false,"family":"Tian","given":"Richard","email":"","affiliations":[{"id":52807,"text":"U.S. Environmental Protection Agency Chesapeake Bay Program","active":true,"usgs":false}],"preferred":false,"id":833366,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Forsyth, Melinda K.","contributorId":274832,"corporation":false,"usgs":false,"family":"Forsyth","given":"Melinda","email":"","middleInitial":"K.","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental 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D. 0000-0001-6808-9193","orcid":"https://orcid.org/0000-0001-6808-9193","contributorId":210994,"corporation":false,"usgs":true,"family":"Keisman","given":"Jennifer","email":"","middleInitial":"L. D.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833369,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tango, Peter J. 0000-0001-6669-6969","orcid":"https://orcid.org/0000-0001-6669-6969","contributorId":274834,"corporation":false,"usgs":true,"family":"Tango","given":"Peter J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833370,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70197250,"text":"70197250 - 2018 - Assessing the impacts of dams and levees on the hydrologic record of the Middle and Lower Mississippi River, USA","interactions":[],"lastModifiedDate":"2018-05-24T10:46:20","indexId":"70197250","displayToPublicDate":"2018-05-24T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the impacts of dams and levees on the hydrologic record of the Middle and Lower Mississippi River, USA","docAbstract":"The impacts of dams and levees on the long-term (>130 years) discharge record was assessed along a ~1200 km segment of the Mississippi River between St. Louis, Missouri, and Vicksburg, Mississippi. To aid in our evaluation of dam impacts, we used data from the U.S. National Inventory of Dams to calculate the rate of reservoir expansion at five long-term hydrologic monitoring stations along the study segment. We divided the hydrologic record at each station into three periods: (1) a pre-rapid reservoir expansion period; (2) a rapid reservoir expansion period; and (3) a post-rapid reservoir expansion period. We then used three approaches to assess changes in the hydrologic record at each station. Indicators of hydrologic alteration (IHA) and flow duration hydrographs were used to quantify changes in flow conditions between the pre- and post-rapid reservoir expansion periods. Auto-regressive interrupted time series analysis (ARITS) was used to assess trends in maximum annual discharge, mean annual discharge, minimum annual discharge, and standard deviation of daily discharges within a given water year. A one-dimensional HEC-RAS hydraulic model was used to assess the impact of levees on flood flows. Our results revealed that minimum annual discharges and low-flow IHA parameters showed the most significant changes. Additionally, increasing trends in minimum annual discharge during the rapid reservoir expansion period were found at three out of the five hydrologic monitoring stations. These IHA and ARITS results support previous findings consistent with the observation that reservoirs generally have the greatest impacts on low-flow conditions. River segment scale hydraulic modeling revealed levees can modestly increase peak flood discharges, while basin-scale hydrologic modeling assessments by the U.S. Army Corps of Engineers showed that tributary reservoirs reduced peak discharges by a similar magnitude (2 to 30%). This finding suggests that the effects of dams and levees on peak flood discharges are in part offsetting one another along the modeled river segments and likely other substantially leveed segments of the Mississippi River.","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2018.01.004","usgsCitation":"Remo, J.W., Ickes, B., Ryherd, J.K., Guida, R.J., and Therrell, M.D., 2018, Assessing the impacts of dams and levees on the hydrologic record of the Middle and Lower Mississippi River, USA: Geomorphology, v. 313, p. 88-100, https://doi.org/10.1016/j.geomorph.2018.01.004.","productDescription":"13 p.","startPage":"88","endPage":"100","ipdsId":"IP-088232","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468733,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2018.01.004","text":"Publisher Index Page"},{"id":354450,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi River","volume":"313","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b38","contributors":{"authors":[{"text":"Remo, Jonathan W.F. 0000-0002-8208-2091","orcid":"https://orcid.org/0000-0002-8208-2091","contributorId":205201,"corporation":false,"usgs":false,"family":"Remo","given":"Jonathan","email":"","middleInitial":"W.F.","affiliations":[{"id":32417,"text":"Southern Illinois University-Carbondale","active":true,"usgs":false}],"preferred":false,"id":736404,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ickes, Brian 0000-0001-5622-3842 bickes@usgs.gov","orcid":"https://orcid.org/0000-0001-5622-3842","contributorId":2925,"corporation":false,"usgs":true,"family":"Ickes","given":"Brian","email":"bickes@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":736403,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryherd, Julia K.","contributorId":205202,"corporation":false,"usgs":false,"family":"Ryherd","given":"Julia","email":"","middleInitial":"K.","affiliations":[{"id":32417,"text":"Southern Illinois University-Carbondale","active":true,"usgs":false}],"preferred":false,"id":736405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guida, Ross J.","contributorId":205203,"corporation":false,"usgs":false,"family":"Guida","given":"Ross","email":"","middleInitial":"J.","affiliations":[{"id":37056,"text":"Sam Houston State University","active":true,"usgs":false}],"preferred":false,"id":736406,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Therrell, Matthew D.","contributorId":172810,"corporation":false,"usgs":false,"family":"Therrell","given":"Matthew","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":736407,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197251,"text":"70197251 - 2018 - Canopy volume removal from oil and gas development activity in the upper Susquehanna River basin in Pennsylvania and New York (USA): An assessment using lidar data","interactions":[],"lastModifiedDate":"2018-05-24T10:43:13","indexId":"70197251","displayToPublicDate":"2018-05-24T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Canopy volume removal from oil and gas development activity in the upper Susquehanna River basin in Pennsylvania and New York (USA): An assessment using lidar data","docAbstract":"Oil and gas development is changing the landscape in many regions of the United States and globally. However, the nature, extent, and magnitude of landscape change and development, and precisely how this development compares to other ongoing land conversion (e.g. urban/sub-urban development, timber harvest) is not well understood. In this study, we examine land conversion from oil and gas infrastructure development in the upper Susquehanna River basin in Pennsylvania and New York, an area that has experienced much oil and gas development over the past 10 years. We quantified land conversion in terms of forest canopy geometric volume loss in contrast to previous studies that considered only areal impacts. For the first time in a study of this type, we use fine-scale lidar forest canopy geometric models to assess the volumetric change due to forest clearing from oil and gas development and contrast this land change to clear cut forest harvesting, and urban and suburban development. Results show that oil and gas infrastructure development removed a large volume of forest canopy from 2006 to 2013, and this removal spread over a large portion of the study area. Timber operations (clear cutting) on Pennsylvania State Forest lands removed a larger total volume of forest canopy during the same time period, but this canopy removal was concentrated in a smaller area. Results of our study point to the need to consider volumetric impacts of oil and gas development on ecosystems, and to place potential impacts in context with other ongoing land conversions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2018.05.041","usgsCitation":"Young, J.A., Maloney, K.O., Slonecker, E.T., Milheim, L., and Siripoonsup, D., 2018, Canopy volume removal from oil and gas development activity in the upper Susquehanna River basin in Pennsylvania and New York (USA): An assessment using lidar data: Journal of Environmental Management, v. 222, p. 66-75, https://doi.org/10.1016/j.jenvman.2018.05.041.","productDescription":"10 p.","startPage":"66","endPage":"75","ipdsId":"IP-089887","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":354448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Pennsylvania","otherGeospatial":"Upper Susquehanna River basin","volume":"222","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b36","contributors":{"authors":[{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":736408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":736409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Slonecker, E. Terrence 0000-0002-5793-0503 tslonecker@usgs.gov","orcid":"https://orcid.org/0000-0002-5793-0503","contributorId":168591,"corporation":false,"usgs":true,"family":"Slonecker","given":"E.","email":"tslonecker@usgs.gov","middleInitial":"Terrence","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":736410,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Milheim, Lesley E. lmilheim@usgs.gov","contributorId":2560,"corporation":false,"usgs":true,"family":"Milheim","given":"Lesley E.","email":"lmilheim@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":false,"id":736411,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Siripoonsup, David dsiripoonsup@usgs.gov","contributorId":197039,"corporation":false,"usgs":true,"family":"Siripoonsup","given":"David","email":"dsiripoonsup@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":736412,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211480,"text":"70211480 - 2018 - A retrospective look at the February 1993 east rift zone intrusion at Kīlauea volcano, Hawaii","interactions":[],"lastModifiedDate":"2020-07-28T22:49:01.833424","indexId":"70211480","displayToPublicDate":"2018-05-23T17:40:45","publicationYear":"2018","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":"A retrospective look at the February 1993 east rift zone intrusion at Kīlauea volcano, Hawaii","docAbstract":"The February 1993 dike intrusion in the East Rift Zone (ERZ) of Kīlauea Volcano, Hawai'i, was recognized from tilt and seismic data, but ground-based geodetic data were too sparse to constrain the characteristics of the intrusion. Analysis of Interferometric Synthetic Aperture Radar (InSAR) from the Japan Aerospace Exploration Agency (JAXA) JERS-1 satellite reveals a maximum of ~30 cm of line-of-sight (LOS) displacement occurring near Makaopuhi Crater in the middle ERZ of Kīlauea. We model this deformation signal as a subvertical dike using a 3D-Mixed Boundary Element Method (3D-MBEM) paired with a nonlinear inversion algorithm to find the best-fit model. The best-fit dike is located just to the west of Makaopuhi Crater striking N50°W, extends to within 100 m of the surface, is ~1.3 km in length by ~4.2 km in width along strike, and has a total volume of ~7.4 × 106 m3. In addition, a post-intrusion interferogram from JERS-1 spanning 1993–1997 was analyzed. Guided by previous results, our model for the 4-year period consists of opening of the deep rift zones by about 0.5 m at 3–8.5 km depth beneath the Southwest Rift Zone (SWRZ), ERZ and the summit. A sub-horizontal detachment fault is connected to the seaward side of the vertical dike-like source to mimic the décollement known to exist beneath the volcano. We classify the 1993 dike intrusion as a passive intrusion similar to those that occurred in 1997 and 1999. Passive intrusions lack precursory inflation at Kīlauea's summit, and the likely triggering mechanism is persistent deep rift opening combined with seaward motion of the south flank along the basal décollement. Passive intrusions make forecasting and hazard assessment difficult since they are not preceded by inflation nor by large increases in seismicity.
Aim
Species richness is a measure of biodiversity often used in spatial conservation assessments and mapped by summing species distribution maps. Commission errors inherent those maps influence richness patterns and conservation assessments. We sought to further the understanding of the sensitivity of hotspot delineation methods and conservation assessments to commission errors, and choice of threshold for hotspot delineation.
Location
United States.
Methods
We created range maps and 30‐m and 1‐km resolution habitat maps for terrestrial vertebrates in the United States and generated species richness maps with each dataset. With the richness maps and the GAP Protected Areas Dataset, we created species richness hotspot maps and calculated the proportion of hotspots within protected areas; calculating protection under a range of thresholds for defining hotspots. Our method allowed us to identify the influence of commission errors by comparing hotspot maps.
Results
Commission errors from coarse spatial grain data and lack of porosity in the range data inflated richness estimates and altered their spatial patterns. Coincidence of hotspots from different data types was low. The 30‐m hotspots were spatially dispersed, and some were very long distances from the hotspots mapped with coarser data. Estimates of protection were low for each of the taxa. The relationship between estimates of hotspot protection and threshold choice was nonlinear and inconsistent among data types (habitat and range) and grain size (30‐m and 1‐km).
Main conclusions
Coarse mapping methods and grain sizes can introduce commission errors into species distribution data that could result in misidentifications of the regions where hotspots occur and affect estimates of hotspot protection. Hotspot conservation assessments are also sensitive to choice of threshold for hotspot delineation. There is value in developing species distribution maps with high resolution and low rates of commission error for conservation assessments.
The U.S. Geological Survey monthly water balance model (MWBM) was enhanced with the capability to simulate glaciers in order to make it more suitable for simulating cold region hydrology. The new model, MWBMglacier, is demonstrated in the heavily glacierized and ecologically important Copper River watershed in Southcentral Alaska. Simulated water budget components compared well to satellite‐based observations and ground measurements of streamflow, evapotranspiration, snow extent, and total water storage, with differences ranging from 0.2% to 7% of the precipitation flux. Nash Sutcliffe efficiency for simulated and observed streamflow was greater than 0.8 for six of eight stream gages. Snow extent matched satellite‐based observations with Nash Sutcliffe efficiency values of greater than 0.89 in the four Copper River ecoregions represented. During the simulation period 1949 to 2009, glacier ice melt contributed 25% of total runoff, ranging from 12% to 45% in different tributaries, and glacierized area was reduced by 6%. Statistically significant (p < 0.05) decreasing and increasing trends in annual glacier mass balance occurred during the multidecade cool and warm phases of the Pacific Decadal Oscillation, respectively, reinforcing the link between climate perturbations and glacier mass balance change. The simulations of glaciers and total runoff for a large, remote region of Alaska provide useful data to evaluate hydrologic, cryospheric, ecologic, and climatic trends. MWBM glacier is a valuable tool to understand when, and to what extent, streamflow may increase or decrease as glaciers respond to a changing climate.
","language":"English","publisher":"AGU","doi":"10.1029/2017JF004482","usgsCitation":"Valentin, M.M., Viger, R.J., Van Beusekom, A.E., Hay, L.E., Hogue, T.S., and Foks, N.L., 2018, Enhancement of a parsimonious water balance model to simulate surface hydrology in a glacierized watershed: Journal of Geophysical Research F: Earth Surface, v. 123, no. 5, p. 1116-1132, https://doi.org/10.1029/2017JF004482.","productDescription":"17 p.","startPage":"1116","endPage":"1132","ipdsId":"IP-094374","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":468839,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017jf004482","text":"Publisher Index Page"},{"id":354424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-23","publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b3c","contributors":{"authors":[{"text":"Valentin, Melissa M.","contributorId":205172,"corporation":false,"usgs":false,"family":"Valentin","given":"Melissa","email":"","middleInitial":"M.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":736281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Viger, Roland J. 0000-0003-2520-714X rviger@usgs.gov","orcid":"https://orcid.org/0000-0003-2520-714X","contributorId":147818,"corporation":false,"usgs":true,"family":"Viger","given":"Roland","email":"rviger@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":false,"id":736280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Beusekom, Ashley E. 0000-0002-6996-978X beusekom@usgs.gov","orcid":"https://orcid.org/0000-0002-6996-978X","contributorId":3992,"corporation":false,"usgs":true,"family":"Van Beusekom","given":"Ashley","email":"beusekom@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":736282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":736283,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":736284,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Foks, Nathan Leon","contributorId":194012,"corporation":false,"usgs":false,"family":"Foks","given":"Nathan","email":"","middleInitial":"Leon","affiliations":[],"preferred":false,"id":736285,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197216,"text":"70197216 - 2018 - Origin of last-glacial loess in the western Yukon-Tanana Upland, central Alaska, USA","interactions":[],"lastModifiedDate":"2018-05-23T10:35:34","indexId":"70197216","displayToPublicDate":"2018-05-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Origin of last-glacial loess in the western Yukon-Tanana Upland, central Alaska, USA","docAbstract":"Loess is widespread over Alaska, and its accumulation has traditionally been associated with glacial periods. Surprisingly, loess deposits securely dated to the last glacial period are rare in Alaska, and paleowind reconstructions for this time period are limited to inferences from dune orientations. We report a rare occurrence of loess deposits dating to the last glacial period, ~19 ka to ~12 ka, in the Yukon-Tanana Upland. Loess in this area is very coarse grained (abundant coarse silt), with decreases in particle size moving south of the Yukon River, implying that the drainage basin of this river was the main source. Geochemical data show, however, that the Tanana River valley to the south is also a likely distal source. The occurrence of last-glacial loess with sources to both the south and north is explained by both regional, synoptic-scale winds from the northeast and opposing katabatic winds that could have developed from expanded glaciers in both the Brooks Range to the north and the Alaska Range to the south. Based on a comparison with recent climate modeling for the last glacial period, seasonality of dust transport may also have played a role in bringing about contributions from both northern and southern sources.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/qua.2018.11","usgsCitation":"Muhs, D., Pigati, J.S., Budahn, J.R., Skipp, G.L., Bettis, E.A., and Jensen, B., 2018, Origin of last-glacial loess in the western Yukon-Tanana Upland, central Alaska, USA: Quaternary Research, v. 89, no. 3, p. 797-819, https://doi.org/10.1017/qua.2018.11.","productDescription":"23 p.","startPage":"797","endPage":"819","ipdsId":"IP-086762","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":354407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"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 -150,\n 65.5\n ],\n [\n -148.75,\n 65.5\n ],\n [\n -148.75,\n 66\n ],\n [\n -150,\n 66\n ],\n [\n -150,\n 65.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"89","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-10","publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b3e","contributors":{"authors":[{"text":"Muhs, Daniel R. 0000-0001-7449-251X dmuhs@usgs.gov","orcid":"https://orcid.org/0000-0001-7449-251X","contributorId":168575,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel R.","email":"dmuhs@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":736257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":736258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budahn, James R. 0000-0001-9794-8882 jbudahn@usgs.gov","orcid":"https://orcid.org/0000-0001-9794-8882","contributorId":1175,"corporation":false,"usgs":true,"family":"Budahn","given":"James","email":"jbudahn@usgs.gov","middleInitial":"R.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":736259,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skipp, Gary L. 0000-0002-9404-0980","orcid":"https://orcid.org/0000-0002-9404-0980","contributorId":201777,"corporation":false,"usgs":true,"family":"Skipp","given":"Gary","email":"","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":736260,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bettis, E. Arthur III 0000-0002-6137-1433","orcid":"https://orcid.org/0000-0002-6137-1433","contributorId":204005,"corporation":false,"usgs":false,"family":"Bettis","given":"E.","suffix":"III","email":"","middleInitial":"Arthur","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":736261,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jensen, Britta","contributorId":184164,"corporation":false,"usgs":false,"family":"Jensen","given":"Britta","affiliations":[],"preferred":false,"id":736262,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197203,"text":"70197203 - 2018 - A framework for modeling scenario-based barrier island storm impacts","interactions":[],"lastModifiedDate":"2018-05-22T13:22:48","indexId":"70197203","displayToPublicDate":"2018-05-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"A framework for modeling scenario-based barrier island storm impacts","docAbstract":"Methods for investigating the vulnerability of existing or proposed coastal features to storm impacts often rely on simplified parametric models or one-dimensional process-based modeling studies that focus on changes to a profile across a dune or barrier island. These simple studies tend to neglect the impacts to curvilinear or alongshore varying island planforms, influence of non-uniform nearshore hydrodynamics and sediment transport, irregular morphology of the offshore bathymetry, and impacts from low magnitude wave events (e.g. cold fronts). Presented here is a framework for simulating regionally specific, low and high magnitude scenario-based storm impacts to assess the alongshore variable vulnerabilities of a coastal feature. Storm scenarios based on historic hydrodynamic conditions were derived and simulated using the process-based morphologic evolution model XBeach. Model results show that the scenarios predicted similar patterns of erosion and overwash when compared to observed qualitative morphologic changes from recent storm events that were not included in the dataset used to build the scenarios. The framework model simulations were capable of predicting specific areas of vulnerability in the existing feature and the results illustrate how this storm vulnerability simulation framework could be used as a tool to help inform the decision-making process for scientists, engineers, and stakeholders involved in coastal zone management or restoration projects.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2018.04.012","usgsCitation":"Mickey, R.C., Long, J., Dalyander, P.S., Plant, N.G., and Thompson, D.M., 2018, A framework for modeling scenario-based barrier island storm impacts: Coastal Engineering, v. 138, p. 98-112, https://doi.org/10.1016/j.coastaleng.2018.04.012.","productDescription":"15 p.","startPage":"98","endPage":"112","ipdsId":"IP-092224","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2018.04.012","text":"Publisher Index Page"},{"id":354386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chandeleur Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89,\n 28.67\n ],\n [\n -87,\n 28.67\n ],\n [\n -87,\n 30.67\n ],\n [\n -89,\n 30.67\n ],\n [\n -89,\n 28.67\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"138","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d7ae4b092d9651e1b4a","contributors":{"authors":[{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":202183,"corporation":false,"usgs":true,"family":"Long","given":"Joseph W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736122,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872 sdalyander@usgs.gov","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":141015,"corporation":false,"usgs":true,"family":"Dalyander","given":"P.","email":"sdalyander@usgs.gov","middleInitial":"Soupy","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736123,"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":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":736124,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, David M. 0000-0002-7103-5740 dthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-7103-5740","contributorId":3502,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"dthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736125,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197202,"text":"70197202 - 2018 - Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes","interactions":[],"lastModifiedDate":"2023-03-27T22:49:54.298718","indexId":"70197202","displayToPublicDate":"2018-05-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes","docAbstract":"Temporal and spatial variability in streams result in heterogeneous gear capture probability (i.e., the proportion of available individuals identified) that confounds interpretation of data used to monitor fish abundance. We modeled tow-barge electrofishing capture probability at multiple spatial scales for nine Ozark Highland stream fishes. In addition to fish size, we identified seven reach-scale environmental characteristics associated with variable capture probability: stream discharge, water depth, conductivity, water clarity, emergent vegetation, wetted width–depth ratio, and proportion of riffle habitat. The magnitude of the relationship between capture probability and both discharge and depth varied among stream fishes. We also identified lithological characteristics among stream segments as a coarse-scale source of variable capture probability. The resulting capture probability model can be used to adjust catch data and derive reach-scale absolute abundance estimates across a wide range of sampling conditions with similar effort as used in more traditional fisheries surveys (i.e., catch per unit effort). Adjusting catch data based on variable capture probability improves the comparability of data sets, thus promoting both well-informed conservation and management decisions and advances in stream-fish ecology.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2016-0422","usgsCitation":"Mollenhauer, R., Mouser, J.B., and Brewer, S.K., 2018, Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 10, p. 1614-1625, https://doi.org/10.1139/cjfas-2016-0422.","productDescription":"12 p.","startPage":"1614","endPage":"1625","ipdsId":"IP-079903","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468741,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/87924","text":"External Repository"},{"id":354387,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.09031466610114,\n 36.913206107808435\n ],\n [\n -95.09031466610114,\n 36.02798264227451\n ],\n [\n -93.63459116212798,\n 36.02798264227451\n ],\n [\n -93.63459116212798,\n 36.913206107808435\n ],\n [\n -95.09031466610114,\n 36.913206107808435\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"75","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d7ae4b092d9651e1b4c","contributors":{"authors":[{"text":"Mollenhauer, Robert","contributorId":176540,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[],"preferred":false,"id":735984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mouser, Joshua B.","contributorId":205087,"corporation":false,"usgs":false,"family":"Mouser","given":"Joshua","email":"","middleInitial":"B.","affiliations":[{"id":37027,"text":"Oklahoma Cooperative Fish and Wildlife Research Unit, Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":735985,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":735983,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196645,"text":"ofr20181065 - 2018 - Streamflow, water quality, and constituent loads and yields, Scituate Reservoir Drainage Area, Rhode Island, water year 2015","interactions":[],"lastModifiedDate":"2018-05-22T10:00:35","indexId":"ofr20181065","displayToPublicDate":"2018-05-21T16:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1065","title":"Streamflow, water quality, and constituent loads and yields, Scituate Reservoir Drainage Area, Rhode Island, water year 2015","docAbstract":"Streamflow and concentrations of sodium and chloride estimated from records of specific conductance were used to calculate loads of sodium and chloride during water year (WY) 2015 (October 1, 2014, through September 30, 2015) for tributaries to the Scituate Reservoir, Rhode Island. Streamflow and water-quality data used in the study were collected by the U.S. Geological Survey and the Providence Water Supply Board. Streamflow was measured or estimated by the U.S. Geological Survey following standard methods at 23 streamgages; 14 of these streamgages are equipped with instrumentation capable of continuously monitoring water level, specific conductance, and water temperature. Water-quality samples were collected at 36 sampling stations by the Providence Water Supply Board and at 14 continuous-record streamgages by the U.S. Geological Survey during WY 2015 as part of a long-term sampling program; all stations are in the Scituate Reservoir drainage area. Water-quality data collected by the Providence Water Supply Board are summarized by using values of central tendency and are used, in combination with measured (or estimated) streamflows, to calculate loads and yields (loads per unit area) of selected water-quality constituents for WY 2015.
The largest tributary to the reservoir (the Ponaganset River, which was monitored by the U.S. Geological Survey) contributed a mean streamflow of 25 cubic feet per second to the reservoir during WY 2015. For the same time period, annual mean streamflows measured (or estimated) for the other monitoring stations in this study ranged from about 0.38 to about 14 cubic feet per second. Together, tributaries (equipped with instrumentation capable of continuously monitoring specific conductance) transported about 1,500,000 kilograms of sodium and 2,400,000 kilograms of chloride to the Scituate Reservoir during WY 2015; sodium and chloride yields for the tributaries ranged from 8,000 to 54,000 kilograms per square mile and from 12,000 to 91,000 kilograms per square mile, respectively.
At the stations where water-quality samples were collected by the Providence Water Supply Board, the medians of the median concentrations were the following: for chloride, 29.5 milligrams per liter; for nitrite, 0.002 milligrams per liter as nitrogen; for nitrate, 0.05 milligrams per liter as nitrogen; for orthophosphate, 0.08 milligrams per liter as phosphate; and for total coliform bacteria and Escherichia coli, 440 and 20 colony forming units per 100 milliliters, respectively. The medians of the median daily loads (and yields) of chloride, nitrite, nitrate, orthophosphate, and total coliform and Escherichia coli bacteria were 170 kilograms per day (79 kilograms per day per square mile), 14 grams per day (5.2 grams per day per square mile), 670 grams per day (190 grams per day per square mile), 640 grams per day (210 grams per day per square mile), 18,000 million colony forming units per day (7,600 million colony forming units per day per square mile), and 1,200 million colony forming units per day (810 million colony forming units per day per square mile), respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181065","collaboration":"Prepared in cooperation with the Providence Water Supply Board","usgsCitation":"Smith, K.P., 2018, Streamflow, water quality, and constituent loads and yields, Scituate Reservoir drainage area, Rhode Island, water year 2015: U.S. Geological Survey Open-File Report 2018–1065, 28 p., https://doi.org/10.3133/ofr20181065.","productDescription":"Report: v, 28 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088040","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":354074,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FJ2FR5","text":"USGS data release","description":"USGS data release","linkHelpText":"Water Quality data from the Providence Water Supply Board for tributary streams to the Scituate Reservoir, water year 2015"},{"id":354073,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1065/ofr20181065.pdf","text":"Report","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1065"},{"id":354072,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1065/coverthb2.jpg"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Scituate Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.61334991455077,\n 41.72546174412562\n ],\n [\n -71.59687042236328,\n 41.73571038089891\n ],\n [\n -71.57112121582031,\n 41.75978824099475\n ],\n [\n -71.5481185913086,\n 41.784369074958185\n ],\n [\n -71.5536117553711,\n 41.82173436916421\n ],\n [\n -71.55635833740234,\n 41.83145600892532\n ],\n [\n -71.5536117553711,\n 41.842199261672384\n ],\n [\n -71.55979156494139,\n 41.85652079326776\n ],\n [\n -71.5707778930664,\n 41.862146232018134\n ],\n [\n -71.59000396728514,\n 41.862146232018134\n ],\n [\n -71.60923004150389,\n 41.877485822442324\n ],\n [\n -71.61128997802734,\n 41.907642945005215\n ],\n [\n -71.6421890258789,\n 41.918373400143004\n ],\n [\n -71.66622161865234,\n 41.91173095014917\n ],\n [\n -71.66416168212889,\n 41.886687809812926\n ],\n [\n -71.66622161865234,\n 41.857543637121026\n ],\n [\n -71.67377471923828,\n 41.854986496815044\n ],\n [\n -71.6861343383789,\n 41.8744181988335\n ],\n [\n -71.69265747070311,\n 41.87262868373214\n ],\n [\n -71.70398712158203,\n 41.86674849564142\n ],\n [\n -71.72321319580077,\n 41.88719899247718\n ],\n [\n -71.73282623291016,\n 41.89435512030307\n ],\n [\n -71.75617218017578,\n 41.895888471963744\n ],\n [\n -71.77196502685547,\n 41.88771017105127\n ],\n [\n -71.76578521728516,\n 41.87237303462768\n ],\n [\n -71.77265167236328,\n 41.854986496815044\n ],\n [\n -71.76647186279297,\n 41.83145600892532\n ],\n [\n -71.7685317993164,\n 41.82122266302155\n ],\n [\n -71.76990509033202,\n 41.81457011105642\n ],\n [\n -71.75754547119139,\n 41.79358445913614\n ],\n [\n -71.74655914306639,\n 41.78129698585321\n ],\n [\n -71.7348861694336,\n 41.77464028786307\n ],\n [\n -71.71634674072264,\n 41.771567732673454\n ],\n [\n -71.7033004760742,\n 41.76081263047197\n ],\n [\n -71.69300079345702,\n 41.75620274904767\n ],\n [\n -71.6861343383789,\n 41.74185877811425\n ],\n [\n -71.6744613647461,\n 41.741346434162764\n ],\n [\n -71.6476821899414,\n 41.73775991204249\n ],\n [\n -71.63875579833983,\n 41.727511602285894\n ],\n [\n -71.61334991455077,\n 41.72546174412562\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
In this report, precipitation data from 2002 to 2012 from the hourly gridded Next-Generation Radar (NEXRAD)-based Multisensor Precipitation Estimate (MPE) precipitation product are compared to precipitation data from two rain gage networks—an automated tipping bucket network of 25 rain gages operated by the U.S. Geological Survey (USGS) and 51 rain gages from the volunteer-operated Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network—in and near DuPage County, Illinois, at a daily time step to test for long-term differences in space, time, and distribution. The NEXRAD–MPE data that are used are from the fifty 2.5-mile grid cells overlying the rain gages from the other networks. Because of the challenges of measuring of frozen precipitation, the analysis period is separated between days with or without the chance of freezing conditions. The NEXRAD–MPE and tipping-bucket rain gage precipitation data are adjusted to account for undercatch by multiplying by a previously determined factor of 1.14. Under nonfreezing conditions, the three precipitation datasets are broadly similar in cumulative depth and distribution of daily values when the data are combined spatially across the networks. However, the NEXRAD–MPE data indicate a significant trend relative to both rain gage networks as a function of distance from the NEXRAD radar just south of the study area. During freezing conditions, of the USGS network rain gages only the heated gages were considered, and these gages indicate substantial mean undercatch of 50 and 61 percent compared to the NEXRAD–MPE and the CoCoRaHS gages, respectively. The heated USGS rain gages also indicate substantially lower quantile values during freezing conditions, except during the most extreme (highest) events. Because NEXRAD precipitation products are continually evolving, the report concludes with a discussion of recent changes in those products and their potential for improved precipitation estimation. An appendix provides an analysis of spatially combined NEXRAD–MPE precipitation data as a function of temperature at an hourly time scale and indicates, among other results, that most precipitation in the study area occurs at moderate temperatures of 30 to 74 degrees Fahrenheit. However, when precipitation does occur, its intensity increases with temperature to about 86 degrees Fahrenheit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181061","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Spies, R.R., Over, T.M., and Ortel, T.W., 2018, Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12: U.S. Geological Survey Open-File Report 2018–1061, 30 p., https://doi.org/10.3133/ofr20181061. ","productDescription":"v, 30 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057485","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":354281,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1061/coverthb.jpg","text":"Report"},{"id":354282,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1061/ofr20181061.pdf","text":"Report","size":"5.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1061"}],"country":"United States","state":"Illinois","county":"DuPage County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.33,\n 41.5833\n ],\n [\n -87.8333,\n 41.5833\n ],\n [\n -87.8333,\n 42.1667\n ],\n [\n -88.33,\n 42.1667\n ],\n [\n -88.33,\n 41.5833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801
Human impacts on rivers threaten the natural function of riverine ecosystems. This paper assesses how channel confinement affects the scour depth and spatial extent of bed disturbance and discusses the implications of these results for salmon-redd disturbance in gravel-bedded rivers. Two-dimensional hydrodynamic models of relatively confined and unconfined reaches of the Cedar River in Washington State, USA, were constructed with surveyed bathymetry and available airborne lidar data then calibrated and verified with field observations of water-surface elevation and streamflow velocity. Simulations showed greater water depths and velocities in the confined reach and greater areas of low-velocity inundation in the unconfined reach at high flows. Data on previously published scour depth of bed disturbance during high flows were compared to simulated bed shear stress to construct a probabilistic logistic-regression model of bed disturbance, which was applied to spatial patterns of simulated bed shear stress to quantify the extent of likely bed disturbance to the burial depth of sockeye and Chinook salmon redds. The disturbance depth was not observed to differ between confined and unconfined reaches; however, results indicated the spatial extent of disturbance to a given depth in the confined reach was roughly twice as large as in the unconfined reach.
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Czuba, Czuba, J.A., Magirl, C.S., Gendaszek, A.S., and Konrad, C.P., 2018, Effect of river confinement on depth and spatial extent of bed disturbance affecting salmon redds: Journal of Ecohydraulics, v. 2, no. 2, p. 1-14, https://doi.org/10.1080/24705357.2018.1457986.","productDescription":"14 p.","startPage":"1","endPage":"14","ipdsId":"IP-066545","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":359514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Cedar River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.29499816894531,\n 47.357431944587034\n ],\n [\n -121.62620544433592,\n 47.357431944587034\n ],\n [\n -121.62620544433592,\n 47.58717856130287\n ],\n [\n -122.29499816894531,\n 47.58717856130287\n ],\n [\n -122.29499816894531,\n 47.357431944587034\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-21","publicationStatus":"PW","scienceBaseUri":"5befe5bde4b045bfcadf7f44","contributors":{"authors":[{"text":"Christiana R. 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These data come from multiple agencies, programs, and sources, often with their own methods and standards for data collection and organization. Coordinating standards and methods is often prohibitively time-intensive and expensive. MonitoringResources.org offers a suite of tools and resources that support coordination of monitoring efforts, cost-effective planning, and sharing of knowledge among organizations. The website was developed by the Pacific Northwest Aquatic Monitoring Partnership—a collaboration of Federal, state, tribal, local, and private monitoring programs—and the U.S. Geological Survey (USGS), with funding from the Bonneville Power Administration and USGS. It is a key component of a coordinated monitoring and information network.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183015","usgsCitation":"Bayer, J.M., Scully, R.A., and Weltzin, J.F., 2018, MonitoringResources.org—Supporting coordinated and cost-effective natural resource monitoring across organizations: U.S. Geological Survey Fact Sheet 2018–3015, 2 p., https://doi.org/10.3133/fs20183015.","productDescription":"2 p.","onlineOnly":"Y","ipdsId":"IP-091401","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":354360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3015/fs20183015.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2018-3015"},{"id":354359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3015/coverthb.jpg"}],"contact":"Populations of Northern Spotted Owls (Strix occidentalis caurina; hereinafter referred to as Spotted Owl) are declining throughout this subspecies’ geographic range. Evidence indicates that competition with invading populations of Barred Owls (S. varia) has contributed significantly to those declines. A pilot study in California showed that localized removal of Barred Owls coupled with conservation of suitable forest conditions can slow or even reverse population declines of Spotted Owls. It remains unknown, however, whether similar results can be obtained in areas with different forest conditions, greater densities of Barred Owls, and fewer remaining Spotted Owls. During 2015–17, we initiated a before-after-control-impact (BACI) experiment at three study areas in Oregon and Washington to determine if removal of Barred Owls can improve population trends of Spotted Owls. Each study area had at least 20 years of pre-treatment demographic data on Spotted Owls, and represented different forest conditions occupied by the two owl species in the Pacific Northwest. This report describes research accomplishments and preliminary results from the first 2.5 years (March 2015–August 2017) of the planned 5-year experiment.
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\"}}]}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Operable Unit 2, Area 8, at Naval Base Kitsap, Keyport is the site of a former chrome-plating facility that released metals (primarily chromium and cadmium), chlorinated volatile organic compounds, and petroleum compounds into the local environment. To ensure long-term protectiveness, as stipulated in the Fourth Five-Year Review for the site, Naval Facilities Engineering Command Northwest collaborated with the U.S. Environmental Protection Agency, the Washington State Department of Ecology, and the Suquamish Tribe, to collect data to monitor the contamination left in place and to ensure the site does not pose a risk to human health or the environment. To support these efforts, refined information was needed on the interaction of fresh groundwater with seawater in response to the up-to 13-ft tidal fluctuations at this nearshore site adjacent to Port Orchard Bay. The information was analyzed to meet the primary objective of this investigation, which was to determine the optimal time during the semi-diurnal and the neap-spring tidal cycles to sample groundwater for freshwater contaminants in Area 8 monitoring wells.
Groundwater levels and specific conductance in five monitoring wells, along with marine water-levels (tidal levels) in Port Orchard Bay, were monitored every 15 minutes during a 3-week duration to determine how nearshore groundwater responds to tidal forcing. Time series data were collected from October 24, 2017, to November 16, 2017, a period that included neap and spring tides. Vertical profiles of specific conductance were also measured once in the screened interval of each well prior to instrument deployment to determine if a freshwater/saltwater interface was present in the well during that particular time.
The vertical profiles of specific conductance were measured only one time during an ebbing tide at approximately the top, middle, and bottom of the saturated thickness within the screened interval of each well. The landward-most well, MW8-8, was completely freshwater, while one of the most seaward wells, MW8-9, was completely saline. A distinct saltwater interface was measured in the three other shallow wells (MW8-11, MW8-12, and MW8-14), with the topmost groundwater occurring fresh underlain by higher conductivity water.
Lag times between minimum spring-tide level and minimum groundwater levels in wells ranged from about 2 to 4.5 hours in the less-than 20-ft deep wells screened across the water table, and was about 7 hours for the single 48-ft deep well screened below the water table. Those lag times were surprisingly long considering the wells are all located within 200-ft of the shoreline and the local geology is largely coarse-grained glacial outwash deposits. Various manmade subsurface features, such as slurry walls and backfilled excavations, likely influence and confuse the connectivity between seawater and groundwater.
The specific-conductance time-series data showed clear evidence of substantial saltwater intrusion into the screened intervals of most shallow wells. Unexpectedly, the intrusion was associated with the neap part of the tidal cycle around November 13–16, when relatively low barometric pressure and high southerly winds led to the highest high and low tides measured during the monitoring period. The data consistently indicated that the groundwater had the lowest specific conductance (was least mixed with seawater) during the prior neap tides around October 30, the same period when the shallow groundwater levels were lowest. Although the specific conductance response is somewhat different between wells, the data do suggest that it is the heights of the actual high-high and low-low tides, regardless of whether or not they occur during the neap or spring part of the cycle, that allows seawater intrusion into the nearshore aquifer at Area 8.
With all the data taken into consideration, the optimal time for sampling the shallow monitoring wells at Area 8 would be centered on a 2–5-hour period following the predicted low-low tide during neap tide, with due consideration of local atmospheric pressure and wind conditions that have the potential to generate tides that can be substantially higher than those predicted from lunar-solar tidal forces. The optimal time for sampling the deeper monitoring wells at Area 8 would be during the 6–8-hour period following a predicted low-low tide, also during the neap tide part of the tidal cycle. The specific time window to sample each well following a low tide can be found in table 5. Those periods are when groundwater in the wells is most fresh and least diluted by seawater intrusion. In addition to timing, consideration should be given to collecting undisturbed samples from the top of the screened interval (or top of the water table if below the top of the interval) to best characterize contaminant concentrations in freshwater. A downhole conductivity probe could be used to identify the saltwater interface, above which would be the ideal depth for sampling.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181082","collaboration":"Prepared in cooperation with the Department of the Navy, Naval Facilities Engineering Command, Northwest","usgsCitation":"Opatz, C.C., and Dinicola, R.S., 2018, Analysis of groundwater response to tidal fluctuations, Operable Unit 2, Area 8, Naval Base Kitsap, Keyport, Washington: U.S. Geological Survey Open-File Report 2018-1082, 20 p., https://doi.org/10.3133/ofr20181082.","productDescription":"Report: iv, 20 p.","onlineOnly":"Y","ipdsId":"IP-095017","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":354378,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1082/coverthb.jpg"},{"id":354379,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1082/ofr20181082.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1082"},{"id":358998,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JW8D5S","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Groundwater and Tidal Time Series Data, Operable Unit 2, Area 8, Naval Base Kitsap, Keyport, Washington"}],"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 -122.64913558959962,\n 47.683072220525\n ],\n [\n -122.59180068969725,\n 47.683072220525\n ],\n [\n -122.59180068969725,\n 47.72627665811123\n ],\n [\n -122.64913558959962,\n 47.72627665811123\n ],\n [\n -122.64913558959962,\n 47.683072220525\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Drylands are the habitat and source of livelihood for about two fifths of the world’s population and are highly susceptible to climate and anthropogenic change. To understand the vulnerability of drylands to changing environmental conditions, land managers need to effectively monitor rates of past change and remote sensing offers a cost-effective means to assess and manage these vast landscapes. Here, we present a novel approach to accurately monitor land-surface phenology in drylands of the Western United States using a regression tree modeling framework that combined information collected by the Operational Land Imager (OLI) onboard Landsat 8 and the Multispectral Instrument (MSI) onboard Sentinel-2. This highly-automatable approach allowed us to precisely characterize seasonal variations in spectral vegetation indices with substantial agreement between observed and predicted values (R2 = 0.98; Mean Absolute Error = 0.01). Derived phenology curves agreed with independent eMODIS phenological signatures of major land cover types (average r-value = 0.86), cheatgrass cover (average r-value = 0.96), and growing season proxies for vegetation productivity (R2 = 0.88), although a systematic bias towards earlier maturity and senescence indicates enhanced monitoring capabilities associated with the use of harmonized Landsat-8 Sentinel-2 data. Overall, our results demonstrate that observations made by the MSI and OLI can be used in conjunction to accurately characterize land-surface phenology and exclusion of imagery from either sensor drastically reduces our ability to monitor dryland environments. Given the declines in MODIS performance and forthcoming decommission with no equivalent replacement planned, data fusion approaches that integrate observations from multispectral sensors will be needed to effectively monitor dryland ecosystems. While the synthetic image stacks are expected to be locally useful, the technical approach can serve a wide variety of applications such as invasive species and drought monitoring, habitat mapping, production of phenology metrics, and land-cover change modeling.
","language":"English","publisher":"MDPI","doi":"10.3390/rs10050791","usgsCitation":"Pastick, N.J., Wylie, B.K., and Wu, Z., 2018, Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems: Remote Sensing, v. 10, no. 5, 15 p., https://doi.org/10.3390/rs10050791.","productDescription":"15 p.","ipdsId":"IP-097826","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468743,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs10050791","text":"Publisher Index Page"},{"id":359420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"10","issue":"5","noUsgsAuthors":false,"publicationDate":"2018-05-19","publicationStatus":"PW","scienceBaseUri":"5bed4274e4b0b3fc5cf91c92","contributors":{"authors":[{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":751236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":751237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":751238,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197124,"text":"70197124 - 2018 - Do downscaled general circulation models reliably simulate historical climatic conditions?","interactions":[],"lastModifiedDate":"2018-05-18T09:43:34","indexId":"70197124","displayToPublicDate":"2018-05-18T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Do downscaled general circulation models reliably simulate historical climatic conditions?","docAbstract":"The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/EI-D-17-0018.1","usgsCitation":"Bock, A.R., Hay, L.E., McCabe, G., Markstrom, S.L., and Atkinson, R.D., 2018, Do downscaled general circulation models reliably simulate historical climatic conditions?: Earth Interactions, v. 22, p. 1-22, https://doi.org/10.1175/EI-D-17-0018.1.","productDescription":"Paper 10; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-090110","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":468746,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/ei-d-17-0018.1","text":"Publisher Index Page"},{"id":354299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-17","publicationStatus":"PW","scienceBaseUri":"5afee6b2e4b0da30c1bfbd46","contributors":{"authors":[{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":735759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":735770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":1453,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory J.","email":"gmccabe@usgs.gov","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":false,"id":735771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":140378,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":735772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atkinson, R. Dwight","contributorId":195660,"corporation":false,"usgs":false,"family":"Atkinson","given":"R.","email":"","middleInitial":"Dwight","affiliations":[],"preferred":false,"id":735773,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}