The Borrego Valley is a small valley (110 square miles) in the northeastern part of San Diego County, California. Although the valley is about 60 miles northeast of city of San Diego, it is separated from the Pacific Ocean coast by the mountains to the west and is mostly within the boundaries of Anza-Borrego Desert State Park. From the time the basin was first settled, groundwater has been the only source of water to the valley. Groundwater is used for agricultural, recreational, and municipal purposes. Over time, groundwater withdrawal through pumping has exceeded the amount of water that has been replenished, causing groundwater-level declines of more than 100 feet in some parts of the basin. Continued pumping has resulted in an increase in pumping lifts, reduced well efficiency, dry wells, changes in water quality, and loss of natural groundwater discharge. As a result, the U.S. Geological Survey began a cooperative study of the Borrego Valley with the Borrego Water District (BWD) in 2009. The purpose of the study was to develop a greater understanding of the hydrogeology of the Borrego Valley Groundwater Basin (BVGB) and to provide tools to help evaluate the potential hydrologic effects of future development. The objectives of the study were to (1) improve the understanding of groundwater conditions and land subsidence, (2) incorporate this improved understanding into a model that would assist in the management of the groundwater resources in the Borrego Valley, and (3) use this model to test several management scenarios. This model provides the capability for the BWD and regional stakeholders to quantify the relative benefits of various options for increasing groundwater storage. The study focuses on the period 1945–2010, with scenarios 50 years into the future.
\nThis report documents and presents (1) an analysis of the conceptual model, (2) a description of the hydrologic features, (3) a compilation and analysis of water-quality data, (4) the measurement and analysis of land subsidence by using geophysical and remote sensing techniques, (5) the development and calibration of a two-dimensional borehole-groundwater-flow model to estimate aquifer hydraulic conductivities, (6) the development and calibration of a three-dimensional (3-D) integrated hydrologic flow model, (7) a water-availability analysis with respect to current climate variability and land use, and (8) potential future management scenarios. The integrated hydrologic model, referred to here as the “Borrego Valley Hydrologic Model” (BVHM), is a tool that can provide results with the accuracy needed for making water-management decisions, although potential future refinements and enhancements could further improve the level of spatial and temporal resolution and model accuracy. Because the model incorporates time-varying inflows and outflows, this tool can be used to evaluate the effects of temporal changes in recharge and pumping and to compare the relative effects of different water-management scenarios on the aquifer system. Overall, the development of the hydrogeologic and hydrologic models, data networks, and hydrologic analysis provides a basis for assessing surface and groundwater availability and potential water-resource management guidelines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155150","collaboration":"Prepared in cooperation with the Borrego Water District","usgsCitation":"Faunt, C.C., Stamos, C.L., Flint, L.E., Wright, M.T., Burgess, M.K., Sneed, Michelle, Brandt, Justin, Martin, Peter, and Coes, A.L., 2015, Hydrogeology, hydrologic effects of development, and simulation of groundwater flow in the Borrego Valley, San Diego County, California: U.S. Geological Survey Scientific Investigations Report 2015–5150, 135 p., https://dx.doi.org/10.3133/sir20155150.","productDescription":"xiv, 135 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-024573","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":311633,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5150/sir20155150.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5150"},{"id":311632,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5150/coverthb.jpg"},{"id":311671,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://dx.doi.org/10.5066/F7S180J9","text":"Borrego Valley Groundwater Conditions"},{"id":314004,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2015/5150/sir20155150_input.zip","text":"Model Input","size":"420 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2015-5150 Model Input files"},{"id":314005,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2015/5150/sir20155150_output.zip","text":"Model Output","size":"45 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2015-5150 Model Output files"}],"country":"United States","state":"California","otherGeospatial":"Borrego Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.54708862304686,\n 32.89342578969234\n ],\n [\n -116.54708862304686,\n 33.34659043589842\n ],\n [\n -115.73272705078124,\n 33.34659043589842\n ],\n [\n -115.73272705078124,\n 32.89342578969234\n ],\n [\n -116.54708862304686,\n 32.89342578969234\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
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Anthropogenic warming contributed to the 2014 East African drought by increasing East African and west Pacific temperatures, and increasing the gradient between standardized western and central Pacific SST causing reduced rainfall, evapotranspiration, and soil moisture.
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Science Center","active":true,"usgs":true}],"preferred":true,"id":589465,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159937,"text":"70159937 - 2015 - Yellowstone wolf (Canis lupus) denisty predicted by elk (Cervus elaphus) biomass","interactions":[],"lastModifiedDate":"2018-09-21T09:17:26","indexId":"70159937","displayToPublicDate":"2015-11-24T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1176,"text":"Canadian Journal of Zoology","active":true,"publicationSubtype":{"id":10}},"title":"Yellowstone wolf (Canis lupus) denisty predicted by elk (Cervus elaphus) biomass","docAbstract":"The Northern Range (NR) of Yellowstone National Park (YNP) hosts a higher prey biomass density in the form of elk (Cervus elaphus L., 1758) than any other system of gray wolves (Canis lupus L., 1758) and prey reported. Therefore, it is important to determine whether that wolf–prey system fits a long-standing model relating wolf density to prey biomass. Using data from 2005 to 2012 after elk population fluctuations dampened 10 years subsequent to wolf reintroduction, we found that NR prey biomass predicted wolf density. This finding and the trajectory of the regression extend the validity of the model to prey densities 19% higher than previous data and suggest that the model would apply to wolf–prey systems of even higher prey biomass.
","language":"English","publisher":"Canadian Journal of Zoology","doi":"10.1139/cjz-2015-0002","usgsCitation":"Mech, L.D., and Barber-Meyer, S., 2015, Yellowstone wolf (Canis lupus) denisty predicted by elk (Cervus elaphus) biomass: Canadian Journal of Zoology, v. 93, no. 6, p. 499-502, https://doi.org/10.1139/cjz-2015-0002.","productDescription":"4 p.","startPage":"499","endPage":"502","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062098","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":488395,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/68840","text":"External Repository"},{"id":311956,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.9619140625,\n 45.205263456162385\n ],\n [\n -110.85205078124999,\n 45.24008561090264\n ],\n [\n -110.7861328125,\n 45.251688256117646\n ],\n [\n -110.67626953125,\n 45.251688256117646\n ],\n [\n -110.5938720703125,\n 45.24395342262324\n ],\n [\n -110.4290771484375,\n 45.182036837015886\n ],\n [\n -110.2972412109375,\n 45.1433047394883\n ],\n [\n -109.4732666015625,\n 45.154927133618465\n ],\n [\n -109.3414306640625,\n 45.18978009667531\n ],\n [\n -109.18212890625,\n 45.20913363773731\n ],\n [\n -109.149169921875,\n 45.166547157856016\n ],\n [\n -109.149169921875,\n 45.06964120886863\n ],\n [\n -109.2315673828125,\n 44.84029065139799\n ],\n [\n -109.2205810546875,\n 44.695992981720714\n ],\n [\n -109.1656494140625,\n 44.59046718130883\n ],\n [\n -109.06677246093749,\n 44.53175879707938\n ],\n [\n -109.072265625,\n 44.469071224701096\n ],\n [\n -109.2205810546875,\n 44.422011314236634\n ],\n [\n -109.34692382812499,\n 44.378839759088585\n ],\n [\n -109.3963623046875,\n 44.34349388385857\n ],\n [\n -109.4073486328125,\n 44.296332880058706\n ],\n [\n -109.3359375,\n 44.3002644115815\n ],\n [\n -109.21508789062499,\n 44.34742225636393\n ],\n [\n -109.127197265625,\n 44.327777761284445\n ],\n [\n -109.09423828125,\n 44.284536706018905\n ],\n [\n -109.1436767578125,\n 44.19402066387343\n ],\n [\n -109.2425537109375,\n 44.142797828180605\n ],\n [\n -109.21508789062499,\n 44.08363928284644\n ],\n [\n -109.1546630859375,\n 44.06390660801779\n ],\n [\n -109.039306640625,\n 44.036269809534616\n ],\n [\n -109.017333984375,\n 43.957236472025635\n ],\n [\n -108.9898681640625,\n 43.810747313446996\n ],\n [\n -109.01184082031249,\n 43.6599240747891\n ],\n [\n -109.017333984375,\n 43.61619382369188\n ],\n [\n -111.3134765625,\n 43.620170616189924\n ],\n [\n -111.29150390625,\n 44.836395454104796\n ],\n [\n -111.3519287109375,\n 44.85586880735725\n ],\n [\n -111.4892578125,\n 44.883120442385646\n ],\n [\n -111.5167236328125,\n 45.00365115687189\n ],\n [\n -111.51123046875,\n 45.178164812206376\n ],\n [\n -111.566162109375,\n 45.259422036351694\n ],\n [\n -111.5277099609375,\n 45.36758436884978\n ],\n [\n -111.434326171875,\n 45.390735154248894\n ],\n [\n -111.2640380859375,\n 45.413876460821086\n ],\n [\n -111.13220214843749,\n 45.4524242413431\n ],\n [\n -111.060791015625,\n 45.43700828867389\n ],\n [\n -111.005859375,\n 45.390735154248894\n ],\n [\n -111.00036621093749,\n 45.31352900692261\n ],\n [\n -110.950927734375,\n 45.259422036351694\n ],\n [\n -110.9619140625,\n 45.205263456162385\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"93","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5662c75ce4b06a3ea36c67d1","contributors":{"authors":[{"text":"Mech, L. David 0000-0003-3944-7769 david_mech@usgs.gov","orcid":"https://orcid.org/0000-0003-3944-7769","contributorId":2518,"corporation":false,"usgs":true,"family":"Mech","given":"L.","email":"david_mech@usgs.gov","middleInitial":"David","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":581136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barber-Meyer, Shannon 0000-0002-3048-2616 sbarber-meyer@usgs.gov","orcid":"https://orcid.org/0000-0002-3048-2616","contributorId":150236,"corporation":false,"usgs":true,"family":"Barber-Meyer","given":"Shannon","email":"sbarber-meyer@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":581137,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159787,"text":"70159787 - 2015 - Persistent U(IV) and U(VI) following in-situ recovery (ISR) mining of a sandstone uranium deposit, Wyoming, USA","interactions":[],"lastModifiedDate":"2018-09-04T16:23:32","indexId":"70159787","displayToPublicDate":"2015-11-23T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Persistent U(IV) and U(VI) following in-situ recovery (ISR) mining of a sandstone uranium deposit, Wyoming, USA","docAbstract":"Drill-core samples from a sandstone-hosted uranium (U) deposit in Wyoming were characterized to determine the abundance and distribution of uranium following in-situ recovery (ISR) mining with oxygen- and carbon dioxide-enriched water. Concentrations of uranium, collected from ten depth intervals, ranged from 5 to 1920 ppm. A composite sample contained 750 ppm uranium with an average oxidation state of 54% U(VI) and 46% U(IV). Scanning electron microscopy (SEM) indicated rare high uranium (∼1000 ppm U) in spatial association with P/Ca and Si/O attributed to relict uranium minerals, possibly coffinite, uraninite, and autunite, trapped within low permeability layers bypassed during ISR mining. Fission track analysis revealed lower but still elevated concentrations of U in the clay/silica matrix and organic matter (several 10 s ppm) and yet higher concentrations associated with Fe-rich/S-poor sites, likely iron oxides, on altered chlorite or euhedral pyrite surfaces (but not on framboidal pyrite). Organic C (<1.62%), total S (<0.31%), and P (<0.03%) were in low abundance relative to the overall bulk composition. Microbial community analysis showed a diverse group of bacteria present with a wide range of putative metabolisms, and provides evidence for a variety of redox microenvironments co-existing in core samples. Although the uranium minerals persisting in low permeability areas in association with organic carbon were less affected by oxidizing solutions during mining, the likely sequestration of uranium within labile iron oxides following mining and sensitivity to changes in redox conditions requires careful attention during groundwater restoration.
\n\n
A groundwater-flow model was developed for the Bad River Watershed and surrounding area by using the U.S. Geological Survey (USGS) finite-difference code MODFLOW-NWT. The model simulates steady-state groundwater-flow and base flow in streams by using the streamflow routing (SFR) package. The objectives of this study were to: (1) develop an improved understanding of the groundwater-flow system in the Bad River Watershed at the regional scale, including the sources of water to the Bad River Band of Lake Superior Chippewa Reservation (Reservation) and groundwater/surface-water interactions; (2) provide a quantitative platform for evaluating future impacts to the watershed, which can be used as a starting point for more detailed investigations at the local scale; and (3) identify areas where more data are needed. This report describes the construction and calibration of the groundwater-flow model that was subsequently used for analyzing potential locations for the collection of additional field data, including new observations of water-table elevation for refining the conceptualization and corresponding numerical model of the hydrogeologic system.
\nThe study area can be conceptually divided into three primary hydrogeologic environments. The first encompasses the southern uplands with relatively low topographic relief, where groundwater-flow is unconfined and occurs primarily in sandy till and glacial outwash overlying Archean-aged crystalline bedrock. The second includes a transitional area of higher topographic relief and shallow depth to bedrock, in the vicinity of ridges formed by steeply dipping, early-Proterozoic aged metasedimentary units of the Marquette Range Supergroup (including the Ironwood Formation), and late-Proterozoic igneous units associated with the Midcontinent Rift System (MRS). Groundwater-flow in this area likely occurs primarily through connected networks of bedrock fractures that are not well characterized, and also in isolated pockets of Quaternary deposits. The third and last hydrogeologic environment includes lowlands along Lake Superior where a deep sandstone aquifer is confined by thick deposits of clay-rich till.
\nModel input was compiled by using both published and unpublished data. Constant flux boundary conditions for the model perimeter were developed from a regional analytic element model described in appendix 1 of this report. Pumping from 26 high-capacity wells within the model area was included. The SFR stream network was developed from the National Hydrography Dataset (NHDPlus Version 2) and hydrography from the Wisconsin Department of Natural Resources (WDNR). Hydraulic conductivity values were determined for each model cell by interpolation from a network of pilot points, within zones representing major hydrogeologic units.
\nRecharge to the groundwater system was estimated on a cell-by-cell basis by using the Soil Water Balance code (SWB), with gridded daily temperature and precipitation data for the period 1980–2011, and GIS coverages of soil and land-surface conditions. Estimated recharge varies considerably, following spatial patterns in the precipitation and soil hydrologic group inputs. The lowest recharge values occur in the Superior lowlands, whereas the highest values occur in the upland areas, especially those underlain by sandy soils, and in the vicinity of bedrock hills.
\nThe model was calibrated to groundwater-levels and base flows obtained from the USGS National Water Information System (NWIS) database, and groundwater-levels obtained from the WDNR and Band River Band well-construction databases. Calibration was performed via nonlinear regression by using the parameter-estimation software suite PEST. Groundwater levels and base-flow observations in the calibration dataset were well simulated by the calibrated model, with reasonable values of hydraulic conductivity. The pilot-point parameters that were most constrained by observations during model calibration coincided with the locations containing the most wells (head observations)—especially the population centers of Ashland, Mellen, and other communities along the major highway corridors.
\nResults from the calibrated model illustrate differences in the nature of groundwater-flow within the watershed. In the southern part of the watershed, where bedrock is shallow, groundwater flow paths are relatively short, extending from local recharge areas to adjacent first and second-order streams. In contrast, laterally continuous deposits of clay-rich till covering the Superior Lowlands isolate most smaller streams from the sandstone aquifer, allowing for longer flow paths toward larger streams such as the Bad, Marengo, and White Rivers. Approximately three-quarters of all first-order stream cells were dry in the Superior Lowlands, compared to only half of first-order stream cells in the southern bedrock uplands.
\nThe model was used to delineate the groundwatershed for the Bad and Kakagon Rivers. “Groundwatershed” is defined as the area contributing groundwater discharge to one of these streams and their tributaries. The groundwatershed was found to align closely with the surface-watershed, with the most notable exception occurring along the southwestern half of Birch Hill, where surface water drains southwest towards the Potato River, and groundwater flows north and east towards Lake Superior. Similarly, the contributing area of groundwater-flow to the Reservation was delineated. Results indicate the off-Reservation groundwater contributing area to be limited in comparison to the extent of the watershed, extending southward into the highlands underlain by MRS igneous rock units, but not further into the area underlain by the Marquette Range Supergroup.
\nStable isotope samples were collected from 54 wells within the watershed, to investigate sources of groundwater. Oxygen-18 (δ 18O) values lower than -13.0 per mil were documented in the sampling, and likely indicate the presence of recharge water from the last glacial period (>9,500 years old) beneath the northern portion of the Reservation, in the vicinity of Odanah, Wisconsin.
\nFinally, a new data-worth analysis of potential new monitoring-well locations was performed by using the model. The relative worth of new measurements was evaluated based on their ability to increase confidence in model predictions of groundwater levels and base flows at 35 locations, under the condition of a proposed open-pit iron mine. Results of the new data-worth analysis, and other inputs and outputs from the Bad River model, are available through an online dynamic web mapping service at (http://wim.usgs.gov/badriver/).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155162","collaboration":"Prepared in cooperation with the Bad River Band of Lake Superior Chippewa; U.S. Bureau of Indian Affairs","usgsCitation":"Leaf, A.T., Fienen, M.N., Hunt, R.J., and Buchwald, C.A., 2015, Groundwater/Surface-Water Interactions in the Bad\n River Watershed, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2015–5162, 110 p., https://dx.doi.org/10.3133/sir20155162.","productDescription":"viii, 110 p.","numberOfPages":"122","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061535","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":311584,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5162/coverthb.jpg"},{"id":311585,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5162/sir20155162.pdf","text":"Report","size":"24.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015=5162"},{"id":332726,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7Z0368H","text":"MODFLOW-NWT model used to evaluate groundwater/surface-water interactions in the Bad River Watershed, Wisconsin"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Bad River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.06842041015625,\n 46.36777895261494\n ],\n [\n -91.06842041015625,\n 46.76996843356982\n ],\n [\n -90.43121337890625,\n 46.76996843356982\n ],\n [\n -90.43121337890625,\n 46.36777895261494\n ],\n [\n -91.06842041015625,\n 46.36777895261494\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wisconsin Water Science Center
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8505 Research W
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http://wi.water.usgs.gov/
In this study, water and whole rock samples from hydraulically fractured wells in the Marcellus Shale (Middle Devonian), and water from conventional wells producing from Upper Devonian sandstones were analyzed for lithium concentrations and isotope ratios (δ7Li). The distribution of lithium concentrations in different mineral groups was determined using sequential extraction. Structurally bound Li, predominantly in clays, accounted for 75-91 wt. % of total Li, whereas exchangeable sites and carbonate cement contain negligible Li (< 3%). Up to 20% of the Li is present in the oxidizable fraction (organic matter and sulfides). The δ7Li values for whole rock shale in Greene Co., Pennsylvania, and Tioga Co., New York, ranged from -2.3 to + 4.3‰, similar to values reported for other shales in the literature. The δ7Li values in shale rocks with stratigraphic depth record progressive weathering of the source region; the most weathered and clay-rich strata with isotopically light Li are found closest to the top of the stratigraphic section. Diagenetic illite-smectite transition could also have partially affected the bulk Li content and isotope ratios of the Marcellus Shale.
\nIn Greene Co., southwest Pennsylvania, the Upper Devonian sandstone formation waters have δ7Li values of + 14.6 ± 1.2 (2SD, n = 25), and are distinct from Marcellus Shale formation waters which have δ7Li of + 10.0 ± 0.8 (2SD, n = 12). These two formation waters also maintain distinctive 87Sr/86Sr ratios suggesting hydrologic separation between these units. Applying temperature-dependent illitilization model to Marcellus Shale, we found that Li concentration in clay minerals increased with Li concentration in pore fluid during diagenetic illite-smectite transition. Samples from north central PA show a much smaller range in both δ7Li and 87Sr/86Sr than in southwest Pennsylvania. Spatial variations in Li and δ7Li values show that Marcellus formation waters are not homogeneous across the Appalachian Basin. Marcellus formation waters in the northeastern Pennsylvania portion of the basin show a much smaller range in both δ7Li and 87Sr/86Sr, suggesting long term, cross-formational fluid migration in this region. Assessing the impact of potential mixing of fresh water with deep formation water requires establishment of a geochemical and isotopic baseline in the shallow, fresh water aquifers, and site specific characterization of formation water, followed by long-term monitoring, particularly in regions of future shale gas development.
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W.","contributorId":150019,"corporation":false,"usgs":false,"family":"Hammack","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":17887,"text":"National Energy Technology Laboratory, Department of Energy","active":true,"usgs":false}],"preferred":false,"id":580430,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70157563,"text":"sir20155138 - 2015 - Geohydrology and water quality of the stratified-drift aquifers in Upper Buttermilk Creek and Danby Creek Valleys, Town of Danby, Tompkins County, New York","interactions":[],"lastModifiedDate":"2015-11-24T09:07:20","indexId":"sir20155138","displayToPublicDate":"2015-11-20T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5138","title":"Geohydrology and water quality of the stratified-drift aquifers in Upper Buttermilk Creek and Danby Creek Valleys, Town of Danby, Tompkins County, New York","docAbstract":"In 2006, the U.S. Geological Survey, in cooperation with the Town of Danby and the Tompkins County Planning Department, began a study of the stratified-drift aquifers in the upper Buttermilk Creek and Danby Creek valleys in the Town of Danby, Tompkins County, New York. In the northern part of the north-draining upper Buttermilk Creek valley, there is only one sand and gravel aquifer, a confined basal unit that overlies bedrock. In the southern part of upper Buttermilk Creek valley, there are as many as four sand and gravel aquifers, two are unconfined and two are confined. In the south-draining Danby Creek valley, there is an unconfined aquifer consisting of outwash and kame sand and gravel (deposited by glacial meltwaters during the late Pleistocene Epoch) and alluvial silt, sand, and gravel (deposited by streams during the Holocene Epoch). In addition, throughout the study area, there are several small local unconfined aquifers where large tributaries deposited alluvial fans in the valley.
\nThe principal sources of recharge to the unconfined aquifers in the study area include direct infiltration of precipitation (rain and snowmelt) at land surface, unchanneled surface runoff from adjacent hillsides that seeps into the aquifer along the edges of the valley, groundwater inflow from adjacent till and bedrock that enters the aquifer along the sides of the valley, and seepage loss from upland-tributary streams where they flow over their alluvial fans in the valley. The percentages of all sources of recharge to the contiguous unconfined aquifer in Danby Creek valley include 16 percent from precipitation that falls directly over the aquifer, 55 percent from unchanneled surface runoff and groundwater inflow from hillsides, and 29 percent from losing tributary streams that cross the aquifer. The total annual recharge to the contiguous unconfined aquifer is 2.56 cubic feet per second (604 million gallons per year).
\nThe principal sources of recharge to the confined aquifers include precipitation that falls directly on the surficial confining unit, which then slowly flows vertically downward through the fine-grained sediments and enters the confined aquifer, and groundwater inflow from till and bedrock that borders the aquifer along adjacent hillsides and at the bottom of the valley. In addition, there is substantial amounts of recharge to the confined aquifers where the confining units are locally absent (forming windows) and where parts of the confining units consist of sediments of low to moderate permeability (forming a semiconfining layer).
\nIn the northern part of the study area (upper Buttermilk Creek valley), groundwater in the stratified-drift aquifers discharges to (1) domestic and commercial wells; (2) Buttermilk Creek in the area near the northern town border, and (3) and a small unnamed stream in a ravine in Buttermilk State Park just north of the town border. In the southern part of the study area (Danby Creek valley), groundwater discharges (1) to domestic, commercial, and farm wells; (2) to Danby Creek; (3) to a large wetland in the central parts of Danby Creek valley; and (4) as losses because of plant uptake and evaporation. About 300 people depend on groundwater from the upper Buttermilk Creek and Danby Creek stratified-drift aquifer system.
\nAn unconfined surficial aquifer about 8,000 feet (ft) long and as much as 800 ft wide, with a saturated thickness of about 20 ft, occupies the lower (southeastern most) 8,000 ft of Danby Creek valley within the Town of Danby. However, because the aquifer is thin, the volume of water stored in the aquifer is small and the potential for induced recharge from Danby Creek during summer periods of low flow is also small, an array of wells would probably be needed to provide sustainable continuous amount of water to large water users such as municipalities and industries. Additional data and a groundwater flow model would be required to estimate sustainable withdrawal from the confined aquifers in upper Buttermilk Creek valley. Well data from water-well drillers through 2012 indicate that the confined aquifers in upper Buttermilk Creek valley are thin (typically about 10 feet thick) and the reported well-yield data suggest these aquifers may not be capable of supplying sufficient water to meet the needs of municipalities and industries. However, additional geohydrologic data leading to calibration of a groundwater flow model would be needed to properly evaluate the long-term (multiple years) potential yield of the confined aquifer system in upper Buttermilk Creek valley and of the unconfined aquifer in Danby Creek valley.
\nDuring 2007–10, groundwater samples were collected from 13 wells including 7 wells that are completed in the confined sand and gravel aquifers, 1 well that is completed in the unconfined aquifer, and 5 wells that are completed in the bedrock aquifers. Calcium dominates the cation composition and bicarbonate dominates the anion composition in most groundwater. Water quality in the study area generally meets state and Federal drinking-water standards but concentrations of some constituents exceeded the standards. The standards that were exceeded include sodium (3 samples), dissolved solids (1 sample), iron (3 samples), manganese (8 samples), and arsenic (1 sample).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155138","collaboration":"Prepared in cooperation with the Town of Danby and the Tompkins County Planning Department","usgsCitation":"Miller, T.S., 2015, Geohydrology and water quality of the stratified-drift aquifers in upper Buttermilk Creek and Danby Creek valleys, Town of Danby, Tompkins County, New York: U.S. Geological Survey Scientific Investigations Report 2015–5138, 66 p., https://dx.doi.org/10.3133/sir20155138.","productDescription":"viii, 66 p.","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-055138","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":311559,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5138/coverthb.jpg"},{"id":311560,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5138/sir20155138.pdf","text":"Report","size":"6.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5138"}],"country":"United States","state":"New York","county":"Tompkins County","city":"Danby","otherGeospatial":"Danby Creek, Upper Buttermilk Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.51290893554688,\n 42.357782825014176\n ],\n [\n -76.51290893554688,\n 42.428524987525385\n ],\n [\n -76.43051147460938,\n 42.428524987525385\n ],\n [\n -76.43051147460938,\n 42.357782825014176\n ],\n [\n -76.51290893554688,\n 42.357782825014176\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
30 Brown Road
Ithaca, NY 14850
http://ny.water.usgs.gov
Information requests:
(518) 285-5602
Next-Generation Radar (NEXRAD) has become an integral component in the estimation of precipitation (Kitzmiller and others, 2013). The high spatial and temporal resolution of NEXRAD has revolutionized the ability to estimate precipitation across vast regions, which is especially beneficial in areas without a dense rain-gage network. With the improved precipitation estimates, hydrologic models can produce reliable streamflow forecasts for areas across the United States. NEXRAD data from the National Weather Service (NWS) has been an invaluable tool used by the U.S. Geological Survey (USGS) for numerous projects and studies; NEXRAD data processing techniques similar to those discussed in this Fact Sheet have been developed within the USGS, including the NWS Quantitative Precipitation Estimates archive developed by Blodgett (2013).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153076","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Ortel, T.W., and Spies, R.R., 2015, NEXRAD Quantitative precipitation estimates, data acquisition, and processing for the DuPage County, Illinois, streamflow-simulation modeling system: U.S. Geological Survey Fact Sheet 2015–3076, 2 p., https://dx.doi.org/10.3133/fs20153076.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057259","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":311538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3076/coverthb.jpg"},{"id":311539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3076/fs20153076.pdf","text":"Report","size":"1.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3076"}],"country":"United States","state":"Illinois","county":"DuPage County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.49212646484374,\n 41.281934557995356\n ],\n [\n -88.49212646484374,\n 42.04929263868686\n ],\n [\n -87.6104736328125,\n 42.04929263868686\n ],\n [\n -87.6104736328125,\n 41.281934557995356\n ],\n [\n -88.49212646484374,\n 41.281934557995356\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801–2347
Phone: (217) 328–USGS (8747)
http://il.water.usgs.gov/
The Great Lakes face a number of serious challenges that cause damage to water quality, habitat, ecology, and coastal health. Excess nutrients from point and nonpoint sources have a history of causing harmful algal blooms (HABs); since the late 1990s, a resurgence of HABs have forced beach closures and resulted in water quality impairments across the Great Lakes. Studies increasingly point to phosphorus (P) runoff from agricultural lands as the cause of these HABs. In 2010, the Great Lakes Restoration Initiative (GLRI) was launched to revitalize the Great Lakes. The GLRI aims to address the challenges facing the Great Lakes and provide a framework for restoration and protection. As part of this effort, the Priority Watersheds Work Group (PWWG), cochaired by the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Agriculture-Natural Resources Conservation Service (USDA–NRCS), is targeting Priority Watersheds (PWs) to reduce the amount of P reaching the Great Lakes. Within the PWs, USDA–NRCS identifies small-scale subbasins with high concentrations of agriculture for coordinated nutrient reduction efforts and enhanced monitoring and modeling. The USDA–NRCS supplies financial and/or technical assistance to producers to install or implement best management practices (BMPs) to lessen the negative effects of agriculture to water quality; additional funding is provided by the GLRI through USDA–NRCS to saturate the small-scale subbasins with BMPs. The watershed modeling component, introduced in this fact sheet, assesses the effectiveness of USDA–NRCS funded BMPs, and nutrient reductions because of GLRI or other funding programs are differentiated. Modeling scenarios consider BMPs that have already been applied and those planned to be implemented across the small-scale subbasins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153067","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture-Natural Resources Conservation Service","usgsCitation":"Merriman, K.R., 2015, Development of an assessment tool for agricultural best management practice Implementation in the Great Lakes Restoration Initiative priority watersheds—Alger Creek, tributary to Saginaw River, Michigan: U.S. Geological Survey Fact Sheet 2015–3067, 6 p., https://dx.doi.org/10.3133/fs20153067.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070206","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":438665,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ST7P39","text":"USGS data release","linkHelpText":"Daily loads of nutrients, sediment, and chloride at Great Lakes Restoration Initiative USGS edge-of-field and tile stations"},{"id":311095,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153065","text":"Fact Sheet 2015-3065","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3067"},{"id":311096,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153066","text":"Fact Sheet 2015-3066","size":"1.28 MB","description":"FS 2015-3067"},{"id":311093,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3067/coverthb.jpg"},{"id":311094,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3067/fs20153067.pdf","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3067"}],"country":"United States","state":"Michigan","otherGeospatial":"Alger Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.166015625,\n 42.52879629320373\n ],\n [\n -85.166015625,\n 43.937461690316646\n ],\n [\n -82.77099609375,\n 43.937461690316646\n ],\n [\n -82.77099609375,\n 42.52879629320373\n ],\n [\n -85.166015625,\n 42.52879629320373\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave>
Urbana, IL 6180
http://il.water.usgs.gov
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The Great Lakes face a number of serious challenges that cause damage to water quality, habitat, ecology, and coastal health. Excess nutrients from point and nonpoint sources have a history of causing harmful algal blooms (HABs); since the late 1990s, a resurgence of HABs have forced beach closures and resulted in water quality impairments across the Great Lakes. Studies increasingly point to phosphorus (P) runoff from agricultural lands as the cause of these HABs. In 2010, the Great Lakes Restoration Initiative (GLRI) was launched to revitalize the Great Lakes. The GLRI aims to address the challenges facing the Great Lakes and provide a framework for restoration and protection. As part of this effort, the Priority Watersheds Work Group (PWWG), cochaired by the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Agriculture-Natural Resources Conservation Service (USDA–NRCS), is targeting Priority Watersheds (PWs) to reduce the amount of P reaching the Great Lakes. Within the PWs, USDA–NRCS identifies small-scale subbasins with high concentrations of agriculture for coordinated nutrient reduction efforts and enhanced monitoring and modeling. The USDA–NRCS supplies financial and/or technical assistance to producers to install or implement best management practices (BMPs) to lessen the negative effects of agriculture to water quality; additional funding is provided by the GLRI through USDA–NRCS to saturate the small-scale subbasins with BMPs. The watershed modeling component, introduced in this fact sheet, assesses the effectiveness of USDA–NRCS funded BMPs, and nutrient reductions because of GLRI or other funding programs are differentiated. Modeling scenarios consider BMPs that have already been applied and those planned to be implemented across the small-scale subbasins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153066","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture-Natural Resources Conservation Service","usgsCitation":"Merriman, K.R., 2015, Development of an assessment tool for agricultural best management practice implementation in the Great Lakes restoration initiative priority watersheds—Eagle Creek, tributary to Maumee River, Ohio: U.S. U.S. Geological Survey Fact Sheet 2015–3066, 6 p., https://dx.doi.org/10.3133/fs20153066.","productDescription":"6 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070205","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":311089,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3066/coverthb.jpg"},{"id":311090,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3066/fs20153066.pdf","text":"Report","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3066"},{"id":311091,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153065","text":"Fact Sheet 2015-3065","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3066"},{"id":311092,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153067","text":"Fact Sheet 2015-3067","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3066"}],"country":"United States","state":"Ohio","otherGeospatial":"Eagle Creek, Maumee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.72749328613281,\n 40.730608477796636\n ],\n [\n -83.72749328613281,\n 41.000629848685385\n ],\n [\n -83.57574462890625,\n 41.000629848685385\n ],\n [\n -83.57574462890625,\n 40.730608477796636\n ],\n [\n -83.72749328613281,\n 40.730608477796636\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave.
Urbana, IL 61801
http://il.water.usgs.gov
The Great Lakes face a number of serious challenges that cause damage to water quality, habitat, ecology, and coastal health. Excess nutrients from point and nonpoint sources have a history of causing harmful algal blooms (HABs); since the late 1990s, a resurgence of HABs have forced beach closures and resulted in water quality impairments across the Great Lakes. Studies increasingly point to phosphorus (P) runoff from agricultural lands as the cause of these HABs. In 2010, the Great Lakes Restoration Initiative (GLRI) was launched to revitalize the Great Lakes. The GLRI aims to address the challenges facing the Great Lakes and provide a framework for restoration and protection. As part of this effort, the Priority Watersheds Work Group (PWWG), cochaired by the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Agriculture-Natural Resources Conservation Service (USDA–NRCS), is targeting Priority Watersheds (PWs) to reduce the amount of P reaching the Great Lakes. Within the PWs, USDA–NRCS identifies small-scale subbasins with high concentrations of agriculture for coordinated nutrient reduction efforts and enhanced monitoring and modeling. The USDA–NRCS supplies financial and/or technical assistance to producers to install or implement best management practices (BMPs) to lessen the negative effects of agriculture to water quality; additional funding is provided by the GLRI through USDA–NRCS to saturate the small-scale subbasins with BMPs. The watershed modeling component, introduced in this fact sheet, assesses the effectiveness of USDA–NRCS funded BMPs, and nutrient reductions because of GLRI or other funding programs are differentiated. Modeling scenarios consider BMPs that have already been applied and those planned to be implemented across the small-scale subbasins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153065","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture-Natural Resources Conservation Service","usgsCitation":"Merriman, K.R., 2015, Development of an assessment tool for agricultural best management practice implementation in the Great Lakes Restoration Initiative priority watersheds—Upper East River, tributary to Green Bay, Wisconsin: U.S. Geological Survey Fact Sheet 2015–3065, 6 p., https://dx.doi.org/10.3133/fs20153065.","productDescription":"6 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070173","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":311075,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153066","text":"Fact Sheet 2015-3066","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3065"},{"id":311076,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153067","text":"Fact Sheet 2015-3067","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3065"},{"id":311073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3065/coverthb.jpg"},{"id":311074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3065/fs20153065.pdf","text":"Report","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3065"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Upper East River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.6376953125,\n 44.21764696919354\n ],\n [\n -88.6376953125,\n 44.66865287227321\n ],\n [\n -87.703857421875,\n 44.66865287227321\n ],\n [\n -87.703857421875,\n 44.21764696919354\n ],\n [\n -88.6376953125,\n 44.21764696919354\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave.
Urbana, IL 61801
http://il.water.usgs.gov
An estimated one-dimensional layered model of electrical resistivity beneath Florida was developed from published geological and geophysical information. The resistivity of each layer is represented by plausible upper and lower bounds as well as a geometric mean resistivity. Corresponding impedance transfer functions, Schmucker-Weidelt transfer functions, apparent resistivity, and phase responses are calculated for inducing geomagnetic frequencies ranging from 10−5 to 100 hertz. The resulting one-dimensional model and response functions can be used to make general estimates of time-varying electric fields associated with geomagnetic storms such as might represent induction hazards for electric-power grid operation. The plausible upper- and lower-bound resistivity structures show the uncertainty, giving a wide range of plausible time-varying electric fields.
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\"}}]}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS–966
Denver, CO 80225-0046
http://geohazards.cr.usgs.gov/
Contours maps (such as topographic maps) compress the information of a function over a two-dimensional area into a discrete set of closed lines that connect points of equal value (isolines), striking a fine balance between expressiveness and cognitive simplicity. They allow humans to perform many common sense reasoning tasks about the underlying function (e.g. elevation).
\nThis paper analyses and formalizes contour semantics in a first-order logic ontology that forms the basis for enabling computational common sense reasoning about contour information. The elicited contour semantics comprises four key concepts – contour regions, contour lines, contour values, and contour sets – and their subclasses and associated relations, which are grounded in an existing qualitative spatial ontology. All concepts and relations are illustrated and motivated by physical-geographic features identifiable on topographic contour maps. The encoding of the semantics of contour concepts in first-order logic and a derived conceptual model as basis for an OWL ontology lay the foundation for fully automated, semantically-aware qualitative and quantitative reasoning about contours.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Spatial information theory: 12th International Conference, COSIT 2015 Santa Fe, NM, USA, October 12–16, 2015, proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-319-23374-1_18","usgsCitation":"Usery, E.L., and Hahmann, T., 2015, What is in a contour map? A region-based logical formalization of contour semantics, chap. of Spatial information theory: 12th International Conference, COSIT 2015 Santa Fe, NM, USA, October 12–16, 2015, proceedings, v. 9368, p. 375-399, https://doi.org/10.1007/978-3-319-23374-1_18.","productDescription":"25 p.","startPage":"375","endPage":"399","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065389","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":311569,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9368","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-15","publicationStatus":"PW","scienceBaseUri":"564ef2bbe4b064dd1d095566","contributors":{"authors":[{"text":"Usery, E. Lynn 0000-0002-2766-2173 usery@usgs.gov","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":231,"corporation":false,"usgs":true,"family":"Usery","given":"E.","email":"usery@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":580288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hahmann, Torsten","contributorId":149994,"corporation":false,"usgs":false,"family":"Hahmann","given":"Torsten","email":"","affiliations":[{"id":17881,"text":"Assistant Professor, University of Maine","active":true,"usgs":false}],"preferred":false,"id":580289,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159728,"text":"70159728 - 2015 - Biofilm formation of Francisella noatunensis subsp. orientalis","interactions":[],"lastModifiedDate":"2016-12-19T11:59:44","indexId":"70159728","displayToPublicDate":"2015-11-19T12:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3685,"text":"Veterinary Microbiology","active":true,"publicationSubtype":{"id":10}},"title":"Biofilm formation of Francisella noatunensis subsp. orientalis","docAbstract":"Francisella noatunensis subsp. orientalis (Fno) is an emergent fish pathogen in both marine and fresh water environments. The bacterium is suspected to persist in the environment even without the presence of a suitable fish host. In the present study, the influence of different abiotic factors such as salinity and temperature were used to study the biofilm formation of different isolates of Fno including intracellular growth loci C (iglC)and pathogenicity determinant protein A (pdpA) knockout strains. Finally, we compared the susceptibility of planktonic and biofilm to three disinfectants used in the aquaculture and ornamental fish industry, namely Virkon®, bleach and hydrogen peroxide. The data indicates that Fno is capable of producing biofilms within 24 h where both salinity as well as temperature plays a role in the growth and biofilm formation of Fno. Mutations in theiglC or pdpA, both known virulence factors, do not appear to affect the capacity of Fno to produce biofilms, and the minimum inhibitory concentration, and minimum biocidal concentration for the three disinfectants were lower than the minimum biofilm eradication concentration values. This information needs to be taken into account if trying to eradicate the pathogen from aquaculture facilities or aquariums.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.vetmic.2015.10.007","usgsCitation":"Soto, E., Halliday-Wimmonds, I., Francis, S., Kearney, M.T., and Hansen, J.D., 2015, Biofilm formation of Francisella noatunensis subsp. orientalis: Veterinary Microbiology, v. 181, no. 3-4, p. 313-317, https://doi.org/10.1016/j.vetmic.2015.10.007.","productDescription":"5 p.","startPage":"313","endPage":"317","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061353","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":311568,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"181","issue":"3-4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564ef2b5e4b064dd1d095552","contributors":{"authors":[{"text":"Soto, Esteban","contributorId":64142,"corporation":false,"usgs":true,"family":"Soto","given":"Esteban","email":"","affiliations":[],"preferred":false,"id":580224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halliday-Wimmonds, Iona","contributorId":149970,"corporation":false,"usgs":false,"family":"Halliday-Wimmonds","given":"Iona","email":"","affiliations":[{"id":17866,"text":"Department of Biomedical Sciences, Ross University School of Veterinary Medicine, PO Box 334, Basseterre, St. Kitts, West Indies","active":true,"usgs":false}],"preferred":false,"id":580225,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Francis, Stewart","contributorId":177541,"corporation":false,"usgs":false,"family":"Francis","given":"Stewart","email":"","affiliations":[],"preferred":false,"id":656130,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kearney, Michael T.","contributorId":149971,"corporation":false,"usgs":false,"family":"Kearney","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":17867,"text":"Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, Louisiana, USA 70803","active":true,"usgs":false}],"preferred":false,"id":580226,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hansen, John D. 0000-0002-3006-2734 jhansen@usgs.gov","orcid":"https://orcid.org/0000-0002-3006-2734","contributorId":3440,"corporation":false,"usgs":true,"family":"Hansen","given":"John","email":"jhansen@usgs.gov","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":580223,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70154881,"text":"70154881 - 2015 - Assessment and Mmanagement of North American horseshoe crab populations, with emphasis on a multispecies framework for Delaware Bay, U.S.A. populations: Chapter 24","interactions":[],"lastModifiedDate":"2016-08-17T11:33:56","indexId":"70154881","displayToPublicDate":"2015-11-19T12:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Assessment and Mmanagement of North American horseshoe crab populations, with emphasis on a multispecies framework for Delaware Bay, U.S.A. populations: Chapter 24","docAbstract":"The horseshoe crab fishery on the US Atlantic coast represents a compelling fishery management story for many reasons, including ecological complexity, health and human safety ramifications, and socio-economic conflicts. Knowledge of stock status and assessment and monitoring capabilities for the species have increased greatly in the last 15 years and permitted managers to make more informed harvest recommendations. Incorporating the bioenergetics needs of migratory shorebirds, which feed on horseshoe crab eggs, into the management framework for horseshoe crabs was identified as a goal, particularly in the Delaware Bay region where the birds and horseshoe crabs exhibit an important ecological interaction. In response, significant effort was invested in studying the population dynamics, migration ecology, and the ecologic relationship of a key migratory shorebird, the Red Knot, to horseshoe crabs. A suite of models was developed that linked Red Knot populations to horseshoe crab populations through a mass gain function where female spawning crab abundance determined what proportion of the migrating Red Knot population reached a critical body mass threshold. These models were incorporated in an adaptive management framework wherein optimal harvest decisions for horseshoe crab are recommended based on several resource-based and value-based variables and thresholds. The current adaptive framework represents a true multispecies management effort where additional data over time are employed to improve the predictive models and reduce parametric uncertainty. The possibility of increasing phenologic asynchrony between the two taxa in response to climate change presents a potential challenge to their ecologic interaction in Delaware Bay.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Changing Global Perspectives on Horseshoe Crab Biology, Conservation and Management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","publisherLocation":"Cham","doi":"10.1007/978-3-319-19542-1_24","usgsCitation":"Millard, M.J., Sweka, J.A., McGowan, C., and Smith, D., 2015, Assessment and Mmanagement of North American horseshoe crab populations, with emphasis on a multispecies framework for Delaware Bay, U.S.A. populations: Chapter 24, chap. of Changing Global Perspectives on Horseshoe Crab Biology, Conservation and Management, p. 407-431, https://doi.org/10.1007/978-3-319-19542-1_24.","startPage":"407","endPage":"431","numberOfPages":"25","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059817","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":326655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57b58abee4b03bcb0104bb5e","contributors":{"authors":[{"text":"Millard, Michael J.","contributorId":23411,"corporation":false,"usgs":false,"family":"Millard","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":645754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sweka, John A.","contributorId":80945,"corporation":false,"usgs":true,"family":"Sweka","given":"John","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":645755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGowan, Conor P. cmcgowan@usgs.gov","contributorId":145496,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor P.","email":"cmcgowan@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":564308,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, David R.","contributorId":173756,"corporation":false,"usgs":false,"family":"Smith","given":"David R.","affiliations":[],"preferred":false,"id":645756,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159331,"text":"sir20155154 - 2015 - Streambed scour evaluations and conditions at selected bridge sites in Alaska, 2012","interactions":[],"lastModifiedDate":"2022-03-15T17:20:23.416494","indexId":"sir20155154","displayToPublicDate":"2015-11-19T12:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5154","title":"Streambed scour evaluations and conditions at selected bridge sites in Alaska, 2012","docAbstract":"Streambed scour potential was evaluated at 18 river- and stream-spanning bridges in Alaska that have unknown foundation details or a lack of existing scour analysis. All sites were evaluated for stream stability and long-term scour potential. Contraction scour and abutment scour were calculated for 17 bridges, and pier scour was calculated for 7 bridges that had piers. Vertical contraction (pressure flow) scour was calculated for sites with overtopping floods (where the modeled water surface was higher than the superstructure of the bridge). In most cases, hydraulic models of the 1- and 0.2-percent annual exceedance probability floods (also known as the 100- and 500-year floods, respectively) were used to derive hydraulic variables for the scour calculations. Alternate flood values were used in scour calculations for sites where smaller floods overtopped a bridge or where standard flood-frequency estimation techniques did not apply. Scour was also calculated for large recorded floods at several sites. Equations for scour in cohesive soils were used for sites where streambed sediment was silt-sized or smaller.
\nChannel instability at four sites was related to human activities (in-channel mining, dredging, and channel relocation). Three of the dredged sites are located on active unstable alluvial fans and were graded to inhibit aggradation. The trend toward aggradation during major floods at these sites greatly reduces confidence in scour estimates.
\nVertical contraction and pressure flow occurred during 1 percent or smaller annual exceedance probability floods at five sites, including three aggradation sites. Contraction scour exceeded 5 feet at two sites, and total scour at piers (pier scour plus contraction scour) exceeded 5 feet at two sites. Debris accumulation increased calculated pier scour at six sites by an average of 1.2 feet. Total scour at abutments including contraction scour exceeded 5 feet at seven sites. Scour estimates seemed excessive at aggradation sites where upstream sediment supply controls scour and deposition processes, at cohesive soil sites where conservative assumptions were made for soil strength and flood duration, and for abutment scour at sites where failure of the embankment and attendant channel widening would reduce scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155154","collaboration":"Prepared in cooperation with the Alaska Department of Transporation and Public Facilities","usgsCitation":"Beebee, R.A., and Schauer, P.V., 2015, Streambed scour evaluations and conditions at selected bridge sites in Alaska, 2012: U.S. Geological Survey Scientific Investigations Report 2015–5154, 45 p., https://dx.doi.org/10.3133/sir20155154.","productDescription":"Report: vi, 45 p.; Appendix","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2012-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-064803","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":311582,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5154/sir20155154_appendixa.xlsx","text":"Appendix A","size":"105 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5154 Appendix A"},{"id":311546,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5154/sir20155154.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5154 PDF"},{"id":311545,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5154/coverthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -131.748046875,\n 54.265224078605655\n ],\n [\n -129.7265625,\n 55.37911044801047\n ],\n [\n -131.044921875,\n 56.70450561416937\n ],\n [\n -135.439453125,\n 59.80063426102869\n ],\n [\n -137.63671875,\n 59.265880628258095\n ],\n [\n -139.658203125,\n 60.326947742998414\n ],\n [\n -140.888671875,\n 60.413852350464914\n ],\n [\n -141.064453125,\n 67.57571741708057\n ],\n [\n -145.546875,\n 67.60922060496382\n ],\n [\n -149.677734375,\n 66.65297740055279\n ],\n [\n -154.16015625,\n 64.20637724320852\n ],\n [\n -154.16015625,\n 61.98026726504402\n ],\n [\n -152.666015625,\n 59.80063426102869\n ],\n [\n -151.962890625,\n 59.085738569819505\n ],\n [\n -148.359375,\n 59.80063426102869\n ],\n [\n -146.162109375,\n 60.45721779774397\n ],\n [\n -142.91015625,\n 59.84481485969105\n ],\n [\n -140.44921875,\n 59.712097173322924\n ],\n [\n -136.845703125,\n 58.03137242177637\n ],\n [\n -133.41796874999997,\n 54.316523240258284\n ],\n [\n -131.748046875,\n 54.265224078605655\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.86328125,\n 57.37393841871411\n ],\n [\n -152.578125,\n 58.53959476664049\n ],\n [\n -151.78710937499997,\n 58.07787626787517\n ],\n [\n -152.490234375,\n 57.18390185831188\n ],\n [\n -154.423828125,\n 56.559482483762245\n ],\n [\n -154.86328125,\n 57.37393841871411\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
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4210 University Drive
Anchorage, Alaska 99508-4560
http://alaska.usgs.gov
Piscine reovirus (PRV) is a double stranded non-enveloped RNA virus detected in farmed and wild salmonids. This study examined the phylogenetic relationships among different PRV sequence types present in samples from salmonids in Western Canada and the US, including Alaska (US), British Columbia (Canada) and Washington State (US). Tissues testing positive for PRV were partially sequenced for segment S1, producing 71 sequences that grouped into 10 unique sequence types. Sequence analysis revealed no identifiable geographical or temporal variation among the sequence types. Identical sequence types were found in fish sampled in 2001, 2005 and 2014. In addition, PRV positive samples from fish derived from Alaska, British Columbia and Washington State share identical sequence types. Comparative analysis of the phylogenetic tree indicated that Canada/US Pacific Northwest sequences formed a subgroup with some Norwegian sequence types (group II), distinct from other Norwegian and Chilean sequences (groups I, III and IV). Representative PRV positive samples from farmed and wild fish in British Columbia and Washington State were subjected to genome sequencing using next generation sequencing methods. Individual analysis of each of the 10 partial segments indicated that the Canadian and US PRV sequence types clustered separately from available whole genome sequences of some Norwegian and Chilean sequences for all segments except the segment S4. In summary, PRV was genetically homogenous over a large geographic distance (Alaska to Washington State), and the sequence types were relatively stable over a 13 year period.
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2015 - Evidence of population resistance to extreme low flows in a fluvial-dependent fish species","interactions":[],"lastModifiedDate":"2015-11-19T09:34:11","indexId":"70159742","displayToPublicDate":"2015-11-19T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of population resistance to extreme low flows in a fluvial-dependent fish species","docAbstract":"Extreme low streamflows are natural disturbances to aquatic populations. Species in naturally intermittent streams display adaptations that enhance persistence during extreme events; however, the fate of populations in perennial streams during unprecedented low-flow periods is not well-understood. Biota requiring swift-flowing habitats may be especially vulnerable to flow reductions. We estimated the abundance and local survival of a native fluvial-dependent fish species (Etheostoma inscriptum) across 5 years encompassing historic low flows in a sixth-order southeastern USA perennial river. Based on capturemark-recapture data, the study shoal may have acted as a refuge during severe drought, with increased young-of-the-year (YOY) recruitment and occasionally high adult immigration. Contrary to expectations, summer and autumn survival rates (30 days) were not strongly depressed during low-flow periods, despite 25%-80% reductions in monthly discharge. Instead, YOY survival increased with lower minimum discharge and in response to small rain events that increased low-flow variability. Age-1+ fish showed the opposite pattern, with survival decreasing in response to increasing low-flow variability. Results from this population dynamics study of a small fish in a perennial river suggest that fluvial-dependent species can be resistant to extreme flow reductions through enhanced YOY recruitment and high survival
","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2015-0173","usgsCitation":"Katz, R.A., and Freeman, M., 2015, Evidence of population resistance to extreme low flows in a fluvial-dependent fish species: Canadian Journal of Fisheries and Aquatic Sciences, v. 11, no. 29, p. 1776-1787, https://doi.org/10.1139/cjfas-2015-0173.","productDescription":"12 p.","startPage":"1776","endPage":"1787","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065219","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":311557,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"11","issue":"29","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564ef2b9e4b064dd1d09555c","contributors":{"authors":[{"text":"Katz, Rachel A.","contributorId":149995,"corporation":false,"usgs":false,"family":"Katz","given":"Rachel","email":"","middleInitial":"A.","affiliations":[{"id":17882,"text":"Odum School of Ecology, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":580305,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":580304,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159743,"text":"70159743 - 2015 - Estimating occupancy dynamics for large-scale monitoring networks: amphibian breeding occupancy across protected areas in the northeast United States","interactions":[],"lastModifiedDate":"2015-11-19T09:30:04","indexId":"70159743","displayToPublicDate":"2015-11-19T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating occupancy dynamics for large-scale monitoring networks: amphibian breeding occupancy across protected areas in the northeast United States","docAbstract":"Regional monitoring strategies frequently employ a nested sampling design where a finite set of study areas from throughout a region are selected within which intensive sub-sampling occurs. This sampling protocol naturally lends itself to a hierarchical analysis to account for dependence among sub-samples. Implementing such an analysis within a classic likelihood framework is computationally prohibitive with species occurrence data when accounting for detection probabilities. Bayesian methods offer an alternative framework to make this analysis feasible. We demonstrate a general approach for estimating occupancy when data come from a nested sampling design. Using data from a regional monitoring program of wood frogs (Lithobates sylvaticus) and spotted salamanders (Ambystoma maculatum) in vernal pools, we analyzed data using static and dynamic occupancy frameworks. We analyzed observations from 2004-2013collected within 14 protected areas located throughout the northeast United States . We use the data set to estimate trends in occupancy at both the regional and individual protected area level. We show that occupancy at the regional level was relatively stable for both species. Much more variation occurred within individual study areas, with some populations declining and some increasing for both species. We found some evidence for a latitudinal gradient in trends among protected areas. However, support for this pattern is overestimated when the hierarchical nature of the data collection is not controlled for in the analysis. For both species, occupancy appeared to be declining in the most southern areas, while occupancy was stable or increasing in more northern areas. These results shed light on the range-level population status of these pond-breeding amphibians and our approach provides a framework that can be used to examine drivers of change including among-year and among-site variation in occurrence dynamics, while properly accounting for nested structure of data collection.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.1679","usgsCitation":"Miller, D.A., and Grant, E., 2015, Estimating occupancy dynamics for large-scale monitoring networks: amphibian breeding occupancy across protected areas in the northeast United States: Ecology and Evolution, v. 5, no. 21, p. 4735-4746, https://doi.org/10.1002/ece3.1679.","productDescription":"12 p.","startPage":"4735","endPage":"4746","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066899","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471634,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.1679","text":"Publisher Index Page"},{"id":311556,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.7158203125,\n 36.61552763134925\n ],\n [\n -81.97998046875,\n 37.49229399862877\n ],\n [\n -82.59521484375,\n 38.18638677411551\n ],\n [\n -82.4853515625,\n 38.496593518947556\n ],\n [\n -82.19970703125,\n 39.06184913429154\n ],\n [\n -80.88134765625,\n 39.80853604144591\n ],\n [\n -80.5517578125,\n 40.697299008636755\n ],\n [\n -80.5517578125,\n 41.983994270935625\n ],\n [\n -78.90380859375,\n 42.81152174509788\n ],\n [\n -79.07958984375,\n 43.1811470593997\n ],\n [\n -79.12353515625,\n 43.34116005412307\n ],\n [\n -78.486328125,\n 43.389081939117496\n ],\n [\n -77.84912109375,\n 43.35713822211053\n ],\n [\n -77.431640625,\n 43.24520272203356\n ],\n [\n -76.22314453125,\n 43.61221676817573\n ],\n [\n -76.35498046875,\n 44.18220395771566\n ],\n [\n -74.8828125,\n 45.02695045318546\n ],\n [\n -71.52099609375,\n 45.042478050891546\n ],\n [\n -71.4111328125,\n 45.27488643704894\n ],\n [\n -70.99365234375,\n 45.321254361171476\n ],\n [\n -70.59814453125,\n 45.644768217751924\n ],\n [\n -70.3125,\n 46.057985244793024\n ],\n [\n -70.29052734375,\n 46.40756396630067\n ],\n [\n -70.07080078125,\n 46.49839225859763\n ],\n [\n -70.02685546875,\n 46.76996843356982\n ],\n [\n -69.23583984375,\n 47.502358951968596\n ],\n [\n -68.994140625,\n 47.368594345213374\n ],\n [\n -68.97216796875,\n 47.234489635299184\n ],\n [\n -68.53271484375,\n 47.29413372501023\n ],\n [\n -68.3349609375,\n 47.41322033016902\n ],\n [\n -67.763671875,\n 47.08508535995384\n ],\n [\n -67.74169921875,\n 45.75219336063106\n ],\n [\n -67.43408203124999,\n 45.598665689820656\n ],\n [\n -67.30224609375,\n 45.27488643704894\n ],\n [\n -67.0166015625,\n 45.058001435398296\n ],\n [\n -66.884765625,\n 44.793530904744074\n ],\n [\n -67.587890625,\n 44.465151013519616\n ],\n [\n -68.66455078125,\n 44.008620115415354\n ],\n [\n -69.80712890625,\n 43.8028187190472\n ],\n [\n -70.55419921875,\n 43.32517767999296\n ],\n [\n -70.77392578125,\n 42.79540065303723\n ],\n [\n -70.83984375,\n 42.374778361114195\n ],\n [\n -70.48828125,\n 41.934976500546604\n ],\n [\n -70.1806640625,\n 41.80407814427237\n ],\n [\n -70.3125,\n 42.114523952464246\n ],\n [\n -69.8291015625,\n 42.00032514831621\n ],\n [\n -69.89501953125,\n 41.623655390686395\n ],\n [\n -70.2685546875,\n 41.475660200278234\n ],\n [\n -71.19140625,\n 41.343824581185686\n ],\n [\n -72.00439453125,\n 41.22824901518532\n ],\n [\n -71.9384765625,\n 40.93011520598305\n ],\n [\n -73.6083984375,\n 40.43022363450859\n ],\n [\n -73.95996093749999,\n 40.27952566881291\n ],\n [\n -74.77294921875,\n 39.01064750994083\n ],\n [\n -75.0146484375,\n 38.856820134743636\n ],\n [\n -75.08056640625,\n 38.09998264736481\n ],\n [\n -75.849609375,\n 37.317751851636906\n ],\n [\n -75.95947265625,\n 36.56260003738548\n ],\n [\n -83.7158203125,\n 36.61552763134925\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","issue":"21","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-27","publicationStatus":"PW","scienceBaseUri":"564ef2b9e4b064dd1d09555a","contributors":{"authors":[{"text":"Miller, David A.W. davidmiller@usgs.gov","contributorId":4043,"corporation":false,"usgs":true,"family":"Miller","given":"David","email":"davidmiller@usgs.gov","middleInitial":"A.W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":580307,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Evan H. Campbell ehgrant@usgs.gov","contributorId":146545,"corporation":false,"usgs":true,"family":"Grant","given":"Evan H. Campbell","email":"ehgrant@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":580306,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159780,"text":"70159780 - 2015 - Determining habitat quality for species that demonstrate dynamic habitat selection","interactions":[],"lastModifiedDate":"2015-12-28T15:27:01","indexId":"70159780","displayToPublicDate":"2015-11-19T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Determining habitat quality for species that demonstrate dynamic habitat selection","docAbstract":"Determining habitat quality for wildlife populations requires relating a species' habitat to its survival and reproduction. Within a season, species occurrence and density can be disconnected from measures of habitat quality when resources are highly seasonal, unpredictable over time, and patchy. Here we establish an explicit link among dynamic selection of changing resources, spatio-temporal species distributions, and fitness for predictive abundance and occurrence models that are used for short-term water management and long-term restoration planning. We used the wading bird distribution and evaluation models (WADEM) that estimate (1) daily changes in selection across resource gradients, (2) landscape abundance of flocks and individuals, (3) conspecific foraging aggregation, and (4) resource unit occurrence (at fixed 400 m cells) to quantify habitat quality and its consequences on reproduction for wetland indicator species. We linked maximum annual numbers of nests detected across the study area and nesting success of Great Egrets (Ardea alba), White Ibises (Eudocimus albus), and Wood Storks (Mycteria americana) over a 20-year period to estimated daily dynamics of food resources produced by WADEM over a 7490 km2 area. For all species, increases in predicted species abundance in March and high abundance in April were strongly linked to breeding responses. Great Egret nesting effort and success were higher when birds also showed greater conspecific foraging aggregation. Synthesis and applications: This study provides the first empirical evidence that dynamic habitat selection processes and distributions of wading birds over environmental gradients are linked with reproductive measures over periods of decades. Further, predictor variables at a variety of temporal (daily-multiannual) resolutions and spatial (400 m to regional) scales effectively explained variation in ecological processes that change habitat quality. The process used here allows managers to develop short- and long-term conservation strategies that (1) consider flexible behavioral patterns and (2) are robust to environmental variation over time.
","language":"English","publisher":"Wiley","publisherLocation":"Oxford","doi":"10.1002/ece3.1813","usgsCitation":"Beerens, J.M., Frederick, P., Noonburg, E.G., and Gawlik, D.E., 2015, Determining habitat quality for species that demonstrate dynamic habitat selection: Ecology and Evolution, v. 5, no. 23, p. 5685-5697, https://doi.org/10.1002/ece3.1813.","productDescription":"13 p.","startPage":"5685","endPage":"5697","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060983","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":471635,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.1813","text":"Publisher Index Page"},{"id":311635,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"South Florida; Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.4364013671875,\n 26.676913083105454\n ],\n [\n -80.3814697265625,\n 26.684275490019488\n ],\n [\n -80.26336669921875,\n 26.5958952526538\n ],\n [\n -80.2056884765625,\n 26.509904531413927\n ],\n [\n -80.26885986328125,\n 26.362342068998764\n ],\n [\n -80.321044921875,\n 26.298339726417737\n ],\n [\n -80.3045654296875,\n 26.2318383390133\n ],\n [\n -80.35400390625,\n 26.145576207592274\n ],\n [\n -80.43090820312499,\n 26.150507192328902\n ],\n [\n -80.452880859375,\n 26.091321632191132\n ],\n [\n -80.43914794921875,\n 25.96545291784179\n ],\n [\n -80.49407958984375,\n 25.886407614212853\n ],\n [\n -80.5023193359375,\n 25.770213848960275\n ],\n [\n -80.49407958984375,\n 25.70588750345636\n ],\n [\n -80.5517578125,\n 25.61428620774471\n ],\n [\n -80.58746337890625,\n 25.517657429994035\n ],\n [\n -80.5902099609375,\n 25.406065958440852\n ],\n [\n -80.80718994140625,\n 25.37877231509496\n ],\n [\n -81.01593017578124,\n 25.60190226111573\n ],\n [\n -81.2933349609375,\n 25.854280326572407\n ],\n [\n -81.49108886718749,\n 25.958044673317843\n ],\n [\n -81.49108886718749,\n 25.99014369697799\n ],\n [\n -81.36474609375,\n 25.99014369697799\n ],\n [\n -81.35101318359374,\n 26.04197744797015\n ],\n [\n -81.112060546875,\n 26.051847947629508\n ],\n [\n -81.10382080078125,\n 26.170229047478472\n ],\n [\n -80.97198486328125,\n 26.170229047478472\n ],\n [\n -80.96923828125,\n 26.06418490332395\n ],\n [\n -80.85937499999999,\n 26.07652055985697\n ],\n [\n -80.83465576171875,\n 26.318036522949075\n ],\n [\n -80.55450439453125,\n 26.354958989429573\n ],\n [\n -80.47210693359375,\n 26.45090222367262\n ],\n [\n -80.4473876953125,\n 26.662186843258542\n ],\n [\n -80.4364013671875,\n 26.676913083105454\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","issue":"23","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-19","publicationStatus":"PW","scienceBaseUri":"56505243e4b0f162148c5cf7","chorus":{"doi":"10.1002/ece3.1813","url":"http://dx.doi.org/10.1002/ece3.1813","publisher":"Wiley-Blackwell","authors":"Beerens James M., Frederick Peter C., Noonburg Erik G., Gawlik Dale E.","journalName":"Ecology and Evolution","publicationDate":"11/19/2015","auditedOn":"11/21/2015"},"contributors":{"authors":[{"text":"Beerens, James M. 0000-0001-8143-916X jbeerens@usgs.gov","orcid":"https://orcid.org/0000-0001-8143-916X","contributorId":143722,"corporation":false,"usgs":true,"family":"Beerens","given":"James","email":"jbeerens@usgs.gov","middleInitial":"M.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":580417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frederick, Peter C","contributorId":150013,"corporation":false,"usgs":false,"family":"Frederick","given":"Peter C","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":580418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Noonburg, Erik G","contributorId":150014,"corporation":false,"usgs":false,"family":"Noonburg","given":"Erik","email":"","middleInitial":"G","affiliations":[{"id":15312,"text":"Florida Atlantic University","active":true,"usgs":false}],"preferred":false,"id":580419,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gawlik, Dale E.","contributorId":88055,"corporation":false,"usgs":true,"family":"Gawlik","given":"Dale","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":580420,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159727,"text":"ofr20151213 - 2015 - Record-high specific conductance and temperature in San Francisco Bay during water year 2014","interactions":[],"lastModifiedDate":"2017-10-30T11:27:07","indexId":"ofr20151213","displayToPublicDate":"2015-11-18T16:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1213","title":"Record-high specific conductance and temperature in San Francisco Bay during water year 2014","docAbstract":"The U.S. Geological Survey (USGS) has operated a water-quality monitoring network in San Francisco Bay since the late 1980s (Buchanan and others, 2015). This network includes 19 stations in the bay; currently, 8 stations are in operation (fig. 1). All eight stations are equipped with specific conductance (which can be related to salinity) and water-temperature sensors that record measurements at 15-minute intervals. Water quality in the bay constantly changes with the ocean tides and with seasonal and interannual differences in river inflows. Our network was designed to observe and characterize some of these changes in the bay across space and over time. Our data demonstrated a high degree of variability both in specific conductance and temperature at time scales from tidal to annual and also revealed longer term changes that are likely to influence overall environmental health in the bay (San Francisco Estuary Institute, 2014). Figure 1. Locations of fixed water-quality monitoring stations in San Francisco Bay, California, for the 2014 water year (October 1, 2013 to September 30, 2014).
\nIn water year (WY) 2014 (October 1, 2013, through September 30, 2014), our network measured record-high values of specific conductance and water temperature at several stations during a period of very little freshwater inflow from the Sacramento–San Joaquin Delta and other tributaries because of severe drought conditions in California. This report summarizes our observations for WY2014 and compares them to previous years that had different levels of freshwater inflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151213","usgsCitation":"Downing-Kunz, M.A., Work, P.A., and Shellenbarger, G.G., 2015, Record-high specific\nconductance and temperature in San Francisco Bay during water year 2014 (ver. 1.1,\nDecember 28, 2015): U.S. Geological Survey Open-File Report 2015–1213, 4 p.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066727","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":311511,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1213/coverthb.jpg"},{"id":313213,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2015/1213/ofr20151213.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2015-1213 Version History"},{"id":311512,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1213/ofr20151213.pdf","text":"Report","size":"1.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1213"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin Delta, 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 -121.70379638671874,\n 37.933366792504366\n ],\n [\n -121.66534423828125,\n 38.04592811939912\n ],\n [\n -121.75048828124999,\n 38.11943249695316\n ],\n [\n -121.90979003906249,\n 38.151837403006766\n ],\n [\n -122.07183837890625,\n 38.18638677411551\n ],\n [\n -122.178955078125,\n 38.20797181420939\n ],\n [\n -122.26684570312499,\n 38.26621945628273\n ],\n [\n -122.39593505859376,\n 38.26621945628273\n ],\n [\n -122.55249023437501,\n 38.16047628099622\n ],\n [\n -122.61291503906249,\n 37.996162679728116\n ],\n [\n -122.5689697265625,\n 37.861844098370945\n ],\n [\n -122.53326416015624,\n 37.77505678240509\n ],\n [\n -122.45635986328124,\n 37.63815995799935\n ],\n [\n -122.39593505859376,\n 37.51626173528878\n ],\n [\n -122.24761962890625,\n 37.413800350662875\n ],\n [\n -122.06359863281249,\n 37.35487607348372\n ],\n [\n -121.85211181640625,\n 37.38107035775657\n ],\n [\n -121.86035156249999,\n 37.5249753680482\n ],\n [\n -122.03613281249999,\n 37.655557695625056\n ],\n [\n -122.15698242187499,\n 37.81846319511331\n ],\n [\n -122.25585937500001,\n 37.92903406232562\n ],\n [\n -122.25036621093749,\n 37.983174833513395\n ],\n [\n -122.18170166015625,\n 38.00049145082287\n ],\n [\n -121.981201171875,\n 37.996162679728116\n ],\n [\n -121.86584472656251,\n 37.96801944035648\n ],\n [\n -121.75048828124999,\n 37.93553306183642\n ],\n [\n -121.70379638671874,\n 37.933366792504366\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
http://ca.water.usgs.gov
This article summarizes the geotechnical effects of the 25 April 2015 M 7.8 Gorkha, Nepal, earthquake and aftershocks, as documented by a reconnaissance team that undertook a broad engineering and scientific assessment of the damage and collected perishable data for future analysis. Brief descriptions are provided of ground shaking, surface fault rupture, landsliding, soil failure, and infrastructure performance. The goal of this reconnaissance effort, led by Geotechnical Extreme Events Reconnaissance, is to learn from earthquakes and mitigate hazards in future earthquakes.
","language":"English","publisher":"Eastern Section, Seismological Society of America","publisherLocation":"El Cerrito, CA","doi":"10.1785/0220150158","usgsCitation":"Moss, R., Thompson, E.M., Kieffer, D.S., Tiwari, B., Hashash, Y.M., Acharya, I., Adhikari, B., Asimaki, D., Clahan, K.B., Collins, B.D., Dahal, S., Jibson, R.W., Khadka, D., Macdonald, A., Madugo, C.L., Mason, H.B., Pehlivan, M., Rayamajhi, D., and Uprety, S., 2015, Geotechnical effects of the 2015 magnitude 7.8 Gorkha, Nepal, earthquake and aftershocks: Seismological Research Letters, v. 86, no. 6, p. 1514-1523, https://doi.org/10.1785/0220150158.","productDescription":"10 p.","startPage":"1514","endPage":"1523","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068466","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471636,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://resolver.caltech.edu/CaltechAUTHORS:20151204-092729660","text":"External Repository"},{"id":311544,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","state":"Gorkha","volume":"86","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-28","publicationStatus":"PW","scienceBaseUri":"564da12ce4b0112df6c62dcb","contributors":{"authors":[{"text":"Moss, Robb E. 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1770 Corporate Drive, Ste. 500
Norcross, GA 30093
678-524-1544
http://water.usgs.gov/watercensus/
The Water Availability Tool for Environmental Resources (WATER) is a decision support system (DSS) for the nontidal part of the Delaware River Basin (DRB) that provides a consistent and objective method of simulating streamflow under historical, forecasted, and managed conditions. WATER integrates geospatial sampling of landscape characteristics, including topographic and soil properties, with a regionally calibrated hillslope-hydrology model, an impervious-surface model, and hydroclimatic models that have been parameterized using three hydrologic response units—forested, agricultural, and developed land cover. It is this integration that enables the regional hydrologic-modeling approach used in WATER without requiring site-specific optimization or those stationary conditions inferred when using a statistical model. The DSS provides a “historical” database, ideal for simulating streamflow for 2001–11, in addition to land-cover forecasts that focus on 2030 and 2060. The WATER Application Utilities are provided with the DSS and apply change factors for precipitation, temperature, and potential evapotranspiration to a 1981–2011 climatic record provided with the DSS. These change factors were derived from a suite of general circulation models (GCMs) and representative concentration pathway (RCP) emission scenarios. These change factors are based on 25-year monthly averages (normals) that are centere on 2030 and 2060. The WATER Application Utilities also can be used to apply a 2010 snapshot of water use for the DRB; a factorial approach enables scenario testing of increased or decreased water use for each simulation. Finally, the WATER Application Utilities can be used to reformat streamflow time series for input to statistical or reservoir management software.
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U.S. Geological Survey
1770 Corporate Drive, Ste. 500
Norcross, GA 30093
678-524-1544
http://water.usgs.gov/watercensus/