We examined the relationship between local- (wetland) and landscape-level factors and breeding bird abundances on 1,190 depressional wetlands in the Prairie Pothole Region of North and South Dakota during the breeding seasons in 1995–97. The surveyed wetlands were selected from five wetland classes (alkali, permanent, semipermanent, seasonal, or temporary), two wetland types (natural or restored), and two landowner groups (private or Federal). We recorded 133 species of birds in the surveyed wetlands during the 3 years. We analyzed the nine most common (or focal) species (that is, species that were present in 25 percent or more of the 1,190 wetlands): the Red-winged Blackbird (Agelaius phoeniceus), Blue-winged Teal (Anas discors), Mallard (Anas platyrhynchos), American Coot (Fulica americana), Gadwall (Anas strepera), Common Yellowthroat (Geothlypis trichas), Yellow-headed Blackbird (Xanthocephalus xanthocephalus), Northern Shoveler (Anas clypeata), and Savannah Sparrow (Passerculus sandwichensis). Our results emphasize the ecological value of all wetland classes, natural and restored wetlands, and publicly and privately owned wetlands in this region, including wetlands that are generally smaller and shallower (that is, temporary and seasonal wetlands) and thus most vulnerable to drainage. Blue-winged Teal, Northern Shoveler, Gadwall, Common Yellowthroat, and Red-winged Blackbird had higher abundances on Federal than on private wetlands. Abundances differed among wetland classes for seven of the nine focal species: Blue-winged Teal, Northern Shoveler, Mallard, American Coot, Common Yellowthroat, Yellow-headed Blackbird, Red-winged Blackbird. American Coot had higher abundances on restored wetlands than on natural wetlands overall, and Gadwall and Common Yellowthroat had higher abundances on private restored wetlands than on private natural wetlands. The Common Yellowthroat was the only species that had higher abundances on restored private wetlands than on restored Federal wetlands. After adjusting for wetland size and the date and location of the surveys, our results demonstrated that incorporating wetland- and landscape-level factors in models can improve our ability to predict abundances of wetland birds in this region. The top model for eight of the nine focal species included wetland- and landscape-level factors, whereas the best model for Blue-winged Teal included only wetland-level attributes. Although local factors (for example, percent open water or emergent vegetation) in individual wetlands are important factors for some wetland breeding birds, it is important that natural resource managers consider landscape-level factors beyond the local factors in their conservation plans for wetland birds.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171096","usgsCitation":"Igl, L.D., Shaffer, J.A., Johnson, D.H., and Buhl, D.A., 2017, The influence of local- and landscape-level factors on wetland breeding birds in the Prairie Pothole Region of North and South Dakota: U.S. Geological Survey Open-File Report 2017–1096, 65 p., https://doi.org/10.3133/ofr20171096.","productDescription":"Report: vii, 65 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086062","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":344903,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX77XM","text":"USGS Data Release","linkHelpText":"The influence of local- and landscape-level factors on wetland breeding birds in the Prairie Pothole Region of North and South Dakota 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U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Historical data for six selected U.S. Geological Survey streamgages along Soldier Creek in northeast Kansas were used in an assessment of streamflow characteristics and trends. This information is required by the Prairie Band Potawatomi Nation for the effective management of tribal water resources, including drought contingency planning. Streamflow data for the period of record at each streamgage were used to assess annual mean streamflow, annual mean base flow, mean monthly flow, annual peak flow, and annual minimum flow.
Annual mean streamflows along Soldier Creek were characterized by substantial year-to-year variability with no pronounced long-term trends. On average, annual mean base flow accounted for about 20 percent of annual mean streamflow. Mean monthly flows followed a general seasonal pattern that included peak values in spring and low values in winter. Annual peak flows, which were characterized by considerable year-to-year variability, were most likely to occur in May and June and least likely to occur during November through February. With the exception of a weak yet statistically significant increasing trend at the Soldier Creek near Topeka, Kansas, streamgage, there were no pronounced long-term trends in annual peak flows. Annual 1-day, 30-day, and 90-day mean minimum flows were characterized by considerable year-to-year variability with no pronounced long-term trend. During an extreme drought, as was the case in the mid-1950s, there may be zero flow in Soldier Creek continuously for a period of one to several months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175061","collaboration":"Prepared in cooperation with the Prairie Band Potawatomi Nation","usgsCitation":"Juracek, K.E., 2017, Streamflow characteristics and trends along Soldier Creek, northeast Kansas: U.S. Geological Survey Scientific Investigations Report 2017–5061, 30 p., https://doi.org/10.3133/sir20175061.","productDescription":"v, 30 p.","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-084908","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":344845,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5061/coverthb.jpg"},{"id":344846,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5061/sir20175061.pdf","text":"Report","size":"5.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5061"}],"country":"United States","state":"Kansas","otherGeospatial":"Soldier Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.2896728515625,\n 38.94018471320357\n ],\n [\n -95.2734375,\n 38.94018471320357\n ],\n [\n -95.2734375,\n 39.8928799002948\n ],\n [\n -96.2896728515625,\n 39.8928799002948\n ],\n [\n -96.2896728515625,\n 38.94018471320357\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
An accurate inventory of irrigated crop acreage is not available at the level of resolution needed to better estimate agricultural water use or to project future water demands in many Florida counties. A detailed digital map and summary of irrigated acreage was developed for Polk County, Florida, during the 2016 growing season. This cooperative project between the U.S. Geological Survey and the Office of Agricultural Water Policy of the Florida Department of Agriculture and Consumer Services is part of an effort to improve estimates of water use and projections of future demands across all counties in the State. The irrigated areas were delineated by using land-use data provided by the Florida Department of Agriculture and Consumer Services, along with information obtained from the South and Southwest Florida Water Management Districts consumptive water-use permits. Delineations were field verified between April and December 2016. Attribute data such as crop type, primary water source, and type of irrigation system were assigned to the irrigated areas.
The results of this inventory and field verification indicate that during the 2016 growing seasons (spring, summer, fall, and winter), an estimated 88,652 acres were irrigated within Polk County. Of the total field-verified crops, 83,995 acres were in citrus; 2,893 acres were in other non-citrus fruit crops (blueberries, grapes, peaches, and strawberries); 621 acres were in row crops (primarily beans and watermelons); 1,117 acres were in nursery (container and tree farms) and sod production; and 26 acres were in field crops including hay and pasture. Of the total inventoried irrigated acreage within Polk County, 98 percent (86,566 acres) was in the Southwest Florida Water Management District, and the remaining 2 percent (2,086 acres) was in the South Florida Water Management District.
About 85,788 acres (96.8 percent of the acreage inventoried) were irrigated by a microirrigation system, including drip, bubblers, and spray emitters. The remaining 3.2 percent of the irrigated acreage was irrigated by a sprinkler system (2,360 acres) or subsurface flood systems (504 acres). Groundwater was the primary source of water used on irrigated acreage (88 percent, or 78,050 acres); the remaining 10,602 acres (12 percent) used groundwater combined with surface water as the irrigation source.
The irrigated acreage estimated by the U.S. Geological Survey (USGS) for this 2016 inventory (88,652 acres) is about 11 percent higher than the 79,869 acres estimated by the U.S. Department of Agriculture (USDA) for 2012. Citrus and pasture in Polk County show the biggest difference in irrigated acreage between the USGS and USDA totals. Irrigated citrus acreage inventoried in 2016 by the USGS totaled 83,996 acres, whereas the USDA reported 78,305 acres of citrus in 2012. The USGS identified 6 acres of irrigated pasture and 20 acres of hay, whereas the USDA reported 6,631 acres of irrigated pasture and 1,349 acres of hay for 2012. In general, differences between the 2016 USGS field-verified acreage totals and acreage published by the USDA for 2012 could be due to (1) irrigated acreage for some specific crops increased or decreased substantially during the 4-year interval between 2012 and 2016 because of production or economic changes, (2) the assumption that if an irrigation system was present, it was used in 2016, when in fact some landowners may not have used their irrigation systems during this growing period even if they had a crop in the field, or (3) the amount of irrigated acreage published by the USDA for selected crops may be underestimated as a result of how information is obtained and formulated by the agency during census compilations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171082","collaboration":"Prepared in cooperation with the Florida Department of Agriculture and Consumer Services Office of Agricultural Water Policy","usgsCitation":"Marella, R.L., Berry, D.R., and Dixon, J.F., 2017, Agricultural irrigated land-use inventory for Polk County, Florida, 2016: U.S. Geological Survey Open-File Report 2017–1082, 14 p., https://doi.org/10.3133/ofr20171082.","productDescription":"14 p.","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080915","costCenters":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"links":[{"id":344885,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1082/coverthb.jpg"},{"id":344888,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76W98BN","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"GIS data and tables associated with irrigated agricultural land use survey in Polk County, Florida, 2016"},{"id":344886,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1082/ofr20171082.pdf","text":"Report","size":"823 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1082"},{"id":344887,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1082/ofr20171082_Appendix01.pdf","text":"Appendix 1","size":"1.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1082 Appendix 1"}],"country":"United 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Most of the land-surface subsidence in the Houston-Galveston region, Texas, has occurred as a direct result of groundwater withdrawals for municipal supply, commercial and industrial use, and irrigation that depressured and dewatered the Chicot and Evangeline aquifers, thereby causing compaction of the aquifer sediments, mostly in the fine-grained silt and clay layers. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, is one in an annual series of reports depicting water-level altitudes and water-level changes in the Chicot, Evangeline, and Jasper aquifers and measured cumulative compaction of subsurface sediments in the Chicot and Evangeline aquifers in the Houston-Galveston region. This report contains regional-scale maps depicting approximate 2017 water-level altitudes (represented by measurements made during December 2016 through March 2017) and long-term water-level changes for the Chicot, Evangeline, and Jasper aquifers; a map depicting locations of borehole-extensometer (hereinafter referred to as “extensometer”) sites; and graphs depicting measured long-term cumulative compaction of subsurface sediments at the extensometers during 1973–2016.
In 2017, water-level-altitude contours for the Chicot aquifer ranged from 200 feet (ft) below the North American Vertical Datum of 1988 (hereinafter referred to as “datum”) in two localized areas in southwestern and northwestern Harris County to 200 ft above datum in west-central Montgomery County. The largest water-level-altitude decline (120 ft) depicted by the 1977–2017 water-level-change contours for the Chicot aquifer was in northwestern Harris County. A broad area where water-level altitudes declined in the Chicot aquifer extends from northwestern, north-central, and southwestern Harris County across parts of north-central, eastern, and south-central Fort Bend County into southeastern Waller County. Adjacent to the areas where water levels declined was a broad area where water levels rose in central, eastern, and southeastern Harris County, most of Galveston County, eastern and northernmost Brazoria County, and northeastern Fort Bend County. The largest rise (200 ft) in water-level altitudes in the Chicot aquifer from 1977 to 2017 was in southeastern Harris County.
The water-level-altitude contours for the Evangeline aquifer in 2017 indicated two areas where the water-level altitudes were 250 ft below datum—one area extending from south-central Montgomery County into north-central Harris County and another area in western Harris County. Water-level altitudes in the Evangeline aquifer ranged from 50 to 200 ft below datum throughout most of Harris County in 2017. In Montgomery County, water-level altitudes in the Evangeline aquifer in 2017 ranged from the aforementioned area where they were 250 ft below datum to an area where they were 200 ft above datum in the northwestern part of the county. The 1977–2017 water-level-change contours for the Evangeline aquifer depict a broad area where water-level altitudes declined in north-central Harris and south-central Montgomery Counties, extending through north-central, northwestern, and southwestern Harris County into western Liberty, southeastern and northeastern Waller, and northeastern and east-central Fort Bend Counties. The largest water-level-altitude decline (280 ft) was in north-central Harris and south-central Montgomery Counties. Water-level altitudes rose in a broad area from central, east-central, and southern Harris County extending into the northernmost part of Brazoria County, the northernmost part of Galveston County, and the southwestern area of Liberty County. The largest rise in water-level altitudes in the Evangeline aquifer from 1977 to 2017 (240 ft) was in southeastern Harris County.
Water-level-altitude contours for the Jasper aquifer in 2017 ranged from 200 ft below datum in three isolated areas of south-central Montgomery County (the westernmost of these areas extended slightly into north-central Harris County) to 250 ft above datum in extreme northwestern Montgomery County, northeastern Grimes County, and southwestern Walker County. The 2000–17 water-level-change contours for the Jasper aquifer depict water-level declines in a broad area throughout most of Montgomery County and in parts of Waller, Grimes, and Harris Counties, with the largest decline (220 ft) in an isolated area in south-central Montgomery County.
Compaction of subsurface sediments (mostly in the fine-grained silt and clay layers) in the Chicot and Evangeline aquifers was recorded continuously by using 13 extensometers at 11 sites that were either activated or installed between 1973 and 1980. During the period of record beginning in 1973 (or later depending on activation or installation date) and ending in late November or December 2016, measured cumulative compaction at the 13 extensometers ranged from 0.096 ft at the Texas City-Moses Lake extensometer to 3.700 ft at the Addicks extensometer. From January through late November or December 2016, the Addicks, Lake Houston, Southwest, and Northeast extensometers recorded net decreases in land-surface elevation, but the Baytown C–1 (shallow), Baytown C–2 (deep), Clear Lake (shallow), Clear Lake (deep), East End, Johnson Space Center, Pasadena, Seabrook, and Texas City-Moses Lake extensometers recorded net increases in land-surface elevation.
The rate of compaction varies from site to site because of differences in rates of groundwater withdrawal in the areas adjacent to each extensometer site; differences among sites in the ratios of sand, silt, and clay and their corresponding compressibilities; and previously established preconsolidation heads. It is not appropriate, therefore, to extrapolate or infer a rate of compaction for an adjacent area on the basis of the rate of compaction recorded by proximal extensometers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175080","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District","usgsCitation":"Kasmarek, M.C., and Ramage, J.K., 2017, Water-level altitudes 2017 and water-level changes in the Chicot, Evangeline, and Jasper aquifers and compaction 1973–2016 in the Chicot and Evangeline aquifers, Houston-Galveston region, Texas: U.S. Geological Survey Scientific Investigations Report 2017–5080, 32 p., https://doi.org/10.3133/sir20175080. ","productDescription":"Report: vii, 32 p.; Data Releases","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-083843","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":344822,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77S7M18","text":"USGS - Data Release","description":"USGS Data Release","linkHelpText":"Water-level measurement data, water-level altitude and long-term water-level altitude change contours (2017) in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston region, Texas"},{"id":344820,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5080/coverthb.jpg"},{"id":344823,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PC30KC","text":"USGS - Data Release","description":"USGS Data Release","linkHelpText":"Cumulative compaction of subsurface sediments (2016) in 13 extensometers completed in the Chicot and Evangeline aquifers in the Houston-Galveston region, Texas"},{"id":344821,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5080/sir20175080.pdf","text":"Report","size":"16.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5080"}],"country":"United States","state":"Texas","city":"Galveston, Houston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.339111328125,\n 28.372068829631633\n ],\n [\n -96.21826171874999,\n 28.44937385955666\n ],\n [\n -95.965576171875,\n 28.58452171937042\n ],\n [\n -95.77880859375,\n 28.65203063036226\n ],\n [\n -95.592041015625,\n 28.719496107557465\n ],\n [\n -95.4052734375,\n 28.825425374477224\n ],\n [\n -95.185546875,\n 28.950475674848008\n ],\n [\n -95.00976562499999,\n 29.094577077511826\n ],\n [\n -94.833984375,\n 29.23847708592805\n ],\n [\n -94.68017578125,\n 29.401319510041485\n ],\n [\n -94.482421875,\n 29.477861195816843\n ],\n [\n -94.19677734375,\n 29.611670115197377\n ],\n [\n -93.966064453125,\n 29.6594160549124\n ],\n [\n -93.80126953124999,\n 29.707139348134145\n ],\n [\n -93.900146484375,\n 29.850173125689896\n ],\n [\n -93.89465332031249,\n 29.888280933159265\n ],\n [\n -93.88916015625,\n 29.92637417863576\n ],\n [\n -93.768310546875,\n 29.964452850852005\n ],\n [\n -93.75732421875,\n 30.021543509740027\n ],\n [\n -93.74633789062499,\n 30.088107753367257\n ],\n [\n -93.724365234375,\n 30.287531589298727\n ],\n [\n -93.74633789062499,\n 30.391830328088137\n ],\n [\n -93.75732421875,\n 30.56226095049944\n ],\n [\n -93.66943359374999,\n 30.675715404167743\n ],\n [\n -93.592529296875,\n 30.826780904779774\n ],\n [\n -93.53759765625,\n 31.015278981711266\n ],\n [\n -93.53759765625,\n 31.147006308556566\n ],\n [\n -93.62548828125,\n 31.27855085894653\n ],\n [\n -93.7353515625,\n 31.512995857454676\n ],\n [\n -93.779296875,\n 31.606609719226917\n ],\n [\n -93.812255859375,\n 31.774877618507386\n ],\n [\n -93.900146484375,\n 31.87755764334002\n ],\n [\n -93.98803710937499,\n 31.952162238024975\n ],\n [\n -94.053955078125,\n 31.99875937194732\n ],\n [\n -94.053955078125,\n 32.32427558887655\n ],\n [\n -95.416259765625,\n 32.16631295696736\n ],\n [\n -96.580810546875,\n 32.03602003973755\n ],\n [\n -96.96533203125,\n 31.970803930433096\n ],\n [\n -96.339111328125,\n 28.372068829631633\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
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The Cascade Range in central Oregon has been shaped by tectonics, volcanism, and hydrology, as well as geomorphic forces that include glaciations. As a result of the rich interplay between these forces, mafic volcanism here can have surprising manifestations, which include relatively large tephra footprints and extensive lava flows, as well as water shortages, transportation and agricultural disruption, and forest fires. Although the focus of this multidisciplinary field trip will be on mafic volcanism, we will also look at the hydrology, geomorphology, and ecology of the area, and we will examine how these elements both influence and are influenced by mafic volcanism. We will see mafic volcanic rocks at the Sand Mountain volcanic field and in the Santiam Pass area, at McKenzie Pass, and in the southern Bend region. In addition, this field trip will occur during a total solar eclipse, the first one visible in the United States in more than 25 years (and the first seen in the conterminous United States in more than 37 years).
The Cascade Range is the result of subduction of the Juan de Fuca plate underneath the North American plate. This north-south-trending volcanic mountain range is immediately downwind of the Pacific Ocean, a huge source of moisture. As moisture is blown eastward from the Pacific on prevailing winds, it encounters the Cascade Range in Oregon, and the resulting orographic lift and corresponding rain shadow is one of the strongest precipitation gradients in the conterminous United States. We will see how the products of the volcanoes in the central Oregon Cascades have had a profound influence on groundwater flow and, thus, on the distribution of Pacific moisture. We will also see the influence that mafic volcanism has had on landscape evolution, vegetation development, and general hydrology.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Field-trip guides to selected volcanoes and volcanic landscapes of the western United States (Scientific Investigations Report 2017-5022)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175022H","usgsCitation":"Deligne, N.I., Mckay, D., Conrey, R.M., Grant, G.E., Johnson, E.R., O’Connor, J., and Sweeney, K., 2017, Field-trip guide to mafic volcanism of the Cascade Range in central Oregon—A volcanic, tectonic, hydrologic, and geomorphic journey: U.S. Geological Survey Scientific Investigations Report 2017–5022–H, 94 p., https://doi.org/10.3133/sir20175022H.","productDescription":"xii, 94 p.","numberOfPages":"110","onlineOnly":"Y","ipdsId":"IP-076209","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":344876,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5022/h/sir20175022h.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5022-H"},{"id":344875,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5022/h/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Cascade Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.1787109375,\n 43.61619382369185\n ],\n [\n -121.0089111328125,\n 43.61619382369185\n ],\n [\n -121.0089111328125,\n 45.57944511437787\n ],\n [\n -123.1787109375,\n 45.57944511437787\n ],\n [\n -123.1787109375,\n 43.61619382369185\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center - Menlo Park
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We report the development and validation of two quantitative PCR (qPCR) assays to detect Nanophyetus salmincola DNA in water samples and in fish and snail tissues. Analytical and diagnostic validation demonstrated good sensitivity, specificity, and repeatability of both qPCR assays. The N. salmincola DNA copy number in kidney tissue was significantly correlated with metacercaria counts based on microscopy. Extraction methods were optimized for the sensitive qPCR detection of N. salmincola DNA in settled water samples. Artificially spiked samples suggested that the 1-cercaria/L threshold corresponded to an estimated log10 copies per liter ≥ 6.0. Significant correlation of DNA copy number per liter and microscopic counts indicated that the estimated qPCR copy number was a good predictor of the number of waterborne cercariae. However, the detection of real-world samples below the estimated 1-cercaria/L threshold suggests that the assays may also detect other N. salmincola life stages, nonintact cercariae, or free DNA that settles with the debris. In summary, the qPCR assays reported here are suitable for identifying and quantifying all life stages of N. salmincola that occur in fish tissues, snail tissues, and water.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/08997659.2017.1365780","usgsCitation":"Purcell, M., Powers, R., Besijn, B., and Hershberger, P., 2017, Detection of Nanophyetus salmincola in water, snails, and fish tissues by quantitative polymerase chain reaction: Journal of Aquatic Animal Health, v. 29, no. 4, p. 189-198, https://doi.org/10.1080/08997659.2017.1365780.","productDescription":"10 p.","startPage":"189","endPage":"198","ipdsId":"IP-086638","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":438246,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FN14QN","text":"USGS data release","linkHelpText":"Purcell_Nanophyetus_Data_Release_2017"},{"id":346361,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-14","publicationStatus":"PW","scienceBaseUri":"59d4a1a7e4b05fe04cc4e0f3","contributors":{"authors":[{"text":"Purcell, Maureen K. mpurcell@usgs.gov","contributorId":3061,"corporation":false,"usgs":true,"family":"Purcell","given":"Maureen K.","email":"mpurcell@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":711860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Powers, Rachel L. 0000-0001-6901-4361","orcid":"https://orcid.org/0000-0001-6901-4361","contributorId":190182,"corporation":false,"usgs":true,"family":"Powers","given":"Rachel L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":711861,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Besijn, Bonnie 0000-0001-9939-9768 bbesijn@usgs.gov","orcid":"https://orcid.org/0000-0001-9939-9768","contributorId":196890,"corporation":false,"usgs":true,"family":"Besijn","given":"Bonnie","email":"bbesijn@usgs.gov","affiliations":[],"preferred":true,"id":711862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hershberger, Paul K. phershberger@usgs.gov","contributorId":1945,"corporation":false,"usgs":true,"family":"Hershberger","given":"Paul K.","email":"phershberger@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":711863,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190171,"text":"70190171 - 2017 - Plasticity in skeletal characteristics of nursery-raised staghorn coral, Acropora cervicornis","interactions":[],"lastModifiedDate":"2017-08-15T17:38:59","indexId":"70190171","displayToPublicDate":"2017-08-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1338,"text":"Coral Reefs","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Plasticity in skeletal characteristics of nursery-raised staghorn coral, Acropora cervicornis","title":"Plasticity in skeletal characteristics of nursery-raised staghorn coral, Acropora cervicornis","docAbstract":"Staghorn coral, Acropora cervicornis, is a threatened species and the primary focus of western Atlantic reef restoration efforts to date. We compared linear extension, calcification rate, and skeletal density of nursery-raised A. cervicornis branches reared for 6 months either on blocks attached to substratum or hanging from PVC trees in the water column. We demonstrate that branches grown on the substratum had significantly higher skeletal density, measured using computerized tomography, and lower linear extension rates compared to water-column fragments. Calcification rates determined with buoyant weighing were not statistically different between the two grow-out methods, but did vary among coral genotypes. Whereas skeletal density and extension rates were plastic traits that depended on grow-out method, calcification rate was conserved. Our results show that the two rearing methods generate the same amount of calcium carbonate skeleton but produce colonies with different skeletal characteristics and suggest that there is genetically based variability in coral calcification performance.
In 2005, the U.S. Geological Survey began a cooperative study with New York City Department of Environmental Protection to characterize the local groundwater-flow system and identify potential sources of seeps on the southern embankment at the Hillview Reservoir in southern Westchester County, New York. Monthly site inspections at the reservoir indicated an approximately 90-square-foot depression in the land surface directly upslope from a seep that has episodically flowed since 2007. In July 2008, the U.S. Geological Survey surveyed the topography of land surface in this depression area by collecting high-accuracy (resolution less than 1 inch) measurements. A point of origin was established for the topographic survey by using differentially corrected positional data collected by a global navigation satellite system. Eleven points were surveyed along the edge of the depression area and at arbitrary locations within the depression area by using robotic land-surveying techniques. The points were surveyed again in March 2012 to evaluate temporal changes in land-surface altitude. Survey measurements of the depression area indicated that the land-surface altitude at 8 of the 11 points decreased beyond the accepted measurement uncertainty during the 44 months from July 2008 to March 2012. Two additional control points were established at stable locations along Hillview Avenue, which runs parallel to the embankment. These points were measured during the July 2008 survey and measured again during the March 2012 survey to evaluate the relative accuracy of the altitude measurements. The relative horizontal and vertical (altitude) accuracies of the 11 topographic measurements collected in March 2012 were ±0.098 and ±0.060 feet (ft), respectively. Changes in topography at 8 of the 11 points ranged from 0.09 to 0.63 ft and topography remained constant, or within the measurement uncertainty, for 3 of the 11 points.
Two cross sections were constructed through the depression area by using land-surface altitude data that were interpolated from positional data collected during the two topographic surveys. Cross section A–A′ was approximately 8.5 ft long and consisted of three surveyed points that trended north to south across the depression. Land-surface altitude change decreased along the entire north-south trending cross section during the 44 months, and ranged from 0.2 to more than 0.6 ft. In general, greater land-surface altitude change was measured north of the midpoint as compared to south of the midpoint of the cross section. Cross section B–B′ was 18 ft long and consisted of six surveyed points that trended east to west across the depression. Land-surface altitude change generally decreased or remained constant along the east-west trending cross section during the 44 months and ranged from 0.0 to 0.3 ft. Volume change of the depression area was calculated by using a three-dimensional geographic information system utility that subtracts interpolated surfaces. The results indicated a net volume loss of approximately 38 ±5 cubic feet of material from the depression area during the 44 months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171028","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Noll, M.L., and Chu, Anthony, 2017, Detecting temporal change in land-surface altitude using robotic land-surveying techniques and geographic information system applications at an earthen dam site in southern Westchester County, New York: U.S. Geological Survey Open-File Report 2017–1028, 15 p., https://doi.org/10.3133/ofr20171028.","productDescription":"vi, 15 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077425","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":344641,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1028/ofr20171028.pdf","text":"Report","size":"1.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1028"},{"id":344640,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1028/coverthb.jpg"}],"country":"United States","state":"New York","county":"Westchester County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.88648986816406,\n 40.894180824484465\n ],\n [\n -73.85250091552734,\n 40.894180824484465\n ],\n [\n -73.85250091552734,\n 40.92726192578736\n ],\n [\n -73.88648986816406,\n 40.92726192578736\n ],\n [\n -73.88648986816406,\n 40.894180824484465\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
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2045 Route 112, Building 4
Coram, NY 11727
Potential contamination bias was estimated for 8 nutrient analytes and 40 pesticides in stream water collected by the U.S. Geological Survey at 147 stream sites from across the United States, and representing a variety of hydrologic conditions and site types, for water years 2002–12. This study updates previous U.S. Geological Survey evaluations of potential contamination bias for nutrients and pesticides. Contamination is potentially introduced to water samples by exposure to airborne gases and particulates, from inadequate cleaning of sampling or analytic equipment, and from inadvertent sources during sample collection, field processing, shipment, and laboratory analysis. Potential contamination bias, based on frequency and magnitude of detections in field blanks, is used to determine whether or under what conditions environmental data might need to be qualified for the interpretation of results in the context of comparisons with background levels, drinking-water standards, aquatic-life criteria or benchmarks, or human-health benchmarks. Environmental samples for which contamination bias as determined in this report applies are those from historical U.S. Geological Survey water-quality networks or programs that were collected during the same time frame and according to the same protocols and that were analyzed in the same laboratory as field blanks described in this report.
Results from field blanks for ammonia, nitrite, nitrite plus nitrate, orthophosphate, and total phosphorus were partitioned by analytical method; results from the most commonly used analytical method for total phosphorus were further partitioned by date. Depending on the analytical method, 3.8, 9.2, or 26.9 percent of environmental samples, the last of these percentages pertaining to all results from 2007 through 2012, were potentially affected by ammonia contamination. Nitrite contamination potentially affected up to 2.6 percent of environmental samples collected between 2002 and 2006 and affected about 3.3 percent of samples collected between 2007 and 2012. The percentages of environmental samples collected between 2002 and 2011 that were potentially affected by nitrite plus nitrate contamination were 7.3 for samples analyzed with the low-level method and 0.4 for samples analyzed with the standard-level method. These percentages increased to 14.8 and 2.2 for samples collected in 2012 and analyzed using replacement low- and standard-level methods, respectively. The maximum potentially affected concentrations for nitrite and for nitrite plus nitrate were much less than their respective maximum contamination levels for drinking-water standards. Although contamination from particulate nitrogen can potentially affect up to 21.2 percent and that from total Kjeldahl nitrogen can affect up to 16.5 percent of environmental samples, there are no critical or background levels for these substances.
For total nitrogen, orthophosphate, and total phosphorus, contamination in a small percentage of environmental samples might be consequential for comparisons relative to impairment risks or background levels. At the low ends of the respective ranges of impairment risk for these nutrients, contamination in up to 5 percent of stream samples could account for at least 23 percent of measured concentrations of total nitrogen, for at least 40 or 90 percent of concentrations of orthophosphate, depending on the analytical method, and for 31 to 76 percent of concentrations of total phosphorus, depending on the time period.
Twenty-six pesticides had no detections in field blanks. Atrazine with 12 and metolachlor with 11 had the highest number of detections, mostly occurring in spring or early summer. At a 99-percent level of confidence, contamination was estimated to be no greater than the detection limit in at least 98 percent of all samples for 38 of 40 pesticides. For metolachlor and atrazine, potential contamination was no greater than 0.0053 and 0.0093 micrograms per liter in 98 percent of samples. For 11 of 14 pesticides with at least one detection, the maximum potentially affected concentration of the environmental sample was less than their respective human-health or aquatic-life benchmarks. Small percentages of environmental samples had concentrations high enough that atrazine contamination potentially could account for the entire aquatic-life benchmark for acute effects on nonvascular plants, that dieldrin contamination could account for up to 100 percent of the cancer health-based screening level, or that chlorpyrifos contamination could account for 13 or 12 percent of the concentrations in the aquatic-life benchmarks for chronic effects on invertebrates or the criterion continuous concentration for chronic effects on aquatic life.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165129","usgsCitation":"Medalie, Laura, and Martin, J.D., 2017, Nutrient and pesticide contamination bias estimated from field blanks collected at surface-water sites in U.S. Geological Survey water-quality networks, 2002–12: U.S. Geological Survey Scientific Investigations Report 2016–5129, 40 p., https://doi.org/10.3133/sir20165129.","productDescription":"Report: vi, 40 p.; Appendixes 1-2","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070500","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":344595,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5129/attachments/sir20165129_appendix2_metadata.txt","text":"Appendix 2","size":"1.43 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Metadata, pesticide field-blank data from surface-water sites"},{"id":344588,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5129/coverthb.jpg"},{"id":344589,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5129/sir20165129.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5129"},{"id":344590,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5129/attachments/sir20165129_appendix1.xlsx","text":"Appendix 1","size":"218 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Dataset and metadata, nutrient field-blank data from surface-water sites"},{"id":344591,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5129/attachments/sir20165129_appendix1_dataset.csv","text":"Appendix 1","size":"286 KB csv","linkHelpText":"- Dataset, nutrient field-blank data from surface-water 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U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
A study was done by the U.S. Geological Survey in cooperation with the Kansas Department of Transportation and the Federal Emergency Management Agency to develop regression models to estimate peak streamflows of annual exceedance probabilities of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent at ungaged locations in Kansas. Peak streamflow frequency statistics from selected streamgages were related to contributing drainage area and average precipitation using generalized least-squares regression analysis. The peak streamflow statistics were derived from 151 streamgages with at least 25 years of streamflow data through 2015. The developed equations can be used to predict peak streamflow magnitude and frequency within two hydrologic regions that were defined based on the effects of irrigation. The equations developed in this report are applicable to streams in Kansas that are not substantially affected by regulation, surface-water diversions, or urbanization. The equations are intended for use for streams with contributing drainage areas ranging from 0.17 to 14,901 square miles in the nonirrigation effects region and, 1.02 to 3,555 square miles in the irrigation-affected region, corresponding to the range of drainage areas of the streamgages used in the development of the regional equations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175063","collaboration":"Prepared in cooperation with the Kansas Department of Transportation and Federal Emergency Management Agency","usgsCitation":"Painter, C.C., Heimann, D.C., and Lanning-Rush, J.L., 2017, Methods for estimating annual exceedance-probability streamflows for streams in Kansas based on data through water year 2015 (ver. 1.1, September 2017): U.S. Geological Survey Scientific Investigations Report 2017–5063, 20 p., https://doi.org/10.3133/sir20175063.","productDescription":"Report: vi, 20 p.; 4 Tables","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087048","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":345864,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5063/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2017–5063 Version History"},{"id":344871,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table5.xlsx","text":"Table 5","size":"47 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 5"},{"id":344870,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table4.xlsx","text":"Table 4","size":"23 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 4"},{"id":344868,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table2.xlsx","text":"Table 2","size":"42 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 2"},{"id":344869,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table3.xlsx","text":"Table 3","size":"60 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 3"},{"id":344698,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5063/coverthb2.jpg"},{"id":344699,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063.pdf","text":"Report","size":"1.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5063"}],"country":"United States","state":"Colorado, Kansas, Missouri, Nebraska, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.48046875,\n 36.38591277287651\n ],\n [\n -93.93310546875,\n 36.38591277287651\n ],\n [\n -93.93310546875,\n 40.713955826286046\n ],\n [\n -102.48046875,\n 40.713955826286046\n ],\n [\n -102.48046875,\n 36.38591277287651\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted August 14, 2017; Version 1.1: September 18, 2017","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
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The Albuquerque Bernalillo County Water Utility Authority (ABCWUA) drinking water supply was almost exclusively sourced from groundwater from within the Albuquerque Basin before 2008. In 2008, the San Juan-Chama Drinking Water Project (SJCDWP) provided surface-water resources to augment the groundwater supply, allowing for a reduction in groundwater pumping in the Albuquerque Basin. In 2013, the U.S. Geological Survey, in cooperation with the ABCWUA, began a study to measure and compare aquifer-system and land-surface elevation change before and after the SJCDWP in 2008. Three methods of data collection with different temporal and spatial resolutions were used for this study: (1) aquifer-system compaction data collected continuously at a single extensometer from 1994 to 2013; (2) land-surface elevation change from Global Positioning System (GPS) surveys of a network of monuments collected in 1994–95, 2005, and 2014; and (3) spatially distributed Interferometric Synthetic Aperture Radar (InSAR) satellite data from 1993 to 2010. Collection of extensometer data allows for direct and continuous measurement of aquifer-system compaction at the extensometer location. The GPS surveys of a network of monuments allow for periodic measurements of land-surface elevation change at monument locations. Interferograms are limited in time by lifespan of the satellite, orbital pattern, and data quality but allow for measurement of gridded land-surface elevation change over the study area. Each of these methods was employed to provide a better understanding of aquifer-system compaction and land-surface elevation change for the Albuquerque Basin.
Results do not show large magnitudes of subsidence in the Albuquerque Basin. High temporal-resolution but low spatial-resolution data measurements of aquifer-system compaction at the Albuquerque extensometer show elastic aquifer-system response to recovering groundwater levels. Results from the GPS survey of the network of monuments show inconsistent land-surface elevation changes over the Albuquerque Basin, likely because of the lack of significant change and the complexity of subsurface stratigraphy in addition to the spatial and temporal heterogeneity of groundwater withdrawals over the study period. Results from the InSAR analysis show areas of land-surface elevation increase after 2008, which could be attributed to elastic recovery of the aquifer system. The spatial extent to which elastic recovery of the aquifer system has resulted in recovery of land-surface elevation is limited to the in-situ measurements at the extensometer. Examination of spatially distributed InSAR data relative to limited spatial extent of the complex heterogeneity subsurface stratigraphy may explain some of the heterogeneity of land-surface elevation changes over this study period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175057","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Driscoll, J.M., and Brandt, J.T., 2017, Land subsidence and recovery in the Albuquerque Basin, New Mexico, 1993–2014: U.S. Geological Survey Scientific Investigations Report 2017–5057, 31 p., https://doi.org/10.3133/sir20175057.","productDescription":"Report: v, 31 p.; Figures: 10A, 10B, 10C","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071011","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":344697,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057_figure10C.pdf","text":"Figure 10C","size":"802 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057 Figure 10C","linkHelpText":"C. InSAR measured elevation change along geology profile CC-CC’"},{"id":344694,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057.pdf","text":"Report","size":"9.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057"},{"id":344695,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057_figure10A.pdf","text":"Figure 10A","size":"594 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057 Figure 10A","linkHelpText":"A. InSAR measured elevation change along geology profile AA-AA’"},{"id":344696,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057_figure10B.pdf","text":"Figure 10B","size":"425 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057 Figure 10B","linkHelpText":"B. InSAR measured elevation change along geology profile BB-BB’"},{"id":344693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5057/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Albuquerque Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107,\n 34.85\n ],\n [\n -106.375,\n 34.85\n ],\n [\n -106.375,\n 35.4\n ],\n [\n -107,\n 35.4\n ],\n [\n -107,\n 34.85\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
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6700 Edith Blvd NE
Albuquerque NM 87113
Bigheaded carps, which include silver and bighead carp, are threatening to invade the Great Lakes. These species vary seasonally in distribution and abundance due to environmental conditions such as precipitation and temperature. Monitoring this seasonal movement is important for management to control the population size and spread of the species. We examined if environmental DNA (eDNA) approaches could detect seasonal changes of these species. To do this, we developed a novel genetic marker that was able to both detect and differentiate bighead and silver carp DNA. We used the marker, combined with a novel occupancy model, to study the occurrence of bigheaded carps at 3 sites on the Wabash River over the course of a year. We studied the Wabash River because of concerns that carps may be able to use the system to invade the Great Lakes via a now closed (ca. 2017) connection at Eagle Marsh between the Wabash River's watershed and the Great Lakes' watershed. We found seasonal trends in the probability of detection and occupancy that varied across sites. These findings demonstrate that eDNA methods can detect seasonal changes in bigheaded carps densities and suggest that the amount of eDNA present changes seasonally. The site that was farthest upstream and had the lowest carp densities exhibited the strongest seasonal trends for both detection probabilities and sample occupancy probabilities. Furthermore, other observations suggest that carps seasonally leave this site, and we were able to detect this with our eDNA approach.
","language":"English","publisher":"International Association for Great Lakes Research","doi":"10.1016/j.jglr.2017.06.003","usgsCitation":"Erickson, R.A., Merkes, C.M., Jackson, C., Goforth, R., and Amberg, J., 2017, Seasonal trends in eDNA detection and occupancy of bigheaded carps: Journal of Great Lakes Research, v. 43, no. 4, p. 762-770, https://doi.org/10.1016/j.jglr.2017.06.003.","productDescription":"9 p.","startPage":"762","endPage":"770","ipdsId":"IP-074701","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":469610,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2017.06.003","text":"Publisher Index Page"},{"id":344854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59b76ec2e4b08b1644ddfac8","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Merkes, Christopher M. 0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707728,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Craig 0000-0003-4023-0276 cjackson@usgs.gov","orcid":"https://orcid.org/0000-0003-4023-0276","contributorId":192276,"corporation":false,"usgs":true,"family":"Jackson","given":"Craig","email":"cjackson@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707729,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goforth, Reuben","contributorId":192277,"corporation":false,"usgs":false,"family":"Goforth","given":"Reuben","affiliations":[],"preferred":false,"id":707730,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Amberg, Jon 0000-0002-8351-4861 jamberg@usgs.gov","orcid":"https://orcid.org/0000-0002-8351-4861","contributorId":149785,"corporation":false,"usgs":true,"family":"Amberg","given":"Jon","email":"jamberg@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707731,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190163,"text":"70190163 - 2017 - Abundant carbon substrates drive extremely high sulfate reduction rates and methane fluxes in Prairie Pothole Wetlands","interactions":[],"lastModifiedDate":"2017-08-15T12:10:56","indexId":"70190163","displayToPublicDate":"2017-08-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Abundant carbon substrates drive extremely high sulfate reduction rates and methane fluxes in Prairie Pothole Wetlands","docAbstract":"Inland waters are increasingly recognized as critical sites of methane emissions to the atmosphere, but the biogeochemical reactions driving such fluxes are less well understood. The Prairie Pothole Region (PPR) of North America is one of the largest wetland complexes in the world, containing millions of small, shallow wetlands. The sediment pore waters of PPR wetlands contain some of the highest concentrations of dissolved organic carbon (DOC) and sulfur species ever recorded in terrestrial aquatic environments. Using a suite of geochemical and microbiological analyses, we measured the impact of sedimentary carbon and sulfur transformations in these wetlands on methane fluxes to the atmosphere. This research represents the first study of coupled geochemistry and microbiology within the PPR and demonstrates how the conversion of abundant labile DOC pools into methane results in some of the highest fluxes of this greenhouse gas to the atmosphere ever reported. Abundant DOC and sulfate additionally supported some of the highest sulfate reduction rates ever measured in terrestrial aquatic environments, which we infer to account for a large fraction of carbon mineralization in this system. Methane accumulations in zones of active sulfate reduction may be due to either the transport of free methane gas from deeper locations or the co-occurrence of methanogenesis and sulfate reduction. If both respiratory processes are concurrent, any competitive inhibition of methanogenesis by sulfate-reducing bacteria may be lessened by the presence of large labile DOC pools that yield noncompetitive substrates such as methanol. Our results reveal some of the underlying mechanisms that make PPR wetlands biogeochemical hotspots, which ultimately leads to their critical, but poorly recognized role in regional greenhouse gas emissions.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13633","usgsCitation":"Martins, P., Hoyt, D.W., Bansal, S., Mills, C., Tfaily, M., Tangen, B., Finocchiaro, R., Johnston, M.D., McAdams, B.C., Solensky, M.J., Smith, G.J., Chin, Y., and Wilkins, M.J., 2017, Abundant carbon substrates drive extremely high sulfate reduction rates and methane fluxes in Prairie Pothole Wetlands: Global Change Biology, v. 23, no. 8, p. 3107-3120, https://doi.org/10.1111/gcb.13633.","productDescription":"14 p.","startPage":"3107","endPage":"3120","ipdsId":"IP-078860","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":438247,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TX3CJ7","text":"USGS data release","linkHelpText":"Dissolved greenhouse gas concentrations and fluxes from Wetlands P7 and P8 of the Cottonwood Lake Study area, Stutsman County, North Dakota, 2015"},{"id":344847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Cottonwood Lake Study Area, Prairie Pothole Wetlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.17770385742188,\n 47.05702528260841\n ],\n [\n -99.03419494628906,\n 47.05702528260841\n ],\n [\n -99.03419494628906,\n 47.14489748555398\n ],\n [\n -99.17770385742188,\n 47.14489748555398\n ],\n [\n -99.17770385742188,\n 47.05702528260841\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-23","publicationStatus":"PW","scienceBaseUri":"59940848e4b0fe2b9fe8af9e","contributors":{"authors":[{"text":"Martins, 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C.","contributorId":195649,"corporation":false,"usgs":false,"family":"McAdams","given":"Brandon","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":707763,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Solensky, Matthew J. 0000-0003-4376-7765 msolensky@usgs.gov","orcid":"https://orcid.org/0000-0003-4376-7765","contributorId":4784,"corporation":false,"usgs":true,"family":"Solensky","given":"Matthew","email":"msolensky@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":707762,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Smith, Garrett J.","contributorId":195646,"corporation":false,"usgs":false,"family":"Smith","given":"Garrett","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":707759,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Chin, Yu-Ping","contributorId":195648,"corporation":false,"usgs":false,"family":"Chin","given":"Yu-Ping","affiliations":[],"preferred":false,"id":707761,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Wilkins, Michael J.","contributorId":195647,"corporation":false,"usgs":false,"family":"Wilkins","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":707760,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70190109,"text":"70190109 - 2017 - Changes in projected spatial and seasonal groundwater recharge in the upper Colorado River Basin","interactions":[],"lastModifiedDate":"2017-08-15T13:16:00","indexId":"70190109","displayToPublicDate":"2017-08-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Changes in projected spatial and seasonal groundwater recharge in the upper Colorado River Basin","docAbstract":"The Colorado River is an important source of water in the western United States, supplying the needs of more than 38 million people in the United States and Mexico. Groundwater discharge to streams has been shown to be a critical component of streamflow in the Upper Colorado River Basin (UCRB), particularly during low-flow periods. Understanding impacts on groundwater in the basin from projected climate change will assist water managers in the region in planning for potential changes in the river and groundwater system. A previous study on changes in basin-wide groundwater recharge in the UCRB under projected climate change found substantial increases in temperature, moderate increases in precipitation, and mostly periods of stable or slight increases in simulated groundwater recharge through 2099. This study quantifies projected spatial and seasonal changes in groundwater recharge within the UCRB from recent historical (1950 to 2015) through future (2016 to 2099) time periods, using a distributed-parameter groundwater recharge model with downscaled climate data from 97 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections. Simulation results indicate that projected increases in basin-wide recharge of up to 15% are not distributed uniformly within the basin or throughout the year. Northernmost subregions within the UCRB are projected an increase in groundwater recharge, while recharge in other mainly southern subregions will decline. Seasonal changes in recharge also are projected within the UCRB, with decreases of 50% or more in summer months and increases of 50% or more in winter months for all subregions, and increases of 10% or more in spring months for many subregions.
","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.12507","usgsCitation":"Tillman, F.D., Gangopadhyay, S., and Pruitt, T., 2017, Changes in projected spatial and seasonal groundwater recharge in the upper Colorado River Basin: Groundwater, v. 55, no. 4, p. 506-518, https://doi.org/10.1111/gwat.12507.","productDescription":"13 p.","startPage":"506","endPage":"518","ipdsId":"IP-078645","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":344783,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Nevada, New Mexico, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.64404296874999,\n 42.147114459220994\n ],\n [\n -108.74267578124999,\n 42.342305278572816\n ],\n [\n -110.28076171874999,\n 41.983994270935625\n ],\n [\n -111.57714843749999,\n 40.74725696280421\n ],\n [\n -112.85156249999999,\n 38.324420427006515\n ],\n [\n -114.52148437499999,\n 37.84015683604134\n ],\n [\n -115.04882812499999,\n 37.54457732085582\n ],\n [\n -115.04882812499999,\n 36.61552763134925\n ],\n [\n -114.19189453124999,\n 34.50655662164561\n ],\n [\n -114.60937499999999,\n 33.797408767572485\n ],\n [\n -114.78515624999999,\n 32.861132322810946\n ],\n [\n -114.96093749999997,\n 32.15701248607008\n ],\n [\n -113.90624999999999,\n 31.74685416292141\n ],\n [\n -113.29101562499999,\n 31.034108344903483\n ],\n [\n -112.41210937499999,\n 30.164126343161097\n ],\n [\n -110.87402343749999,\n 30.543338954230222\n ],\n [\n -109.24804687499997,\n 31.259769987394286\n ],\n [\n -107.13867187499999,\n 32.97180377635759\n ],\n [\n -106.17187499999999,\n 36.43896124085945\n ],\n [\n -105.95214843749999,\n 39.740986355883564\n ],\n [\n -106.39160156249999,\n 41.52502957323801\n ],\n [\n -107.64404296874999,\n 42.147114459220994\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-16","publicationStatus":"PW","scienceBaseUri":"59901397e4b09fa1cb178921","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":707516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":707517,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pruitt, Tom 0000-0002-3543-1324","orcid":"https://orcid.org/0000-0002-3543-1324","contributorId":173440,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","email":"","affiliations":[{"id":27228,"text":"Reclamation","active":true,"usgs":false}],"preferred":false,"id":707518,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190106,"text":"70190106 - 2017 - Use of alternating and pulsed direct current electrified fields for zebra mussel control","interactions":[],"lastModifiedDate":"2017-08-12T08:55:26","indexId":"70190106","displayToPublicDate":"2017-08-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Use of alternating and pulsed direct current electrified fields for zebra mussel control","docAbstract":"Alternatives to chemicals for controlling dreissenid mussels are desirable for environmental compatibility, but few alternatives exist. Previous studies have evaluated the use of electrified fields for stunning and/or killing planktonic life stages of dreissenid mussels, however, the available literature on the use of electrified fields to control adult dreissenid mussels is limited. We evaluated the effects of sinusoidal alternating current (AC) and 20% duty cycle square-wave pulsed direct current (PDC) exposure on the survival of adult zebra mussels at water temperatures of 10, 15, and 22 °C. Peak voltage gradients of ~ 17 and 30 Vp/cm in the AC and PDC exposures, respectively, were continuously applied for 24, 48, or 72 h. Peak power densities ranged from 77,999 to 107,199 µW/cm3 in the AC exposures and 245,320 to 313,945 µW/cm3 in the PDC exposures. The peak dose ranged from 6,739 to 27,298 Joules/cm3 and 21,306 to 80,941 Joules/cm3 in the AC and PDC exposures, respectively. The applied power ranged from 16.6 to 68.9 kWh in the AC exposures and from 22.2 to 86.4 kWh in the PDC exposures. Mortality ranged from 2.7 to 92.7% in the AC exposed groups and from 24.0 to 98.7% in PDC exposed groups. Mortality increased with corresponding increases in water temperature and exposure duration, and we observed more zebra mussel mortality in the PDC exposures. Exposures conducted with AC required less of a peak dose (Joules/cm3) but more applied power (kWh) to achieve the same level of adult zebra mussel mortality as corresponding PDC exposures. The results demonstrate that 20% duty cycle square-wave PDC requires less energy than sinusoidal AC to inducing the same level of adult zebra mussel mortality.
","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre","doi":"10.3391/mbi.2017.8.3.05","usgsCitation":"Luoma, J.A., Dean, J.C., Severson, T.J., Wise, J.K., and Barbour, M., 2017, Use of alternating and pulsed direct current electrified fields for zebra mussel control: Management of Biological Invasions, v. 8, no. 3, p. 311-324, https://doi.org/10.3391/mbi.2017.8.3.05.","productDescription":"14 p.","startPage":"311","endPage":"324","ipdsId":"IP-080213","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":469611,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2017.8.3.05","text":"Publisher Index Page"},{"id":344784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59901398e4b09fa1cb178923","contributors":{"authors":[{"text":"Luoma, James A. 0000-0003-3556-0190 jluoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":4449,"corporation":false,"usgs":true,"family":"Luoma","given":"James","email":"jluoma@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dean, Jan C.","contributorId":195579,"corporation":false,"usgs":false,"family":"Dean","given":"Jan","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":707508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Severson, Todd J. 0000-0001-5282-3779 tseverson@usgs.gov","orcid":"https://orcid.org/0000-0001-5282-3779","contributorId":4749,"corporation":false,"usgs":true,"family":"Severson","given":"Todd","email":"tseverson@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wise, Jeremy K. 0000-0003-0184-6959 jwise@usgs.gov","orcid":"https://orcid.org/0000-0003-0184-6959","contributorId":5009,"corporation":false,"usgs":true,"family":"Wise","given":"Jeremy","email":"jwise@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707510,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barbour, Matthew T. 0000-0002-0095-9188 mbarbour@usgs.gov","orcid":"https://orcid.org/0000-0002-0095-9188","contributorId":195580,"corporation":false,"usgs":true,"family":"Barbour","given":"Matthew","email":"mbarbour@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707511,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190141,"text":"70190141 - 2017 - Dam removal: Listening in","interactions":[],"lastModifiedDate":"2019-04-24T16:24:39","indexId":"70190141","displayToPublicDate":"2017-08-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Dam removal: Listening in","docAbstract":"Dam removal is widely used as an approach for river restoration in the United States. The increase in dam removals—particularly large dams—and associated dam-removal studies over the last few decades motivated a working group at the USGS John Wesley Powell Center for Analysis and Synthesis to review and synthesize available studies of dam removals and their findings. Based on dam removals thus far, some general conclusions have emerged: (1) physical responses are typically fast, with the rate of sediment erosion largely dependent on sediment characteristics and dam-removal strategy; (2) ecological responses to dam removal differ among the affected upstream, downstream, and reservoir reaches; (3) dam removal tends to quickly reestablish connectivity, restoring the movement of material and organisms between upstream and downstream river reaches; (4) geographic context, river history, and land use significantly influence river restoration trajectories and recovery potential because they control broader physical and ecological processes and conditions; and (5) quantitative modeling capability is improving, particularly for physical and broad-scale ecological effects, and gives managers information needed to understand and predict long-term effects of dam removal on riverine ecosystems. Although these studies collectively enhance our understanding of how riverine ecosystems respond to dam removal, knowledge gaps remain because most studies have been short (< 5 years) and do not adequately represent the diversity of dam types, watershed conditions, and dam-removal methods in the U.S.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017WR020457","usgsCitation":"Foley, M.M., Bellmore, J., O'Connor, J., Duda, J.J., East, A., Grant, G.G., Anderson, C.W., Bountry, J.A., Collins, M.J., Connolly, P., Craig, L.S., Evans, J.E., Greene, S., Magilligan, F.J., Magirl, C.S., Major, J.J., Pess, G.R., Randle, T.J., Shafroth, P.B., Torgersen, C.E., Tullos, D.D., and Wilcox, A.C., 2017, Dam removal: Listening in: Water Resources Research, v. 53, no. 7, p. 5229-5246, https://doi.org/10.1002/2017WR020457.","productDescription":"18 p.","startPage":"5229","endPage":"5246","ipdsId":"IP-083383","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":29789,"text":"John 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1969–2015","interactions":[],"lastModifiedDate":"2017-08-11T17:47:51","indexId":"70190148","displayToPublicDate":"2017-08-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Water quality measurements in San Francisco Bay by the U.S. Geological Survey, 1969–2015","docAbstract":"The U.S. Geological Survey (USGS) maintains a place-based research program in San Francisco Bay (USA) that began in 1969 and continues, providing one of the longest records of water-quality measurements in a North American estuary. Constituents include salinity, temperature, light extinction coefficient, and concentrations of chlorophyll-a, dissolved oxygen, suspended particulate matter, nitrate, nitrite, ammonium, silicate, and phosphate. We describe the sampling program, analytical methods, structure of the data record, and how to access all measurements made from 1969 through 2015. We provide a summary of how these data have been used by USGS and other researchers to deepen understanding of how estuaries are structured and function differently from the river and ocean ecosystems they bridge.
","language":"English","publisher":"Nature","doi":"10.1038/sdata.2017.98","usgsCitation":"Schraga, T., and Cloern, J.E., 2017, Water quality measurements in San Francisco Bay by the U.S. Geological Survey, 1969–2015: Scientific Data, v. 4, Article 170098: 14 p., https://doi.org/10.1038/sdata.2017.98.","productDescription":"Article 170098: 14 p.","ipdsId":"IP-086767","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":469616,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2017.98","text":"Publisher Index Page"},{"id":438248,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7D21WGF","text":"USGS data release","linkHelpText":"USGS Measurements of Water Quality in San Francisco Bay (CA), 2016-2021 (ver. 4.0, March 2023)"},{"id":344767,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.15673828124999,\n 37.06394430056685\n ],\n [\n -121.37695312499999,\n 37.06394430056685\n ],\n [\n -121.37695312499999,\n 39.036252959636606\n ],\n [\n -123.15673828124999,\n 39.036252959636606\n ],\n [\n -123.15673828124999,\n 37.06394430056685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-08","publicationStatus":"PW","scienceBaseUri":"598e9038e4b09fa1cb160966","contributors":{"authors":[{"text":"Schraga, Tara 0000-0002-2108-5846 tschraga@usgs.gov","orcid":"https://orcid.org/0000-0002-2108-5846","contributorId":1118,"corporation":false,"usgs":true,"family":"Schraga","given":"Tara","email":"tschraga@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":707701,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cloern, James E. 0000-0002-5880-6862 jecloern@usgs.gov","orcid":"https://orcid.org/0000-0002-5880-6862","contributorId":1488,"corporation":false,"usgs":true,"family":"Cloern","given":"James","email":"jecloern@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":707700,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190137,"text":"70190137 - 2017 - Comparison of sediment and nutrient export and runoff characteristics from watersheds with centralized versus distributed stormwater management","interactions":[],"lastModifiedDate":"2017-08-11T18:26:47","indexId":"70190137","displayToPublicDate":"2017-08-11T00:00:00","publicationYear":"2017","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":"Comparison of sediment and nutrient export and runoff characteristics from watersheds with centralized versus distributed stormwater management","docAbstract":"Stormwater control measures (SCMs) are used to retain stormwater and pollutants. SCMs have traditionally been installed in a centralized manner using detention to mitigate peak flows. Recently, distributed SCM networks that treat runoff near the source have been increasingly utilized. The aim of this study was to evaluate differences among watersheds that vary in SCM arrangement by assessing differences in baseflow nutrient (NOx-N and PO4−) concentrations and fluxes, stormflow export of suspended sediments and particulate phosphorus (PP), and runoff characteristics. A paired watershed approach was used to compare export between 2004 and 2016 from one forested watershed (For-MD), one suburban watershed with centralized SCMs (Cent-MD), and one suburban watershed with distributed SCMs (Dist-MD). Results indicated baseflow nitrate (NOx-N) concentrations typically exceeded 1 mg-N/L in all watersheds and were highest in Dist-MD. Over the last 10 years in Dist-MD, nitrate concentrations in both stream baseflow and in a groundwater well declined as land use shifted from agriculture to suburban. Baseflow nitrate export temporarily increased during the construction phase of SCM development in Dist-MD. This temporary pulse of nitrate may be attributed to the conversion of sediment control facilities to SCMs and increased subsurface flushing as infiltration SCMs came on line. During storm flow, Dist-MD tended to have less runoff and lower maximum specific discharge than Cent-MD for small events (<1.3 cm), but runoff responses became increasingly similar to Cent-MD with increasing precipitation (>1.3 cm). Mass export estimated during paired storm events indicated Dist-MD exported 30% less sediment and 31% more PP than Cent-MD. For large precipitation events, export of sediment and PP was similar among all three watersheds. Results suggest that distributed SCMs can reduce runoff and sediment loads during small rain events compared to centralized SCMs, but these differences become less evident for large events when peak discharge likely leads to substantial bank erosion.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2017.07.067","usgsCitation":"Hopkins, K.G., Loperfido, J., Craig, L.S., Noe, G.E., and Hogan, D.M., 2017, Comparison of sediment and nutrient export and runoff characteristics from watersheds with centralized versus distributed stormwater management: Journal of Environmental Management, v. 203, no. 1, p. 286-298, https://doi.org/10.1016/j.jenvman.2017.07.067.","productDescription":"13 p.","startPage":"286","endPage":"298","ipdsId":"IP-086456","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":344772,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"203","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"598e903be4b09fa1cb16096c","contributors":{"authors":[{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":707625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loperfido, J.V.","contributorId":90970,"corporation":false,"usgs":true,"family":"Loperfido","given":"J.V.","email":"","affiliations":[],"preferred":false,"id":707626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Craig, Laura S.","contributorId":195611,"corporation":false,"usgs":false,"family":"Craig","given":"Laura","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":707627,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":707628,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hogan, Dianna M. 0000-0003-1492-4514 dhogan@usgs.gov","orcid":"https://orcid.org/0000-0003-1492-4514","contributorId":131137,"corporation":false,"usgs":true,"family":"Hogan","given":"Dianna","email":"dhogan@usgs.gov","middleInitial":"M.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":707629,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70187180,"text":"ds1031 - 2017 - Archive of bathymetry data collected in South Florida from 1995 to 2015","interactions":[],"lastModifiedDate":"2017-08-10T17:27:37","indexId":"ds1031","displayToPublicDate":"2017-08-10T15:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1031","title":"Archive of bathymetry data collected in South Florida from 1995 to 2015","docAbstract":"Land development and alterations of the ecosystem in south Florida over the past 100 years have decreased freshwater and increased nutrient flows into many of Florida's estuaries, bays, and coastal regions. As a result, there has been a decrease in the water quality in many of these critical habitats, often prompting seagrass die-offs and reduced fish and aquatic life populations. Restoration of water quality in many of these habitats will depend partly upon using numerical-circulation and sediment-transport models to establish water-quality targets and to assess progress toward reaching restoration targets. Application of these models is often complicated because of complex sea floor topography and tidal flow regimes. Consequently, accurate and modern sea-floor or bathymetry maps are critical for numerical modeling research. Modern bathymetry data sets will also permit a comparison to historical data in order to help assess sea-floor changes within these critical habitats. New and detailed data sets also support marine biology studies to help understand migratory and feeding habitats of marine life.
This data series is a compilation of 13 mapping projects conducted in south Florida between 1995 and 2015 and archives more than 45 million bathymetric soundings. Data were collected primarily with a single beam sound navigation and ranging (sonar) system called SANDS developed by the U.S. Geological Survey (USGS) in 1993. Bathymetry data for the Estero Bay project were supplemented with the National Aeronautics and Space Administration's (NASA) Experimental Advanced Airborne Research Lidar (EAARL) system. Data from eight rivers in southwest Florida were collected with an interferometric swath bathymetry system. The projects represented in this data series were funded by the USGS Coastal and Marine Geology Program (CMGP), the USGS South Florida Ecosystem Restoration Project- formally named Placed Based Studies, and other non-Federal agencies. The purpose of the data collection for all these projects was to support one or more of the following scientific aspects: numerical model applications, sea floor change analysis, or marine habitat investigations.
This report serves as an archive of processed bathymetry sounding data, digital bathymetric contours, digital bathymetric maps, sea floor surface grids, and formal Federal Geographic Data Committee (FGDC) metadata. Refer to the Abbreviations page for explanations of acronyms and abbreviations used in this report. Since 2006, the USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) assigns a unique identifier or Field Activity Number (FAN) for each field data collection. Projects described in this report conducted prior to 2006 do not have a FAN.
Data from the 13 projects presented in this report provided critical hydrographic information to support multiple science projects in south Florida. The projects and the types of sounding data collected are:
Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
MODFLOW is a popular open-source groundwater flow model distributed by the U.S. Geological Survey. Growing interest in surface and groundwater interactions, local refinement with nested and unstructured grids, karst groundwater flow, solute transport, and saltwater intrusion, has led to the development of numerous MODFLOW versions. Often times, there are incompatibilities between these different MODFLOW versions. The report describes a new MODFLOW framework called MODFLOW 6 that is designed to support multiple models and multiple types of models. The framework is written in Fortran using a modular object-oriented design. The primary framework components include the simulation (or main program), Timing Module, Solutions, Models, Exchanges, and Utilities. The first version of the framework focuses on numerical solutions, numerical models, and numerical exchanges. This focus on numerical models allows multiple numerical models to be tightly coupled at the matrix level.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Groundwater in Book 6 Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A57","usgsCitation":"Hughes, J.D., Langevin, C.D., and Banta, E.R., 2017, Documentation for the MODFLOW 6 framework: U.S. Geological Survey Techniques and Methods, book 6, chap. A57, 40 p., https://doi.org/10.3133/tm6A57.","productDescription":"Report: 42 p.; Application Site; Companion FIles","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-081538","costCenters":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"links":[{"id":343721,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/tm6A55","text":"Techniques and Methods 6A-55","linkHelpText":"- Documentation for the MODFLOW 6 Groundwater Flow Model"},{"id":343720,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a57/tm6a57.pdf","text":"Report","size":"2.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6A-57"},{"id":343722,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/tm6A56","text":"Techniques and Methods 6A-56","linkHelpText":"- Documentation for the \"XT3D\" Option in the Node Property Flow (NPF) Package of MODFLOW"},{"id":344650,"rank":5,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/F76Q1VQV","linkHelpText":"- MODFLOW 6"},{"id":343719,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a57/coverthb.jpg"}],"publicComments":"This report is Chapter 57 of Section A: Groundwater in Book 6 Modeling techniques.","contact":"Office of Groundwater
U.S. Geological Survey
Mail Stop 411
12201 Sunrise Valley Drive
Reston, VA 20192
This report documents the Groundwater Flow (GWF) Model for a new version of MODFLOW called MODFLOW 6. The GWF Model for MODFLOW 6 is based on a generalized control-volume finite-difference approach in which a cell can be hydraulically connected to any number of surrounding cells. Users can define the model grid using one of three discretization packages, including (1) a structured discretization package for defining regular MODFLOW grids consisting of layers, rows, and columns, (2) a discretization by vertices package for defining layered unstructured grids consisting of layers and cells, and (3) a general unstructured discretization package for defining flexible grids comprised of cells and their connection properties. For layered grids, a new capability is available for removing thin cells and vertically connecting cells overlying and underlying the thin cells. For complex problems involving water-table conditions, an optional Newton-Raphson formulation, based on the formulations in MODFLOW-NWT and MODFLOW-USG, can be activated. Use of the Newton-Raphson formulation will often improve model convergence and allow solutions to be obtained for difficult problems that cannot be solved using the traditional wetting and drying approach. The GWF Model is divided into “packages,” as was done in previous MODFLOW versions. A package is the part of the model that deals with a single aspect of simulation. Packages included with the GWF Model include those related to internal calculations of groundwater flow (discretization, initial conditions, hydraulic conductance, and storage), stress packages (constant heads, wells, recharge, rivers, general head boundaries, drains, and evapotranspiration), and advanced stress packages (streamflow routing, lakes, multi-aquifer wells, and unsaturated zone flow). An additional package is also available for moving water available in one package into the individual features of the advanced stress packages. The GWF Model also has packages for obtaining and controlling output from the model. This report includes detailed explanations of physical and mathematical concepts on which the GWF Model and its packages are based.
Like its predecessors, MODFLOW 6 is based on a highly modular structure; however, this structure has been extended into an object-oriented framework. The framework includes a robust and generalized numerical solution object, which can be used to solve many different types of models. The numerical solution object has several different matrix preconditioning options as well as several methods for solving the linear system of equations. In this new framework, the GWF Model itself is an object as are each of the GWF Model packages. A benefit of the object-oriented structure is that multiple objects of the same type can be used in a single simulation. Thus, a single forward run with MODFLOW 6 may contain multiple GWF Models. GWF Models can be hydraulically connected using GWF-GWF Exchange objects. Connecting GWF models in different ways permits the user to utilize a local grid refinement strategy consisting of parent and child models or to couple adjacent GWF Models. An advantage of the approach implemented in MODFLOW 6 is that multiple models and their exchanges can be incorporated into a single numerical solution object. With this design, models can be tightly coupled at the matrix level.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Groundwater in Book 6 Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A55","collaboration":"Prepared in cooperation with the U.S. Geological Survey Water Availability and Use Science Program ","usgsCitation":"Langevin, C.D., Hughes, J.D., Banta, E.R., Niswonger, R.G., Panday, Sorab, and Provost, A.M., 2017, Documentation for the MODFLOW 6 Groundwater Flow Model: U.S. Geological Survey Techniques and Methods, book 6, chap. A55, 197 p., https://doi.org/10.3133/tm6A55. ","productDescription":"Report: 197 p.; Application Site; Companion Files","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-078755","costCenters":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"links":[{"id":343646,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/tm6A56","text":"Techniques and Methods 6A-56","linkHelpText":"- Documentation for the \"XT3D\" Option in the Node Property Flow (NPF) Package of MODFLOW "},{"id":343647,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/tm6A57","text":"Techniques and Methods 6A-57","linkHelpText":"- Documentation for the MODFLOW 6 Framework"},{"id":343639,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a55/coverthb.jpg"},{"id":343640,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a55/tm6a55.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6A-55"},{"id":344648,"rank":5,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/F76Q1VQV","linkHelpText":"- MODFLOW 6"}],"publicComments":"This report is Chapter 55 of Section A: Groundwater in Book 6 Modeling techniques.","contact":"Office of Groundwater
U.S. Geological Survey
Mail Stop 411
12201 Sunrise Valley Drive
Reston, VA 20192
This report describes the “XT3D” option in the Node Property Flow (NPF) Package of MODFLOW 6. The XT3D option extends the capabilities of MODFLOW by enabling simulation of fully three-dimensional anisotropy on regular or irregular grids in a way that properly takes into account the full, three-dimensional conductivity tensor. It can also improve the accuracy of groundwater-flow simulations in cases in which the model grid violates certain geometric requirements. Three example problems demonstrate the use of the XT3D option to simulate groundwater flow on irregular grids and through three-dimensional porous media with anisotropic hydraulic conductivity.
Conceptually, the XT3D method of estimating flow between two MODFLOW 6 model cells can be viewed in terms of three main mathematical steps: construction of head-gradient estimates by interpolation; construction of fluid-flux estimates by application of the full, three-dimensional form of Darcy’s Law, in which the conductivity tensor can be heterogeneous and anisotropic; and construction of the flow expression by enforcement of continuity of flow across the cell interface. The resulting XT3D flow expression, which relates the flow across the cell interface to the values of heads computed at neighboring nodes, is the sum of terms in which conductance-like coefficients multiply head differences, as in the conductance-based flow expression the NPF Package uses by default. However, the XT3D flow expression contains terms that involve “neighbors of neighbors” of the two cells for which the flow is being calculated. These additional terms have no analog in the conductance-based formulation. When assembled into matrix form, the XT3D formulation results in a larger stencil than the conductance-based formulation; that is, each row of the coefficient matrix generally contains more nonzero elements. The “RHS” suboption can be used to avoid expanding the stencil by placing the additional terms on the right-hand side of the matrix equation and evaluating them at the previous iteration or time step.
The XT3D option can be an alternative to the Ghost-Node Correction (GNC) Package. However, the XT3D formulation is typically more computationally intensive than the conductance-based formulation the NPF Package uses by default, either with or without ghost nodes. Before deciding whether to use the GNC Package or XT3D option for production runs, the user should consider whether the conductance-based formulation alone can provide acceptable accuracy for the particular problem being solved.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Groundwater in Book 6 Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A56","usgsCitation":"Provost, A.M., Langevin, C.D., and Hughes, J.D., 2017, Documentation for the “XT3D” option in the Node\nProperty Flow (NPF) Package of MODFLOW 6: U.S. Geological Survey Techniques and Methods, book 6, chap. A56, 40 p., https://doi.org/10.3133/tm6A56.","productDescription":"vi, 27 p.","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-081540","costCenters":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"links":[{"id":343661,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a56/coverthb.jpg"},{"id":343663,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/tm6A55","text":"Techniques and Methods 6A-55","linkHelpText":"- Documentation for the MODFLOW 6 Groundwater Flow Model"},{"id":343664,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/tm6A57","text":"Techniques and Methods 6A-57","linkHelpText":"- Documentation for the MODFLOW 6 Framework "},{"id":344649,"rank":5,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/F76Q1VQV","linkHelpText":"- MODFLOW 6"},{"id":343662,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a56/tm6a56.pdf","text":"Report","size":"3.92 MB"}],"publicComments":"This report is Chapter 56 of Section A: Groundwater in Book 6 Modeling techniques.","contact":"Office of Groundwater
U.S. Geological Survey
Mail Stop 411
12201 Sunrise Valley Drive
Reston, VA 20192
The stochastic empirical loading and dilution model (SELDM) was used to demonstrate methods for estimating risks for water-quality exceedances of event-mean concentrations (EMCs) of total-copper. Monte Carlo methods were used to simulate stormflow, total-hardness, suspended-sediment, and total-copper EMCs as stochastic variables. These simulations were done for the Charles River Basin upstream of Interstate 495 in Bellingham, Massachusetts. The hydrology and water quality of this site were simulated with SELDM by using data from nearby, hydrologically similar sites. Three simulations were done to assess the potential effects of the highway on receiving-water quality with and without highway-runoff treatment by a structural best-management practice (BMP). In the low-development scenario, total copper in the receiving stream was simulated by using a sediment transport curve, sediment chemistry, and sediment-water partition coefficients. In this scenario, neither the highway runoff nor the BMP effluent caused concentration exceedances in the receiving stream that exceed the once in three-year threshold (about 0.54 percent). In the second scenario, without the highway, runoff from the large urban areas in the basin caused exceedances in the receiving stream in 2.24 percent of runoff events. In the third scenario, which included the effects of the urban runoff, neither the highway runoff nor the BMP effluent increased the percentage of exceedances in the receiving stream. Comparison of the simulated geometric mean EMCs with data collected at a downstream monitoring site indicates that these simulated values are within the 95-percent confidence interval of the geometric mean of the measured EMCs.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"World environmental and water resources congress 2017: Watershed management, irrigation and drainage, and water resources planning and management","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"World Environmental and Water Resources Congress 2017","conferenceDate":"May 21-25, 2017","conferenceLocation":"Sacremento, CA","language":"English","publisher":"American Society of Civil Engineers","publisherLocation":"Reston, VA","doi":"10.1061/9780784480601.028","isbn":"978-0-7844-8060-1","usgsCitation":"Granato, G.E., and Jones, S.C., 2017, Estimating risks for water-quality exceedances of total-copper from highway and urban runoff under predevelopment and current conditions with the Stochastic Empirical Loading and Dilution Model (SELDM), in World environmental and water resources congress 2017: Watershed management, irrigation and drainage, and water resources planning and management, Sacremento, CA, May 21-25, 2017, p. 313-327, https://doi.org/10.1061/9780784480601.028.","productDescription":"15 p.","startPage":"313","endPage":"327","ipdsId":"IP-074316","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":344706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-18","publicationStatus":"PW","scienceBaseUri":"598c1f3ee4b09fa1cb0ffef3","contributors":{"editors":[{"text":"Dunn, Christopher N.","contributorId":195552,"corporation":false,"usgs":false,"family":"Dunn","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":707424,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Van Weele, Brian","contributorId":176821,"corporation":false,"usgs":false,"family":"Van Weele","given":"Brian","email":"","affiliations":[],"preferred":false,"id":707425,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Granato, Gregory E. 0000-0002-2561-9913 ggranato@usgs.gov","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":147346,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory","email":"ggranato@usgs.gov","middleInitial":"E.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":false,"id":707417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Susan C. 0000-0002-5891-5209","orcid":"https://orcid.org/0000-0002-5891-5209","contributorId":64716,"corporation":false,"usgs":false,"family":"Jones","given":"Susan","email":"","middleInitial":"C.","affiliations":[{"id":34302,"text":"Federal Highway Administration (United States)","active":true,"usgs":false}],"preferred":false,"id":707418,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190051,"text":"70190051 - 2017 - Hierarchical, quantitative biogeographic provinces for all North American turtles and their contribution to the biogeography of turtles and the continent","interactions":[],"lastModifiedDate":"2017-08-07T16:53:26","indexId":"70190051","displayToPublicDate":"2017-08-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1896,"text":"Herpetological Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Hierarchical, quantitative biogeographic provinces for all North American turtles and their contribution to the biogeography of turtles and the continent","docAbstract":"Our study represents the first attempt to describe biogeographic provinces for North American (México, United States, and Canada) turtles. We analyzed three nested data sets separately: (1) all turtles, (2) freshwater turtles, and (3) aquatic turtles. We georeferenced North American turtle distributions, then we created presence–absence matrices for each of the three data sets. We used watershed unit as biogeographic units. We conducted an unweighted pair-group method with arithmetic mean clustering analysis on each Jaccard index distance matrix from our watershed species matrices to delineate biogeographic provinces. Provinces were then tested for significant differences in species compositions in a global model with the use of a one-way analysis of similarity. We conducted a best subset of environmental variables with maximum (rank) correlation with community dissimilarities that determined the best model of abiotic variables explaining province delineation (i.e., climate, topography, and stream channel). To identify which species contributed the most to province delineations, we conducted an indicator species analysis and a similarity-percentage analysis. There were 16 all-turtle provinces, 15 freshwater provinces, and 13 aquatic provinces. Species compositions delineating the provinces were explained by abiotic variables, including mean annual precipitation, mean precipitation seasonality, and diversity of streams. Province delineations correspond closely with geographical boundaries, many of which have Pleistocene origins. For example, rivers with a history of carrying glacial runoff (e.g., Arkansas, Mississippi) sometimes dissect upland provinces, especially for aquatic and semiaquatic turtles. Compared with freshwater fishes, turtles show greater sensitivity to decreased temperature with restriction of most taxa south of the last permafrost maximum. Turtles also exhibit higher sensitivity to climatic, geomorphic, and tectonic instability, with richness and endemism concentrated along the more stable Gulf of México and Atlantic (south of the last permafrost maximum) coasts. Although distribution data indicate two aquatic turtles are most cold tolerant (i.e., Chrysemys picta, Chelydra serpentina), aquatic turtles overall show the most restriction to warmer, wetter climates. Sequential addition of semiaquatic and terrestrial turtles into analyses shows, as expected, that these taxa flesh out turtle faunas in climatically harsh (e.g., grasslands) or remote (e.g., California, Sonoran Desert) regions. The turtle assemblages of southwestern versus southeastern North America are distinct. But there is a transition zone across the semiarid plains of the Texas Gulf Coast, High Plains, and Chihuahuan Desert, including a strong boundary congruent with the Cochise Filter-Barrier. This is not a simple subdivision of Neotropical versus Nearctic taxa, as some lineages from both realms span the transition zone.
","language":"English","publisher":"The Herpetologists’ League","doi":"10.1655/HERPMONOGRAPHS-D-16-00013","usgsCitation":"Ennen, J.R., Matamoros, W.A., Agha, M., Lovich, J.E., Sweat, S.C., and Hoagstrom, C.W., 2017, Hierarchical, quantitative biogeographic provinces for all North American turtles and their contribution to the biogeography of turtles and the continent: Herpetological Monographs, v. 31, no. 1, p. 114-140, https://doi.org/10.1655/HERPMONOGRAPHS-D-16-00013.","productDescription":"27 p.","startPage":"114","endPage":"140","ipdsId":"IP-080651","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":344620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"North America","volume":"31","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59897c0ee4b09fa1cb0c2bf1","contributors":{"authors":[{"text":"Ennen, Joshua R.","contributorId":83858,"corporation":false,"usgs":true,"family":"Ennen","given":"Joshua","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":707330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matamoros, Wilfredo A.","contributorId":172518,"corporation":false,"usgs":false,"family":"Matamoros","given":"Wilfredo","email":"","middleInitial":"A.","affiliations":[{"id":27060,"text":"Facultad de Ciencias Biologicas, Universidad de Cencias y Artes de Chiapas, Museo de Zoologia, Tuxtla Gutiérrez, Chiapas, México Apartado Postal 29000, México","active":true,"usgs":false}],"preferred":false,"id":707332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Agha, Mickey","contributorId":22235,"corporation":false,"usgs":false,"family":"Agha","given":"Mickey","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false},{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":707333,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":707329,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sweat, Sarah C.","contributorId":195519,"corporation":false,"usgs":false,"family":"Sweat","given":"Sarah","email":"","middleInitial":"C.","affiliations":[{"id":13216,"text":"Tennessee Aquarium Conservation Institute","active":true,"usgs":false}],"preferred":false,"id":707331,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hoagstrom, Christopher W.","contributorId":195520,"corporation":false,"usgs":false,"family":"Hoagstrom","given":"Christopher","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":707334,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}