In 2012, a late season tropical depression developed into a tropical storm and later a hurricane. The hurricane, named “Hurricane Sandy,” gained strength to a Category 3 storm on October 25, 2012, and underwent several transitions on its approach to the mid-Atlantic region of the eastern coast of the United States. By October 28, 2012, Hurricane Sandy had strengthened into the largest hurricane ever recorded in the North Atlantic and was tracking parallel to the east coast of United States, heading toward New Jersey. On October 29, 2012, the storm turned west-northwest and made landfall near Atlantic City, N.J. The high winds and wind-driven storm surge caused massive damage along the entire coastline of New Jersey. Millions of people were left without power or communication networks. Many homes were completely destroyed. Sand dunes were eroded, and the barrier island at Mantoloking was breached, connecting the ocean with Barnegat Bay.
Several days before the storm made landfall in New Jersey, the U.S. Geological Survey (USGS) made a decision to deploy a temporary network of storm-tide sensors and barometric pressure sensors from Virginia to Maine to supplement the existing USGS and National Oceanic and Atmospheric Administration (NOAA) networks of permanent tide monitoring stations. After the storm made landfall, the USGS conducted a sensor data recovery and high-water-mark collection campaign in cooperation with the Federal Emergency Management Agency (FEMA).
Peak storm-tide elevations documented at USGS tide gages, tidal crest-stage gages, temporary storm sensor locations, and high-water-mark sites indicate the area from southern Monmouth County, N.J., north through Raritan Bay, N.J., had the highest peak storm-tide elevations during this storm. The USGS tide gages at Raritan River at South Amboy and Raritan Bay at Keansburg, part of the New Jersey Tide Telemetry System, each recorded peak storm-tide elevations of greater than 13 feet (ft)—more than 5 ft higher than the previously recorded period-of-record maximum. A comparison of peak storm-tide elevations to preliminary FEMA Coastal Flood Insurance Study flood elevations indicated that these areas experienced the highest recurrence intervals along the coast of New Jersey. Analysis showed peak storm-tide elevations exceeded the 100-year FEMA flood elevations in many parts of Middlesex, Union, Essex, Hudson, and Bergen Counties, and peak storm-tide elevations at many locations in Monmouth County exceeded the 500-year recurrence interval.
A level 1 HAZUS (HAZards United States) analysis was done for the counties in New Jersey affected by flooding to estimate total building stock losses. The aggregated total building stock losses estimated by HAZUS for New Jersey, on the basis of the final inundation verified by USGS high-water marks, was almost $19 billion. A comparison of Hurricane Sandy with historic coastal storms showed that peak storm-tide elevations associated with Hurricane Sandy exceeded most of the previously documented elevations associated with the storms of December 1992, March 1962, September 1960, and September 1944 at many coastal communities in New Jersey. This scientific investigation report was prepared in cooperation with FEMA to document flood processes and flood damages resulting from this storm and to assist in future flood mitigation actions in New Jersey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165085","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Suro, T.P., Deetz, Anna, and Hearn, Paul, 2016, Documentation and hydrologic analysis of Hurricane Sandy in New Jersey, October 29–30, 2012: U.S. Geological Survey Scientific Investigations Report 2016–5085, 73 p., https://dx.doi.org/10.3133/sir20165085.","productDescription":"Report: ix, 73 p.; 5 Tables","numberOfPages":"87","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-055579","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":330616,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table4.xls","text":"Table 4","size":"45 KB xls","description":"SIR 2016-5085","linkHelpText":"- Description of U.S. Geological Survey sensors temporarily deployed for Hurricane Sandy with peak storm tide elevations, annual exceedance probabilities, and estimated recurrence intervals in New Jersey, October 29–30, 2012 \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330617,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table5.xls","text":"Table 5","size":"144 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations at 169 high-water-mark sites along the coast of New Jersey during Hurricane Sandy, October 29–30, 2012, and the corresponding Federal Emergency Management Agency flood elevations for the 10-, 50-, 100-, and 500-year recurrence intervals \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330618,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table6.xls","text":"Table 6","size":"61 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations for selected historic coastal floods and peak storm-tide elevations during Hurricane Sandy, October 29–30, 2012, at selected U.S. Geological Survey permanent monitoring tide gages in New Jersey"},{"id":330619,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table7.xls","text":"Table 7","size":"74 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations at 82 high-water-mark sites flagged and surveyed after the December 1992 storm in New Jersey, peak storm-tide elevations from the closest high-water-mark sites flagged and surveyed after Hurricane Sandy, October 29–30, 2012, and peak storm-tide elevations from the nearest U.S. Geological Survey tide gage along the coast of New Jersey during Hurricane Sandy, October 29–30, 2012\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330614,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085.pdf","text":"Report","size":"85.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5085"},{"id":330615,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table3.xls","text":"Table 3","size":"77.5 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak-of-record tide elevations and peak storm-tide elevations at U.S. Geological Survey permanent monitoring tide gages in New Jersey, October 29–30, 2012\t\t\t\t\t\t"},{"id":330613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5085/coverthb.jpg"}],"country":"United States","state":"New 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Jersey\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville NJ, 08648
http://nj.usgs.gov/
In this study, the U.S. Geological Survey investigated the use of insects as bioindicators of climate change in sagebrush steppe shrublands and grasslands in the Upper Columbia Basin. The research was conducted in the Stinkingwater and Pueblo mountain ranges in eastern Oregon on lands administered by the Bureau of Land Management.
We used a “space-for-time” sampling design that related insect communities to climate and weather along elevation gradients. We analyzed our insect dataset at three levels of organization: (1) whole-community, (2) feeding guilds (detritivores, herbivores, nectarivores, parasites, and predators), and (3) orders within nectarivores (i.e., pollinators). We captured 59,517 insects from 176 families and 10 orders at the Pueblo Mountains study area and 112,305 insects from 185 families and 11 orders at the Stinkingwater Mountains study area in 2012 and 2013. Of all the individuals captured at the Stinkingwater Mountains study area, 77,688 were from the family Cecidomyiidae (Diptera, gall gnats).
We found that the composition of insect communities was associated with variability in long-term (30-yr) temperature and interannual fluctuations in temperature. We found that captures of certain fly, bee, moth, and butterfly pollinators were more strongly associated with some climate and vegetation variables than others. We found that timing of emergence, as measured by first detection of families, was associated with elevation. When analyzed by feeding guilds, we found that all guilds emerged later at high elevations except for detritivores, which emerged earlier at high elevations. The abundance of most taxa varied through time, mostly in response to temperature and precipitation. Of the pollinators, bees (particularly, Halictidae and Megachilidae) peaked in abundance in late June and early July, whereas butterflies and moths peaked in August. Flies peaked in abundance in July.
Overall, our interpretation of these patterns is that insect communities respond positively and negatively to weather and local vegetation more than to long-term climate. Given increasing variability in weather and high probability of extreme weather events, insect communities in sagebrush steppe also may experience considerable fluctuations in composition and abundance, as well as phenology. These findings have implications for many ecosystem services, including pollination, decomposition, and food resources for predatory birds and other vertebrates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161183","collaboration":"Prepared in cooperation with the Bureau of Land Management under Interagency Agreement L10PG00804 for the project: “Forecasting Insect Community Responses to Changes in Land Management and Climate in Upper Columbia Basin Sagebrush Steppe”","usgsCitation":"Pilliod, D.S., and Rohde, A.T., 2016, Insect community responses to climate and weather across elevation gradients in the Sagebrush Steppe, eastern Oregon: U.S. Geological Open-File Report 2016–1083, 50 p., https://doi.org/10.3133/ofr20161183.","productDescription":"vi, 50 p.","numberOfPages":"61","onlineOnly":"Y","ipdsId":"IP-074398","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":331101,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1183/ofr20161183.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1183"},{"id":331100,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1183/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Sagebrush Steppe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.33624267578124,\n 43.51668853502906\n ],\n [\n -119.33624267578124,\n 42.70262285884388\n ],\n [\n -118.5809326171875,\n 42.71069600569497\n ],\n [\n -118.13598632812499,\n 42.71473218539458\n ],\n [\n -118.2073974609375,\n 43.69965122967144\n ],\n [\n -118.4600830078125,\n 43.72744458647464\n ],\n [\n -119.33624267578124,\n 43.51668853502906\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
http://fresc.usgs.gov/
Despite widespread and ongoing implementation of conservation practices throughout the Chesapeake Bay watershed, water quality continues to be degraded by excess sediment and nutrient inputs. While the Chesapeake Bay Program has developed and maintains a large-scale and long-term monitoring network to detect improvements in water quality throughout the watershed, fewer resources have been allocated for monitoring smaller watersheds, even though water-quality improvements that may result from the implementation of conservation practices are likely to be first detected at smaller watershed scales.
In 2010, the U.S. Geological Survey partnered with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture to initiate water-quality monitoring in four selected small watersheds that were targeted for increased implementation of conservation practices. Smith Creek watershed is an agricultural watershed in the Shenandoah Valley of Virginia that is dominated by cattle and poultry production, and the Upper Chester River watershed is an agricultural watershed on the Eastern Shore of Maryland that is dominated by row-cropping activities. The Conewago Creek watershed is an agricultural watershed in southeastern Pennsylvania that is characterized by mixed agricultural activities. The fourth watershed, Difficult Run, is a suburban watershed in northern Virginia that is dominated by medium density residential development. The objective of this study was to investigate spatial and temporal variations in water chemistry and suspended sediment in these four relatively small watersheds that represent a range of land-use patterns and underlying geology to (1) characterize current water-quality conditions in these watersheds, and (2) identify the dominant sources, sinks, and transport processes in each watershed.
The general study design involved two components. The first included intensive routine water-quality monitoring at an existing streamgage within each study area (including continuous water-quality monitoring as well as discrete water-quality sampling) to develop a detailed understanding of the temporal and hydrologic variability in stream chemistry and sediment transport in each watershed. The second component involved extensive water-quality monitoring at various sites throughout each watershed to develop a detailed understanding of spatial patterns. Both components were used to improve understanding of sources and transport processes affecting stream chemistry, including nutrients and suspended sediments, and their implications for detecting long-term trends related to best management practices. This report summarizes the results of monitoring that was performed from April 2010 through September 2013.
Summaries for each of the four small watersheds are presented below. Each watershed has a more descriptive and detailed section in the report, but these summaries may be particularly useful for some watershed managers and stakeholders desiring slightly less technical detail.
Smith Creek is a 105.39-mi2 watershed within the Shenandoah Valley that drains to the North Fork Shenandoah River. The long-term Smith Creek base-flow index is 72.3 percent, indicating that on average, approximately 72 percent of Smith Creek flow was base flow, which suggests that Smith Creek streamflow is dominated by groundwater discharge rather than stormwater runoff. A series of cluster and principal components analyses demonstrated that the majority of the variability in Smith Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed for turbidity, suspended sediments, total nitrogen, ammonium, orthophosphate, iron, total phosphorus, and the ratio of calcium to magnesium. Statistically significant inverse correlations with flow were observed for specific conductance, magnesium, δ15N of nitrate, pH, bicarbonate, calcium, and δ18O of nitrate. Of particular note, flow and nitrate were not statistically significantly correlated, likely because of the relatively complex concentration-discharge relationship observed in continuous and discrete datasets. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, turbidity, orthophosphate, total phosphorus, suspended-sediment concentration, and silica were higher during the warm season, but pH, dissolved oxygen, and sulfate were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loads in Smith Creek using the continuous water-quality monitors. The mean Smith Creek in-stream sediment load was approximately 6,900 tons per year, with nearly 90 percent of the sediment load over the 3-year study period contributed during the eight largest storm events during that period. The Smith Creek total phosphorus load was approximately 21,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods, although a substantial phosphorus load still occurs during base-flow conditions. The Smith Creek total nitrogen load was approximately 400,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions (as was the case for sediment and total phosphorus) and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Smith Creek watershed revealed how the complex geology and hydrology interacted to result in variable water chemistry. During relatively dry and low base-flow periods, much of the discharge in Smith Creek was contributed by a single dominant spring—Lacey Spring. During wetter base-flow periods, the flows in Smith Creek were largely generated by a mixture of headwater springs and forested mountain tributaries with very different geochemical composition. The headwater springs generally issued from limestone bedrock and were characterized as having relatively high nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The undeveloped, high-gradient, forested mountain sites were generally characterized by low ionic strength waters with low nutrient concentrations. Nitrate isotope data from the limestone springs generally were consistent with manure-derived nitrogen sources (such as cattle and poultry), although the possibility of other mixed sources cannot be excluded. Nitrate isotope data from the undeveloped, high-gradient forested mountain sites were more consistent with nitrogen from undisturbed soils, atmospheric deposition, or nitrogen fixation. Regardless of the nitrogen source, oxygen isotope data indicate that the nitrate was largely a result of nitrification. Land-use data indicate that manure sources of nitrogen dominated watershed nitrogen inputs. Phosphorus sources were less well studied. The presence of a single point-source discharge near the town of New Market contributed the majority of the phosphorus to Smith Creek under base-flow conditions, but nonpoint sources of phosphorus dominated the loading to Smith Creek during stormflow periods.
Implementation of conservation practices increased in the Smith Creek watershed during the study period, and even though a broad range of practice types was implemented, the most common practices included stream fencing (for cattle exclusion), the development of nutrient management plans, conservation crop rotation, and the planting of cover crops. While the implementation of these conservation practices is encouraging, results indicate small increases in nitrate concentrations at the streamgage over the last 29 years, concurrent with small decreases in nitrate fluxes. It will likely be years before the cumulative effect of these practices can be detected in the Smith Creek water quality, and the magnitude of the effect of these conservation practices detected in Smith Creek will depend largely on whether nutrient loading (of manure and commercial fertilizer) is reduced over time.
The Upper Chester River watershed includes the 36-square-mile (mi2) watershed area around several nontidal tributaries that drain into the tidal Chester River. The streamgage is on Chesterville Branch, the largest nontidal tributary (approximately 6.12 mi2) and is the site for continuous water-quality monitoring for this project. The base-flow index at Chesterville Branch is about 72 percent and indicates that, as in most of the Coastal Plain, groundwater is the greatest contributor to streamflow. As such, more than 90 percent of the nitrogen in the stream is in the form of nitrate from groundwater. Continuous and discrete data collected at Chesterville Branch show the effects of streamflow and season on water quality. Significantly positive correlations with flow were observed for ammonium, dissolved and total phosphorus, sediment, and turbidity as runoff carried these constituents from the land surface into Chesterville Branch. Other constituents that increased significantly with flow include potassium, sulfate, iron, and manganese, which are likely contributed from near-stream areas and ponds with high organic-matter content. Total nitrogen, pH, and specific conductance, along with chemical constituents associated with groundwater inputs including nitrate, calcium, ratio of calcium to magnesium, silica, bicarbonate, and sodium, were negatively correlated with flow because concentrations of these constituents were diluted by runoff.
Seasonal differences in water chemistry, which are most likely related to increased biologic effects on the uptake and release of chemicals in the stream and near-stream areas, also were observed. Water temperature, orthophosphate, δ15N of nitrate, bicarbonate, sodium, and the ratio of sodium to chloride were higher during the warm season, and dissolved oxygen, total nitrogen, nitrate, magnesium, sulfate, and manganese were higher during the cool season.
Surrogate-regression models developed by using continuous water-quality data showed that the annual sediment load for the 2013 water year was about 2,600 tons, with more than 90 percent of this sediment contributed during two storms. The total phosphorus load in 2013 was about 13,000 pounds with more than 90 percent contributed during the same two storms as sediment. The load of total nitrogen, 140,000 pounds, accumulated steadily throughout the 2013 water year as nitrate in groundwater continuously discharged into the stream. The same two large storms that contributed 90 percent of the suspended-sediment and total phosphorus load only contributed about 20 percent of the annual total nitrogen load.
Extensive water-quality monitoring of stream base flow throughout the Upper Chester River watershed identified how differences in land use and hydrogeology affected water chemistry. In parts of the watershed with well-drained soil and thick sandy aquifer sediments, concentrations of nitrate and other chemicals associated with fertilizer and lime application increased in streams as agricultural land use increased. More than 90 percent of the nitrogen in streams from these areas was in the form of nitrate, and concentrations ranged from about 5 milligrams per liter (mg/L) to 8 mg/L as nitrogen in the two largest tributaries. Stream nitrate concentrations were about 1 mg/L as nitrogen where soils were more poorly drained, the surficial aquifer sediments were thinner, and forests and wetlands were more widespread than agriculture. Nitrate isotope data were consistent with inorganic fertilizers ± atmospheric deposition and N2 fixation as sources of nitrogen, and with nitrification as the dominant nitrate-forming process. Nitrate reduction was indicated by elevated δ15N and δ18O values in some samples from streams draining watersheds with poorly drained soils. An analysis of land-use data and SPARROW modeling input data attributed almost 90 percent of the nitrogen sources in the Upper Chester River watershed to inorganic fertilizer and fixation of atmospheric nitrogen by legumes, which is in agreement with the isotopic characteristics of nitrate in this watershed. Local sources of manure are limited in this area. Total phosphorus concentrations during base flow ranged from below detection to about 0.2 mg/L. Stream phosphorus concentrations during base flow were generally lower than those measured during storms because most phosphorus transport likely occurs as phosphorus attached to sediment particles during runoff. Because manure is not widely used in this area, the major source of phosphorus is likely fertilizer.
The implementation of conservation practices in the Upper Chester River watershed increased substantially during the study period, with a total implementation of 1,194 U.S. Department of Agriculture-compliant practices. The most frequently used practices were oriented towards nutrient and sediment control, including cover crops, nutrient management planning, conservation crop rotation, conservation tillage, and irrigation management. The current Chesapeake Bay model for this area predicts that implementation of best management practices should result in a 13-percent decrease in overall delivery of nitrogen to the Upper Chester River. Because most nitrogen travels through the groundwater system for years to decades before being discharged to streams, the time period of monitoring was not sufficient to see the effects of these practices on water quality. The magnitude of the effect that may eventually be detected will depend on the degree to which nitrate leaching into the groundwater system is reduced over time. Loadings of phosphorus and sediment are primarily transported during large runoff events and are difficult to control and analyze for trends because of their timing and episodic nature.
Conewago Creek has two primary monitoring locations—one near the middle of the 47-mi2 watershed and the other near the outlet just upstream of the Susquehanna River. The base-flow index was 47.3 percent for 2012–2013, indicating that on average, approximately 53 percent of the streamflow in Conewago Creek exited the watershed as surface flow, which suggests that the stormwater runoff was somewhat greater than groundwater discharge (base flow). A series of cluster and principal components analyses demonstrated that the majority of the variability in the Conewago Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed at both monitoring sites for ammonium, total phosphorus, orthophosphate, iron, and manganese; additionally, at the upstream monitoring station, total nitrogen demonstrated a statistically significant positive correlation with flow. Statistically significant inverse correlations with flow were observed at both sites for water temperature, specific conductance (at the downstream site only), sulfate, chloride, calcium, and magnesium. Statistically significant seasonal patterns were observed for several water-quality constituents. Water temperature, phosphorus (upstream site only), and orthophosphate were higher during the warm season, and nitrate and total nitrogen (upstream site only) were higher during the cool season.
Surrogate regression models were developed to compute sediment and nutrient load in Conewago Creek by using the continuous water-quality monitors and water-quality samples. Conewago Creek sediment load was approximately 9,900 tons in 2012 and approximately 18,900 tons in 2013, with nearly 80 percent of the sediment load in 2013 contributed by the three largest storm events. Annual total nitrogen loads could not be estimated due to poor model performance. The addition of continued monitoring or a continuously recording nitrate sensor could improve estimates of total nitrogen loads. During 2012 and 2013, phosphorus loads in Conewago Creek were approximately 50,000 pounds in each year.
Combining data from one high-flow synoptic sampling with the data from routine sampling revealed how the geology and hydrology interact to result in variable water chemistry throughout the Conewago Creek watershed. The areas above the upstream gage in the headwaters are generally underlain by forested non-carbonate bedrock and are characterized by relatively low nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The more developed, agricultural areas below the upstream site were generally characterized by higher ionic strength waters with higher nutrient and metal concentrations. An analysis of land-use data and SPAtially Referenced Regressions On Watershed (SPARROW) modeling data indicates that manure sources of nitrogen dominate the input of nitrogen to the watershed.
Implementation of conservation practices increased in the Conewago Creek watershed during the study period, and while a broad range of practice types were implemented, the most common practices included residue and tillage management, cover crops, nutrient management, terracing, and stream fencing (for animal exclusion or bank restoration). While the implementation of these conservation practices is encouraging, the cumulative effects of these practices probably will not be detected in Conewago Creek water quality for several years. The magnitude of the effects of these conservation practices on water quality in Conewago Creek will depend largely on the extent to which nutrient loading (septic, manure, and commercial fertilizer) and sediment-producing activities are reduced over time.
The Difficult Run watershed is a 57.82-mi2 watershed that drains to the Potomac River. The long-term Difficult Run base-flow index (from 1936 to 2010) was 57.9, indicating that approximately 58 percent of streamflow exited the watershed as base flow and 42 percent as stormflow; however, with continued development and urbanization of the watershed, the base-flow index has decreased to 50 percent during the last 20 years. This base-flow index was less than those of the other watersheds evaluated in this study, likely because the Difficult Run watershed largely is underlain by crystalline piedmont metamorphic rocks and has a greater proportion of impervious urban land cover. A series of cluster and principal components analyses indicated that most of the variability in Difficult Run water quality could be attributed to hydrologic variability and seasonality. Statistically significant positive correlations with flow were observed for turbidity, dissolved oxygen, suspended sediments, ammonium, orthophosphate, iron, and total phosphorus. Statistically significant inverse correlations with flow were observed for water temperature, pH, specific conductance, bicarbonate, calcium, magnesium, nitrate, δ15N of nitrate, and silica. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, ammonium, orthophosphate, and δ15N of nitrate were higher during the warm season, and dissolved oxygen, nitrate, and manganese were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loading rates. The Difficult Run sediment load was approximately 8,000 tons per year, with greater than 95 percent of the sediment load in the 2013 water year contributed by the seven largest storm events. The total phosphorus load in Difficult Run was approximately 14,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods. The total nitrogen load in Difficult Run is estimated to have been approximately 140,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions than that of phosphorus and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Difficult Run watershed revealed relatively uniform generation of flow per unit of watershed area, as well as spatial variation in water quality that is strongly related to land-use activities. Elevated nitrate concentrations were observed in a subset of monitoring sites that are inversely correlated with population density and positively correlated to the septic system density within each subwatershed. The majority of the elevated nitrate concentrations for these sites are hypothesized to be caused by nitrate leaching from septic systems, more so than homeowner fertilizer usage among these subwatersheds that have lower population densities than other parts of the watershed. Nitrate isotope data, temporal patterns in the water-quality data, mass-balance computations, and a separate land-use analysis all generally indicate that leachate from septic systems was the likely source of the elevated nitrate. Another group of water-quality sites have relatively low nitrogen concentrations, are located in areas that are served by city sewer lines, and have experienced stream restoration activities. A final group of sites drained the areas with the highest imperviousness and had strongly elevated specific conductance, chloride, and sodium, which were likely caused by a combination of road salting and other anthropogenic sources draining these urbanized areas in the watershed. A fourth group of sites represents a mixture of water sources and had water quality similar to that at the Difficult Run streamgage. Analysis of the nitrate isotope data generally indicates a broad range of composition indicative of mixed natural and anthropogenic nitrogen sources. Implementation of conservation practices increased in the Difficult Run watershed during the study period, and while a broad range of practice types was implemented, the most common practices included stream restoration. While the implementation of these conservation practices is encouraging, the cumulative effect of these practices probably will not be detected in Difficult Run water quality for several years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165093","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program","usgsCitation":"Hyer, K.E., Denver, J.M., Langland, M.J., Webber, J.S., Böhlke, J.K., Hively, W.D., and Clune, J.W., 2016, Spatial and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013: U.S. Geological Survey Scientific Investigations Report 2016–5093, 211 p., https://dx.doi.org/10.3133/sir20165093.","productDescription":"Report: xix, 211 p.","startPage":"1","endPage":"211","numberOfPages":"236","onlineOnly":"N","ipdsId":"IP-067371","costCenters":[{"id":614,"text":"Virginia Water Science 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U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Beryllium is a lightweight metallic element used in a wide variety of specialty and industrial applications. As a function of its unique chemical and physical properties, such as a high stiffness-to-weight ratio, resistance to temperature extremes, and high thermal conductivity, beryllium cannot be easily replaced by substitute materials in applications where combinations of these properties make it the material of choice. Because the number of beryllium producers is limited and the use of substitute materials in specific defense-related applications that are vital to national security is inadequate, several studies have categorized beryllium as a critical and strategic material. This categorization has led to the United States Government recommending that beryllium be stockpiled for use in the event of a national emergency. As of December 31, 2015, the National Defense Stockpile inventory of hot-pressed beryllium metal powder, structured beryllium metal powder, and vacuum-cast beryllium metal totaled 78 metric tons (t).
The U.S. Geological Survey (USGS) Mineral Resources Program supports research on the occurrence, quality, quantity, and availability of mineral resources vital to the economy and national security. The USGS, through its National Minerals Information Center (NMIC), collects, analyzes, and disseminates information on more than 90 nonfuel mineral commodities from more than 180 countries. This fact sheet provides information on the production, consumption, supply chain, geology, and resource availability of beryllium in a global context.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163081","usgsCitation":"Lederer, G.W., Foley, N.K., Jaskula, B.W., and Ayuso, R.A., 2016, Beryllium—A critical mineral commodity—Resources, production, and supply chain: U.S. Geological Survey Fact Sheet 2016–3081, 4 p., https://dx.doi.org/10.3133/fs20163081.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-077851","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":330523,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3081/coverthb3.jpg"},{"id":330524,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3081/fs20163081.pdf","text":"Report","size":"339 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3081"}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
Or visit the USGS Minerals Information Web site at: http://minerals.usgs.gov/minerals
","tableOfContents":"During September–November 2012, the U.S. Geological Survey, in cooperation with the Puerto Rico Aqueduct and Sewer Authority, conducted a sedimentation survey of Lago Caonillas to estimate current (2012) reservoir storage capacity and the recent (2000–2012) reservoir sedimentation rate by comparing the 2012 bathymetric survey data with the February 2000 data. The Lago Caonillas storage capacity, which was 42.27 million cubic meters in February 2000, decreased to 39.55 million cubic meters by September–November 2012. The intersurvey (2000–2012) storage capacity loss was about 6 percent, corresponding to a decrease of about 0.5 percent per year; this loss represents a reservoir sedimentation rate of about 226,670 cubic meters per year between 2000 and 2012. On a long-term basis, however, the sedimentation rate has remained nearly constant, decreasing from about 257,500 to 251,720 cubic meters per year during 1948–2000 and 1948–2012, respectively. Most of the sediment accumulation and associated storage capacity loss of Lago Caonillas has occurred within the eastern and Río Caonillas branches of the reservoir. In the vicinity of the Caonillas Dam, minor sediment deposition and scour have occurred. The Lago Caonillas drainage area sediment yield has decreased by about 2 percent since the previous survey, from 1,266 cubic meters per square kilometer per year in 2000 to 1,237 cubic meters per square kilometer per year in 2012. If the long-term sedimentation rate of 251,720 cubic meters per year remains constant, the useful life of Lago Caonillas may end in about 2169.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3368","collaboration":"Prepared in cooperation with the Puerto Rico Aqueduct and Sewer Authority","usgsCitation":"Soler-López, L.R., 2016, Sedimentation survey of Lago Caonillas, Utuado, Puerto Rico, September–November 2012: U.S. Geological Survey Scientific Investigations Map 3368, 1 sheet, https://dx.doi.org/10.3133/sim3368.","productDescription":"29.00 x 30.83 inches","numberOfPages":"1","onlineOnly":"Y","ipdsId":"IP-055426","costCenters":[{"id":156,"text":"Caribbean Water Science Center","active":true,"usgs":true}],"links":[{"id":438510,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74M92NT","text":"USGS data release","linkHelpText":"Spatial Data for Sedimentation Survey of Lago Caonillas, Utuado, Puerto Rico, SeptemberNovember 2012"},{"id":330594,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3368/sim3368.pdf","text":"Sheet 1","size":"1.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3368"},{"id":330593,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3368/coverthb.jpg"},{"id":330666,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F74M92NT","text":"USGS data release - Spatial Data for Sedimentation Survey of Lago Caonillas, Utuado, Puerto Rico, September–November 2012"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.8,\n 18.2\n ],\n [\n -66.8,\n 18.5\n ],\n [\n -66.5,\n 18.5\n ],\n [\n -66.5,\n 18.2\n ],\n [\n -66.8,\n 18.2\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Apatite fission track (AFT) and vitrinite reflectance data from five exploration wells and three seafloor cores illuminate the thermal history of the underexplored United States Chukchi shelf. On the northeastern shelf, Triassic strata in the Chevron 1 Diamond well record apatite annealing followed by cooling, possibly during the Triassic to Middle Jurassic, which is a thermal history likely related to Canada Basin rifting. Jurassic strata exhumed in the hanging wall of the frontal Herald Arch thrust fault record a history of probable Late Jurassic to Early Cretaceous structural burial in the Chukotka fold and thrust belt, followed by rapid exhumation to near-surface temperatures at 104 ± 30 Ma. This history of contractional tectonism is in good agreement with inherited fission track ages in low-thermal-maturity, Cretaceous–Cenozoic strata in the Chukchi foreland, providing complementary evidence for the timing of exhumation and suggesting a source-to-sink relationship. In the central Chukchi foreland, inverse modeling of reset AFT samples from the Shell 1 Klondike and Shell 1 Crackerjack wells reveals several tens of degrees of cooling from maximum paleo-temperatures, with maximum heating permissible at any time from about 100 to 50 Ma, and cooling persisting to as recent as 30 Ma. Similar histories are compatible with partially reset AFT samples from other Chukchi wells (Shell 1 Popcorn, Shell 1 Burger, and Chevron 1 Diamond) and are probable in light of regional geologic evidence. Given geologic context provided by regional seismic reflection data, we interpret these inverse models to reveal a Late Cretaceous episode of cyclical burial and erosion across the central Chukchi shelf, possibly partially overprinted by Cenozoic cooling related to decreasing surface temperatures. Regionally, we interpret this kinematic history to be reflective of moderate, transpressional deformation of the Chukchi shelf during the final phases of contractional tectonism in the Chukotkan orogen (lasting until ∼70 Ma), followed by renewed subsidence of the Chukchi shelf in the latest Cretaceous and Cenozoic. This history maintained modest thermal maturities at the base of the Brookian sequence across the Chukchi shelf, because large sediment volumes bypassed to adjacent depocenters. Therefore, the Chukchi shelf appears to be an area with the potential for widespread preservation of petroleum systems in the oil window.
","language":"English","publisher":"American Association of Petroleum Geologists","publisherLocation":"Tulsa, OK","doi":"10.1306/09291515010","usgsCitation":"Craddock, W.H., and Houseknecht, D.W., 2016, Cretaceous–Cenozoic burial and exhumation history of the Chukchi shelf, offshore Arctic Alaska: AAPG Bulletin, v. 100, no. 1, p. 63-100, https://doi.org/10.1306/09291515010.","productDescription":"38 p.","startPage":"63","endPage":"100","ipdsId":"IP-063616","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":330700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -175,\n 75\n ],\n [\n -175,\n 64\n ],\n [\n -165,\n 64\n ],\n [\n -165,\n 75\n ],\n [\n -175,\n 75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"100","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"581c4cc0e4b09688d6e90f9b","contributors":{"authors":[{"text":"Craddock, William H. 0000-0002-4181-4735 wcraddock@usgs.gov","orcid":"https://orcid.org/0000-0002-4181-4735","contributorId":3411,"corporation":false,"usgs":true,"family":"Craddock","given":"William","email":"wcraddock@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":652897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houseknecht, David W. 0000-0002-9633-6910 dhouse@usgs.gov","orcid":"https://orcid.org/0000-0002-9633-6910","contributorId":645,"corporation":false,"usgs":true,"family":"Houseknecht","given":"David","email":"dhouse@usgs.gov","middleInitial":"W.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":652898,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178129,"text":"70178129 - 2016 - Population dynamics of mallards breeding in eastern Washington","interactions":[],"lastModifiedDate":"2016-11-03T12:49:57","indexId":"70178129","displayToPublicDate":"2016-11-03T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Population dynamics of mallards breeding in eastern Washington","docAbstract":"Variation in regional population trends for mallards breeding in the western United States indicates that additional research into factors that influence demographics could contribute to management and understanding the population demographics of mallards across North America. We estimated breeding incidence and adult female, nest, and brood survival in eastern Washington in 2006 and 2007 by monitoring female mallards with radio telemetry and tested how those parameters were influenced by study year (2006 vs. 2007), landscape type (agricultural vs. natural), and age (second year [SY] vs. after second year [ASY]). We also investigated the effects of female body condition and capture date on breeding incidence, and nest initiation date and hatch date on nest and brood survival, respectively. We included population parameters in a stage-based demographic model and conducted a perturbation analysis to identify which vital rates were most influential on population growth rate (λ). Adult female survival was best modeled with a constant weekly survival rate (0.994, SE = 0.003). Breeding incidence differed between years and was higher for birds in better body condition. Nest survival was higher for ASY females (0.276, SE = 0.118) than SY females (0.066, SE = 0.052), and higher on publicly managed lands (0.383, SE = 0.212) than agricultural (0.114, SE = 0.058) landscapes. Brood survival was best modeled with a constant rate for the 7-week monitoring period (0.50, SE = 0.155). The single variable having the greatest influence on λ was non-breeding season survival, but the combination of parameters from the breeding grounds explained a greater percent of the variance in λ. Mallard population growth rate was most sensitive to changes in non-breeding survival, nest success, brood survival, and breeding incidence. Future management decisions should focus on activities that improve these vital rates if managers want to increase the production of mallards in eastern Washington.
","language":"English","publisher":"Wildlife Society","publisherLocation":"Menasha, WI","doi":"10.1002/jwmg.1030","usgsCitation":"Dugger, B., Coluccy, J.M., Dugger, K.M., Fox, T.T., Kraege, D.K., and Petrie, M.J., 2016, Population dynamics of mallards breeding in eastern Washington: Journal of Wildlife Management, v. 80, no. 3, p. 500-509, https://doi.org/10.1002/jwmg.1030.","productDescription":"10 p.","startPage":"500","endPage":"509","ipdsId":"IP-058959","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":330697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Columbia Basin Irrigation Project","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.00915527343749,\n 46.24824991289166\n ],\n [\n -120.00915527343749,\n 47.956823800497475\n ],\n [\n -117.9107666015625,\n 47.956823800497475\n ],\n [\n -117.9107666015625,\n 46.24824991289166\n ],\n [\n -120.00915527343749,\n 46.24824991289166\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"80","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-06","publicationStatus":"PW","scienceBaseUri":"581c4cc0e4b09688d6e90f97","contributors":{"authors":[{"text":"Dugger, Bruce D.","contributorId":81236,"corporation":false,"usgs":true,"family":"Dugger","given":"Bruce D.","affiliations":[],"preferred":false,"id":652902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coluccy, John M.","contributorId":111382,"corporation":false,"usgs":true,"family":"Coluccy","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":652917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dugger, Katie M. 0000-0002-4148-246X","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":36037,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":517,"text":"Oregon Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":652918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fox, Trevor T.","contributorId":176632,"corporation":false,"usgs":false,"family":"Fox","given":"Trevor","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":652919,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kraege, Donald K.","contributorId":19738,"corporation":false,"usgs":false,"family":"Kraege","given":"Donald","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":652920,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Petrie, Mark J.","contributorId":89655,"corporation":false,"usgs":true,"family":"Petrie","given":"Mark","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":652921,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70177172,"text":"ds1024 - 2016 - Characterization of sediment and measurement of groundwater levels and temperatures, Camas National Wildlife Refuge, eastern Idaho","interactions":[],"lastModifiedDate":"2016-11-03T07:32:31","indexId":"ds1024","displayToPublicDate":"2016-11-02T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1024","title":"Characterization of sediment and measurement of groundwater levels and temperatures, Camas National Wildlife Refuge, eastern Idaho","docAbstract":"The Camas National Wildlife Refuge (Refuge) in eastern Idaho, established in 1937, contains wetlands, ponds, and wet meadows that are essential resting and feeding habitat for migratory birds and nesting habitat for waterfowl. Initially, natural sources of water supported these habitats. However, during the past few decades, climate change and changes in surrounding land use have altered and reduced natural groundwater and surface water inflows such that the wetlands, ponds, and wet meadows are now maintained through water management and groundwater pumping. These water management activities have proven to be inefficient and costly, prompting the Refuge to develop alternative water management options that are more efficient and less expensive. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, is studying the hydrogeology at the Refuge to provide information for developing alternative water management options.
The hydrogeologic studies at the Refuge included characterizing the type, distribution, and hydraulic conductivity of surficial sediments and measuring water levels and temperatures in monitoring wells. Four monitoring wells and seven soil probe coreholes were drilled at the Refuge. Seven water level and temperature data loggers were installed in the wells and water levels and temperatures were continuously recorded from November 2014 to June 2016. Sediment cores were collected from the coreholes and sediment type and distribution were characterized from drillers’ notes, geophysical logs, corehole samples, and particle grain-size analysis. The hydraulic conductivities of sediments were estimated using the measured average grain size and the assumed textural maturity of the sediment, and ranged from about 20 to 290 feet per day.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1024","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Twining, B.V., and Rattray, G.W., 2016, Characterization of sediment and measurement of groundwater levels and temperatures, Camas National Wildlife Refuge, eastern Idaho: U.S. Geological Survey Data Series 1024, 23 p.,\nhttps://dx.doi.org/10.3133/ds1024.","productDescription":"Report: v, 23 p.; Appendix","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078192","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":330654,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1024/ds1024.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1024"},{"id":330653,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1024/coverthb.jpg"},{"id":330655,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1024/ds1024_appendixa.pdf","text":"Appendix A","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1024 Appendix A"}],"country":"United States","state":"Idaho","otherGeospatial":"Camas National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.2480010986328,\n 43.99318499277654\n ],\n [\n -112.23066329956055,\n 43.99306149560464\n ],\n [\n -112.22946166992188,\n 43.98194569774652\n ],\n [\n -112.24285125732422,\n 43.98219273809204\n ],\n [\n -112.24250793457031,\n 43.97836349721919\n ],\n [\n -112.24748611450195,\n 43.97823996920823\n ],\n [\n -112.24817276000977,\n 43.9576071863508\n ],\n [\n -112.22328186035156,\n 43.95797789836312\n ],\n [\n -112.21710205078125,\n 43.939439464527474\n ],\n [\n -112.2169303894043,\n 43.92794273182484\n ],\n [\n -112.22705841064453,\n 43.92769546584876\n ],\n [\n -112.22688674926758,\n 43.921018895951235\n ],\n [\n -112.24164962768555,\n 43.92089524870123\n ],\n [\n -112.24164962768555,\n 43.917309366668476\n ],\n [\n -112.24714279174805,\n 43.917185711709344\n ],\n [\n -112.24714279174805,\n 43.899500406811846\n ],\n [\n -112.2776985168457,\n 43.8997477899657\n ],\n [\n -112.27787017822266,\n 43.902839992663196\n ],\n [\n -112.28233337402344,\n 43.90321104619439\n ],\n [\n -112.28250503540038,\n 43.90642674658866\n ],\n [\n -112.28748321533203,\n 43.90667410096243\n ],\n [\n -112.28765487670898,\n 43.91001328440555\n ],\n [\n -112.30258941650389,\n 43.910136954268836\n ],\n [\n -112.30224609374999,\n 43.906797777763856\n ],\n [\n -112.31306076049803,\n 43.90667410096243\n ],\n [\n -112.31271743774414,\n 43.92448091462121\n ],\n [\n -112.3077392578125,\n 43.924851833243785\n ],\n [\n -112.30791091918945,\n 43.92794273182484\n ],\n [\n -112.29246139526367,\n 43.92880815464654\n ],\n [\n -112.29314804077148,\n 43.942653207310386\n ],\n [\n -112.30241775512695,\n 43.942653207310386\n ],\n [\n -112.30224609374999,\n 43.957236472025635\n ],\n [\n -112.29280471801758,\n 43.9576071863508\n ],\n [\n -112.29297637939453,\n 43.96502098715404\n ],\n [\n -112.27924346923827,\n 43.96477387536573\n ],\n [\n -112.27684020996094,\n 43.96625653067646\n ],\n [\n -112.27237701416016,\n 43.96625653067646\n ],\n [\n -112.27117538452148,\n 43.965268097914425\n ],\n [\n -112.26688385009766,\n 43.965268097914425\n ],\n [\n -112.26671218872069,\n 43.96810979777519\n ],\n [\n -112.26448059082031,\n 43.970951361689934\n ],\n [\n -112.25761413574219,\n 43.97144553284128\n ],\n [\n -112.25812911987305,\n 43.97836349721919\n ],\n [\n -112.25263595581055,\n 43.979104659888236\n ],\n [\n -112.25366592407227,\n 43.98169865637306\n ],\n [\n -112.26327896118164,\n 43.98219273809204\n ],\n [\n -112.26327896118164,\n 43.98540416903878\n ],\n [\n -112.25263595581055,\n 43.98614524381678\n ],\n [\n -112.25332260131836,\n 43.98923295580709\n ],\n [\n -112.2480010986328,\n 43.98972697481996\n ],\n [\n -112.2480010986328,\n 43.99318499277654\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
http://id.water.usgs.gov
Sympatric populations of native Brook Trout Salvelinus fontinalis and naturalized Brown Trout Salmo truttaexist throughout the eastern USA. An understanding of habitat use by sympatric populations is of importance for fisheries management agencies because of the close association between habitat and population dynamics. Moreover, habitat use by stream-dwelling salmonids may be further complicated by several factors, including the potential for fish to display scale-dependent habitat use. Discrete-choice models were used to (1) evaluate fall and early winter daytime habitat use by sympatric Brook Trout and Brown Trout populations based on available residual pool habitat within a stream network and (2) assess the sensitivity of inferred habitat use to changes in the spatial scale of the assumed available habitat. Trout exhibited an overall preference for pool habitats over nonpool habitats; however, the use of pools was nonlinear over time. Brook Trout displayed a greater preference for deep residual pool habitats than for shallow pool and nonpool habitats, whereas Brown Trout selected for all pool habitat categories similarly. Habitat use by both species was found to be scale dependent. At the smallest spatial scale (50 m), habitat use was primarily related to the time of year and fish weight. However, at larger spatial scales (250 and 450 m), habitat use varied over time according to the study stream in which a fish was located. Scale-dependent relationships in seasonal habitat use by Brook Trout and Brown Trout highlight the importance of considering scale when attempting to make inferences about habitat use; fisheries managers may want to consider identifying the appropriate spatial scale when devising actions to restore and protect Brook Trout populations and their habitats.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2016.1167777","usgsCitation":"Davis, L.A., and Wagner, T., 2016, Scale-dependent seasonal pool habitat use by sympatric Wild Brook Trout and Brown Trout populations: Transactions of the American Fisheries Society, v. 145, p. 888-902, https://doi.org/10.1080/00028487.2016.1167777.","productDescription":"15 p.","startPage":"888","endPage":"902","ipdsId":"IP-071257","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":337175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Hunts Run Watershed ","volume":"145","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-29","publicationStatus":"PW","scienceBaseUri":"58c277d8e4b014cc3a3e76b3","contributors":{"authors":[{"text":"Davis, Lori A.","contributorId":187762,"corporation":false,"usgs":false,"family":"Davis","given":"Lori","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":681596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":681459,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70179744,"text":"70179744 - 2016 - Evaluation of gas production potential from gas hydrate deposits in National Petroleum Reserve Alaska using numerical simulations","interactions":[],"lastModifiedDate":"2017-01-17T10:26:02","indexId":"70179744","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5264,"text":"Journal of Natural Gas Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of gas production potential from gas hydrate deposits in National Petroleum Reserve Alaska using numerical simulations","docAbstract":"An evaluation of the gas production potential of Sunlight Peak gas hydrate accumulation in the eastern portion of the National Petroleum Reserve Alaska (NPRA) of Alaska North Slope (ANS) is conducted using numerical simulations, as part of the U.S. Geological Survey (USGS) gas hydrate Life Cycle Assessment program. A field scale reservoir model for Sunlight Peak is developed using Advanced Processes & Thermal Reservoir Simulator (STARS) that approximates the production design and response of this gas hydrate field. The reservoir characterization is based on available structural maps and the seismic-derived hydrate saturation map of the study region. A 3D reservoir model, with heterogeneous distribution of the reservoir properties (such as porosity, permeability and vertical hydrate saturation), is developed by correlating the data from the Mount Elbert well logs. Production simulations showed that the Sunlight Peak prospect has the potential of producing 1.53 × 109 ST m3 of gas in 30 years by depressurization with a peak production rate of around 19.4 × 104 ST m3/day through a single horizontal well. To determine the effect of uncertainty in reservoir properties on the gas production, an uncertainty analysis is carried out. It is observed that for the range of data considered, the overall cumulative production from the Sunlight Peak will always be within the range of ±4.6% error from the overall mean value of 1.43 × 109 ST m3. A sensitivity analysis study showed that the proximity of the reservoir from the base of permafrost and the base of hydrate stability zone (BHSZ) has significant effect on gas production rates. The gas production rates decrease with the increase in the depth of the permafrost and the depth of BHSZ. From the overall analysis of the results it is concluded that Sunlight Peak gas hydrate accumulation behaves differently than other Class III reservoirs (Class III reservoirs are composed of a single layer of hydrate with no underlying zone of mobile fluids) due to its smaller thickness and high angle of dip.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jngse.2016.11.021","usgsCitation":"Nandanwar, M.S., Anderson, B.J., Ajayi, T., Collett, T.S., and Zyrianova, M.V., 2016, Evaluation of gas production potential from gas hydrate deposits in National Petroleum Reserve Alaska using numerical simulations: Journal of Natural Gas Science and Engineering, v. 36, no. A, p. 760-772, https://doi.org/10.1016/j.jngse.2016.11.021.","productDescription":"13 p.","startPage":"760","endPage":"772","ipdsId":"IP-079065","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":333231,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.86328125,\n 69.38804929116819\n ],\n [\n -154.86328125,\n 70.90226826757711\n ],\n [\n -151.402587890625,\n 70.90226826757711\n ],\n [\n -151.402587890625,\n 69.38804929116819\n ],\n [\n -154.86328125,\n 69.38804929116819\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"A","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"587f3c31e4b0d96de2564547","contributors":{"authors":[{"text":"Nandanwar, Manish S.","contributorId":178323,"corporation":false,"usgs":false,"family":"Nandanwar","given":"Manish","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":658498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Brian J.","contributorId":147120,"corporation":false,"usgs":false,"family":"Anderson","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":658499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ajayi, Taiwo","contributorId":178324,"corporation":false,"usgs":false,"family":"Ajayi","given":"Taiwo","email":"","affiliations":[],"preferred":false,"id":658500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":658501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zyrianova, Margarita V. 0000-0002-3669-1320 rita@usgs.gov","orcid":"https://orcid.org/0000-0002-3669-1320","contributorId":1203,"corporation":false,"usgs":true,"family":"Zyrianova","given":"Margarita","email":"rita@usgs.gov","middleInitial":"V.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":658497,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193641,"text":"70193641 - 2016 - Multiple browsers structure tree recruitment in logged temperate forests","interactions":[],"lastModifiedDate":"2017-11-13T14:51:14","indexId":"70193641","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Multiple browsers structure tree recruitment in logged temperate forests","docAbstract":"Historical extirpations have resulted in depauperate large herbivore assemblages in many northern forests. In eastern North America, most forests are inhabited by a single wild ungulate species, white-tailed deer (Odocoileus virginianus), and relationships between deer densities and impacts on forest regeneration are correspondingly well documented. Recent recolonizations by moose (Alces americanus) in northeastern regions complicate established deer density thresholds and predictions of browsing impacts on forest dynamics because size and foraging differences between the two animals suggest a lack of functional redundancy. We asked to what extent low densities of deer + moose would structure forest communities differently from that of low densities of deer in recently logged patch cuts of Massachusetts, USA. In each site, a randomized block with three treatment levels of large herbivores–no-ungulates (full exclosure), deer (partial exclosure), and deer + moose (control) was established. After 6–7 years, deer + moose reduced stem densities and basal area by 2-3-fold, Prunus pensylvanica and Quercus spp. recruitment by 3–6 fold, and species richness by 1.7 species (19%). In contrast, in the partial exclosures, deer had non-significant effects on stem density, basal area, and species composition, but significantly reduced species richness by 2.5 species on average (28%). Deer browsing in the partial exclosure was more selective than deer + moose browsing together, perhaps contributing to the decline in species richness in the former treatment and the lack of additional decline in the latter. Moose used the control plots at roughly the same frequency as deer (as determined by remote camera traps), suggesting that the much larger moose was the dominant browser species in terms of animal biomass in these cuts. A lack of functional redundancy with respect to foraging behavior between sympatric large herbivores may explain combined browsing effects that were both large and complex.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0166783","usgsCitation":"Faison, E.K., DeStefano, S., Foster, D., Rapp, J.M., and Compton, J., 2016, Multiple browsers structure tree recruitment in logged temperate forests: PLoS ONE, v. 11, no. 11, p. 1-14, https://doi.org/10.1371/journal.pone.0166783.","productDescription":"e0166783; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-076434","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":482069,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0166783","text":"Publisher Index Page"},{"id":348722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.44247436523438,\n 42.249868245939325\n ],\n [\n -71.9000244140625,\n 42.249868245939325\n ],\n [\n -71.9000244140625,\n 42.63496903887609\n ],\n [\n -72.44247436523438,\n 42.63496903887609\n ],\n [\n -72.44247436523438,\n 42.249868245939325\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"11","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-28","publicationStatus":"PW","scienceBaseUri":"5a60fc9ce4b06e28e9c2404a","contributors":{"authors":[{"text":"Faison, Edward K.","contributorId":191559,"corporation":false,"usgs":false,"family":"Faison","given":"Edward","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":721857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeStefano, Stephen 0000-0003-2472-8373 destef@usgs.gov","orcid":"https://orcid.org/0000-0003-2472-8373","contributorId":166706,"corporation":false,"usgs":true,"family":"DeStefano","given":"Stephen","email":"destef@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":719728,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foster, David R.","contributorId":149881,"corporation":false,"usgs":false,"family":"Foster","given":"David R.","affiliations":[{"id":16810,"text":"Harvard Univ.","active":true,"usgs":false}],"preferred":false,"id":721858,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rapp, Joshua M.","contributorId":200307,"corporation":false,"usgs":false,"family":"Rapp","given":"Joshua","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":721859,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Compton, Justin A.","contributorId":200308,"corporation":false,"usgs":false,"family":"Compton","given":"Justin A.","affiliations":[],"preferred":false,"id":721860,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178360,"text":"70178360 - 2016 - Environmental drivers of differences in microbial community structure in crude oil reservoirs across a methanogenic gradient","interactions":[],"lastModifiedDate":"2016-11-15T11:53:03","indexId":"70178360","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"title":"Environmental drivers of differences in microbial community structure in crude oil reservoirs across a methanogenic gradient","docAbstract":"Stimulating in situ microbial communities in oil reservoirs to produce natural gas is a potentially viable strategy for recovering additional fossil fuel resources following traditional recovery operations. Little is known about what geochemical parameters drive microbial population dynamics in biodegraded, methanogenic oil reservoirs. We investigated if microbial community structure was significantly impacted by the extent of crude oil biodegradation, extent of biogenic methane production, and formation water chemistry. Twenty-two oil production wells from north central Louisiana, USA, were sampled for analysis of microbial community structure and fluid geochemistry. Archaea were the dominant microbial community in the majority of the wells sampled. Methanogens, including hydrogenotrophic and methylotrophic organisms, were numerically dominant in every well, accounting for, on average, over 98% of the total Archaea present. The dominant Bacteria groups were Pseudomonas, Acinetobacter, Enterobacteriaceae, and Clostridiales, which have also been identified in other microbially-altered oil reservoirs. Comparing microbial community structure to fluid (gas, water, and oil) geochemistry revealed that the relative extent of biodegradation, salinity, and spatial location were the major drivers of microbial diversity. Archaeal relative abundance was independent of the extent of methanogenesis, but closely correlated to the extent of crude oil biodegradation; therefore, microbial community structure is likely not a good sole predictor of methanogenic activity, but may predict the extent of crude oil biodegradation. However, when the shallow, highly biodegraded, low salinity wells were excluded from the statistical analysis, no environmental parameters could explain the differences in microbial community structure. This suggests that the microbial community structure of the 5 shallow, up-dip wells was different than the 17 deeper, down-dip wells. Also, the 17 down-dip wells had statistically similar microbial communities despite significant changes in environmental parameters between oil fields. Together, this implies that no single microbial population is a reliable indicator of a reservoir's ability to degrade crude oil to methane, and that geochemistry may be a more important indicator for selecting a reservoir suitable for microbial enhancement of natural gas generation.
","language":"English","publisher":"frontiers","doi":"10.3389/fmicb.2016.01535","usgsCitation":"Shelton, J., Akob, D.M., McIntosh, J.C., Fierer, N., Spear, J.R., Warwick, P.D., and McCray, J.E., 2016, Environmental drivers of differences in microbial community structure in crude oil reservoirs across a methanogenic gradient: Frontiers in Microbiology, v. 7, Article 1535; 12 p., https://doi.org/10.3389/fmicb.2016.01535.","productDescription":"Article 1535; 12 p.","ipdsId":"IP-079283","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":470454,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2016.01535","text":"Publisher Index Page"},{"id":331007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.40737915039062,\n 31.425733532703752\n ],\n [\n -92.40737915039062,\n 31.809895002118832\n ],\n [\n -91.96105957031249,\n 31.809895002118832\n ],\n [\n -91.96105957031249,\n 31.425733532703752\n ],\n [\n -92.40737915039062,\n 31.425733532703752\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-28","publicationStatus":"PW","scienceBaseUri":"582c2ce4e4b0c253be072c02","contributors":{"authors":[{"text":"Shelton, Jenna L. 0000-0002-1377-0675 jlshelton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":5025,"corporation":false,"usgs":true,"family":"Shelton","given":"Jenna L.","email":"jlshelton@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":653765,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Akob, Denise M. 0000-0003-1534-3025 dakob@usgs.gov","orcid":"https://orcid.org/0000-0003-1534-3025","contributorId":4980,"corporation":false,"usgs":true,"family":"Akob","given":"Denise","email":"dakob@usgs.gov","middleInitial":"M.","affiliations":[{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":653766,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McIntosh, Jennifer C.","contributorId":139870,"corporation":false,"usgs":false,"family":"McIntosh","given":"Jennifer","email":"","middleInitial":"C.","affiliations":[{"id":13301,"text":"Department of Hydrology and Water Resources, University of Arizona, Tucson, Arizona","active":true,"usgs":false}],"preferred":false,"id":653767,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fierer, Noah","contributorId":138711,"corporation":false,"usgs":false,"family":"Fierer","given":"Noah","email":"","affiliations":[{"id":6713,"text":"University of Colorado, Boulder CO","active":true,"usgs":false}],"preferred":false,"id":653768,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spear, John R.","contributorId":176847,"corporation":false,"usgs":false,"family":"Spear","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":653769,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Warwick, Peter D. 0000-0002-3152-7783 pwarwick@usgs.gov","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":762,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter","email":"pwarwick@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":653770,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McCray, John E.","contributorId":169186,"corporation":false,"usgs":false,"family":"McCray","given":"John","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":653771,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178628,"text":"70178628 - 2016 - Chemical and isotopic changes in Williston Basin brines during long-term oil production: An example from the Poplar dome, Montana","interactions":[],"lastModifiedDate":"2017-04-27T10:14:39","indexId":"70178628","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":605,"text":"AAPG Bulletin","printIssn":"0149-1423","active":true,"publicationSubtype":{"id":10}},"title":"Chemical and isotopic changes in Williston Basin brines during long-term oil production: An example from the Poplar dome, Montana","docAbstract":"Brine samples were collected from 30 conventional oil wells producing mostly from the Charles Formation of the Madison Group in the East and Northwest Poplar oil fields on the Fort Peck Indian Reservation, Montana. Dissolved concentrations of major ions, trace metals, Sr isotopes, and stable isotopes (oxygen and hydrogen) were analyzed to compare with a brine contaminant that affected groundwater northeast of the town of Poplar. Two groups of brine compositions, designated group I and group II, are identified on the basis of chemistry and 87Sr/86Sr ratios. The solute chemistry and Sr isotopic composition of group I brines are consistent with long-term residency in Mississippian carbonate rocks, and brines similar to these contaminated the groundwater. Group II brines probably resided in clastic rocks younger than the Mississippian limestones before moving into the Poplar dome to replenish the long-term fluid extraction from the Charles Formation. Collapse of strata at the crest of the Poplar dome resulting from dissolution of Charles salt in the early Paleogene probably developed pathways for the ingress of group II brines from overlying clastic aquifers into the Charles reservoir. Such changes in brine chemistry associated with long-term oil production may be a widespread phenomenon in the Williston Basin.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/05261615114","usgsCitation":"Peterman, Z.E., and Thamke, J., 2016, Chemical and isotopic changes in Williston Basin brines during long-term oil production: An example from the Poplar dome, Montana: AAPG Bulletin, v. 100, no. 10, p. 1619-1632, https://doi.org/10.1306/05261615114.","productDescription":"14 p.","startPage":"1619","endPage":"1632","ipdsId":"IP-066379","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":331392,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","volume":"100","issue":"10","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"584144dee4b04fc80e50739b","contributors":{"authors":[{"text":"Peterman, Zell E. 0000-0002-5694-8082 peterman@usgs.gov","orcid":"https://orcid.org/0000-0002-5694-8082","contributorId":167699,"corporation":false,"usgs":true,"family":"Peterman","given":"Zell","email":"peterman@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":654631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thamke, Joanna N. 0000-0002-6917-1946 jothamke@usgs.gov","orcid":"https://orcid.org/0000-0002-6917-1946","contributorId":1012,"corporation":false,"usgs":true,"family":"Thamke","given":"Joanna N.","email":"jothamke@usgs.gov","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":654632,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193096,"text":"70193096 - 2016 - Evaluation of fisher (Pekania pennanti) restoration in Olympic National Park and the Olympic Recovery Area: 2015 final annual progress report","interactions":[],"lastModifiedDate":"2017-11-27T14:59:21","indexId":"70193096","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/OLYM/NRR—2016/1274","displayTitle":"Evaluation of fisher (Pekania pennanti) restoration in Olympic National Park and the Olympic Recovery Area: 2015 final annual progress report","title":"Evaluation of fisher (Pekania pennanti) restoration in Olympic National Park and the Olympic Recovery Area: 2015 final annual progress report","docAbstract":"With the translocation and release of 90 fishers (Pekania pennanti) from British Columbia to Olympic National Park during 2008–2010, the National Park Service (NPS) and Washington Department of Fish and Wildlife (WDFW) accomplished the first phase of fisher restoration in Washington State. Beginning in 2013, we initiated a new research project to determine the current status of fishers on Washington’s Olympic Peninsula 3–8 years after the releases and evaluate the short-term success of the restoration program. Objectives of the study are to determine the current distribution of fishers and proportion of the recovery area that is currently occupied by fishers, determine several genetic characteristics of the reintroduced population, and determine reproductive success of the founding animals through genetic studies.
During 2015, we continued working with a broad coalition of cooperating agencies, tribes, and nongovernmental organizations (NGO) to collect data on fisher distribution and genetics using noninvasive sampling methods. The primary sampling frame consisted of 157 24-km2 hexagons (hexes) distributed across all major land ownerships within the Olympic Peninsula target survey area. In 2014 we expanded the study by adding 58 more hexes to an expanded study area in response to incidental fisher observations outside of the target area obtained in 2013; 49 hexes were added south and 9 to the east of the target area. During 2015, Federal, State, Tribal and NGO biologists and volunteers established three Distributioned motion-sensing camera stations, paired with hair snaring devices, in 87 hexes; 75 in the targeted area and 12 in the expansion areas. Each paired camera/hair station was left in place for approximately 6 weeks, with three checks on 2-week intervals. We documented fisher presence in 7 of the 87 hexagons. Four fishers were identified through microsatellite DNA analyses. The 4 identified fishers included 1 of the original founding population of 90 and 3 new recruits to the population. Three additional fishers were detected with cameras but not DNA, consequently their identities were unknown. All fisher detections were in the target area. Additionally, we identified 46 other species of wildlife at the baited camera stations. We also obtained 4 additional confirmed records of fishers in the study area through photographs provided by the public and incidental live capture.
During 2016, we plan to resample 69 hexagons sampled in the target area in 2014 and 12 new hexes in the expansion area. In addition, we plan to sample non-selected hexes in-between hexes where we had a cluster of fishers in 2014, to provide better understanding of occupancy patterns and minimum number of individuals in an area where fishers appear to be concentrating.
","language":"English","publisher":"National Park Service","publisherLocation":"Fort Collins, CO","usgsCitation":"Happe, P.J., Jenkins, K.J., Kay, T.J., Pilgrim, K., Schwartz, M.K., Lewis, J.C., and Aubry, K.B., 2016, Evaluation of fisher (Pekania pennanti) restoration in Olympic National Park and the Olympic Recovery Area: 2015 final annual progress report: Natural Resource Report NPS/OLYM/NRR—2016/1274, ix, 34 p.","productDescription":"ix, 34 p.","numberOfPages":"48","ipdsId":"IP-088873","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":349384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":349382,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.nps.gov/olym/learn/nature/upload/OLYM_Fisher_2015ProgressReport_20160815.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Washington","otherGeospatial":"Olympic 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K.","contributorId":199035,"corporation":false,"usgs":false,"family":"Schwartz","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":717971,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lewis, Jeffrey C.","contributorId":141090,"corporation":false,"usgs":false,"family":"Lewis","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":13674,"text":"WDFW","active":true,"usgs":false}],"preferred":false,"id":717972,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Aubry, Keith B.","contributorId":141091,"corporation":false,"usgs":false,"family":"Aubry","given":"Keith","email":"","middleInitial":"B.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":717973,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70184987,"text":"70184987 - 2016 - Pb-Sr isotopic and geochemical constraints on sources and processes of lead contamination in well waters and soil from former fruit orchards, Pennsylvania, USA: A legacy of anthropogenic activities","interactions":[],"lastModifiedDate":"2017-03-13T13:29:57","indexId":"70184987","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2302,"text":"Journal of Geochemical Exploration","active":true,"publicationSubtype":{"id":10}},"title":"Pb-Sr isotopic and geochemical constraints on sources and processes of lead contamination in well waters and soil from former fruit orchards, Pennsylvania, USA: A legacy of anthropogenic activities","docAbstract":"Isotopic discrimination can be an effective tool in establishing a direct link between sources of Pb contamination and the presence of anomalously high concentrations of Pb in waters, soils, and organisms. Residential wells supplying water containing up to 1600 ppb Pb to houses built on the former Mohr orchards commercial site, near Allentown, PA, were evaluated to discern anthropogenic from geogenic sources. Pb (n = 144) and Sr (n = 40) isotopic data and REE (n = 29) data were determined for waters from residential wells, test wells (drilled for this study), and surface waters from pond and creeks. Local soils, sediments, bedrock, Zn-Pb mineralization and coal were also analyzed (n = 94), together with locally used Pb-As pesticide (n = 5). Waters from residential and test wells show overlapping values of 206Pb/207Pb, 208Pb/207Pb and 87Sr/86Sr. Larger negative Ce anomalies (Ce/Ce*) distinguish residential wells from test wells. Results show that residential and test well waters, sediments from residential water filters in water tanks, and surface waters display broad linear trends in Pb isotope plots. Pb isotope data for soils, bedrock, and pesticides have contrasting ranges and overlapping trends. Contributions of Pb from soils to residential well waters are limited and implicated primarily in wells having shallow water-bearing zones and carrying high sediment contents. Pb isotope data for residential wells, test wells, and surface waters show substantial overlap with Pb data reflecting anthropogenic actions (e.g., burning fossil fuels, industrial and urban processing activities). Limited contributions of Pb from bedrock, soils, and pesticides are evident. High Pb concentrations in the residential waters are likely related to sediment build up in residential water tanks. Redox reactions, triggered by influx of groundwater via wells into the residential water systems and leading to subtle changes in pH, are implicated in precipitation of Fe oxyhydroxides, oxidative scavenging of Ce(IV), and desorption and release of Pb into the residential water systems. The Pb isotope features in the residences and the region are best interpreted as reflecting a legacy of industrial Pb present in underlying aquifers that currently supply the drinking water wells.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gexplo.2016.08.008","usgsCitation":"Ayuso, R.A., and Foley, N.K., 2016, Pb-Sr isotopic and geochemical constraints on sources and processes of lead contamination in well waters and soil from former fruit orchards, Pennsylvania, USA: A legacy of anthropogenic activities: Journal of Geochemical Exploration, v. 170, p. 125-147, https://doi.org/10.1016/j.gexplo.2016.08.008.","productDescription":"23 p.","startPage":"125","endPage":"147","ipdsId":"IP-070677","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":337434,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","volume":"170","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58c7af9fe4b0849ce9795e96","contributors":{"authors":[{"text":"Ayuso, Robert A. 0000-0002-8496-9534 rayuso@usgs.gov","orcid":"https://orcid.org/0000-0002-8496-9534","contributorId":2654,"corporation":false,"usgs":true,"family":"Ayuso","given":"Robert","email":"rayuso@usgs.gov","middleInitial":"A.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":683834,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foley, Nora K. 0000-0003-0124-3509 nfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-0124-3509","contributorId":4010,"corporation":false,"usgs":true,"family":"Foley","given":"Nora","email":"nfoley@usgs.gov","middleInitial":"K.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":683835,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70177938,"text":"70177938 - 2016 - Uncertainty in biological monitoring: a framework for data collection and analysis to account for multiple sources of sampling bias","interactions":[],"lastModifiedDate":"2016-11-01T09:18:02","indexId":"70177938","displayToPublicDate":"2016-10-31T12:15:55","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty in biological monitoring: a framework for data collection and analysis to account for multiple sources of sampling bias","docAbstract":"Identifying environmental metrics specific to lesser scaup (Aythya affinis; scaup) spring migration chronology may help inform development of conservation, management and population monitoring. Our objective was to determine how environmental conditions influence spring migration of lesser scaup to assess the effectiveness of the Waterfowl Breeding Population and Habitat Survey in accurately estimating scaup populations. We first compared peak timing of mallard (Anas platyrhynchos) and scaup migration from weekly ground surveys in North Dakota, USA because the Waterfowl Breeding Population and Habitat Survey is designed to capture annual mallard migration. As predicted, we detected that peak timing of scaup and mallard migrations differed in 25 of 36 years investigated (1980–2010). We marked scaup with satellite transmitters (n = 78; 7,403 locations) at Long Point, Lake Erie, Ontario, Canada; Pool 19 of the Mississippi River, Iowa and Illinois, USA; and Presque Isle Bay, Lake Erie, Pennsylvania, USA. We tested the assumption that our marked scaup were representative of the continental population using the traditional survey area by comparing timing of migration of marked birds and scaup counted in the North Dakota Game and Fish Department survey. We detected a strong positive correlation between marked scaup and the survey data, which indicated that marked scaup were representative of the population. We subsequently used our validated sample of marked scaup to investigate the effects of annual variation in temperature, precipitation, and ice cover on spring migration chronology in the traditional and eastern survey areas of the Waterfowl Breeding Population and Habitat Survey, 2005–2010. We evaluated competing environmental models to explain variation in timing and rate of scaup migration at large-scale and local levels. Spring migration of scaup occurred earlier and faster during springs with warmer temperatures and greater precipitation, variables known to influence energy budgets and wetland availability. Our results suggest that surveys designed to index abundance of breeding mallards is imprecise for estimating scaup abundance, and inaccurate at estimating breeding population size by survey stratum.
","language":"English","publisher":"Wildlife Society","publisherLocation":"Washington, D.C.","doi":"10.1002/jwmg.21131","usgsCitation":"Finger, T.A., Afton, A.D., Schummer, M.L., Petrie, S.A., Badzinski, S.S., Johnson, M.A., Szymanski, M.L., Jacobs, K.J., Olsen, G.H., and Mitchell, M., 2016, Environmental factors influence lesser scaup migration chronology and population monitoring: Journal of Wildlife Management, v. 80, no. 8, p. 1437-1449, https://doi.org/10.1002/jwmg.21131.","productDescription":"13 p.","startPage":"1437","endPage":"1449","ipdsId":"IP-068193","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":330488,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","volume":"80","issue":"8","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-22","publicationStatus":"PW","scienceBaseUri":"5813125de4b0b5a0c12ab662","contributors":{"authors":[{"text":"Finger, Taylor A.","contributorId":176345,"corporation":false,"usgs":false,"family":"Finger","given":"Taylor","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":652253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Afton, Alan D. 0000-0002-0436-8588 aafton@usgs.gov","orcid":"https://orcid.org/0000-0002-0436-8588","contributorId":139582,"corporation":false,"usgs":false,"family":"Afton","given":"Alan","email":"aafton@usgs.gov","middleInitial":"D.","affiliations":[{"id":368,"text":"Louisiana Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":652254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schummer, Michael L.","contributorId":176347,"corporation":false,"usgs":false,"family":"Schummer","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":652255,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Petrie, Scott A.","contributorId":141223,"corporation":false,"usgs":false,"family":"Petrie","given":"Scott","email":"","middleInitial":"A.","affiliations":[{"id":13717,"text":"Long Point Waterfowl","active":true,"usgs":false}],"preferred":false,"id":652256,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Badzinski, Shannon S.","contributorId":176348,"corporation":false,"usgs":false,"family":"Badzinski","given":"Shannon","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":652257,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Michael A.","contributorId":174789,"corporation":false,"usgs":false,"family":"Johnson","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":652258,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Szymanski, Michael L.","contributorId":176349,"corporation":false,"usgs":false,"family":"Szymanski","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":652259,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jacobs, Kevin J.","contributorId":176350,"corporation":false,"usgs":false,"family":"Jacobs","given":"Kevin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":652260,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Olsen, Glenn H. 0000-0002-7188-6203 golsen@usgs.gov","orcid":"https://orcid.org/0000-0002-7188-6203","contributorId":40918,"corporation":false,"usgs":true,"family":"Olsen","given":"Glenn","email":"golsen@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":652252,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mitchell, M.E.","contributorId":176351,"corporation":false,"usgs":false,"family":"Mitchell","given":"M.E.","affiliations":[],"preferred":false,"id":652285,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70177881,"text":"70177881 - 2016 - Snake fungal disease: An emerging threat to wild snakes","interactions":[],"lastModifiedDate":"2023-06-20T15:41:47.714161","indexId":"70177881","displayToPublicDate":"2016-10-25T16:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3048,"text":"Philosophical Transactions of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Snake fungal disease: An emerging threat to wild snakes","docAbstract":"Since 2006, there has been a marked increase in the number of reports of severe and often fatal fungal skin infections in wild snakes in the eastern USA. The emerging condition, referred to as snake fungal disease (SFD), was initially documented in rattlesnakes, where the infections were believed to pose a risk to the viability of affected populations. The disease is caused byOphidiomyces ophiodiicola, a fungus recently split from a complex of fungi long referred to as the Chrysosporium anamorph of Nannizziopsis vriesii (CANV). Here we review the current state of knowledge about O. ophiodiicola and SFD. In addition, we provide original findings which demonstrate that O. ophiodiicola is widely distributed in eastern North America, has a broad host range, is the predominant cause of fungal skin infections in wild snakes and often causes mild infections in snakes emerging from hibernation. This new information, together with what is already available in the scientific literature, advances our knowledge of the cause, pathogenesis and ecology of SFD. However, additional research is necessary to elucidate the factors driving the emergence of this disease and develop strategies to mitigate its impacts.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rstb.2015.0457","usgsCitation":"Lorch, J.M., Knowles, S., Lankton, J.S., Michell, K., Edwards, J.L., Kapfer, J.M., Staffen, R.A., Wild, E.R., Schmidt, K.Z., Ballmann, A., Blodgett, D., Farrell, T.M., Glorioso, B.M., Last, L.A., Price, S.J., Schuler, K.L., Smith, C., Wellehan, J.F., and Blehert, D., 2016, Snake fungal disease: An emerging threat to wild snakes: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 371, 20150457; 8 p.; Data Release, https://doi.org/10.1098/rstb.2015.0457.","productDescription":"20150457; 8 p.; Data Release","ipdsId":"IP-075547","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":462053,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rstb.2015.0457","text":"Publisher Index Page"},{"id":438529,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Z31WRB","text":"USGS data release","linkHelpText":"Snake dermatitis data"},{"id":330379,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":337088,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7Z31WRB","text":"Snake fungal disease: an emerging threat to wild snakes"}],"volume":"371","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-05","publicationStatus":"PW","scienceBaseUri":"58106f96e4b0f497e7961105","chorus":{"doi":"10.1098/rstb.2015.0457","url":"http://dx.doi.org/10.1098/rstb.2015.0457","publisher":"The Royal Society","authors":"Lorch Jeffrey M., Knowles Susan, Lankton Julia S., Michell Kathy, Edwards Jaime L., Kapfer Joshua M., Staffen Richard A., Wild Erik R., Schmidt Katie Z., Ballmann Anne E., Blodgett Doug, Farrell Terence M., Glorioso Brad M., Last Lisa A., Price Steven J., Schuler Krysten L., Smith Christopher E., Wellehan James F. X., Blehert David S.","journalName":"Philosophical Transactions of the Royal Society B: Biological Sciences","publicationDate":"10/24/2016","publiclyAccessibleDate":"10/24/2016"},"contributors":{"authors":[{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":5565,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":651987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knowles, Susan 0000-0002-0254-6491 sknowles@usgs.gov","orcid":"https://orcid.org/0000-0002-0254-6491","contributorId":5254,"corporation":false,"usgs":true,"family":"Knowles","given":"Susan","email":"sknowles@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health 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X. Jr.","contributorId":23859,"corporation":false,"usgs":true,"family":"Wellehan","given":"James","suffix":"Jr.","email":"","middleInitial":"F. X.","affiliations":[],"preferred":false,"id":652004,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Blehert, David S. 0000-0002-1065-9760 dblehert@usgs.gov","orcid":"https://orcid.org/0000-0002-1065-9760","contributorId":1816,"corporation":false,"usgs":true,"family":"Blehert","given":"David S.","email":"dblehert@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":652005,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70174874,"text":"sir20165048 - 2016 - Assessment of hydrogeologic terrains, well-construction characteristics, groundwater hydraulics, and water-quality and microbial data for determination of surface-water-influenced groundwater supplies in West Virginia","interactions":[],"lastModifiedDate":"2016-10-24T13:52:21","indexId":"sir20165048","displayToPublicDate":"2016-10-24T10:50:00","publicationYear":"2016","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":"2016-5048","title":"Assessment of hydrogeologic terrains, well-construction characteristics, groundwater hydraulics, and water-quality and microbial data for determination of surface-water-influenced groundwater supplies in West Virginia","docAbstract":"In January 2014, a storage tank leaked, spilling a large quantity of 4-methylcyclohexane methanol into the Elk River in West Virginia and contaminating the water supply for more than 300,000 people. In response, the West Virginia Legislature passed Senate Bill 373, which requires the West Virginia Department of Health and Human Resources (WVDHHR) to assess the susceptibility and vulnerability of public surface-water-influenced groundwater supply sources (SWIGS) and surface-water intakes statewide. In response to this mandate for reassessing SWIGS statewide, the U.S. Geological Survey (USGS), in cooperation with the WVDHHR, Bureau of Public Health, Office of Environmental Health Services, compiled available data and summarized the results of previous groundwater studies to provide the WVDHHR with data that could be used as part of the process for assessing and determining SWIGS.
\nExisting geologic, hydrologic, well-construction, water-quality, and other related data and information from previous U.S. Geological Survey (USGS) hydrogeologic studies and the USGS National Water Information System (NWIS) database, in conjunction with data from the West Virginia Bureau for Public Health (WVBPH) Department of Health and Human Resources (WVDHHR) and the West Virginia Department of Environmental Protection database and files, were collected, compiled, and analyzed to help the WVDHHR to better assess public groundwater supply wells that may meet the definition of a surface-water-influenced- groundwater supply (SWIGS).
\nIn this study, measures of intrinsic susceptibility, which are characterized by the physical properties that affect the ease with which water moves through the unsaturated zone and, subsequently, into the saturated zone within an aquifer, showed that karst limestone aquifers are the aquifers most intrinsically susceptible to contamination within the State of West Virginia. Karst limestone aquifers are present within Cambrian- and Ordovician-age formations within West Virginia’s eastern panhandle and in Mississippian-age limestones within the Greenbrier River valley. Solution development within these limestone aquifers allows rapid recharge and flow of groundwater within the aquifer, both of which allow surface contaminants to easily enter the aquifer and travel long distances in a short period of time.
\nAlluvial aquifers bordering the Ohio River in western West Virginia are also potentially highly susceptible to contamination because these alluvial aquifers can receive significant recharge from the adjacent Ohio River. Any potential contaminants that may be present in the river have the potential to enter the aquifer and contaminate wells completed within the sand and gravel alluvial sediments within which the wells are completed. These same alluvial sediments, however, help to retard the movement of bacteria and other potentially pathogenic organisms, such as Cryptosporidia and Giardia lamblia, into the aquifer. As a result, samples from alluvial aquifers bordering the Ohio River and elsewhere within the State do not commonly test positive for indicator bacteria, such as total coliform, fecal coliform, or Escherichia coli (E. coli). The alluvial sediments do not, however, provide assimilative capacity with respect to water soluble compounds such as nitrate and certain volatile and semi-volatile organic compounds. Therefore, the Ohio River alluvial aquifers are highly susceptible to organic compounds present in the river or on the land surface near a well. These aquifers are also susceptible to nitrate contamination from fertilizers, pesticides, and manure, which are commonly used on the fertile agricultural soils present on terraces along the Ohio River.
\nAbandoned-coal-mine aquifers, which are typically used as a source of groundwater in southern West Virginia, are moderately susceptible to contamination. The vast network of voids from mine entries provide vast storage for groundwater in abandoned mine aquifers, and fracturing of overburden strata, which is common in areas of past or current mining, can allow rapid infiltration of contaminants to the aquifer. Where streams cross over below-drainage underground coal mines, there is an increased potential for contamination of coal-mine aquifers. As a result, above-drainage underground coal mines, those mines that are present at an elevation above local tributary drainage, are probably less susceptible to contamination than are below-drainage underground coal mines. Public groundwater supplies in abandoned coal mines need to be evaluated on a case-by-case basis to assess the potential for recharge of contaminated surface water to enter below-drainage underground coal-mine aquifers and to assess potential hydraulic conductivity to nearby surface-water bodies, such as lakes, ponds, rivers, or streams.
\nFractured-rock aquifers compose an additional major type of aquifer within the State of West Virginia. Owing to their low permeability and their typically small groundwater capture areas, fractured-rock aquifers within the State of West Virginia typically have low susceptibility to contamination. However, there are exceptions, and wells completed in fractured-rock aquifers that are in close proximity to streams may be adversely affected by induced recharge from the stream. Where such systems are present, frequent bacterial testing of the source water can be used to ascertain the potential for microbial contamination of the aquifer.
\nIntrinsic susceptibility alone does not fully predict whether or not a well is vulnerable to contamination, only that the hydrogeologic terrain is suitable for rapid transport of pathogenic organisms or chemical compounds to and within the aquifer. However, contaminants may or may not be present in the recharge water to an individual well or well field. Therefore, an assessment of potential contaminant sources, such as nearby gas wells, landfills, underground storage tanks, above ground storage tanks, major transportation corridors, surface or underground coal mines, and flood plains, is needed to assess vulnerability. The assessments need to be conducted on a case-by-case basis or, as has been done in this study, by collecting and compiling the number of potential contaminant sources that may be present in the source-water-protection area for an individual public groundwater supply source.
\nGroundwater public-supply systems in areas of high intrinsic susceptibility and with a large number of potential contaminant sources within the recharge or source-water-protection area of individual wells or well fields are potentially vulnerable to contamination and probably warrant further evaluation as potential SWIGS. However, measures can be taken to educate the local population and initiate safety protocols and protective strategies to appropriately manage contaminant sources to prevent release of contaminants to the aquifer, therefore, reducing vulnerability of these systems to contamination. However, each public groundwater supply source needs to be assessed on an individual basis. Data presented in this report can be used to categorize and prioritize wells and springs that have a high potential for intrinsic susceptibility or vulnerability to contamination.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165048","collaboration":"Prepared in cooperation with the West Virginia Department of Health and Human Resources, Bureau of Public Health, Office of Environmental Health Services","usgsCitation":"Kozar, M.D., and Paybins, K.S., 2016, Assessment of hydrogeologic terrains, well-construction characteristics, groundwater hydraulics, and water-quality and microbial data for determination of surface-water-influenced groundwater supplies in West Virginia (ver. 1.1, October 2016): U.S. Geological Survey Scientific Investigations Report 2016–5048, 55 p., https://dx.doi.org/10.3133/sir20165048.","productDescription":"Report: vii, 54 p.; 2 Figures; 3 Appendixes","numberOfPages":"67","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-065870","costCenters":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":325448,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_figure3A.pdf","text":"Figure 3A -","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Major Geologic Formations in West Virginia"},{"id":325450,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix1.xlsx","text":"Appendix 1 - ","size":"168 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"Description of 324 wells in West Virginia sampled as part of the U.S. Geological Survey and West Virginia Department of Environmental Protection statewide Ambient Groundwater Quality Monitoring Network"},{"id":325449,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_figure3B.pdf","text":"Figure 3B -","size":"745 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Major geologic formations in the study area of the Blue Ridge Physiographic Province USGS National Water Quality Assessment study in Virginia and North Carolina"},{"id":325446,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5048/coverthb2.jpg"},{"id":325452,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix3.xlsx","text":"Appendix 3 - ","size":"111 KB","linkHelpText":" Permit data for public groundwater supplies in West Virginia with accompanying counts of number of potential sources of contamination within the respective source-water-protection area for each public groundwater supply source"},{"id":325447,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048.pdf","text":"Report","size":"25.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5048"},{"id":325451,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix2.xlsx","text":"Appendix 2 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Virginia\",\"nation\":\"USA \"}}]}","edition":"Version 1.0: Originally posted August 30, 2016; Version 1.1: October 24, 2016","contact":"Director, West Virginia Water Science Center
U.S. Geological Survey
11 Dunbar Street
Charleston, WV 25301
http://wv.usgs.gov
Rapid population growth since the 1930s in Ocean County and vicinity, New Jersey, has placed increasing demands upon the area’s freshwater resources. To examine effects of groundwater withdrawals, a three-dimensional groundwater-flow model was developed to simulate the groundwater-flow systems of five area aquifers: the unconfined Kirkwood-Cohansey aquifer system and Vincentown aquifer, and three confined aquifers— the Rio Grande water-bearing zone, the Atlantic City 800-foot sand, and the Piney Point aquifer. The influence of withdrawals is evaluated by using transient groundwater-flow model simulations that incorporate three withdrawal schemes. These are (1) no-withdrawal conditions; (2) 2000–03 withdrawal conditions, using reported monthly withdrawals at all production wells from January 2000 through December 2003; and (3) maximum-allocation withdrawal conditions using the maximum withdrawal allowed by New Jersey Department of Environmental Protection permits at each well. Particle tracking analysis, using results from model simulations, delineated particle flow paths from production wells to the point of recharge, and estimated particle travel times.
Compared with no-withdrawal conditions, 2000–03 withdrawal conditions reduced the amount of groundwater flow out of the Kirkwood-Cohansey aquifer system into streams, increased the net flow of water into other layers, reduced net flow into or out of storage, and reduced flow from the Kirkwood-Cohansey aquifer system to constant head cells.
Freshwater discharging to the Barnegat Bay-Little Egg Harbor estuary from streams and groundwater is essential to maintaining the ecology of the bay. Examination of selected stress periods indicates that simulated base flow in streams flowing into the Barnegat Bay-Little Egg Harbor estuary is reduced by as much as 49 cubic feet per second for 2000 to 2003 withdrawal conditions when compared with no-withdrawal conditions.
In the three confined aquifers, water levels during periods of low recharge and high withdrawals, and high recharge and low withdrawals, were examined to determine seasonal effects on the confined flow systems. The simulated potentiometric surface of the Rio Grande water-bearing zone and the Atlantic City 800-foot sand during selected stress periods indicates substantial declines from no-withdrawal conditions to 2000–03 conditions as a result of groundwater withdrawals. Cones of depression in Toms River Township, Seaside Heights and Seaside Park Boroughs, and Barnegat Light Borough developed in the potentiometric surface of the Piney Point aquifer in response to withdrawals.
Maximum-allocation withdrawals decreased flow out of the Kirkwood-Cohansey aquifer system to constant head cells, increased flow out of the aquifer system to adjacent and lower layers, and reduced groundwater discharge to streams when compared with 2000–03 withdrawal conditions. Increases in withdrawals from the Rio Grande water-bearing zone, the Atlantic City 800-foot sand, and the Piney Point aquifer result in an increase in simulated net groundwater flow into these aquifers. Base-flow reduction from 2000–03 conditions to maximum-allocation conditions of 25 to 29 cubic feet per second in all streams draining to the Barnegat Bay-Little Egg Harbor also is indicated. Potentiometric surfaces of the Rio Grande water-bearing zone, Atlantic City 800-foot sand, and the Piney Point aquifer during two stress periods of simulated maximum-allocation withdrawal conditions indicated the expansion of several cones of depression developed during 2000–03 withdrawals.
Simulation of average 2000–03 withdrawal conditions indicated the extent to which the groundwater-flow system is susceptible to potential saltwater intrusion into near-shore wells. Travel time from recharge to discharge location ranged from 11 to approximately 50,700 years in near-shore Kirkwood-Cohansey aquifer system wells. Those in Seaside Heights Borough, in Island Beach State Park (Berkeley Township), and in Ship Bottom Borough have particle travel times from 140 to 12,000 years and flow paths that originated under Barnegat Bay or the Atlantic Ocean from the simulation of average maximum-allocation withdrawal conditions.
Travel time along flow paths to wells screened in the Rio Grande water-bearing zone and the Atlantic City 800-foot sand from recharge to discharge point ranged from nearly 530 years to greater than 3.73 million years from the simulation of average 2000–03 withdrawal conditions. Particle tracking indicated that most wells screened in these aquifers derived a large part of their recharge from the Oswego River Basin, with a small portion of flow originating either beneath Barnegat Bay or to the east beneath the Atlantic Ocean. Travel time along flow paths that start beneath either Barnegat Bay or the Atlantic Ocean ranged from 2,300 to approximately 134,000 years from the simulation of average maximum-allocation withdrawal conditions.\"
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165035","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Cauller, S.J., Voronin, L.M., and Chepiga, M.M., 2016, Simulated effects of groundwater withdrawals from aquifers in Ocean County and vicinity, New Jersey: U.S. Geological Survey Scientific Investigations Report 2016–5035, 77 p., https://dx.doi.org/10.3133/sir20165035.","productDescription":"x, 77 p.","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-020396","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":438535,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H99392","text":"USGS data release","linkHelpText":"MODFLOW2005 model used to simulate the effects of groundwater withdrawals from aquifers in Ocean County and vicinity, New Jersey"},{"id":330264,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5035/coverthb.jpg"},{"id":330265,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5035/sir20165035.pdf","text":"Report","size":"47.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5035 Report"},{"id":332215,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7H99392"}],"country":"United States","state":"New Jersey","county":"Ocean County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.03549194335938,\n 40.10118506258701\n ],\n [\n -74.04647827148438,\n 40.10538669840983\n ],\n [\n -74.0643310546875,\n 40.092781012494065\n ],\n [\n -74.09042358398438,\n 40.10013461308659\n ],\n [\n -74.08905029296875,\n 40.12219064672336\n ],\n [\n -74.19754028320312,\n 40.20195268954057\n ],\n [\n -74.33212280273438,\n 40.22397567550632\n ],\n [\n -74.55459594726562,\n 40.08122374895389\n ],\n [\n -74.53399658203125,\n 39.68393975392731\n ],\n [\n -74.45709228515625,\n 39.577114881737586\n ],\n [\n -74.4158935546875,\n 39.536880650643056\n ],\n [\n -74.410400390625,\n 39.49874248613119\n ],\n [\n -74.3609619140625,\n 39.487084981687495\n ],\n [\n -74.32525634765625,\n 39.49556336059472\n ],\n [\n -74.2401123046875,\n 39.46058338433589\n ],\n [\n -74.02999877929686,\n 39.74521015328692\n ],\n [\n -73.9764404296875,\n 40.091730433255\n ],\n [\n -74.03549194335938,\n 40.10118506258701\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
http://nj.usgs.gov
Probabilistic seismic‐hazard assessment (PSHA) requires an estimate of Mmax, the moment magnitude M of the largest earthquake that could occur within a specified area. Sparse seismicity hinders Mmax estimation in the central and eastern United States (CEUS) and tectonically similar regions worldwide (stable continental regions [SCRs]). A new global catalog of moderate‐to‐large SCR earthquakes is analyzed with minimal assumptions about enigmatic geologic controls on SCR Mmax. An earlier observation that SCR earthquakes of M 7.0 and larger occur in young (250–23 Ma) passive continental margins and associated rifts but not in cratons is not strongly supported by the new catalog. SCR earthquakes of M 7.5 and larger are slightly more numerous and reach slightly higher M in young passive margins and rifts than in cratons. However, overall histograms of M from young margins and rifts and from cratons are statistically indistinguishable. This conclusion is robust under uncertainties inM, the locations of SCR boundaries, and which of two available global SCR catalogs is used. The conclusion stems largely from recent findings that (1) large southeast Asian earthquakes once thought to be SCR were in actively deforming crust and (2) long escarpments in cratonic Australia were formed by prehistoric faulting. The 2014 seismic‐hazard model of the U.S. Geological Survey represents CEUS Mmax as four‐point probability distributions. The distributions have weighted averages of M 7.0 in cratons and M 7.4 in passive margins and rifts. These weighted averages are consistent with Mmax estimates of other SCR PSHAs of the CEUS, southeastern Canada, Australia, and India.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160048","usgsCitation":"Wheeler, R.L., 2016, Maximum magnitude (Mmax) in the central and eastern United States for the 2014 U.S. Geological Survey Hazard Model: Bulletin of the Seismological Society of America, v. 106, no. 5, p. 2154-2167, https://doi.org/10.1785/0120160048.","productDescription":"14 p.","startPage":"2154","endPage":"2167","ipdsId":"IP-076405","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":329732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-30","publicationStatus":"PW","scienceBaseUri":"58088686e4b0f497e78e24bd","contributors":{"authors":[{"text":"Wheeler, Russell L. wheeler@usgs.gov","contributorId":858,"corporation":false,"usgs":true,"family":"Wheeler","given":"Russell","email":"wheeler@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":false,"id":651258,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70175979,"text":"sir20165126 - 2016 - Geologic framework, age, and lithologic characteristics of the North Park Formation in North Park, north-central Colorado","interactions":[],"lastModifiedDate":"2016-10-19T09:30:47","indexId":"sir20165126","displayToPublicDate":"2016-10-18T00:00:00","publicationYear":"2016","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":"2016-5126","title":"Geologic framework, age, and lithologic characteristics of the North Park Formation in North Park, north-central Colorado","docAbstract":"Deposits of the North Park Formation of late Oligocene and Miocene age are locally exposed at small, widely spaced outcrops along the margins of the roughly northwest-trending North Park syncline in the southern part of North Park, a large intermontane topographic basin in Jackson County in north-central Colorado. These outcrops suggest that rocks and sediments of the North Park Formation consist chiefly of poorly consolidated sand, weakly cemented sandstone, and pebbly sandstone; subordinate amounts of pebble conglomerate; minor amounts of cobbly pebble gravel, siltstone, and sandy limestone; and rare beds of cobble conglomerate and altered tuff. These deposits partly filled North Park as well as a few small nearby valleys and half grabens. In North Park, deposits of the North Park Formation probably once formed a broad and relatively thick sedimentary apron composed chiefly of alluvial slope deposits (mostly sheetwash and stream-channel alluvium) that extended, over a distance of at least 150 kilometers (km), northwestward from the Never Summer Mountains and northward from the Rabbit Ears Range across North Park and extended farther northwestward into the valley of the North Platte River slightly north of the Colorado-Wyoming border. The maximum preserved thickness of the formation in North Park is about 550 meters near the southeastern end of the North Park syncline.
The deposition of the North Park Formation was coeval in part with local volcanism, extensional faulting, development of half grabens, and deposition of the Browns Park Formation and Troublesome Formation and was accompanied by post-Laramide regional epeirogenic uplift. Regional deposition of extensive eolian sand sheets and loess deposits, coeval with the deposition of the North Park Formation, suggests that semiarid climatic conditions prevailed during the deposition of the North Park Formation during the late Oligocene and Miocene.
The North Park Formation locally contains a 28.1-mega-annum (Ma, million years ago) ash-flow tuff near its base at Owl Ridge and is interbedded with 29-Ma rhyodacite lava flows and volcanic breccia at Owl Mountain. The formation locally contains vertebrate fossils at least as young as Barstovian age (about 15.9–12.6 Ma) and overlies rocks as young as the White River Formation, which contains vertebrate fossils of Chadronian age (about 37–33.8 Ma) in North Park and a bed of 36.0-Ma volcanic ash in the upper part of the Laramie River valley about 30 km northeast of Walden, Colorado. Based on the ages of the vertebrate fossils, folding of the rocks and sediments in the North Park syncline may be much younger than about 16 Ma.
Bedding characteristics of the North Park Formation suggest that (1) some or much of the sand, sandstone, and pebbly sandstone may have been deposited as sheetwash alluvium; (2) much of the siltstone may have been deposited as sheetwash alluvium or ephemeral pond or marsh deposits; (3) beds of sandy limestone probably were deposited as ephemeral pond or marsh deposits; and (4) altered tuff probably was deposited in ephemeral ponds or marshes. Most of the conglomerate and gravel in the North Park Formation are stream-channel deposits that were deposited by high-energy ephemeral or intermittent streams that issued from volcanic terrain rather than debris-flow deposits in relatively near-source fan deposits dominated by sediment gravity flow. Laccolithic doming, uplift, and tilting in the Never Summer Mountains near the Mount Richthofen stock, as well as the formation of volcanic edifices in the Never Summer Mountains and the Rabbit Ears Range during the late Oligocene and Miocene, significantly steepened stream gradients and greatly increased the erosive power and transport capacity of streams that transported large rock fragments and finer sediment eroded from volcanic and sedimentary sources and deposited them in the North Park Formation.
Much of the material that makes up the rocks and sediments of the North Park Formation was derived from the erosion of volcanic, intrusive, and sedimentary rocks. Clasts in the North Park Formation were derived chiefly from the erosion of volcanic and intrusive igneous rocks of late Oligocene and Miocene age that range in composition from rhyolite to trachybasalt. These rocks are locally exposed along the west flank of the Never Summer Mountains, the north flank of the Rabbit Ears Range, and the east flank of the Park Range at and near Rabbit Ears Peak. The minor amount of igneous and metamorphic clasts of Proterozoic age in the North Park Formation are commonly composed of durable rock types that are resistant to both physical and chemical weathering. Many of these clasts may have been derived from the erosion of conglomerate and conglomeratic sandstone in the Coalmont Formation rather than from basement rocks currently at or near the ground surface in the Never Summer Mountains. Much of the sand and finer grained particles in the North Park Formation probably were derived from the erosion of sandstone, shale, and sandy claystone of the Coalmont Formation. Likewise, much of the abundant sand-sized quartz and feldspar in sand, sandstone, and pebbly sandstone of the North Park Formation probably was derived from the erosion of sandstone, conglomeratic sandstone, and conglomerate of the Coalmont Formation. Some of the fine sand, very fine sand, and silt in very fine grained sandstone and siltstone of the North Park Formation may be derived from the erosion of coeval eolian sand and loess in the Browns Park Formation that was transported across the Park Range by westerly or southwesterly winds.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165126","usgsCitation":"Shroba, R.R., 2016, Geologic framework, age, and lithologic characteristics of the North Park Formation in North Park, north-central Colorado: U.S. Geological Survey Scientific Investigations Report 2016–5126, 28 p., https://dx.doi.org/10.3133/sir20165126.","productDescription":"v, 28 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-059910","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":329640,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5126/coverthb.jpg"},{"id":329637,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5126/sir20165126.pdf","text":"Report","size":"5.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5126"}],"country":"United States","state":"Colorado","otherGeospatial":"North Park Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.45820617675781,\n 40.558156335842106\n ],\n [\n -106.45820617675781,\n 40.689229364982054\n ],\n [\n -106.19453430175781,\n 40.689229364982054\n ],\n [\n -106.19453430175781,\n 40.558156335842106\n ],\n [\n -106.45820617675781,\n 40.558156335842106\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
Water samples were collected and analyzed for arsenic and radon from 36 private, mostly domestic wells that tap the Rancocas aquifer in southern New Castle and northern Kent Counties, Delaware, during the summer of 2015. Both arsenic and radon are from natural mineral sources, in particular glauconitic and other marine-derived sediments, which are important components of the geologic formations comprising the Rancocas aquifer. Routine testing of domestic wells is not required in Delaware; as a result, many homeowners are not aware of potential water-quality problems with these chemicals in their well water. Arsenic has previously been detected at levels of potential concern for human health in this aquifer in adjacent parts of Maryland where it is referred to as the Aquia aquifer. Arsenic and radon also have previously been detected in several Rancocas aquifer wells in Delaware. The Delaware Department of Natural Resources and Environmental Control intends to use the data from this project to better identify areas with potential for levels of concern for domestic well owners. This report includes chemical results and maps showing the distribution of sampled wells and concentrations of arsenic and radon. All data collected for this study also are available in the U.S. Geological Survey’s National Water Information System database.
Arsenic was detected above the minimum reporting limit of 0.1 micrograms per liter (µg/L) in 34 of the 36 wells sampled with concentrations ranging from about 0.11 to 27 µg/L. In 15 of the samples, arsenic concentrations were at or above the U.S. Environmental Protection Agency (EPA) Maximum Contaminant Level (MCL) of 10 µg/L for public wells. Most of the higher concentrations are clustered along a band running from the southwest to northeast in the southern part of the study area.
Radon, which is an inert gas derived from radium, was detected in all water samples with concentrations ranging from 85 to 1,870 picocuries per liter (pCi/L). Currently, the EPA has not set a MCL for radon in public water systems. There were no samples where radon was detected at a concentration exceeding the proposed alternative MCL of 4,000 pCi/L. Samples from 16 of 36 wells were above the lower proposed MCL of 300 pCi/L. Most of these samples were from wells greater than 200 feet deep located in a similar part of the aquifer as the higher concentrations of arsenic along an east-northeasterly line in the southern part of the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161143","isbn":"978-1-4113 4086-2","collaboration":"Prepared in cooperation with the Delaware Department of Natural Resources and Environmental Control (DNREC) Water Supply Section, Groundwater Protection Branch","usgsCitation":"Denver, J.M., 2016, Occurrence and distribution of arsenic and radon in water from private wells in the Rancocas aquifer, southern New Castle and northern Kent Counties, Delaware, 2015: U.S. Geological Survey Open-File Report 2016–1143, 15 p., https://dx.doi.org/10.3133/ofr20161143. ","productDescription":"vi, 15 p.","numberOfPages":"26","onlineOnly":"N","ipdsId":"IP-076094","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":329414,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1143/ofr20161143.pdf","text":"Report","size":"1.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1143"},{"id":329413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1143/coverthb.jpg"}],"country":"United States","state":"Delaware","county":"Kent County, New Castle County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.43624877929688,\n 39.310925412127155\n ],\n [\n -75.76034545898438,\n 39.298705113102244\n ],\n [\n -75.77888488769531,\n 39.50827899034114\n ],\n [\n -75.57220458984375,\n 39.51834388059882\n ],\n [\n -75.43624877929688,\n 39.310925412127155\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
Or visit our Web site at:
http://md.water.usgs.gov
The Mw 5.1 Fairview, Oklahoma, earthquake on 13 February 2016 and its associated seismicity produced the largest moment release in the central and eastern United States since the 2011 Mw 5.7 Prague, Oklahoma, earthquake sequence and is one of the largest earthquakes potentially linked to wastewater injection. This energetic sequence has produced five earthquakes with Mw 4.4 or larger. Almost all of these earthquakes occur in Precambrian basement on a partially unmapped 14 km long fault. Regional injection into the Arbuckle Group increased approximately sevenfold in the 36 months prior to the start of the sequence (January 2015). We suggest far-field pressurization from clustered, high-rate wells greater than 12 km from this sequence induced these earthquakes. As compared to the Fairview sequence, seismicity is diffuse near high-rate wells, where pressure changes are expected to be largest. This points to the critical role that preexisting faults play in the occurrence of large induced earthquakes.
","language":"English","publisher":"Wiley","doi":"10.1002/2016GL070861","usgsCitation":"Yeck, W.L., Weingarten, M., Benz, H.M., McNamara, D.E., Bergman, E., Herrmann, R., Rubinstein, J.L., and Earle, P.S., 2016, Far-field pressurization likely caused one of the largest injection induced earthquakes by reactivating a large pre-existing basement fault structure: Geophysical Research Letters, v. 43, no. 19, p. 10,198-10,207, https://doi.org/10.1002/2016GL070861.","productDescription":"10 p. ","startPage":"10,198","endPage":"10,207","ipdsId":"IP-079896","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":336735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"19","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-11","publicationStatus":"PW","scienceBaseUri":"58b7eba5e4b01ccd5500baf9","contributors":{"authors":[{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":673897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weingarten, Matthew","contributorId":138656,"corporation":false,"usgs":false,"family":"Weingarten","given":"Matthew","email":"","affiliations":[{"id":12481,"text":"Department of Geological Sciences, University of Colorado, Boulder, Colorado","active":true,"usgs":false}],"preferred":false,"id":673898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":673899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":673900,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergman, E.","contributorId":184179,"corporation":false,"usgs":false,"family":"Bergman","given":"E.","email":"","affiliations":[],"preferred":false,"id":673901,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herrmann, R.B","contributorId":184256,"corporation":false,"usgs":false,"family":"Herrmann","given":"R.B","email":"","affiliations":[],"preferred":false,"id":673902,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rubinstein, Justin L. 0000-0003-1274-6785 jrubinstein@usgs.gov","orcid":"https://orcid.org/0000-0003-1274-6785","contributorId":2404,"corporation":false,"usgs":true,"family":"Rubinstein","given":"Justin","email":"jrubinstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":673903,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":673904,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70176830,"text":"70176830 - 2016 - Potential effects of climate change on streamflow for seven watersheds in eastern and central Montana","interactions":[],"lastModifiedDate":"2017-03-10T11:22:18","indexId":"70176830","displayToPublicDate":"2016-10-11T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Potential effects of climate change on streamflow for seven watersheds in eastern and central Montana","docAbstract":"Eastern and central Montana.
Fish in Northern Great Plains streams tolerate extreme conditions including heat, cold, floods, and drought; however changes in streamflow associated with long-term climate change may render some prairie streams uninhabitable for current fish species. To better understand future hydrology of these prairie streams, the Precipitation-Runoff Modeling System model and output from the RegCM3 Regional Climate model were used to simulate streamflow for seven watersheds in eastern and central Montana, for a baseline period (water years 1982–1999) and three future periods: water years 2021–2038 (2030 period), 2046–2063 (2055 period), and 2071–2088 (2080 period).
Projected changes in mean annual and mean monthly streamflow vary by the RegCM3 model selected, by watershed, and by future period. Mean annual streamflows for all future periods are projected to increase (11–21%) for two of the four central Montana watersheds: Middle Musselshell River and Cottonwood Creek. Mean annual streamflows for all future periods are projected to decrease (changes of −24 to −75%) for Redwater River watershed in eastern Montana. Mean annual streamflows are projected to increase slightly (2–15%) for the 2030 period and decrease (changes of −16 to −44%) for the 2080 period for the four remaining watersheds.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2016.06.001","usgsCitation":"Chase, K.J., Haj, A., Regan, R.S., and Viger, R., 2016, Potential effects of climate change on streamflow for seven watersheds in eastern and central Montana: Journal of Hydrology: Regional Studies, v. 7, p. 69-81, https://doi.org/10.1016/j.ejrh.2016.06.001.","productDescription":"13 p.","startPage":"69","endPage":"81","ipdsId":"IP-062632","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":470510,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2016.06.001","text":"Publisher Index Page"},{"id":438538,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P26W5S","text":"USGS data release","linkHelpText":"Documentation of the Precipitation-Runoff Modeling System and Output from the RegCM3 Regional Climate Model Used to Estimate Potential Effects of Climate 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