A reconnaissance survey of Fe-Mn crusts from the 5000 km long (~31°S to 10°N) Ninetyeast Ridge (NER) in the Indian Ocean shows their widespread occurrence along the ridge as well as with water depth on the ridge flanks. The crusts are hydrogenetic based in growth rates and discrimination plots. Twenty samples from 12 crusts from 9 locations along the ridge were analyzed for chemical and mineralogical compositions, growth rates, and statistical relationships (Q-mode factor analysis, correlation coefficients) were calculated. The crusts collected are relatively thin (maximum 40 mm), and those analyzed varied from 4 mm to 32 mm. However, crusts as thick as 80 mm can be expected to occur based on the age of rocks that comprise the NER and the growth rates calculated here. Growth rates of the crusts increase to the north along the NER and with water depth. The increase to the north resulted from an increased supply of Mn from the oxygen minimum zone (OMZ) to depths below the OMZ combined with an increased supply of Fe at depth from the dissolution of biogenic carbonate and from deep-sourced hydrothermal Fe. These increased supplies of Fe increased growth rates of the deeper-water crusts along the entire NER. Because of the huge terrigenous (rivers, eolian, pyroclastic) and hydrothermal (three spreading centers) inputs to the Indian Ocean, and the history of primary productivity, Fe-Mn crust compositions vary from those analyzed from open-ocean locations in the Pacific.
\nThe sources of detrital material in the crusts changed along the NER and reflect, from north to south, the decreasing influence of the Ganga River system and volcanic arcs located to the east, with increasing influence of sediment derived from Australia to the south. In addition, weathering of NER basalt likely contributed to the aluminosilicate fraction of the crusts. The southernmost sample has a relatively large detrital component compared to other southern NER crust samples, which was probably derived predominantly from weathering of local volcanic outcrops.
\nFe-Mn crusts from a dredge haul at 3412 m water depth, 2°S latitude, are pervasively phosphatized along with the substrate rocks (site D7). Phosphatization took place through replacement of carbonate, preferential replacement of Fe oxyhydroxide relative to Mn oxide in the crusts, preferential replacement of silica-rich phases relative to Al-rich phases in the crusts, and precipitation of carbonate fluorapatite in pore space. The preferentially replaced silica may have been Si adsorbed on the Fe oxyhydroxide. The enrichment of Ni, Zn, and Cu in the phosphatized crust reflects preferential adsorption into the tunnel structure of todorokite. The rare earth element plus yttrium (REY) patterns indicate a lower oxidation potential during phosphatization of the NER crusts compared to Pacific phosphatized crusts. NER phosphatization occurred in a deeper-water environment than typical for phosphatization of Pacific crusts, occurred post-middle Miocene, a younger age than phosphatization the Pacific crusts, and had in part a different set of chemical changes produced by the phosphatization than did the Pacific crusts.
\nThe southern third of NER has Fe-Mn crusts with the highest Co (0.91%), Ni (0.43%), ΣREY (0.33%), Cu (0.22%), Te (146 ppm), Pt (1.5 ppm), Ru (52 ppb), and Rh (99 ppb) contents. These are among the highest Pt, Ru, and Rh concentrations measured in marine Fe-Mn deposits. Because of these high metal concentrations, exploration is warranted for the southern sector of the NER, especially at shallower-water sites where the platinum group elements (PGE) and Co are likely to be even more enriched.
","language":"English","publisher":"Permagon Press","doi":"10.1016/j.dsr.2015.11.006","usgsCitation":"Hein, J.R., Conrad, T., Mizell, K., Banakar, V.K., Frey, F.A., and Sager, W.W., 2016, Controls on ferromanganese crust composition and reconnaissance resource potential, Ninetyeast Ridge, Indian Ocean: Deep-Sea Research Part I: Oceanographic Research Papers, v. 110, p. 1-19, https://doi.org/10.1016/j.dsr.2015.11.006.","productDescription":"19 p.","startPage":"1","endPage":"19","numberOfPages":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064986","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":318361,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Ninetyeast Ridge, Indian Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 86.2646484375,\n -30.48655084258847\n ],\n [\n 112.8515625,\n -30.48655084258847\n ],\n [\n 112.8515625,\n -4.34641127533318\n ],\n [\n 86.2646484375,\n -4.34641127533318\n ],\n [\n 86.2646484375,\n -30.48655084258847\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ced431e4b015c306ec2fde","contributors":{"authors":[{"text":"Hein, James R. 0000-0002-5321-899X jhein@usgs.gov","orcid":"https://orcid.org/0000-0002-5321-899X","contributorId":140835,"corporation":false,"usgs":true,"family":"Hein","given":"James","email":"jhein@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":621234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conrad, Tracey A.","contributorId":52540,"corporation":false,"usgs":true,"family":"Conrad","given":"Tracey A.","affiliations":[],"preferred":false,"id":621235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mizell, Kira 0000-0002-5066-787X kmizell@usgs.gov","orcid":"https://orcid.org/0000-0002-5066-787X","contributorId":4914,"corporation":false,"usgs":true,"family":"Mizell","given":"Kira","email":"kmizell@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":621236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Banakar, Virupaxa K.","contributorId":167153,"corporation":false,"usgs":false,"family":"Banakar","given":"Virupaxa","email":"","middleInitial":"K.","affiliations":[{"id":24631,"text":"Council for Scientific & Industrial Research, National Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":621237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frey, Frederick A.","contributorId":167154,"corporation":false,"usgs":false,"family":"Frey","given":"Frederick","email":"","middleInitial":"A.","affiliations":[{"id":24632,"text":"Earth, Atmospheric & Planetary Sciences, MIT","active":true,"usgs":false}],"preferred":false,"id":621238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sager, William W.","contributorId":167155,"corporation":false,"usgs":false,"family":"Sager","given":"William","email":"","middleInitial":"W.","affiliations":[{"id":24633,"text":"Earth & Atmospheric Sciences, University of Houston","active":true,"usgs":false}],"preferred":false,"id":621239,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70161867,"text":"sir20155185 - 2016 - Stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural streamflow","interactions":[],"lastModifiedDate":"2017-10-12T19:59:21","indexId":"sir20155185","displayToPublicDate":"2016-02-24T13:00:00","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":"2015-5185","title":"Stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural streamflow","docAbstract":"The Souris River Basin is a 61,000-square-kilometer basin in the Provinces of Saskatchewan and Manitoba and the State of North Dakota. In May and June of 2011, record-setting rains were seen in the headwater areas of the basin. Emergency spillways of major reservoirs were discharging at full or nearly full capacity, and extensive flooding was seen in numerous downstream communities. To determine the probability of future extreme floods and droughts, the U.S. Geological Survey, in cooperation with the North Dakota State Water Commission, developed a stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural (unregulated) streamflow. Simulations from the model can be used in future studies to simulate regulated streamflow, design levees, and other structures; and to complete economic cost/benefit analyses.
Long-term climatic variability was analyzed using tree-ring chronologies to hindcast precipitation to the early 1700s and compare recent wet and dry conditions to earlier extreme conditions. The extended precipitation record was consistent with findings from the Devils Lake and Red River of the North Basins (southeast of the Souris River Basin), supporting the idea that regional climatic patterns for many centuries have consisted of alternating wet and dry climate states.
A stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration for the Souris River Basin was developed using recorded meteorological data and extended precipitation records provided through tree-ring analysis. A significant climate transition was seen around1970, with 1912–69 representing a dry climate state and 1970–2011 representing a wet climate state. Although there were some distinct subpatterns within the basin, the predominant differences between the two states were higher spring through early fall precipitation and higher spring potential evapotranspiration for the wet compared to the dry state.
A water-balance model was developed for simulating monthly natural (unregulated) mean streamflow based on precipitation, temperature, and potential evapotranspiration at select streamflow-gaging stations. The model was calibrated using streamflow data from the U.S. Geological Survey and Environment Canada, along with natural (unregulated) streamflow data from the U.S. Army Corps of Engineers. Correlation coefficients between simulated and natural (unregulated) flows generally were high (greater than 0.8), and the seasonal means and standard deviations of the simulated flows closely matched the means and standard deviations of the natural (unregulated) flows. After calibrating the model for a monthly time step, monthly streamflow for each subbasin was disaggregated into three values per month, or an approximately 10-day time step, and a separate routing model was developed for simulating 10-day streamflow for downstream gages.
The stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration was combined with the water-balance model to simulate potential future sequences of 10-day mean streamflow for each of the streamflow-gaging station locations. Flood risk, as determined by equilibrium flow-frequency distributions for the dry (1912–69) and wet (1970–2011) climate states, was considerably higher for the wet state compared to the dry state. Future flood risk will remain high until the wet climate state ends, and for several years after that, because there may be a long lag-time between the return of drier conditions and the onset of a lower soil-moisture storage equilibrium.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155185","collaboration":"Prepared in cooperation with the North Dakota State Water Commission","usgsCitation":"Kolars, K.A., Vecchia, A.V., and Ryberg, K.R., 2016, Stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural streamflow: U.S. Geological Survey Scientific Investigations Report 2015–5185, 55 p., https://dx.doi.org/10.3133/sir20155185.","productDescription":"viii, 55 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068149","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":318270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5185/sir20155185.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5185"},{"id":318269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5185/coverthb.jpg"}],"country":"Canada, United States","state":"Manitoba, North Dakota, Saskatchewan","otherGeospatial":"Souris River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.04052734375,\n 48.99824008113872\n ],\n [\n -104.74365234375,\n 49.42884000063522\n ],\n [\n -104.7930908203125,\n 50.004208515595614\n ],\n [\n -103.480224609375,\n 50.52041218671901\n ],\n [\n -102.0245361328125,\n 50.604159488561\n ],\n [\n -101.195068359375,\n 50.25071752130677\n ],\n [\n -100.65673828125,\n 49.745781306155735\n ],\n [\n -99.60891723632812,\n 49.648069803718805\n ],\n [\n -99.18594360351562,\n 49.577773933420914\n ],\n [\n -99.2340087890625,\n 49.39131220507362\n ],\n [\n -99.76547241210936,\n 49.413653634531116\n ],\n [\n -99.4482421875,\n 48.100094697973795\n ],\n [\n -101.502685546875,\n 47.99727386804474\n ],\n [\n -103.568115234375,\n 48.52388120259336\n ],\n [\n -104.04052734375,\n 48.99824008113872\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS North Dakota Water Science Center
821 East Interstate Avenue
Bismarck, North Dakota 58503
Tritium–helium-3 groundwater ages of the Edwards aquifer in south-central Texas were determined as part of a long-term study of groundwater flow and recharge in the Edwards and Trinity aquifers. These ages help to define groundwater residence times and to provide constraints for calibration of groundwater flow models. A suite of 17 samples from public and private supply wells within Uvalde County were collected for active and noble gases, and for tritium–helium-3 analyses from the confined and unconfined parts of the Edwards aquifer. Samples were collected from monitoring wells at discrete depths in open boreholes as well as from integrated pumped well-head samples. The data indicate a fairly uniform groundwater flow system within an otherwise structurally complex geologic environment comprised of regionally and locally faulted rock units, igneous intrusions, and karst features within carbonate rocks. Apparent ages show moderate, downward average, linear velocities in the Uvalde area with increasing age to the east along a regional groundwater flow path. Though the apparent age data show a fairly consistent distribution across the study area, many apparent ages indicate mixing of both modern (less than 60 years) and premodern (greater than 60 years) waters. This mixing is most evident along the “bad water” line, an arbitrary delineation of 1,000 milligrams per liter dissolved solids that separates the freshwater zone of the Edwards aquifer from the downdip saline water zone. Mixing of modern and premodern waters also is indicated within the unconfined zone of the aquifer by high excess helium concentrations in young waters. Excess helium anomalies in the unconfined aquifer are consistent with possible subsurface discharge of premodern groundwater from the underlying Trinity aquifer into the younger groundwater of the Edwards aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155163","usgsCitation":"Hunt, A.G., Landis, G.P., and Faith, J.R., 2016, Groundwater ages from the freshwater zone of the Edwards Aquifer, Uvalde County, Texas—Insights into groundwater flow and recharge: U.S. Geological Survey Scientific Investigations Report 2015–5163, 28 p., https://dx.doi.org/10.3133/sir20155163.","productDescription":"viii, 28 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065915","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":318180,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5163/coverthb.jpg"},{"id":318181,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5163/sir20155163.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5163"}],"country":"United States","state":"Texas","county":"Uvalde County","otherGeospatial":"Edwards Aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.4132,29.6253],[-99.4107,29.087],[-99.6813,29.0872],[-100.1119,29.0844],[-100.1112,29.3486],[-100.111,29.6236],[-100.0145,29.6237],[-99.6173,29.6257],[-99.6033,29.6257],[-99.4132,29.6253]]]},\"properties\":{\"name\":\"Uvalde\",\"state\":\"TX\"}}]}","contact":"Center Director, USGS Crustal Geophysics and Geochemistry Science Center
Box 25046, Mail Stop 964
Denver, CO 80225
In 2013, the U.S. Geological Survey and the Miami Conservancy District investigated the effectiveness of methods used to remove arsenic from drinking water at 11 homes in southwestern and central Ohio. The untreated (raw) ground-water had arsenic concentrations of 7.7–382 micrograms per liter (µg/L), and the median concentration was 30 µg/L. The pH was neutral to slightly alkaline, and redox conditions were strongly reducing, as indicated by high concentrations of iron. The predominant arsenic species was arsenite (As3+), which is difficult to treat because it exists in water as an uncharged compound (H3AsO3).
The water-treatment systems included (1) seven single-tap reverse-osmosis systems, (2) two whole-house oxidation/filtration systems, and (3) two systems that included wholehouse anion exchange and single-tap reverse osmosis. All but one system included pretreatment by a water softener, and two systems included preoxidation to convert arsenite (As3+) to arsenate (As5+) before treatment by anion exchange.
None of the treatment systems removed all of the arsenic from the drinking water. About one-half of the systems decreased the arsenic concentration to less than the maximum contamination level of 10 µg/L. The effectiveness of the systems varied widely; the percentage of arsenic removed ranged from 2 to 90 percent, and the median was 65 percent.
At some sites, the low effectiveness of arsenic removal may have been related to system maintenance and(or) operation issues. At two sites, homeowners acknowledged that the treatment systems had not been maintained for several years. At two other sites, the treatment systems were being maintained, but the water-quality data indicated that one of the components was not working, unbeknownst to the homeowner. EPA research at a small number of sites in Ohio indicated that operation and maintenance of some arsenic-treatment systems was not always simple.
Another factor that affected system effectiveness was the quality of the raw water. In general, the treatment systems were less effective at treating higher concentrations of arsenic. For five sites with raw-water arsenic concentrations of 10–30 µg/L, the systems removed 65–81 percent of the arsenic, and the final concentrations were less than the maximum contamination level. For three sites with higher raw-water arsenic concentrations (50–75 µg/L), the systems removed 22–34 percent of the arsenic; and the final concentrations were 4–5 times more than the maximum contamination level. Other characteristics of the raw water may have affected the performance of treatment systems; in general, raw water with the higher arsenic concentrations also had higher pH, higher concentrations of organic carbon and ammonia, and more reducing (methanogenic) redox conditions.
For sites with raw-water arsenic concentrations of 10–30 µg/L, two types of systems (reverse osmosis and oxidation/filtration) removed similar amounts of arsenic, but the quality of the treated water differed in other respects. Reverse osmosis caused substantial decreases in pH, alkalinity, and concentrations of most ions. On the other hand, oxidation/filtration using manganese-based media caused a large increase of manganese concentrations, from less than 50 µg/L in raw water to more than 700 µg/L in outflow from the oxidation filtration units.
It is not known if the results of this study are widely applicable; the number of systems sampled was relatively small, and each system was sampled only once. Further study may be warranted to investigate whether available methods of arsenic removal are effective/practical for residential use in areas like Ohio, were groundwater with elevated arsenic concentrations is strongly reducing, and the predominant arsenic species is arsenite (As3+).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155156","isbn":"978-1-4113-4006-0","collaboration":"Prepared in cooperation with Miami Conservancy District","usgsCitation":"Thomas, M.A., and Ekberg, Mike, 2016, The effectiveness of water-treatment systems for arsenic used in 11 homes in Southwestern and Central Ohio, 2013: U.S. Geological Survey Scientific Investigations Report 2015–5156, 26 p., https://dx.doi.org/10.3133/sir20155156.","productDescription":"Report: vi, 26 p.; Table","numberOfPages":"37","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-065490","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":318264,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2015/5156/sir20155156_table2.xlsx","text":"Table 2. Water-quality results from 11 domestic wells in southwestern and central Ohio, 2013","size":"36.0 kb","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5156 Table 2"},{"id":318263,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5156/sir20155156.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5156"},{"id":318262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5156/coverthb.jpg"}],"country":"United States","state":"Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.825439453125,\n 39.24927084622338\n ],\n [\n -84.825439453125,\n 40.43858586704328\n ],\n [\n -81.815185546875,\n 40.43858586704328\n ],\n [\n -81.815185546875,\n 39.24927084622338\n ],\n [\n -84.825439453125,\n 39.24927084622338\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS Ohio Water Science Center
6480 Doubletree Ave
Columbus, OH 43229-1111
","tableOfContents":"
Arsenic concentrations were measured in samples from 168 domestic wells in Licking County, Ohio, to document arsenic concentrations in a wide variety of wells and to identify hydrogeologic factors associated with arsenic concentrations in groundwater. Elevated concentrations of arsenic (greater than 10.0 micrograms per liter [µg/L]) were detected in 12 percent of the wells (about 1 in 8). The maximum arsenic concentration of about 44 µg/L was detected in two wells in the same township.
A subset of 102 wells was also sampled for iron, sulfate, manganese, and nitrate, which were used to estimate redox conditions of the groundwater. Elevated arsenic concentrations were detected only in strongly reducing groundwater. Almost 20 percent of the samples with iron concentrations high enough to produce iron staining (greater than 300 µg/L) also had elevated concentrations of arsenic.
In groundwater, arsenic primarily occurs as two inorganic species—arsenite and arsenate. Arsenic speciation was determined for a subset of nine samples, and arsenite was the predominant species. Of the two species, arsenite is more difficult to remove from water, and is generally considered to be more toxic to humans.
Aquifer and well-construction characteristics were compiled from 99 well logs. Elevated concentrations of arsenic (and iron) were detected in glacial and bedrock aquifers but were more prevalent in glacial aquifers. The reason may be that the glacial deposits typically contain more organic carbon than the Paleozoic bedrock. Organic carbon plays a role in the redox reactions that cause arsenic (and iron) to be released from the aquifer matrix. Arsenic concentrations were not significantly different for different types of bedrock (sandstone, shale, sandstone/shale, or other). However, arsenic concentrations in bedrock wells were correlated with two well-construction characteristics; higher arsenic concentrations in bedrock wells were associated with (1) shorter open intervals and (2) deeper open intervals, relative to the water level.
The spatial distribution of arsenic concentrations was compared to hydrogeologic characteristics of Licking County. Elevated concentrations of arsenic (and iron) were associated with areas of flat topography and thick (greater than 100 feet),clay-rich glacial deposits. These characteristics are conducive to development of strongly reducing redox conditions, which can cause arsenic associated with iron oxyhydroxides in the aquifer matrix to be released to the groundwater.
Hydrogeologic characteristics conducive to the development of strongly reducing groundwater are relatively wide-spread in the western part of Licking County, which is part of the Central Lowland physiographic province. In this area, a thick layer of clay-rich glacial deposits obscures the bedrock surface and creates flat to gently rolling landscape with poorly developed drainage networks. In the eastern part of the county, which is part of the Appalachian Plateaus physiographic province, the landscape includes steep-sided valleys and bedrock uplands. In this area, elevated arsenic concentrations were detected in buried valleys but not in the bedrock uplands, where glacial deposits are thin or absent. The observation that elevated concentrations of arsenic (and iron) were more prevalent in the western part of Licking County is true for both glacial and bedrock aquifers.
In Licking County, thick, clay-rich glacial deposits (and elevated concentrations of arsenic) are associated with two hydrogeologic settings—buried valley and complex thick drift. Most wells in the buried-valley setting had low arsenic concentrations, but a few samples had very high concentrations (30–44 µg/L) and very reducing redox conditions (methanogenic and near-methanogenic). For wells in the complex-thick-drift setting, elevated arsenic concentrations are more prevalent, but the maximum concentration was lower (about 21 µg/L). Similar observations were made about arsenic concentrations in parts of southwestern Ohio.
The hydrogeologic settings and characteristics associated with arsenic in Licking County also exist in other parts of Ohio. The statewide extent of these characteristics roughly corresponds to areas where elevated concentrations of arsenic are known to exist. This preliminary conceptual model can be tested and revised as additional wells are sampled for arsenic.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155148","isbn":"978-1-4113-4008-4","collaboration":"Prepared in cooperation with the Ohio Water Development Authority","usgsCitation":"Thomas, M.A., 2016, Arsenic in groundwater of Licking County, Ohio, 2012—Occurrence and relation to hydrogeology:\nU.S. Geological Survey Scientific Investigations Report 2015–5148, 38 p., https://dx.doi.org/10.3133/sir20155148.","productDescription":"Report: vii, 38 p.; Table","numberOfPages":"50","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-065867","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":318250,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2015/5148/sir20155148_table2.xlsx","text":"Table 2. Water-quality and hydrogeologic data for 168 domestic wells in Licking County, Ohio, 2012.","size":"108 kb","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5148 Table 2."},{"id":318247,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5148/coverthb.jpg"},{"id":318248,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5148/sir20155148.pdf","text":"Report","size":"9.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5148"}],"country":"United States","state":"Ohio","county":"Licking County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.96875,\n 39.95185892663003\n ],\n [\n -82.96875,\n 40.50544628405211\n ],\n [\n -81.89208984375,\n 40.50544628405211\n ],\n [\n -81.89208984375,\n 39.95185892663003\n ],\n [\n -82.96875,\n 39.95185892663003\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS Ohio Water Science Center
6480 Doubletree Ave
Columbus, OH 43229-1111
Sediment accumulation threatens the viability and hydrologic functioning of many naturally formed depressional wetlands across the interior regions of North America. These wetlands provide many ecosystem services and vital habitats for diverse plant and animal communities. Climate change may further impact sediment accumulation rates in the context of current land use patterns. We estimated sediment accretion in wetlands within a region renowned for its large populations of breeding waterfowl and migrant shorebirds and examined the relative roles of precipitation and land use context in the sedimentation process. We modeled rates of sediment accumulation from 1971 through 2100 using the Revised Universal Soil Loss Equation (RUSLE) with a sediment delivery ratio and the Unit Stream Power Erosion Deposition model (USPED). These models predicted that by 2100, 21–33 % of wetlands filled completely with sediment and 27–46 % filled by half with sediments; estimates are consistent with measured sediment accumulation rates in the region reported by empirical studies. Sediment accumulation rates were strongly influenced by size of the catchment, greater coverage of tilled landscape within the catchment, and steeper slopes. Conservation efforts that incorporate the relative risk of infilling of wetlands with sediments, thus emphasizing areas of high topographic relief and large watersheds, may benefit wetland-dependent biota.
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rainfall occurred across South Carolina during October 1–5, 2015, as a result of an upper atmospheric low-pressure system that funneled tropical moisture from Hurricane Joaquin into the State. The storm caused major flooding in the central and coastal parts of South Carolina. Almost 27 inches of rain fell near Mount Pleasant in Charleston County during this period. U.S. Geological Survey (USGS) streamgages recorded peaks of record at 17 locations, and 15 other locations had peaks that ranked in the top 5 for the period of record. During the October 2015 flood event, USGS personnel made about 140 streamflow measurements at 86 locations to verify, update, or extend existing rating curves (which are used to compute streamflow from monitored river stage). Immediately after the storm event, USGS personnel documented 602 high-water marks, noting the location and height of the water above land surface. Later in October, 50 additional high-water marks were documented near bridges for South Carolina Department of Transportation. Using a subset of these high-water marks, 20 flood-inundation maps of 12 communities were created. Digital datasets of the inundation area, modeling boundary, and water depth rasters are all available for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161019","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Musser, J.W., Watson, K.M., Painter, J.A., and Gotvald, A.J., 2016, Flood-inundation maps of selected areas affected by the flood of October 2015 in central and coastal South Carolina: U.S. Geological Survey Open-File Report 2016–1019, 81 p., https://dx.doi.org/10.3133/ofr20161019.","productDescription":"Report: v, 81 p.; Raw Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072657","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":318176,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1019/ofr20161019.pdf","text":"Report","size":"47.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1019"},{"id":318175,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1019/coverthb.jpg"},{"id":318184,"rank":3,"type":{"id":19,"text":"Raw Data"},"url":"https://water.usgs.gov/floods/events/2015/Joaquin/data_ofr20161019","text":"USGS Flood Information (Data Download)"}],"country":"United States","state":"South Carolina","otherGeospatial":"Salkehatchie River Basin, Savannah River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.541259765625,\n 33.86129311351553\n ],\n [\n -78.64013671875,\n 33.76088200086917\n ],\n [\n -78.94775390625,\n 33.61461929233378\n ],\n [\n -79.1015625,\n 33.348884792201694\n ],\n [\n -79.1455078125,\n 33.22030778968541\n ],\n [\n -79.34326171875,\n 32.99945000822837\n ],\n [\n -79.530029296875,\n 32.96258644191747\n ],\n [\n -79.60693359375,\n 32.85190345738802\n ],\n [\n -79.815673828125,\n 32.74108223150125\n ],\n [\n -80.057373046875,\n 32.55607364492029\n ],\n [\n -80.2880859375,\n 32.491230287947594\n ],\n [\n -80.386962890625,\n 32.41706632846282\n ],\n [\n -80.474853515625,\n 32.287132632616384\n ],\n [\n -80.628662109375,\n 32.175612478499346\n ],\n [\n -80.7275390625,\n 32.06395559466043\n ],\n [\n -80.85937499999999,\n 31.942839972853083\n ],\n [\n -80.936279296875,\n 31.99875937194732\n ],\n [\n -81.123046875,\n 32.12910537866886\n ],\n [\n -81.14501953125,\n 32.25926542645936\n ],\n [\n -81.14501953125,\n 32.36140331527543\n ],\n [\n -81.2548828125,\n 32.44488496716713\n ],\n [\n -81.298828125,\n 32.537551746769\n ],\n [\n -81.375732421875,\n 32.58384932565662\n ],\n [\n -81.4306640625,\n 32.685619853722\n ],\n [\n -81.474609375,\n 32.879587173066305\n ],\n [\n -81.507568359375,\n 33.00866349457558\n ],\n [\n -81.58447265624999,\n 33.073130945006625\n ],\n [\n -81.71630859375,\n 33.146750228776455\n ],\n [\n -81.815185546875,\n 33.23868752757414\n ],\n [\n -81.88110351562499,\n 33.32134852669881\n ],\n [\n -81.990966796875,\n 33.37641235124676\n ],\n [\n -81.947021484375,\n 33.458942753687644\n ],\n [\n -82.078857421875,\n 33.568861182555544\n ],\n [\n -82.166748046875,\n 33.642062504753675\n ],\n [\n -82.19970703125,\n 33.715201644740844\n ],\n [\n -82.28759765625,\n 33.80653802509606\n ],\n [\n -82.430419921875,\n 33.897777013859475\n ],\n [\n -82.55126953124999,\n 34.00713506435885\n ],\n [\n -82.496337890625,\n 34.17999758688084\n ],\n [\n -82.254638671875,\n 34.75966612466248\n ],\n [\n -81.947021484375,\n 35.11990857099681\n ],\n [\n -81.837158203125,\n 35.191766965947394\n ],\n [\n -81.39770507812499,\n 35.16482750605027\n ],\n [\n -81.046142578125,\n 35.146862906756304\n ],\n [\n -81.024169921875,\n 35.074964853989556\n ],\n [\n -80.91430664062499,\n 35.110921809704756\n ],\n [\n -80.771484375,\n 34.95799531086792\n ],\n [\n -80.782470703125,\n 34.84987503195418\n ],\n [\n -80.5517578125,\n 34.831841149828676\n ],\n [\n -80.255126953125,\n 34.804782919572425\n ],\n [\n -79.716796875,\n 34.82282272723702\n ],\n [\n -78.541259765625,\n 33.86129311351553\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
Net fluxes (change between upstream and downstream margins) for water, methylmercury (MeHg), total mercury (THg), dissolved organic carbon (DOC), and chloride (Cl) were assessed twice in an Adirondack stream reach (Sixmile Brook, USA), to test the hypothesized importance of wetland-stream hydraulic and chemical gradients as fundamental controls on fluvial mercury (Hg) supply. The 500 m study reach represented less than 4% of total upstream basin area. During a snowmelt high-flow event in May 2009 surface water, DOC, and chloride fluxes increased by 7.1±1.3%, 8.0±1.3%, and 9.0±1.3%, respectively, within the reach, demonstrating that the adjacent wetlands are important sources of water and solutes to the stream. However, shallow groundwater Hg concentrations lower than in the surface water limited groundwater-surface water Hg exchange and no significant changes in Hg (filtered MeHg and THg) fluxes were observed within the reach despite the favorable hydraulic gradient. In August 2009, the lack of significant wetland-stream hydraulic gradient resulted in no net flux of water or solutes (MeHg, THg, DOC, or Cl) within the reach. The results are consistent with the wetland-Hg-source hypothesis and indicate that hydraulic and chemical gradient (direction and magnitude) interactions are fundamental controls on the supply of wetland Hg to the stream.
","language":"English","publisher":"Symbiosis Group","doi":"10.15226/24754706/1/1/00102","usgsCitation":"Bradley, P.M., Burns, D.A., Harvey, J., Journey, C.A., Brigham, M.E., and Riva-Murray, K., 2016, Hydraulic and biochemical gradients limit wetland mercury supply to an Adirondack stream: SOJ Aquatic Research, v. 1, no. 17, p. 1-9, https://doi.org/10.15226/24754706/1/1/00102.","productDescription":"9 p.","startPage":"1","endPage":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054962","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":471213,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15226/24754706/1/1/00102","text":"Publisher Index Page"},{"id":324656,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, South Carolina","otherGeospatial":"Fishing Brook, McTier Creek basin, Sixmile Brook","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.990966796875,\n 33.442901319379345\n ],\n [\n -81.990966796875,\n 34.01396527491264\n ],\n [\n -81.09283447265625,\n 34.01396527491264\n ],\n [\n -81.09283447265625,\n 33.442901319379345\n ],\n [\n -81.990966796875,\n 33.442901319379345\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.7125244140625,\n 43.69965122967144\n ],\n [\n -74.7125244140625,\n 44.3670601700202\n ],\n [\n -73.6578369140625,\n 44.3670601700202\n ],\n [\n -73.6578369140625,\n 43.69965122967144\n ],\n [\n -74.7125244140625,\n 43.69965122967144\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","issue":"17","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-22","publicationStatus":"PW","scienceBaseUri":"5774f25ae4b07dd077c6a300","contributors":{"authors":[{"text":"Bradley, Paul M. 0000-0001-7522-8606 pbradley@usgs.gov","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":361,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul","email":"pbradley@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":641285,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Douglas A. 0000-0001-6516-2869 daburns@usgs.gov","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":1237,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas","email":"daburns@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":641286,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harvey, Judson 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":140228,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":641287,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Journey, Celeste A. 0000-0002-2284-5851 cjourney@usgs.gov","orcid":"https://orcid.org/0000-0002-2284-5851","contributorId":2617,"corporation":false,"usgs":true,"family":"Journey","given":"Celeste","email":"cjourney@usgs.gov","middleInitial":"A.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":641288,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brigham, Mark E. 0000-0001-7412-6800 mbrigham@usgs.gov","orcid":"https://orcid.org/0000-0001-7412-6800","contributorId":1840,"corporation":false,"usgs":true,"family":"Brigham","given":"Mark","email":"mbrigham@usgs.gov","middleInitial":"E.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":641289,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Riva-Murray, Karen 0000-0001-6683-2238 krmurray@usgs.gov","orcid":"https://orcid.org/0000-0001-6683-2238","contributorId":168876,"corporation":false,"usgs":true,"family":"Riva-Murray","given":"Karen","email":"krmurray@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":641290,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70169298,"text":"70169298 - 2016 - Are the Columbia River Basalts, Columbia Plateau, Idaho, Oregon, and Washington, USA, a viable geothermal target? A preliminary analysis","interactions":[],"lastModifiedDate":"2017-04-21T16:05:35","indexId":"70169298","displayToPublicDate":"2016-02-22T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Are the Columbia River Basalts, Columbia Plateau, Idaho, Oregon, and Washington, USA, a viable geothermal target? A preliminary analysis","docAbstract":"The successful development of a geothermal electric power generation facility relies on (1) the identification of sufficiently high temperatures at an economically viable depth and (2) the existence of or potential to create and maintain a permeable zone (permeability >10-14 m2) of sufficient size to allow efficient long-term extraction of heat from the reservoir host rock. If both occur at depth under the Columbia Plateau, development of geothermal resources there has the potential to expand both the magnitude and spatial extent of geothermal energy production. However, a number of scientific and technical issues must be resolved in order to evaluate the likelihood that the Columbia River Basalts, or deeper geologic units under the Columbia Plateau, are viable geothermal targets.
Recent research has demonstrated that heat flow beneath the Columbia Plateau Regional Aquifer System may be higher than previously measured in relatively shallow (<600 m depth) wells, indicating that sufficient temperatures for electricity generation occur at depths 5 km. The remaining consideration is evaluating the likelihood that naturally high permeability exists, or that it is possible to replicate the high average permeability (approximately 10-14 to 10-12 m2) characteristic of natural hydrothermal reservoirs. From a hydraulic perspective, Columbia River Basalts are typically divided into dense, impermeable flow interiors and interflow zones comprising the top of one flow, the bottom of the overlying flow, and any sedimentary interbed. Interflow zones are highly variable in texture but, at depths <600 m, some of them form highly permeable regional aquifers with connectivity over many tens of kilometers. Below depths of ~600 m, permeability reduction occurs in many interflow zones, caused by the formation of low-temperature hydrothermal alteration minerals (corresponding to temperatures above ~35 °C). However, some high permeability (>10-14 m2) interflows are documented at depths up to ~1,400 m. If the elevated permeability in these zones persists to greater depths, they may provide natural permeability of sufficient magnitude to allow their exploitation as conventional geothermal reservoirs. Alternatively, if the permeability in these interflow zones is less than 10-14 m2 at depth, it may be possible to use hydraulic and thermal stimulation to enhance the permeability of both the interflow zones and the natural jointing within the low-permeability interior portions of individual basalt flows in order to develop Enhanced/Engineered Geothermal System (EGS) reservoirs. The key challenge for an improved Columbia Plateau geothermal assessment is acquiring and interpreting comprehensive field data that can provide quantitative constraints on the recovery of heat from the Columbia River Basalts at depths greater than those currently tested by deep boreholes.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Proceedings, 41st Workshop on Geothermal Reservoir Engineering","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"conferenceTitle":"41st Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 22-24, 2016","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","publisherLocation":"Stanford, CA","usgsCitation":"Burns, E.R., Williams, C.F., Tolan, T., and Kaven, J.O., 2016, Are the Columbia River Basalts, Columbia Plateau, Idaho, Oregon, and Washington, USA, a viable geothermal target? A preliminary analysis, in Proceedings, 41st Workshop on Geothermal Reservoir Engineering, Stanford, CA, February 22-24, 2016, 11 p.","productDescription":"11 p.","ipdsId":"IP-071284","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":340099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Columbia Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.25,\n 44.25\n ],\n [\n -115.25,\n 44.25\n ],\n [\n -115.25,\n 48.5\n ],\n [\n -122.25,\n 48.5\n ],\n [\n -122.25,\n 44.25\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4ee4b0c3010a8087c5","contributors":{"authors":[{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":3094,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":623483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Colin F. 0000-0003-2196-5496 colin@usgs.gov","orcid":"https://orcid.org/0000-0003-2196-5496","contributorId":274,"corporation":false,"usgs":true,"family":"Williams","given":"Colin","email":"colin@usgs.gov","middleInitial":"F.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":623484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tolan, Terry","contributorId":55489,"corporation":false,"usgs":true,"family":"Tolan","given":"Terry","affiliations":[],"preferred":false,"id":623485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kaven, Joern Ole","contributorId":148002,"corporation":false,"usgs":false,"family":"Kaven","given":"Joern","email":"","middleInitial":"Ole","affiliations":[],"preferred":false,"id":623486,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70177889,"text":"70177889 - 2016 - Toward a quantitative and empirical dissolved organic carbon budget for the Gulf of Maine, a semienclosed shelf sea","interactions":[],"lastModifiedDate":"2016-10-26T14:12:02","indexId":"70177889","displayToPublicDate":"2016-02-20T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1836,"text":"Global Biogeochemical Cycles","active":true,"publicationSubtype":{"id":10}},"title":"Toward a quantitative and empirical dissolved organic carbon budget for the Gulf of Maine, a semienclosed shelf sea","docAbstract":"A time series of organic carbon export from Gulf of Maine (GoM) watersheds was compared to a time series of biological, chemical, bio-optical, and hydrographic properties, measured across the GoM between Yarmouth, NS, Canada, and Portland, ME, U.S. Optical proxies were used to quantify the dissolved organic carbon (DOC) and particulate organic carbon in the GoM. The Load Estimator regression model applied to river discharge data demonstrated that riverine DOC export (and its decadal variance) has increased over the last 80 years. Several extraordinarily wet years (2006–2010) resulted in a massive pulse of chromophoric dissolved organic matter (CDOM; proxy for DOC) into the western GoM along with unidentified optically scattering material (<0.2 μm diameter). A survey of DOC in the GoM and Scotian Shelf showed the strong influence of the Gulf of Saint Lawrence on the DOC that enters the GoM. A deep plume of CDOM-rich water was observed near the coast of Maine which decreased in concentration eastward. The Forel-Ule color scale was derived and compared to the same measurements made in 1912–1913 by Henry Bigelow. Results show that the GoM has yellowed in the last century, particularly in the region of the extension of the Eastern Maine Coastal Current. Time lags between DOC discharge and its appearance in the GoM increased with distance from the river mouths. Algae were also a significant source of DOC but not CDOM. Gulf-wide algal primary production has decreased. Increases in precipitation and DOC discharge to the GoM are predicted over the next century.","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015GB005332","usgsCitation":"Balch, W., Huntington, T.G., Aiken, G.R., Drapeau, D., Bowler, B., Lubelczyk, L., and Butler, K.D., 2016, Toward a quantitative and empirical dissolved organic carbon budget for the Gulf of Maine, a semienclosed shelf sea: Global Biogeochemical Cycles, v. 30, no. 2, p. 268-292, https://doi.org/10.1002/2015GB005332.","productDescription":"25 p.","startPage":"268","endPage":"292","ipdsId":"IP-072221","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":471216,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gb005332","text":"Publisher Index Page"},{"id":330416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Gulf of Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72,\n 42\n ],\n [\n -72,\n 47\n ],\n [\n -65,\n 47\n ],\n [\n -65,\n 42\n ],\n [\n -72,\n 42\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-20","publicationStatus":"PW","scienceBaseUri":"5811c0f3e4b0f497e79a5a7f","contributors":{"authors":[{"text":"Balch, William","contributorId":176267,"corporation":false,"usgs":false,"family":"Balch","given":"William","affiliations":[],"preferred":false,"id":652037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huntington, Thomas G. 0000-0002-9427-3530 thunting@usgs.gov","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":1884,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas","email":"thunting@usgs.gov","middleInitial":"G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":652036,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drapeau, David","contributorId":176268,"corporation":false,"usgs":false,"family":"Drapeau","given":"David","email":"","affiliations":[],"preferred":false,"id":652039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowler, Bruce","contributorId":176269,"corporation":false,"usgs":false,"family":"Bowler","given":"Bruce","email":"","affiliations":[],"preferred":false,"id":652040,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lubelczyk, Laura","contributorId":176270,"corporation":false,"usgs":false,"family":"Lubelczyk","given":"Laura","email":"","affiliations":[],"preferred":false,"id":652041,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Butler, Kenna D. kebutler@usgs.gov","contributorId":3283,"corporation":false,"usgs":true,"family":"Butler","given":"Kenna","email":"kebutler@usgs.gov","middleInitial":"D.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":652042,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70173794,"text":"70173794 - 2016 - Potential foraging decisions by a desert ungulate to balance water and nutrient intake in a water-stressed environment","interactions":[],"lastModifiedDate":"2016-06-10T13:21:40","indexId":"70173794","displayToPublicDate":"2016-02-19T14:30:00","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":"Potential foraging decisions by a desert ungulate to balance water and nutrient intake in a water-stressed environment","docAbstract":"Arid climates have unpredictable precipitation patterns, and wildlife managers often provide supplemental water to help desert ungulates endure the hottest, driest periods. When surface water is unavailable, the only source of water for ungulates comes from the forage they consume, and they must make resourceful foraging decisions to meet their requirements. We compared two desert bighorn sheep (Ovis canadensis nelsoni) populations in Arizona, USA: a treatment population with supplemental water removed during treatment, and a control population. We examined whether sheep altered their seasonal diets without supplemental water. We calculated water and nutrient intake and metabolic water production from dry matter intake and forage moisture and nitrogen content, to determine whether sheep could meet their seasonal daily water and nutrient requirements solely from forage. Diets of sheep were higher in protein (all seasons) and moisture (autumn and winter) during treatment compared to pretreatment. During treatment, sheep diet composition was similar between the treatment and control populations, which suggests, under the climatic conditions of this study, water removal did not influence sheep diets. We estimated that under drought conditions, without any surface water available (although small ephemeral potholes would contain water after rains), female and male sheep would be unable to meet their daily water requirements in all seasons, except winter, when reproductive females had a nitrogen deficit. We determined that sheep could achieve water and nutrient balances in all seasons by shifting their total diet proportions by 8–55% from lower to higher moisture and nitrogen forage species. We elucidate how seasonal forage quality and foraging decisions by desert ungulates allow them to cope with their xeric and uncertain environment, and suggest that, with the forage conditions observed in our study area during this study period, providing supplemental water during water-stressed periods may not be necessary for desert bighorn sheep.
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\"}}]}","volume":"11","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-19","publicationStatus":"PW","scienceBaseUri":"575be4ace4b04f417c27f531","contributors":{"authors":[{"text":"Gedir, Jay V.","contributorId":171735,"corporation":false,"usgs":false,"family":"Gedir","given":"Jay","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":638454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. 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This high-resolution elevation dataset was collected on June 11, 2014, to characterize beach topography and document ongoing beach evolution and recovery, and is part of the ongoing beach monitoring within the Hurricane Sandy Supplemental Project GS2-2B. This USGS data series includes the resulting processed elevation point data (xyz) and an interpolated digital elevation model (DEM).
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Seasonal mean daily flow data from 119 U.S. Geological Survey streamflow-gaging stations in North Dakota; the surrounding states of Montana, Minnesota, and South Dakota; and the Canadian provinces of Manitoba and Saskatchewan with 10 or more years of unregulated flow record were used to develop regression equations for flow duration, n-day high flow and n-day low flow using ordinary least-squares and Tobit regression techniques. Regression equations were developed for seasonal flow durations at the 10th, 25th, 50th, 75th, and 90th percent exceedances; the 1-, 7-, and 30-day seasonal mean high flows for the 10-, 25-, and 50-year recurrence intervals; and the 1-, 7-, and 30-day seasonal mean low flows for the 2-, 5-, and 10-year recurrence intervals. Basin and climatic characteristics determined to be significant explanatory variables in one or more regression equations included drainage area, percentage of basin drainage area that drains to isolated lakes and ponds, ruggedness number, stream length, basin compactness ratio, minimum basin elevation, precipitation, slope ratio, stream slope, and soil permeability. The adjusted coefficient of determination for the n-day high-flow regression equations ranged from 55.87 to 94.53 percent. The Chi2 values for the duration regression equations ranged from 13.49 to 117.94, whereas the Chi2 values for the n-day low-flow regression equations ranged from 4.20 to 49.68.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155184","collaboration":"Prepared in cooperation with the North Dakota State Water Commission, North Dakota Department of Transportation, North Dakota Department of Health, Red River Joint Water Resources Board, and Devils Lake Basin Joint Water Resource Board","usgsCitation":"Williams-Sether, Tara, and Gross, T.A., 2016, Regression equations to estimate seasonal flow duration, n-day high-flow frequency, and n-day low-flow frequency at sites in North Dakota using data through water year 2009: U.S. Geological Survey Scientific Investigations Report 2015–5184, 12 p., https://dx.doi.org/10.3133/sir20155184.","productDescription":"Report: iv, 12 p.; 1 Table","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069878","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":316691,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2015/5184/sir20155184_table1.xlsx","text":"Table 1","size":"72.0 kb","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5184 Table 1"},{"id":316670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5184/sir20155184.pdf","text":"Report","size":"3.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5184"},{"id":316669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5184/coverthb.jpg"}],"country":"Canada, United States","state":"Manitoba, Minnesota, Montana, North Dakota, Saskatchewan, South Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.31591796875,\n 49.63917719651036\n ],\n [\n -101.854248046875,\n 49.94415035164548\n ],\n [\n -103.128662109375,\n 50.21206446065373\n ],\n [\n -104.359130859375,\n 50.240178884797025\n ],\n [\n -105.13916015625,\n 49.90171121726089\n ],\n [\n -105.3369140625,\n 48.64016871811908\n ],\n [\n -105.567626953125,\n 47.39834920035926\n ],\n [\n -105.27099609375,\n 46.59661864884465\n ],\n [\n -105.128173828125,\n 45.71385093029221\n ],\n [\n -105.46875,\n 44.574817404670306\n ],\n [\n -103.9306640625,\n 44.62175409623324\n ],\n [\n -103.524169921875,\n 45.54483149242463\n ],\n [\n -102.513427734375,\n 45.43700828867389\n ],\n [\n -101.019287109375,\n 45.205263456162385\n ],\n [\n -99.635009765625,\n 44.98811302615805\n ],\n [\n -98.668212890625,\n 45.0502402697946\n ],\n [\n -97.85522460937499,\n 44.89479576469787\n ],\n [\n -96.339111328125,\n 44.56699093657141\n ],\n [\n -95.185546875,\n 44.91813929958515\n ],\n [\n -95.987548828125,\n 45.95878764035642\n ],\n [\n -95.701904296875,\n 47.03269459852135\n ],\n [\n -95.767822265625,\n 47.82053186746053\n ],\n [\n -96.0205078125,\n 48.62564740882851\n ],\n [\n -96.427001953125,\n 49.24629332459796\n ],\n [\n -96.99829101562499,\n 49.986552130506176\n ],\n [\n -97.5146484375,\n 49.66762782262192\n ],\n [\n -97.97607421875,\n 49.50380954152213\n ],\n [\n -98.6572265625,\n 49.75997752330658\n ],\n [\n -99.29443359375,\n 49.61070993807422\n ],\n [\n -100.008544921875,\n 49.489538473066474\n ],\n [\n -100.34912109375,\n 49.36806633482156\n ],\n [\n -101.31591796875,\n 49.63917719651036\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS North Dakota Water Science Center
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Soil mottles generally are interpreted as a product of reducing conditions during periods of water saturation. The upland soils of the Santa Cruz, CA, marine terrace chronosequence display an evolving sequence of reticulate mottling from the youngest soil (65 ka) without mottles to the oldest soil (225 ka) with well-developed mottles. The mottles consist of an interconnected network of clay and C-enriched regions (gray, 2.5Y 6/1) bordered by leached parent material (white, 2.5Y 8/1) within a diminishing matrix of oxidized parent material (orange, 7.5YR 5/8). The mottles develop in soils that formed from relatively uniform nearshore sediments and occur below the depth of soil bioturbation. To explore how a presumably wetland feature occurs in an unsaturated upland soil, physical and chemical characteristics of mottle separates (orange, gray, and white) were compared through the deep time represented by the soil chronosequence. Mineralogical, isotopic, and surface-area differences among mottle separates indicate that rhizogenic centimeter-scale mass transfer acting across millennia is an integral part of weathering, pedogenesis, and C and nutrient transfer. Elemental analysis, electron microscopy, and Fe-isotope systematics indicate that mottle development is driven by deep roots together with their fungal and microbial symbionts. Taken together, these data suggest that deep soil horizons on old stable landforms can develop reticulate mottling as the long-term imprint of rhizospheric processes. The processes of rhizogenic mottle formation appear to regulate pedogenesis, nutrients, and C sequestration at depth in unsaturated zones.
","language":"English","publisher":"Soil Science Society of America","publisherLocation":"Madison, WI","doi":"10.2136/vzj2015.07.0102","usgsCitation":"Schulz, M., Stonestrom, D.A., Lawrence, C.R., Bullen, T.D., Fitzpatrick, J., Kyker-Snowman, E., Manning, J., and Mnich, M., 2016, Structured heterogeneity in a marine terrace chronosequence: Upland mottling: Vadose Zone Journal, v. 15, no. 2, 14 p., https://doi.org/10.2136/vzj2015.07.0102.","productDescription":"14 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066928","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":471218,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2136/vzj2015.07.0102","text":"Publisher Index Page"},{"id":318284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.15,\n 36.95\n ],\n [\n -122.15,\n 37\n ],\n [\n -122.1,\n 37\n ],\n [\n -122.1,\n 36.95\n ],\n [\n -122.15,\n 36.95\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-19","publicationStatus":"PW","scienceBaseUri":"56cc4003e4b059daa47e46b5","contributors":{"authors":[{"text":"Schulz, Marjorie S. 0000-0001-5597-6447 mschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-6447","contributorId":3720,"corporation":false,"usgs":true,"family":"Schulz","given":"Marjorie S.","email":"mschulz@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - 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The coupled effect of bivalve grazing at the sediment-water interface and sinking of phytoplankton cells to that bottom filtration zone could influence the relative biomass of sinking (diatoms) and non-sinking phytoplankton. Simulations with a pseudo-2D numerical model showed that benthic filter feeding can interact with sinking to alter diatom:non-diatom ratios. Cases with the smallest proportion of diatom biomass were those with the fastest sinking speeds and strongest bivalve grazing rates. Hydrodynamics modulated the coupled sinking-grazing influence on phytoplankton communities. For example, in simulations with persistent stratification, the non-sinking forms accumulated in the surface layer away from bottom grazers while the sinking forms dropped out of the surface layer toward bottom grazers. Tidal-scale stratification also influenced vertical gradients of the two groups in opposite ways. The model was applied to Suisun Bay, a low-salinity habitat of the San Francisco Bay system that was transformed by the introduction of the exotic clam Potamocorbula amurensis. Simulation results for this Bay were similar to (but more muted than) those for generic habitats, indicating that P. amurensis grazing could have caused a disproportionate loss of diatoms after its introduction. Our model simulations suggest bivalve grazing affects both phytoplankton biomass and community composition in shallow waters. We view these results as hypotheses to be tested with experiments and more complex modeling approaches.
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llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":2181,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":620812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cloern, James E. 0000-0002-5880-6862 jecloern@usgs.gov","orcid":"https://orcid.org/0000-0002-5880-6862","contributorId":1488,"corporation":false,"usgs":true,"family":"Cloern","given":"James","email":"jecloern@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":620813,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Janet K. 0000-0002-1528-8452 jthompso@usgs.gov","orcid":"https://orcid.org/0000-0002-1528-8452","contributorId":1009,"corporation":false,"usgs":true,"family":"Thompson","given":"Janet","email":"jthompso@usgs.gov","middleInitial":"K.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":620814,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stacey, Mark T.","contributorId":13367,"corporation":false,"usgs":true,"family":"Stacey","given":"Mark T.","affiliations":[],"preferred":false,"id":620816,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koseff, Jeffrey K.","contributorId":167033,"corporation":false,"usgs":false,"family":"Koseff","given":"Jeffrey K.","affiliations":[{"id":24597,"text":"Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA","active":true,"usgs":false}],"preferred":false,"id":620815,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70168556,"text":"70168556 - 2016 - Simulating future water temperatures in the North Santiam River, Oregon","interactions":[],"lastModifiedDate":"2016-02-19T10:09:47","indexId":"70168556","displayToPublicDate":"2016-02-18T11:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Simulating future water temperatures in the North Santiam River, Oregon","docAbstract":"A previously calibrated two-dimensional hydrodynamic and water-quality model (CE-QUAL-W2) of Detroit Lake in western Oregon was used in conjunction with inflows derived from Precipitation-Runoff Modeling System (PRMS) hydrologic models to examine in-lake and downstream water temperature effects under future climate conditions. Current and hypothetical operations and structures at Detroit Dam were imposed on boundary conditions derived from downscaled General Circulation Models in base (1990–1999) and future (2059–2068) periods. Compared with the base period, future air temperatures were about 2 °C warmer year-round. Higher air temperature and lower precipitation under the future period resulted in a 23% reduction in mean annual PRMS-simulated discharge and a 1 °C increase in mean annual estimated stream temperatures flowing into the lake compared to the base period. Simulations incorporating current operational rules and minimum release rates at Detroit Dam to support downstream habitat, irrigation, and water supply during key times of year resulted in lower future lake levels. That scenario results in a lake level that is above the dam’s spillway crest only about half as many days in the future compared to historical frequencies. Managing temperature downstream of Detroit Dam depends on the ability to blend warmer water from the lake’s surface with cooler water from deep in the lake, and the spillway is an important release point near the lake’s surface. Annual average in-lake and release temperatures from Detroit Lake warmed 1.1 °C and 1.5 °C from base to future periods under present-day dam operational rules and fill schedules. Simulated dam operations such as beginning refill of the lake 30 days earlier or reducing minimum release rates (to keep more water in the lake to retain the use of the spillway) mitigated future warming to 0.4 and 0.9 °C below existing operational scenarios during the critical autumn spawning period for endangered salmonids. A hypothetical floating surface withdrawal at Detroit Dam improved temperature control in summer and autumn (0.6 °C warmer in summer, 0.6 °C cooler in autumn compared to existing structures) without altering release rates or lake level management rules.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Hydrology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"New York","doi":"10.1016/j.jhydrol.2016.01.062","collaboration":"USACE","usgsCitation":"Buccola, N.L., Risley, J.C., and Rounds, S.A., 2016, Simulating future water temperatures in the North Santiam River, Oregon: Journal of Hydrology, v. 535, p. 318-330, https://doi.org/10.1016/j.jhydrol.2016.01.062.","productDescription":"13 p.","startPage":"318","endPage":"330","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066718","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":471223,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2016.01.062","text":"Publisher Index Page"},{"id":318165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.178955078125,\n 44.79158175909386\n ],\n [\n -122.21603393554688,\n 44.775986224030376\n ],\n [\n -122.30255126953126,\n 44.775986224030376\n ],\n [\n -122.31491088867186,\n 44.73795445686983\n ],\n [\n -122.32177734375,\n 44.692088041727814\n ],\n [\n -122.31353759765624,\n 44.66962952692013\n ],\n [\n -122.2723388671875,\n 44.65009330721101\n ],\n [\n -122.22427368164064,\n 44.63934558051711\n ],\n [\n -122.14187622070311,\n 44.623708968901205\n ],\n [\n -122.08694458007812,\n 44.58068656459206\n ],\n [\n -122.09793090820311,\n 44.524905626703834\n ],\n [\n -122.02926635742188,\n 44.4867089169177\n ],\n [\n -121.91940307617188,\n 44.473971118440275\n ],\n [\n -121.79718017578124,\n 44.4808302785626\n ],\n [\n -121.76971435546874,\n 44.56601255400719\n ],\n [\n -121.74087524414064,\n 44.67548910920999\n ],\n [\n -121.74911499023438,\n 44.73600343509071\n ],\n [\n -121.74911499023438,\n 44.790607161582656\n ],\n [\n -121.79580688476562,\n 44.811070253260006\n ],\n [\n -121.90292358398438,\n 44.82860426955568\n ],\n [\n -121.95785522460936,\n 44.8500274926005\n ],\n [\n -121.99905395507812,\n 44.87046950217272\n ],\n [\n -122.06909179687501,\n 44.84808025602074\n ],\n [\n -122.1240234375,\n 44.824708282300236\n ],\n [\n -122.178955078125,\n 44.79158175909386\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"535","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56c84acce4b0b3c9ae3810a3","contributors":{"authors":[{"text":"Buccola, Norman L. 0000-0002-9590-2458 nbuccola@usgs.gov","orcid":"https://orcid.org/0000-0002-9590-2458","contributorId":139096,"corporation":false,"usgs":true,"family":"Buccola","given":"Norman","email":"nbuccola@usgs.gov","middleInitial":"L.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":620889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Risley, John C. 0000-0002-8206-5443 jrisley@usgs.gov","orcid":"https://orcid.org/0000-0002-8206-5443","contributorId":2698,"corporation":false,"usgs":true,"family":"Risley","given":"John","email":"jrisley@usgs.gov","middleInitial":"C.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":620891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":620890,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70168497,"text":"70168497 - 2016 - Book review: Ducks, geese, and swans of North America","interactions":[],"lastModifiedDate":"2018-01-02T11:05:20","indexId":"70168497","displayToPublicDate":"2016-02-18T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3111,"text":"Prairie Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Book review: Ducks, geese, and swans of North America","docAbstract":"As pointed out in the book’s introduction by Richard McCabe, very few books deserve being called a classic. First published in 1942, the various editions of Ducks, Geese, and Swans of North America—authored by Francis K. Kortright (1942), Frank C. Bellrose (1976, 1981), and this new edition by Guy Baldassarre (2014)—are deservedly placed in that category among the waterfowl literature. This book has been a valuable resource for the scientific community and waterfowl enthusiasts, and I was excited to learn that a new version has been published. As expected, this new edition did not disappoint and is a remarkable volume in terms of incorporating current research into each species account in a way that does not overwhelm either professional or amateur readers.
\nReview info: Ducks, geese, and swans of North America. By Guy Baldassarre, 2014. ISBN: 978-1421407517, 1088 pp.
","language":"English","publisher":"South Dakota State University","usgsCitation":"Wilson, R.E., 2016, Book review: Ducks, geese, and swans of North America: Prairie Naturalist, v. 47, no. 1, p. 55-55.","productDescription":"1 p.","startPage":"55","endPage":"55","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064631","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":318126,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":318094,"type":{"id":15,"text":"Index Page"},"url":"https://www.sdstate.edu/nrm/organizations/gpnss/tpn/2015-archives.cfm"}],"volume":"47","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56c6eb28e4b0946c6523b0bd","contributors":{"authors":[{"text":"Wilson, Robert E. 0000-0003-1800-0183 rewilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":5718,"corporation":false,"usgs":true,"family":"Wilson","given":"Robert","email":"rewilson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":620636,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70168659,"text":"70168659 - 2016 - Testing the suitability of geologic frameworks for extrapolating hydraulic properties across regional scales","interactions":[],"lastModifiedDate":"2016-12-16T10:51:16","indexId":"70168659","displayToPublicDate":"2016-02-18T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Testing the suitability of geologic frameworks for extrapolating hydraulic properties across regional scales","docAbstract":"The suitability of geologic frameworks for extrapolating hydraulic conductivity (K) to length scales commensurate with hydraulic data is difficult to assess. A novel method is presented for evaluating assumed relations between K and geologic interpretations for regional-scale groundwater modeling. The approach relies on simultaneous interpretation of multiple aquifer tests using alternative geologic frameworks of variable complexity, where each framework is incorporated as prior information that assumes homogeneous K within each model unit. This approach is tested at Pahute Mesa within the Nevada National Security Site (USA), where observed drawdowns from eight aquifer tests in complex, highly faulted volcanic rocks provide the necessary hydraulic constraints. The investigated volume encompasses 40 mi3 (167 km3) where drawdowns traversed major fault structures and were detected more than 2 mi (3.2 km) from pumping wells. Complexity of the five frameworks assessed ranges from an undifferentiated mass of rock with a single unit to 14 distinct geologic units. Results show that only four geologic units can be justified as hydraulically unique for this location. The approach qualitatively evaluates the consistency of hydraulic property estimates within extents of investigation and effects of geologic frameworks on extrapolation. Distributions of transmissivity are similar within the investigated extents irrespective of the geologic framework. In contrast, the extrapolation of hydraulic properties beyond the volume investigated with interfering aquifer tests is strongly affected by the complexity of a given framework. Testing at Pahute Mesa illustrates how this method can be employed to determine the appropriate level of geologic complexity for large-scale groundwater modeling.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-016-1375-1","usgsCitation":"Mirus, B.B., Halford, K.J., Sweetkind, D.S., and Fenelon, J.M., 2016, Testing the suitability of geologic frameworks for extrapolating hydraulic properties across regional scales: Hydrogeology Journal, v. 24, no. 5, p. 1133-1146, https://doi.org/10.1007/s10040-016-1375-1.","productDescription":"14 p.","startPage":"1133","endPage":"1146","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-033309","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":490008,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-016-1375-1","text":"Publisher Index Page"},{"id":318311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Pahute Mesa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.7,\n 37.3\n ],\n [\n -116.7,\n 37\n ],\n [\n -116.3,\n 37\n ],\n [\n -116.3,\n 37.3\n ],\n [\n -116.7,\n 37.3\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"5","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-18","publicationStatus":"PW","scienceBaseUri":"56cc4007e4b059daa47e46e5","contributors":{"authors":[{"text":"Mirus, Benjamin B.","contributorId":12348,"corporation":false,"usgs":false,"family":"Mirus","given":"Benjamin","email":"","middleInitial":"B.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":false,"id":621173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halford, Keith J. 0000-0002-7322-1846 khalford@usgs.gov","orcid":"https://orcid.org/0000-0002-7322-1846","contributorId":1374,"corporation":false,"usgs":true,"family":"Halford","given":"Keith","email":"khalford@usgs.gov","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":621176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweetkind, Donald S. 0000-0003-0892-4796 dsweetkind@usgs.gov","orcid":"https://orcid.org/0000-0003-0892-4796","contributorId":139913,"corporation":false,"usgs":true,"family":"Sweetkind","given":"Donald","email":"dsweetkind@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":621174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fenelon, Joseph M. 0000-0003-4449-245X jfenelon@usgs.gov","orcid":"https://orcid.org/0000-0003-4449-245X","contributorId":2355,"corporation":false,"usgs":true,"family":"Fenelon","given":"Joseph","email":"jfenelon@usgs.gov","middleInitial":"M.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":621175,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168509,"text":"70168509 - 2016 - Cumulative drought and land-use impacts on perennial vegetation across a North American dryland region","interactions":[],"lastModifiedDate":"2016-06-15T16:18:08","indexId":"70168509","displayToPublicDate":"2016-02-17T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":849,"text":"Applied Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"Cumulative drought and land-use impacts on perennial vegetation across a North American dryland region","docAbstract":"The decline and loss of perennial vegetation in dryland ecosystems due to global change pressures can alter ecosystem properties and initiate land degradation processes. We tracked changes of perennial vegetation using remote sensing to address the question of how prolonged drought and land-use intensification have affected perennial vegetation cover across a desert region in the early 21st century?
\nMojave Desert, southeastern California, southern Nevada, southwestern Utah and northwestern Arizona, USA.
\nWe coupled the Moderate-Resolution Imaging Spectroradiometer Enhanced Vegetation Index (MODIS-EVI) with ground-based measurements of perennial vegetation cover taken in about 2000 and about 2010. Using the difference between these years, we determined perennial vegetation changes in the early 21st century and related these shifts to climate, soil and landscape properties, and patterns of land use.
\nWe found a good fit between MODIS-EVI and perennial vegetation cover (2000: R2 = 0.83 and 2010: R2 = 0.74). The southwestern, far southeastern and central Mojave Desert had large declines in perennial vegetation cover in the early 21st century, while the northeastern and southeastern portions of the desert had increases. These changes were explained by 10-yr precipitation anomalies, particularly in the cool season and during extreme dry or wet years. Areas heavily impacted by visitor use or wildfire lost perennial vegetation cover, and vegetation in protected areas increased to a greater degree than in unprotected areas.
\nWe find that we can extrapolate previously documented declines of perennial plant cover to an entire desert, and demonstrate that prolonged water shortages coupled with land-use intensification create identifiable patterns of vegetation change in dryland regions.
\nThe U.S. Geological Survey (USGS), as part of the National Assessment of Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms (Morgan, 2009). On August 30-31, 2011, the USGS conducted an oblique aerial photographic survey from Ocracoke Inlet, North Carolina, to Virginia Beach, Virginia, aboard a Piper Navajo Chieftain (aircraft) at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was flown to collect post-Hurricane Irene data for assessing incremental changes in the beach and nearshore area since the last survey, flown in May 2008, and the data can be used in the assessment of future coastal change.
\nThe photographs provided in this report are Joint Photographic Experts Group (JPEG) images. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. The photograph locations are an estimate of the position of the aircraft at the time the photograph was taken and do not indicate the location of any feature in the images (see the Navigation Data page). These photographs document the state of the barrier islands and other coastal features at the time of the survey. Pages containing thumbnail images of the photographs, referred to as contact sheets, were created in 5-minute segments of flight time. These segments can be found on the Photos and Maps page. Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet.
\nTable 1 provides detailed information about the GPS location, image name, date, and time for each of the 2,688 photographs that were taken along with links to each photograph.
In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML also shows the track of Hurricane Irene. The KML files were created using the photographic navigation files. These KML file(s) can be found in the kml folder.
St. Petersburg Coastal and Marine Science Center
600 4th Street South
St. Petersburg, FL 33701
(727) 502-8000
http://coastal.er.usgs.gov/
This circular provides an overview of selected activities that were conducted within the U.S. Geological Survey (USGS) Integrated Methods Development Project, an interdisciplinary project designed to develop new tools and conduct innovative research requiring integration of geologic, geophysical, geochemical, and remote-sensing expertise. The project was supported by the USGS Mineral Resources Program, and its products and acquired capabilities have broad applications to missions throughout the USGS and beyond.
In addressing challenges associated with understanding the location, quantity, and quality of mineral resources, and in investigating the potential environmental consequences of resource development, a number of field and laboratory capabilities and interpretative methodologies evolved from the project that have applications to traditional resource studies as well as to studies related to ecosystem health, human health, disaster and hazard assessment, and planetary science. New or improved tools and research findings developed within the project have been applied to other projects and activities. Specifically, geophysical equipment and techniques have been applied to a variety of traditional and nontraditional mineral- and energy-resource studies, military applications, environmental investigations, and applied research activities that involve climate change, mapping techniques, and monitoring capabilities. Diverse applied geochemistry activities provide a process-level understanding of the mobility, chemical speciation, and bioavailability of elements, particularly metals and metalloids, in a variety of environmental settings. Imaging spectroscopy capabilities maintained and developed within the project have been applied to traditional resource studies as well as to studies related to ecosystem health, human health, disaster assessment, and planetary science. Brief descriptions of capabilities and laboratory facilities and summaries of some applications of project products and research findings are included in this circular. The work helped support the USGS mission to “provide reliable scientific information to describe and understand the Earth; minimize loss of life and property from natural disasters; manage water, biological, energy, and mineral resources; and enhance and protect our quality of life.” Activities within the project include the following:
This circular consists of eight sections that contain summaries of various activities under the project. The eight sections are listed below:
Center Director, USGS Crustal Geophysics and Geochemistry Science Center
Box 25046, Mail Stop 964
Denver, CO 80225
Or visit the Crustal Geophysics and Geochemistry Science Center Web site at:
http://crustal.usgs.gov/
Marine seismic reflection data from southern Puget Sound, Washington, were collected to investigate the nature of shallow structures associated with the Tacoma fault zone and the Olympia structure. Growth folding and probable Holocene surface deformation were imaged within the Tacoma fault zone beneath Case and Carr Inlets. Shallow faults near potential field anomalies associated with the Olympia structure were imaged beneath Budd and Eld Inlets. Beneath Case Inlet, the Tacoma fault zone includes an ∼350-m wide section of south-dipping strata forming the upper part of a fold (kink band) coincident with the southern edge of an uplifted shoreline terrace. An ∼2 m change in the depth of the water bottom, onlapping postglacial sediments, and increasing stratal dips with increasing depth are consistent with late Pleistocene to Holocene postglacial growth folding above a blind fault. Geologic data across a topographic lineament on nearby land indicate recent uplift of late Holocene age. Profiles acquired in Carr Inlet 10 km to the east of Case Inlet showed late Pleistocene or Holocene faulting at one location with ∼3 to 4 m of vertical displacement, south side up. North of this fault the data show several other disruptions and reflector terminations that could mark faults within the broad Tacoma fault zone. Seismic reflection profiles across part of the Olympia structure beneath southern Puget Sound show two apparent faults about 160 m apart having 1 to 2 m of displacement of subhorizontal bedding. Directly beneath one of these faults, a dipping reflector that may mark the base of a glacial channel shows the opposite sense of throw, suggesting strike-slip motion. Deeper seismic reflection profiles show disrupted strata beneath these faults but little apparent vertical offset, consistent with strike-slip faulting. These faults and folds indicate that the Tacoma fault and Olympia structure include active structures with probable postglacial motion.
","language":"English","publisher":"Seismological Society of America","publisherLocation":"Albany, CA","doi":"10.1785/0120080306","usgsCitation":"Odum, J., Stephenson, W.J., Pratt, T.L., and Blakely, R.J., 2016, High-resolution seismic reflection imaging of growth folding and shallow faults beneath the Southern Puget Lowland, Washington State: Bulletin of the Seismological Society of America, v. 100, no. 4, p. 1710-1723, https://doi.org/10.1785/0120080306.","productDescription":"14 p.","startPage":"1710","endPage":"1723","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-076890","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":488465,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1785/0120080306","text":"External Repository"},{"id":324161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","county":"Island, Jefferson, King Kitsap, Mason, Pierce, San Juan, Skagit, Snohomish, Thurston, Whatcom","city":"Seattle","otherGeospatial":"Northwest coast of Washington State; part of the Saliah Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.86010742187499,\n 48.88639177703194\n ],\n [\n -123.18969726562499,\n 48.672826384100354\n ],\n [\n -123.18969726562499,\n 48.574789910928864\n ],\n [\n -123.06884765625,\n 48.425555463221045\n ],\n [\n -123.20068359374999,\n 48.23565029755306\n ],\n [\n -123.4149169921875,\n 46.81885778879603\n ],\n [\n -121.2176513671875,\n 46.90149244734082\n ],\n [\n -121.3275146484375,\n 48.86832824998009\n ],\n [\n -122.86010742187499,\n 48.88639177703194\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"100","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2010-07-27","publicationStatus":"PW","scienceBaseUri":"576a653be4b07657d1a11db0","contributors":{"authors":[{"text":"Odum, Jackson K. 0000-0003-4697-2430 odum@usgs.gov","orcid":"https://orcid.org/0000-0003-4697-2430","contributorId":1365,"corporation":false,"usgs":true,"family":"Odum","given":"Jackson K.","email":"odum@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":640150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":640151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":640152,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blakely, Richard J. 0000-0003-1701-5236 blakely@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-5236","contributorId":1540,"corporation":false,"usgs":true,"family":"Blakely","given":"Richard","email":"blakely@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":640153,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70164455,"text":"70164455 - 2016 - Wetting and drying of soil in response to precipitation: Data analysis, modeling, and forecasting","interactions":[],"lastModifiedDate":"2016-12-20T11:32:48","indexId":"70164455","displayToPublicDate":"2016-02-17T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Wetting and drying of soil in response to precipitation: Data analysis, modeling, and forecasting","docAbstract":"This paper investigates methods to analyze and forecast soil moisture time series. We extend an existing Antecedent Water Index (AWI) model, which expresses soil moisture as a function of time and rainfall. Unfortunately, the existing AWI model does not forecast effectively for time periods beyond a few hours. To overcome this limitation, we develop a novel AWI-based model. Our model accumulates rainfall over a time interval and can fit a diverse range of wetting and drying curves. In addition, parameters in our model reflect hydrologic redistribution processes of gravity and suction.We validate our models using experimental soil moisture and rainfall time series data collected from steep gradient post-wildfire sites in Southern California, where rapid landscape change was observed in response to small to moderate rain storms. We found that our novel model fits the data for three distinct soil textures, occurring at different depths below the ground surface (5, 15, and 30 cm). Our model also successfully forecasts soil moisture trends, such as drying and wetting rate.","conferenceTitle":"13th Conference of the Association for the Advancement of Artificial Intelligence","conferenceDate":"February 12–17, 2016","conferenceLocation":" Phoenix, Arizona ","language":"English","publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","collaboration":"Carnegie Mellon University","usgsCitation":"Basak, A., Kulkarni, C., Schmidt, K.M., and Mengshoel, O., 2016, Wetting and drying of soil in response to precipitation: Data analysis, modeling, and forecasting, 13th Conference of the Association for the Advancement of Artificial Intelligence, Phoenix, Arizona , February 12–17, 2016.","ipdsId":"IP-068964","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":332337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":316604,"type":{"id":15,"text":"Index Page"},"url":"https://www.aaai.org/Conferences/AAAI/aaai16.php"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"585a51bfe4b01224f329b5ed","contributors":{"authors":[{"text":"Basak, Aniruddha","contributorId":156329,"corporation":false,"usgs":false,"family":"Basak","given":"Aniruddha","email":"","affiliations":[{"id":20319,"text":"Carnegie Mellon University, Silicon Valley","active":true,"usgs":false}],"preferred":false,"id":597456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kulkarni, Chinmay","contributorId":156330,"corporation":false,"usgs":false,"family":"Kulkarni","given":"Chinmay","email":"","affiliations":[{"id":20319,"text":"Carnegie Mellon University, Silicon Valley","active":true,"usgs":false}],"preferred":false,"id":597457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmidt, Kevin M. 0000-0003-2365-8035 kschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-2365-8035","contributorId":1985,"corporation":false,"usgs":true,"family":"Schmidt","given":"Kevin","email":"kschmidt@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":597455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mengshoel, Ole","contributorId":156331,"corporation":false,"usgs":false,"family":"Mengshoel","given":"Ole","email":"","affiliations":[{"id":20319,"text":"Carnegie Mellon University, Silicon Valley","active":true,"usgs":false}],"preferred":false,"id":597458,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168482,"text":"70168482 - 2016 - Spatial and temporal trends of drought effects in a heterogeneous semi-arid forest ecosystem","interactions":[],"lastModifiedDate":"2016-02-16T13:26:05","indexId":"70168482","displayToPublicDate":"2016-02-16T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal trends of drought effects in a heterogeneous semi-arid forest ecosystem","docAbstract":"Drought has long been recognized as a driving mechanism in the forests of western North America and drought-induced mortality has been documented across genera in recent years. Given the frequency of these events are expected to increase in the future, understanding patterns of mortality and plant response to severe drought is important to resource managers. Drought can affect the functional, physiological, structural, and demographic properties of forest ecosystems. Remote sensing studies have documented changes in forest properties due to direct and indirect effects of drought; however, few studies have addressed this at local scales needed to characterize highly heterogeneous ecosystems in the forest-shrubland ecotone. We analyzed a 22-year Landsat time series (1985–2012) to determine changes in forest in an area that experienced a relatively dry decade punctuated by two years of extreme drought. We assessed the relationship between several vegetation indices and field measured characteristics (e.g. plant area index and canopy gap fraction) and applied these indices to trend analysis to uncover the location, direction and timing of change. Finally, we assessed the interaction of climate and topography by forest functional type. The Normalized Difference Moisture Index (NDMI), a measure of canopy water content, had the strongest correlation with short-term field measures of plant area index (R2 = 0.64) and canopy gap fraction (R2 = 0.65). Over the entire time period, 25% of the forested area experienced a significant (p-value < 0.05) negative trend in NDMI, compared to less than 10% in a positive trend. Coniferous forests were more likely to be associated with a negative NDMI trend than deciduous forest. Forests on southern aspects were least likely to exhibit a negative trend while north aspects were most prevalent. Field plots with a negative trend had a lower live density, and higher amounts of standing dead and down trees compared to plots with no trend. Our analysis identifies spatially explicit patterns of long-term trends anchored with ground based evidence to highlight areas of forest that are resistant, persistent or vulnerable to severe drought. The results provide a long-term perspective for the resource management of this area and can be applied to similar ecosystems throughout western North America.
","language":"English","publisher":"Elsevier Science Pub. Co.","publisherLocation":"New York, NY","doi":"10.1016/j.foreco.2016.01.017","usgsCitation":"Assal, T.J., Anderson, P.J., and Sibold, J., 2016, Spatial and temporal trends of drought effects in a heterogeneous semi-arid forest ecosystem: Forest Ecology and Management, v. 365, p. 137-151, https://doi.org/10.1016/j.foreco.2016.01.017.","productDescription":"15 p.","startPage":"137","endPage":"151","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070450","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471230,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2016.01.017","text":"Publisher Index Page"},{"id":318076,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah, Wyoming","otherGeospatial":"Wyoming Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.5,\n 40.5\n ],\n [\n -109.5,\n 41.5\n ],\n [\n -108.5,\n 41.5\n ],\n [\n -108.5,\n 40.5\n ],\n [\n -109.5,\n 40.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"365","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56c44831e4b0946c65211715","contributors":{"authors":[{"text":"Assal, Timothy J. 0000-0001-6342-2954 assalt@usgs.gov","orcid":"https://orcid.org/0000-0001-6342-2954","contributorId":2203,"corporation":false,"usgs":true,"family":"Assal","given":"Timothy","email":"assalt@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":620492,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Patrick J. 0000-0003-2281-389X andersonpj@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-389X","contributorId":3590,"corporation":false,"usgs":true,"family":"Anderson","given":"Patrick","email":"andersonpj@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":620493,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sibold, Jason","contributorId":10724,"corporation":false,"usgs":false,"family":"Sibold","given":"Jason","affiliations":[],"preferred":false,"id":620494,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70168373,"text":"70168373 - 2016 - Mercury remediation in wetland sediment using zero-valent iron and granular activated carbon","interactions":[],"lastModifiedDate":"2019-09-04T14:37:31","indexId":"70168373","displayToPublicDate":"2016-02-16T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Mercury remediation in wetland sediment using zero-valent iron and granular activated carbon","docAbstract":"Wetlands are hotspots for production of toxic methylmercury (MeHg) that can bioaccumulate in the food web. The objective of this study was to determine whether the application of zero-valent iron (ZVI) or granular activated carbon (GAC) to wetland sediment could reduce MeHg production and bioavailability to benthic organisms. Field mesocosms were installed in a wetland fringing Hodgdon Pond (Maine, USA), and ZVI and GAC were applied. Pore-water MeHg concentrations were lower in treated compared with untreated mesocosms; however, sediment MeHg, as well as total Hg (THg), concentrations were not significantly different between treated and untreated mesocosms, suggesting that smaller pore-water MeHg concentrations in treated sediment were likely due to adsorption to ZVI and GAC, rather than inhibition of MeHg production. In laboratory experiments with intact vegetated sediment clumps, amendments did not significantly change sediment THg and MeHg concentrations; however, the mean pore-water MeHg and MeHg:THg ratios were lower in the amended sediment than the control. In the laboratory microcosms, snails (Lymnaea stagnalis) accumulated less MeHg in sediment treated with ZVI or GAC. The study results suggest that both GAC and ZVI have potential for reducing MeHg bioaccumulation in wetland sediment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2015.11.047","usgsCitation":"Lewis, A.S., Huntington, T.G., Marvin-DiPasquale, M.C., and Amirbahman, A., 2016, Mercury remediation in wetland sediment using zero-valent iron and granular activated carbon: Environmental Pollution, v. 212, p. 366-373, https://doi.org/10.1016/j.envpol.2015.11.047.","productDescription":"8 p.","startPage":"366","endPage":"373","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067067","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":318036,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"212","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56c4482ce4b0946c652116f1","contributors":{"authors":[{"text":"Lewis, Ariel S.","contributorId":166710,"corporation":false,"usgs":false,"family":"Lewis","given":"Ariel","email":"","middleInitial":"S.","affiliations":[{"id":24494,"text":"Univ. of Maine","active":true,"usgs":false}],"preferred":false,"id":619821,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huntington, Thomas G. 0000-0002-9427-3530 thunting@usgs.gov","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":1884,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas","email":"thunting@usgs.gov","middleInitial":"G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":619822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marvin-DiPasquale, Mark C. 0000-0002-8186-9167 mmarvin@usgs.gov","orcid":"https://orcid.org/0000-0002-8186-9167","contributorId":1485,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","email":"mmarvin@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":619820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amirbahman, Aria","contributorId":44031,"corporation":false,"usgs":true,"family":"Amirbahman","given":"Aria","email":"","affiliations":[],"preferred":false,"id":619823,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}