During landfall of extratropical cyclones between 2005 and 2011, nearly 1400 precipitation samples were collected at intervals of 30-min time resolution with novel automated collectors at four NOAA sites in northern California [Alta (ATA), Bodega Bay (BBY), Cazadero (CZD) and Shasta Dam (STD)] during 43 events. Substantial decreases were commonly followed hours later by substantial increases in hydrogen isotopic composition (δ2HVSMOW where VSMOW is Vienna Standard Mean Ocean Water) and oxygen isotopic composition (δ18OVSMOW) of precipitation. These variations likely occur as pre-cold frontal precipitation generation transitions from marine vapour masses having low rainout to cold cloud layers having much higher rainout (with concomitant brightband signatures measured by an S-band profiling radar and lower δ2HVSMOW values of precipitation), and finally to shallower, warmer precipitating clouds having lower rainout (with non-brightband signatures and higher δ2HVSMOW values of precipitation), in accord with ‘seeder–feeder’ precipitation. Of 82 intervals identified, a remarkable 100.5 ‰ decrease in δ2HVSMOW value was observed for a 21 January 2010 event at BBY. Of the 61 intervals identified with increases in δ2HVSMOW values as precipitation transitioned to shallower, warmer clouds having substantially less rainout (the feeder part of the seeder–feeder mechanism), a remarkable increase in δ2HVSMOW value of precipitation of 82.3 ‰ was observed for a 10 February 2007 event at CZD. All CZD and ATA events having δ2HVSMOW values of precipitation below −105 ‰ were atmospheric rivers (ARs), and of the 13 events having δ2HVSMOWvalues of precipitation below −80 ‰, 77 % were ARs. Cloud echo-top heights (a proxy for atmospheric temperature) were available for 23 events. The mean echo-top height is greater for higher rainout periods than that for lower rainout periods in 22 of the 23 events. The lowest δ2HVSMOW of precipitation of 28 CZD events was −137.9 ‰ on 16 February 2009 during an AR with cold precipitating clouds and very high rainout with tops >6.5 km altitude. An altitude effect of −2.5 ‰ per 100 m was measured from BBY and CZD δ2HVSMOW data and of −1.8 ‰ per 100 m for CZD and ATA δ2HVSMOW data. We present a new approach to categorise rainfall intervals using δ2HVSMOW values of precipitation and rainfall rates. We term this approach the algorithmic-isotopic categorisation of rainfall, and we were able to identify higher rainout and/or lower rainout periods during all events in this study. We conclude that algorithmic-isotopic categorisation of rainfall can enable users to distinguish between tropospheric vapour masses having relatively high rainout (typically with brightband rain and that commonly are ARs) and vapour masses having lower rainout (commonly with non-brightband rain).
","language":"English","publisher":"International Meteorological Institute","publisherLocation":"Stockholm, Sweden","doi":"10.3402/tellusb.v67.28574","usgsCitation":"Coplen, T.B., Paul J. Neiman, Allen B. White, and Ralph, F.M., 2015, Categorisation of northern California rainfall for periods with and without a radar brightband using stable isotopes and a novel automated precipitation collector: Tellus, Series A: Dynamic Meteorology and Oceanography, v. 67, p. 1-48, https://doi.org/10.3402/tellusb.v67.28574.","productDescription":"48 p.","startPage":"1","endPage":"48","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069509","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":471616,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3402/tellusb.v67.28574","text":"Publisher Index Page"},{"id":311949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":311948,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.3402/tellusb.v67.28574"}],"country":"United States","state":"California","otherGeospatial":"Northern California: Bodega Bay, Cazadero, Alta and Shasta Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.05837631225586,\n 38.33600115904974\n ],\n [\n -123.04987907409668,\n 38.33566453597907\n ],\n [\n -123.03897857666014,\n 38.32825843276099\n ],\n [\n -123.03091049194336,\n 38.316339750609366\n ],\n [\n -123.03245544433592,\n 38.31283784445829\n ],\n [\n -123.05322647094727,\n 38.29936739855925\n ],\n [\n -123.05940628051756,\n 38.2978854967878\n ],\n [\n -123.0743408203125,\n 38.31088478487631\n ],\n [\n -123.07863235473634,\n 38.32334305552793\n ],\n [\n -123.07502746582031,\n 38.33034568390225\n ],\n [\n -123.05940628051756,\n 38.33640510467016\n ],\n [\n -123.05837631225586,\n 38.33600115904974\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.09742927551268,\n 38.53829709805414\n ],\n [\n -123.07588577270506,\n 38.54010974905484\n ],\n [\n -123.07228088378906,\n 38.526211590726355\n ],\n [\n -123.09021949768065,\n 38.52211548602912\n ],\n [\n -123.09863090515137,\n 38.53796141693008\n ],\n [\n -123.09742927551268,\n 38.53829709805414\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.42185592651367,\n 40.720331161623065\n ],\n [\n -122.43988037109374,\n 40.71278477995827\n ],\n [\n -122.43095397949219,\n 40.7066689811733\n ],\n [\n -122.4151611328125,\n 40.71668818761883\n ],\n [\n -122.4203109741211,\n 40.72072146844198\n ],\n [\n -122.42185592651367,\n 40.720331161623065\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.82300186157227,\n 39.21363530066982\n ],\n [\n -120.8027458190918,\n 39.217093273094804\n ],\n [\n -120.79673767089844,\n 39.1987374726247\n ],\n [\n -120.81768035888672,\n 39.1955446697901\n ],\n [\n -120.82471847534181,\n 39.21297028644833\n ],\n [\n -120.82300186157227,\n 39.21363530066982\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"67","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-01-01","publicationStatus":"PW","scienceBaseUri":"5662c745e4b06a3ea36c67b1","contributors":{"authors":[{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":581361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paul J. 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By September 16, 2013, as much as 12 to 20 inches of rain had fallen in the foothills of the Front Range of the Southern Rocky Mountains and adjacent plains near Colorado Springs, Colorado, north to the Colorado-Wyoming border. The rain caused major flooding during September 9–18, 2013, in a large part of the South Platte River Basin and in the Fountain Creek Basin. The floods resulted in several fatalities, more than 31,000 damaged or destroyed structures, and an estimated 3 billion dollars in damages. The U.S. Geological Survey (USGS) documented peak stage, streamflow, or both from the flood event for 80 sites located on selected rivers and streams in the South Platte River and Fountain Creek Basins and on the Platte River in Nebraska. The majority of flood-peak streamflows occurred on September 12 or 13, 2013, coinciding with the period of maximum rainfall. The flood resulted in new record peak streamflows at 17 streamgages having at least 10 years of record; 13 in the South Platte River Basin and 4 in the Fountain Creek Basin.
\nFlooding in the South Platte River Basin was primarily contained to select streams in Aurora and the Denver metropolitan area, most of the mountain tributaries joining the main stem South Platte River from Denver to Greeley, and in the main stem South Platte River from Denver to the Colorado-Nebraska State line. In Aurora, where about 15 inches of rain fell, streamflow peaked at 5,470 cubic feet per second (ft3/s) in Toll Gate Creek, a tributary to Sand Creek. Downstream from Aurora near the confluence with the South Platte River, Sand Creek peaked at 14,900 ft3/s, which was the highest streamflow since at least 1992, but less than the peak of 25,500 ft3/s in 1957 that occurred 4 miles upstream from the mouth. Flood-peak streamflows in the Denver metropolitan area were generally below historic records. The peak of 3,930 ft3/s on September 12 at the State of Colorado streamgage South Platte River at Denver ranked 59 out of 116 peaks and was less than the 1965 peak of 40,300 ft3/s. Ten of the 13 streamgages in the South Platte River Basin with new record peak streamflows were located on the mountain tributaries; Bear Creek, Fourmile Creek, Boulder Creek, St. Vrain Creek, the Big Thompson River, and the Cache la Poudre River. A daily average streamflow of 8,910 ft3/s on September 13 in Boulder Creek at the confluence with St. Vrain Creek was more than twice the previous instantaneous peak of 4,410 ft3/s from 1938. The USGS calculated a peak streamflow of 23,800 ft3/s for the St. Vrain Creek at Lyons; the highest streamflow on record at this State of Colorado streamgage (122 years of record) is 10,500 ft3/s from 1941. A peak streamflow of 16,200 ft3/s was calculated for the Big Thompson River at mouth of canyon near Drake streamgage, which is the second highest peak in 90 years of record and about one-half the magnitude of the peak of 31,200 ft3/s from July 31, 1976. A streamflow of 60,000 ft3/s in the South Platte River at Fort Morgan (September 15, 2013) suggests that a new record streamflow occurred in the main stem in the Greeley area, about 45 miles upstream from Fort Morgan. The current peak of record at a State of Colorado streamgage at Kersey, about 6.5 miles downstream from Greeley, is 31,500 ft3/s from 1973. Given that there was minimal inflow between Kersey and Fort Morgan, the USGS estimates there was probably at least 60,000 ft3/s at Kersey, which would be almost double the peak streamflow of record from 1973.
\nFlooding in the Fountain Creek Basin was primarily contained to Fountain Creek from southern Colorado Springs to its confluence with the Arkansas River in Pueblo, in lower Monument Creek, and in several mountain tributaries. New record peak streamflows occurred at four mountain tributary streamgages having at least 10 years of record; Bear Creek, Cheyenne Creek, Rock Creek, and Little Fountain Creek. Five streamgages with at least 10 years of record in a 32-mile reach of Fountain Creek extending from Colorado Springs to Piñon had peak streamflows in the top five for the period of record. A peak of 15,300 ft3/s at Fountain Creek near Fountain was the highest streamflow recorded in the Fountain Creek Basin during the September 2013 event and ranks the third highest peak in 46 years. Near the mouth of the basin, a peak of 11,800 ft3/s in Pueblo was only the thirteenth highest annual peak in 74 years. A new Colorado record for daily rainfall of 11.85 inches was recorded at a USGS rain gage in the Little Fountain Creek Basin on September 12, 2013.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA, 20192
http://water.usgs.gov/osw/
Fish consumption advisories are used to inform citizens in the United States about noncommercial game fish with hazardous levels of methylmercury (MeHg). The US Environmental Protection Agency (USEPA) suggests issuing a fish consumption advisory when concentrations of MeHg in fish exceed a human health screening value of 300 ng/g. However, states have authority to develop their own systems for issuing fish consumption advisories for MeHg. Five states in the south central United States (Arkansas, Louisiana, Mississippi, Oklahoma, and Texas) issue advisories for the general human population when concentrations of MeHg exceed 700 ng/g to 1000 ng/g. The objective of the present study was to estimate the increase in fish consumption advisories that would occur if these states followed USEPA recommendations. The authors used the National Descriptive Model of Mercury in Fish to estimate the mercury concentrations in 5 size categories of largemouth bass–equivalent fish at 766 lentic and lotic sites within the 5 states. The authors found that states in this region have not issued site‐specific fish consumption advisories for most of the water bodies that would have such advisories if USEPA recommendations were followed. One outcome of the present study may be to stimulate discussion between scientists and policy makers at the federal and state levels about appropriate screening values to protect the public from the health hazards of consuming MeHg‐contaminated game fish.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.3185","usgsCitation":"Adams, K., Drenner, R.W., Chumchal, M.M., and Donato, D.I., 2015, Disparity between state fish consumption advisory systems for methylmercury and US Environmental Protection Agency recommendations: A case study of the South Central United States: Environmental Toxicology and Chemistry, v. 35, no. 1, p. 247-251, https://doi.org/10.1002/etc.3185.","productDescription":"5 p.","startPage":"247","endPage":"251","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064147","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":318156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.330078125,\n 30.391830328088137\n ],\n [\n -88.505859375,\n 32.0639555946604\n ],\n [\n -88.04443359375,\n 35.02999636902566\n ],\n [\n -90.17578124999999,\n 35.10193405724606\n ],\n [\n -89.6484375,\n 36.01356058518153\n ],\n [\n -90.3515625,\n 36.03133177633187\n ],\n [\n -90.02197265625,\n 36.36822190085111\n ],\n [\n -90.263671875,\n 36.527294814546245\n ],\n [\n -94.59228515625,\n 36.527294814546245\n ],\n [\n -94.6142578125,\n 37.020098201368114\n ],\n [\n -103.0078125,\n 37.03763967977139\n ],\n [\n -103.02978515625,\n 36.527294814546245\n ],\n [\n -103.1396484375,\n 31.970803930433096\n ],\n [\n -106.69921875,\n 31.98944183792288\n ],\n [\n -106.5673828125,\n 31.89621446335144\n ],\n [\n -104.87548828125,\n 30.486550842588485\n ],\n [\n -104.58984375,\n 29.668962525992505\n ],\n [\n -103.1396484375,\n 28.92163128242129\n ],\n [\n -102.67822265625,\n 29.80251790576445\n ],\n [\n -102.23876953125,\n 29.80251790576445\n ],\n [\n -101.337890625,\n 29.7453016622136\n ],\n [\n -100.5029296875,\n 28.844673680771795\n ],\n [\n -99.7119140625,\n 27.586197857692664\n ],\n [\n -99.49218749999999,\n 27.078691552927534\n ],\n [\n -98.96484375,\n 26.352497858154024\n ],\n [\n -98.02001953125,\n 26.05678288577881\n ],\n [\n -97.36083984375,\n 25.780107118422244\n ],\n [\n -97.05322265625,\n 25.958044673317843\n ],\n [\n -97.294921875,\n 27.01998400798257\n ],\n [\n -96.78955078125,\n 27.955591004642553\n ],\n [\n -94.85595703125,\n 29.19053283229458\n ],\n [\n -93.8232421875,\n 29.554345125748267\n ],\n [\n -93.2080078125,\n 29.649868677972304\n ],\n [\n -92.28515625,\n 29.439597566602902\n ],\n [\n -92.0654296875,\n 29.439597566602902\n ],\n [\n -91.7578125,\n 29.286398892934763\n ],\n [\n -91.0986328125,\n 28.97931203672246\n ],\n [\n -89.71435546875,\n 28.998531814051795\n ],\n [\n -88.92333984375,\n 28.9023972285585\n ],\n [\n -88.330078125,\n 30.391830328088137\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-01","publicationStatus":"PW","scienceBaseUri":"56c6f93fe4b0946c6524072d","contributors":{"authors":[{"text":"Adams, Kimberly","contributorId":167057,"corporation":false,"usgs":false,"family":"Adams","given":"Kimberly","email":"","affiliations":[{"id":24605,"text":"Biology Department, Texas Christian University, Fort Worth, Texas, USA","active":true,"usgs":false}],"preferred":false,"id":620875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drenner, Ray W.","contributorId":46407,"corporation":false,"usgs":true,"family":"Drenner","given":"Ray","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":620876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chumchal, Matthew M.","contributorId":84659,"corporation":false,"usgs":true,"family":"Chumchal","given":"Matthew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":620877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donato, David I. 0000-0002-5412-0249 didonato@usgs.gov","orcid":"https://orcid.org/0000-0002-5412-0249","contributorId":2234,"corporation":false,"usgs":true,"family":"Donato","given":"David","email":"didonato@usgs.gov","middleInitial":"I.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":620874,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159787,"text":"70159787 - 2015 - Persistent U(IV) and U(VI) following in-situ recovery (ISR) mining of a sandstone uranium deposit, Wyoming, USA","interactions":[],"lastModifiedDate":"2018-09-04T16:23:32","indexId":"70159787","displayToPublicDate":"2015-11-23T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Persistent U(IV) and U(VI) following in-situ recovery (ISR) mining of a sandstone uranium deposit, Wyoming, USA","docAbstract":"Drill-core samples from a sandstone-hosted uranium (U) deposit in Wyoming were characterized to determine the abundance and distribution of uranium following in-situ recovery (ISR) mining with oxygen- and carbon dioxide-enriched water. Concentrations of uranium, collected from ten depth intervals, ranged from 5 to 1920 ppm. A composite sample contained 750 ppm uranium with an average oxidation state of 54% U(VI) and 46% U(IV). Scanning electron microscopy (SEM) indicated rare high uranium (∼1000 ppm U) in spatial association with P/Ca and Si/O attributed to relict uranium minerals, possibly coffinite, uraninite, and autunite, trapped within low permeability layers bypassed during ISR mining. Fission track analysis revealed lower but still elevated concentrations of U in the clay/silica matrix and organic matter (several 10 s ppm) and yet higher concentrations associated with Fe-rich/S-poor sites, likely iron oxides, on altered chlorite or euhedral pyrite surfaces (but not on framboidal pyrite). Organic C (<1.62%), total S (<0.31%), and P (<0.03%) were in low abundance relative to the overall bulk composition. Microbial community analysis showed a diverse group of bacteria present with a wide range of putative metabolisms, and provides evidence for a variety of redox microenvironments co-existing in core samples. Although the uranium minerals persisting in low permeability areas in association with organic carbon were less affected by oxidizing solutions during mining, the likely sequestration of uranium within labile iron oxides following mining and sensitivity to changes in redox conditions requires careful attention during groundwater restoration.
\n\n
A groundwater-flow model was developed for the Bad River Watershed and surrounding area by using the U.S. Geological Survey (USGS) finite-difference code MODFLOW-NWT. The model simulates steady-state groundwater-flow and base flow in streams by using the streamflow routing (SFR) package. The objectives of this study were to: (1) develop an improved understanding of the groundwater-flow system in the Bad River Watershed at the regional scale, including the sources of water to the Bad River Band of Lake Superior Chippewa Reservation (Reservation) and groundwater/surface-water interactions; (2) provide a quantitative platform for evaluating future impacts to the watershed, which can be used as a starting point for more detailed investigations at the local scale; and (3) identify areas where more data are needed. This report describes the construction and calibration of the groundwater-flow model that was subsequently used for analyzing potential locations for the collection of additional field data, including new observations of water-table elevation for refining the conceptualization and corresponding numerical model of the hydrogeologic system.
\nThe study area can be conceptually divided into three primary hydrogeologic environments. The first encompasses the southern uplands with relatively low topographic relief, where groundwater-flow is unconfined and occurs primarily in sandy till and glacial outwash overlying Archean-aged crystalline bedrock. The second includes a transitional area of higher topographic relief and shallow depth to bedrock, in the vicinity of ridges formed by steeply dipping, early-Proterozoic aged metasedimentary units of the Marquette Range Supergroup (including the Ironwood Formation), and late-Proterozoic igneous units associated with the Midcontinent Rift System (MRS). Groundwater-flow in this area likely occurs primarily through connected networks of bedrock fractures that are not well characterized, and also in isolated pockets of Quaternary deposits. The third and last hydrogeologic environment includes lowlands along Lake Superior where a deep sandstone aquifer is confined by thick deposits of clay-rich till.
\nModel input was compiled by using both published and unpublished data. Constant flux boundary conditions for the model perimeter were developed from a regional analytic element model described in appendix 1 of this report. Pumping from 26 high-capacity wells within the model area was included. The SFR stream network was developed from the National Hydrography Dataset (NHDPlus Version 2) and hydrography from the Wisconsin Department of Natural Resources (WDNR). Hydraulic conductivity values were determined for each model cell by interpolation from a network of pilot points, within zones representing major hydrogeologic units.
\nRecharge to the groundwater system was estimated on a cell-by-cell basis by using the Soil Water Balance code (SWB), with gridded daily temperature and precipitation data for the period 1980–2011, and GIS coverages of soil and land-surface conditions. Estimated recharge varies considerably, following spatial patterns in the precipitation and soil hydrologic group inputs. The lowest recharge values occur in the Superior lowlands, whereas the highest values occur in the upland areas, especially those underlain by sandy soils, and in the vicinity of bedrock hills.
\nThe model was calibrated to groundwater-levels and base flows obtained from the USGS National Water Information System (NWIS) database, and groundwater-levels obtained from the WDNR and Band River Band well-construction databases. Calibration was performed via nonlinear regression by using the parameter-estimation software suite PEST. Groundwater levels and base-flow observations in the calibration dataset were well simulated by the calibrated model, with reasonable values of hydraulic conductivity. The pilot-point parameters that were most constrained by observations during model calibration coincided with the locations containing the most wells (head observations)—especially the population centers of Ashland, Mellen, and other communities along the major highway corridors.
\nResults from the calibrated model illustrate differences in the nature of groundwater-flow within the watershed. In the southern part of the watershed, where bedrock is shallow, groundwater flow paths are relatively short, extending from local recharge areas to adjacent first and second-order streams. In contrast, laterally continuous deposits of clay-rich till covering the Superior Lowlands isolate most smaller streams from the sandstone aquifer, allowing for longer flow paths toward larger streams such as the Bad, Marengo, and White Rivers. Approximately three-quarters of all first-order stream cells were dry in the Superior Lowlands, compared to only half of first-order stream cells in the southern bedrock uplands.
\nThe model was used to delineate the groundwatershed for the Bad and Kakagon Rivers. “Groundwatershed” is defined as the area contributing groundwater discharge to one of these streams and their tributaries. The groundwatershed was found to align closely with the surface-watershed, with the most notable exception occurring along the southwestern half of Birch Hill, where surface water drains southwest towards the Potato River, and groundwater flows north and east towards Lake Superior. Similarly, the contributing area of groundwater-flow to the Reservation was delineated. Results indicate the off-Reservation groundwater contributing area to be limited in comparison to the extent of the watershed, extending southward into the highlands underlain by MRS igneous rock units, but not further into the area underlain by the Marquette Range Supergroup.
\nStable isotope samples were collected from 54 wells within the watershed, to investigate sources of groundwater. Oxygen-18 (δ 18O) values lower than -13.0 per mil were documented in the sampling, and likely indicate the presence of recharge water from the last glacial period (>9,500 years old) beneath the northern portion of the Reservation, in the vicinity of Odanah, Wisconsin.
\nFinally, a new data-worth analysis of potential new monitoring-well locations was performed by using the model. The relative worth of new measurements was evaluated based on their ability to increase confidence in model predictions of groundwater levels and base flows at 35 locations, under the condition of a proposed open-pit iron mine. Results of the new data-worth analysis, and other inputs and outputs from the Bad River model, are available through an online dynamic web mapping service at (http://wim.usgs.gov/badriver/).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155162","collaboration":"Prepared in cooperation with the Bad River Band of Lake Superior Chippewa; U.S. Bureau of Indian Affairs","usgsCitation":"Leaf, A.T., Fienen, M.N., Hunt, R.J., and Buchwald, C.A., 2015, Groundwater/Surface-Water Interactions in the Bad\n River Watershed, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2015–5162, 110 p., https://dx.doi.org/10.3133/sir20155162.","productDescription":"viii, 110 p.","numberOfPages":"122","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061535","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":311584,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5162/coverthb.jpg"},{"id":311585,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5162/sir20155162.pdf","text":"Report","size":"24.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015=5162"},{"id":332726,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7Z0368H","text":"MODFLOW-NWT model used to evaluate groundwater/surface-water interactions in the Bad River Watershed, Wisconsin"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Bad River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.06842041015625,\n 46.36777895261494\n ],\n [\n -91.06842041015625,\n 46.76996843356982\n ],\n [\n -90.43121337890625,\n 46.76996843356982\n ],\n [\n -90.43121337890625,\n 46.36777895261494\n ],\n [\n -91.06842041015625,\n 46.36777895261494\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wisconsin Water Science Center
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8505 Research W
Middleton, WI USA 53562
http://wi.water.usgs.gov/
In this study, water and whole rock samples from hydraulically fractured wells in the Marcellus Shale (Middle Devonian), and water from conventional wells producing from Upper Devonian sandstones were analyzed for lithium concentrations and isotope ratios (δ7Li). The distribution of lithium concentrations in different mineral groups was determined using sequential extraction. Structurally bound Li, predominantly in clays, accounted for 75-91 wt. % of total Li, whereas exchangeable sites and carbonate cement contain negligible Li (< 3%). Up to 20% of the Li is present in the oxidizable fraction (organic matter and sulfides). The δ7Li values for whole rock shale in Greene Co., Pennsylvania, and Tioga Co., New York, ranged from -2.3 to + 4.3‰, similar to values reported for other shales in the literature. The δ7Li values in shale rocks with stratigraphic depth record progressive weathering of the source region; the most weathered and clay-rich strata with isotopically light Li are found closest to the top of the stratigraphic section. Diagenetic illite-smectite transition could also have partially affected the bulk Li content and isotope ratios of the Marcellus Shale.
\nIn Greene Co., southwest Pennsylvania, the Upper Devonian sandstone formation waters have δ7Li values of + 14.6 ± 1.2 (2SD, n = 25), and are distinct from Marcellus Shale formation waters which have δ7Li of + 10.0 ± 0.8 (2SD, n = 12). These two formation waters also maintain distinctive 87Sr/86Sr ratios suggesting hydrologic separation between these units. Applying temperature-dependent illitilization model to Marcellus Shale, we found that Li concentration in clay minerals increased with Li concentration in pore fluid during diagenetic illite-smectite transition. Samples from north central PA show a much smaller range in both δ7Li and 87Sr/86Sr than in southwest Pennsylvania. Spatial variations in Li and δ7Li values show that Marcellus formation waters are not homogeneous across the Appalachian Basin. Marcellus formation waters in the northeastern Pennsylvania portion of the basin show a much smaller range in both δ7Li and 87Sr/86Sr, suggesting long term, cross-formational fluid migration in this region. Assessing the impact of potential mixing of fresh water with deep formation water requires establishment of a geochemical and isotopic baseline in the shallow, fresh water aquifers, and site specific characterization of formation water, followed by long-term monitoring, particularly in regions of future shale gas development.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2015.11.003","usgsCitation":"Phan, T.T., Capo, R.C., Stewart, B.W., Macpherson, G., Rowan, E.L., and Hammack, R.W., 2015, Factors controlling Li concentration and isotopic composition in formation waters and host rocks of Marcellus Shale, Appalachian Basin: Chemical Geology, v. 420, p. 162-179, https://doi.org/10.1016/j.chemgeo.2015.11.003.","productDescription":"18 p.","startPage":"162","endPage":"179","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068352","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":471632,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1397544","text":"Publisher Index Page"},{"id":311641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Pennsylvania","county":"Greene 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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"565438a9e4b071e7ea53d492","contributors":{"authors":[{"text":"Phan, Thai T.","contributorId":150016,"corporation":false,"usgs":false,"family":"Phan","given":"Thai","email":"","middleInitial":"T.","affiliations":[{"id":17887,"text":"National Energy Technology Laboratory, Department of Energy","active":true,"usgs":false}],"preferred":false,"id":580427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Capo, Rosemary C","contributorId":150015,"corporation":false,"usgs":false,"family":"Capo","given":"Rosemary","email":"","middleInitial":"C","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":580426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Brian W.","contributorId":150017,"corporation":false,"usgs":false,"family":"Stewart","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":580428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Macpherson, Gwen","contributorId":150018,"corporation":false,"usgs":false,"family":"Macpherson","given":"Gwen","email":"","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":580429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rowan, Elisabeth L. 0000-0001-5753-6189 erowan@usgs.gov","orcid":"https://orcid.org/0000-0001-5753-6189","contributorId":2075,"corporation":false,"usgs":true,"family":"Rowan","given":"Elisabeth","email":"erowan@usgs.gov","middleInitial":"L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":580425,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hammack, Richard W.","contributorId":150019,"corporation":false,"usgs":false,"family":"Hammack","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":17887,"text":"National Energy Technology Laboratory, Department of Energy","active":true,"usgs":false}],"preferred":false,"id":580430,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70159538,"text":"fs20153065 - 2015 - Development of an assessment tool for agricultural best management practice implementation in the Great Lakes Restoration Initiative priority watersheds—Upper East River, tributary to Green Bay, Wisconsin","interactions":[],"lastModifiedDate":"2026-06-29T18:53:33.514072","indexId":"fs20153065","displayToPublicDate":"2015-11-19T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-3065","displayTitle":"Development of an Assessment Tool for Agricultural Best Management Practice Implementation in the Great Lakes Restoration Initiative Priority Watersheds—Upper East River, Tributary to Green Bay, Wisconsin","title":"Development of an assessment tool for agricultural best management practice implementation in the Great Lakes Restoration Initiative priority watersheds—Upper East River, tributary to Green Bay, Wisconsin","docAbstract":"The Great Lakes face a number of serious challenges that cause damage to water quality, habitat, ecology, and coastal health. Excess nutrients from point and nonpoint sources have a history of causing harmful algal blooms (HABs); since the late 1990s, a resurgence of HABs have forced beach closures and resulted in water quality impairments across the Great Lakes. Studies increasingly point to phosphorus (P) runoff from agricultural lands as the cause of these HABs. In 2010, the Great Lakes Restoration Initiative (GLRI) was launched to revitalize the Great Lakes. The GLRI aims to address the challenges facing the Great Lakes and provide a framework for restoration and protection. As part of this effort, the Priority Watersheds Work Group (PWWG), cochaired by the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Agriculture-Natural Resources Conservation Service (USDA–NRCS), is targeting Priority Watersheds (PWs) to reduce the amount of P reaching the Great Lakes. Within the PWs, USDA–NRCS identifies small-scale subbasins with high concentrations of agriculture for coordinated nutrient reduction efforts and enhanced monitoring and modeling. The USDA–NRCS supplies financial and/or technical assistance to producers to install or implement best management practices (BMPs) to lessen the negative effects of agriculture to water quality; additional funding is provided by the GLRI through USDA–NRCS to saturate the small-scale subbasins with BMPs. The watershed modeling component, introduced in this fact sheet, assesses the effectiveness of USDA–NRCS funded BMPs, and nutrient reductions because of GLRI or other funding programs are differentiated. Modeling scenarios consider BMPs that have already been applied and those planned to be implemented across the small-scale subbasins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153065","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture-Natural Resources Conservation Service","usgsCitation":"Merriman, K.R., 2015, Development of an assessment tool for agricultural best management practice implementation in the Great Lakes Restoration Initiative priority watersheds—Upper East River, tributary to Green Bay, Wisconsin: U.S. Geological Survey Fact Sheet 2015–3065, 6 p., https://dx.doi.org/10.3133/fs20153065.","productDescription":"6 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070173","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":311076,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.er.usgs.gov/publication/fs20153067","text":"Fact Sheet 2015-3067","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3065"},{"id":506276,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_103689.htm","linkFileType":{"id":5,"text":"html"}},{"id":311074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3065/fs20153065.pdf","text":"Report","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3065"},{"id":311073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3065/coverthb.jpg"},{"id":311075,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.er.usgs.gov/publication/fs20153066","text":"Fact Sheet 2015-3066","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3065"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Upper East River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.6376953125,\n 44.21764696919354\n ],\n [\n -88.6376953125,\n 44.66865287227321\n ],\n [\n -87.703857421875,\n 44.66865287227321\n ],\n [\n -87.703857421875,\n 44.21764696919354\n ],\n [\n -88.6376953125,\n 44.21764696919354\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave.
Urbana, IL 61801
http://il.water.usgs.gov
Approximately two thirds of migratory songbirds in eastern North America negotiate the Gulf of Mexico (GOM), where inclement weather coupled with no refueling or resting opportunities can be lethal. However, decisions made when navigating such features and their consequences remain largely unknown due to technological limitations of tracking small animals over large areas. We used automated radio telemetry to track three songbird species (Red-eyed Vireo, Swainson’s Thrush, Wood Thrush) from coastal Alabama to the northern Yucatan Peninsula (YP) during fall migration. Detecting songbirds after crossing ∼1,000 km of open water allowed us to examine intrinsic (age, wing length, fat) and extrinsic (weather, date) variables shaping departure decisions, arrival at the YP, and crossing times. Large fat reserves and low humidity, indicative of beneficial synoptic weather patterns, favored southward departure across the Gulf. Individuals detected in the YP departed with large fat reserves and later in the fall with profitable winds, and flight durations (mean = 22.4 h) were positively related to wind profit. Age was not related to departure behavior, arrival, or travel time. However, vireos negotiated the GOM differently than thrushes, including different departure decisions, lower probability of detection in the YP, and longer crossing times. Defense of winter territories by thrushes but not vireos and species-specific foraging habits may explain the divergent migratory behaviors. Fat reserves appear extremely important to departure decisions and arrival in the YP. As habitat along the GOM is degraded, birds may be limited in their ability to acquire fat to cross the Gulf.
","language":"English","publisher":"National Academy of Sciences of the United States of America","publisherLocation":"Washington, D.C.","doi":"10.1073/pnas.1503381112","usgsCitation":"Deppe, J.L., Ward, M.P., Bolus, R.T., Diehl, R.H., Celis-Murillo, A., Zenzal, T., Moore, F.R., Benson, T.J., Smolinsky, J., Schofield, L.N., Enstrom, D.A., Paxton, E., Bohrer, G., Beveroth, T.A., Raim, A., Obringer, R.L., Delaney, D., and Cochran, W.W., 2015, Fat, weather, and date affect migratory songbirds’ departure decisions, routes, and time it takes to cross the Gulf of Mexico: Proceedings of the National Academy of Sciences, v. 112, no. 46, p. E6331-E6338, https://doi.org/10.1073/pnas.1503381112.","startPage":"E6331","endPage":"E6338","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063912","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":471638,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://doi.org/10.1073/pnas.1503381112","text":"External Repository"},{"id":326420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.395263671875,\n 18.542116654448996\n ],\n [\n -91.395263671875,\n 21.95323560036448\n ],\n [\n -86.319580078125,\n 21.95323560036448\n ],\n [\n -86.319580078125,\n 18.542116654448996\n ],\n [\n 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IL","active":true,"usgs":false}],"preferred":false,"id":645277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ward, Michael P.","contributorId":173620,"corporation":false,"usgs":false,"family":"Ward","given":"Michael","email":"","middleInitial":"P.","affiliations":[{"id":27257,"text":"Dept of Nat Resources and Env Sciences, University of Illinois, Urbana, IL","active":true,"usgs":false}],"preferred":false,"id":645278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bolus, Rachel T. rbolus@usgs.gov","contributorId":173621,"corporation":false,"usgs":true,"family":"Bolus","given":"Rachel","email":"rbolus@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":false,"id":645280,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diehl, Robert H. 0000-0001-9141-1734 rhdiehl@usgs.gov","orcid":"https://orcid.org/0000-0001-9141-1734","contributorId":3396,"corporation":false,"usgs":true,"family":"Diehl","given":"Robert","email":"rhdiehl@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":645276,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Celis-Murillo, A.","contributorId":57991,"corporation":false,"usgs":true,"family":"Celis-Murillo","given":"A.","affiliations":[],"preferred":false,"id":645279,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zenzal, Theodore J. 0000-0001-7342-1373","orcid":"https://orcid.org/0000-0001-7342-1373","contributorId":173622,"corporation":false,"usgs":false,"family":"Zenzal","given":"Theodore J.","affiliations":[{"id":27258,"text":"Dept of Biological Sciences, The University of Southern Mississippi, Hattiesburg, MS","active":true,"usgs":false}],"preferred":false,"id":645281,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moore, Frank R.","contributorId":54582,"corporation":false,"usgs":false,"family":"Moore","given":"Frank","email":"","middleInitial":"R.","affiliations":[{"id":12981,"text":"Department of Biological Sciences, University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":645282,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Benson, Thomas J.","contributorId":173625,"corporation":false,"usgs":false,"family":"Benson","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":27259,"text":"Illinois Natural History Survey, University of Illinois, Champaign, IL 61820","active":true,"usgs":false}],"preferred":false,"id":645288,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smolinsky, Jaclyn A.","contributorId":9175,"corporation":false,"usgs":true,"family":"Smolinsky","given":"Jaclyn A.","affiliations":[],"preferred":false,"id":645283,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Schofield, Lynn N.","contributorId":173623,"corporation":false,"usgs":false,"family":"Schofield","given":"Lynn","email":"","middleInitial":"N.","affiliations":[{"id":27256,"text":"Dept of Biological Sciences, Eastern Illinois University, Charleston, IL","active":true,"usgs":false}],"preferred":false,"id":645284,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Enstrom, David A.","contributorId":173624,"corporation":false,"usgs":false,"family":"Enstrom","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":27259,"text":"Illinois Natural History Survey, University of Illinois, Champaign, IL 61820","active":true,"usgs":false}],"preferred":false,"id":645285,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Paxton, Eben H. 0000-0001-5578-7689 epaxton@usgs.gov","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":438,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben H.","email":"epaxton@usgs.gov","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":false,"id":645286,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Bohrer, Gil","contributorId":66569,"corporation":false,"usgs":true,"family":"Bohrer","given":"Gil","affiliations":[],"preferred":false,"id":645287,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Beveroth, Tara A.","contributorId":173626,"corporation":false,"usgs":false,"family":"Beveroth","given":"Tara","email":"","middleInitial":"A.","affiliations":[{"id":27259,"text":"Illinois Natural History Survey, University of Illinois, Champaign, IL 61820","active":true,"usgs":false}],"preferred":false,"id":645289,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Raim, Arlo","contributorId":173635,"corporation":false,"usgs":false,"family":"Raim","given":"Arlo","email":"","affiliations":[],"preferred":false,"id":645300,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Obringer, Renee L.","contributorId":173627,"corporation":false,"usgs":false,"family":"Obringer","given":"Renee","email":"","middleInitial":"L.","affiliations":[{"id":27260,"text":"Dept of Civil, Env and Geodetic Engineering, Ohio State University, Columbus, OH 43210","active":true,"usgs":false}],"preferred":false,"id":645290,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Delaney, David","contributorId":75444,"corporation":false,"usgs":true,"family":"Delaney","given":"David","affiliations":[],"preferred":false,"id":645291,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Cochran, William W.","contributorId":173628,"corporation":false,"usgs":false,"family":"Cochran","given":"William","email":"","middleInitial":"W.","affiliations":[{"id":27259,"text":"Illinois Natural History Survey, University of Illinois, Champaign, IL 61820","active":true,"usgs":false}],"preferred":false,"id":645292,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70159678,"text":"70159678 - 2015 - Karst mapping in the United States: Past, present and future","interactions":[],"lastModifiedDate":"2017-04-14T10:20:04","indexId":"70159678","displayToPublicDate":"2015-11-17T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1727,"text":"GSA Special Papers","active":true,"publicationSubtype":{"id":10}},"title":"Karst mapping in the United States: Past, present and future","docAbstract":"The earliest known comprehensive karst map of the entire USA was published by Stringfield and LeGrand (1969), based on compilations of William E. Davies of the U.S. Geological Survey (USGS). Various versions of essentially the same map have been published since. The USGS recently published new digital maps and databases depicting the extent of known karst, potential karst, and pseudokarst areas of the United States of America including Puerto Rico and the U.S. Virgin Islands (Weary and Doctor, 2014). These maps are based primarily on the extent of potentially karstic soluble rock types, and rocks with physical properties conducive to the formation of pseudokarst features. These data were compiled and refined from multiple sources at various spatial resolutions, mostly as digital data supplied by state geological surveys. The database includes polygons delineating areas with potential for karst and that are tagged with attributes intended to facilitate classification of karst regions. Approximately 18% of the surface of the fifty United States is underlain by significantly soluble bedrock. In the eastern United States the extent of outcrop of soluble rocks provides a good first-approximation of the distribution of karst and potential karst areas. In the arid western states, the extent of soluble rock outcrop tends to overestimate the extent of regions that might be considered as karst under current climatic conditions, but the new dataset encompasses those regions nonetheless. This database will be revised as needed, and the present map will be updated as new information is incorporated.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2015.2516(04)","usgsCitation":"Weary, D.J., and Doctor, D.H., 2015, Karst mapping in the United States: Past, present and future: GSA Special Papers, v. 516, p. 177-211, https://doi.org/10.1130/2015.2516(04).","productDescription":"15 p.","startPage":"177","endPage":"211","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062964","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":311430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"516","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564c4fbbe4b0ebfbef0d3459","contributors":{"authors":[{"text":"Weary, David J. 0000-0002-6115-6397 dweary@usgs.gov","orcid":"https://orcid.org/0000-0002-6115-6397","contributorId":545,"corporation":false,"usgs":true,"family":"Weary","given":"David","email":"dweary@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":580049,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":580050,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159668,"text":"70159668 - 2015 - Experimental infection of snakes with Ophidiomyces ophiodiicola causes pathological changes that typify snake fungal disease","interactions":[],"lastModifiedDate":"2018-02-01T16:58:33","indexId":"70159668","displayToPublicDate":"2015-11-17T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3819,"text":"mBio","active":true,"publicationSubtype":{"id":10}},"title":"Experimental infection of snakes with Ophidiomyces ophiodiicola causes pathological changes that typify snake fungal disease","docAbstract":"Snake fungal disease (SFD) is an emerging skin infection of wild snakes in eastern North America. The fungus Ophidiomyces ophiodiicola is frequently associated with the skin lesions that are characteristic of SFD, but a causal relationship between the fungus and the disease has not been established. We experimentally infected captive-bred corn snakes (Pantherophis guttatus) in the laboratory with pure cultures of O. ophiodiicola. All snakes in the infected group (n = 8) developed gross and microscopic lesions identical to those observed in wild snakes with SFD; snakes in the control group (n = 7) did not develop skin infections. Furthermore, the same strain of O. ophiodiicola used to inoculate snakes was recovered from lesions of all animals in the infected group, but no fungi were isolated from individuals in the control group. Monitoring progression of lesions throughout the experiment captured a range of presentations of SFD that have been described in wild snakes. The host response to the infection included marked recruitment of granulocytes to sites of fungal invasion, increased frequency of molting, and abnormal behaviors, such as anorexia and resting in conspicuous areas of enclosures. While these responses may help snakes to fight infection, they could also impact host fitness and may contribute to mortality in wild snakes with chronic O. ophiodiicola infection. This work provides a basis for understanding the pathogenicity of O. ophiodiicola and the ecology of SFD by using a model system that incorporates a host species that is easy to procure and maintain in the laboratory.
\nIMPORTANCE Skin infections in snakes, referred to as snake fungal disease (SFD), have been reported with increasing frequency in wild snakes in the eastern United States. While most of these infections are associated with the fungusOphidiomyces ophiodiicola, there has been no conclusive evidence to implicate this fungus as a primary pathogen. Furthermore, it is not understood why the infections affect different host populations differently. Our experiment demonstrates that O. ophiodiicola is the causative agent of SFD and can elicit pathological changes that likely impact fitness of wild snakes. This information, and the laboratory model we describe, will be essential in addressing unresolved questions regarding disease ecology and outcomes of O. ophiodiicola infection and helping to conserve snake populations threatened by the disease. The SFD model of infection also offers utility for exploring larger concepts related to comparative fungal virulence, host response, and host-pathogen evolution.
","language":"English","publisher":"American Society for Microbiology","doi":"10.1128/mBio.01534-15","usgsCitation":"Lorch, J.M., Lankton, J.S., Werner, K., Falendysz, E.A., McCurley, K., and Blehert, D., 2015, Experimental infection of snakes with Ophidiomyces ophiodiicola causes pathological changes that typify snake fungal disease: mBio, v. 6, no. 6, e01534-15; 9 p., https://doi.org/10.1128/mBio.01534-15.","productDescription":"e01534-15; 9 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068390","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":471645,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1128/mbio.01534-15","text":"Publisher Index Page"},{"id":311413,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"6","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564c4fb9e4b0ebfbef0d3455","contributors":{"authors":[{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":5565,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":579987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lankton, Julia S. 0000-0002-6843-4388 jlankton@usgs.gov","orcid":"https://orcid.org/0000-0002-6843-4388","contributorId":5888,"corporation":false,"usgs":true,"family":"Lankton","given":"Julia","email":"jlankton@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":579988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Werner, Katrien kwerner@usgs.gov","contributorId":149910,"corporation":false,"usgs":true,"family":"Werner","given":"Katrien","email":"kwerner@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":579989,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falendysz, Elizabeth A. 0000-0003-2895-8918 efalendysz@usgs.gov","orcid":"https://orcid.org/0000-0003-2895-8918","contributorId":127735,"corporation":false,"usgs":true,"family":"Falendysz","given":"Elizabeth","email":"efalendysz@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":579990,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCurley, Kevin","contributorId":149911,"corporation":false,"usgs":false,"family":"McCurley","given":"Kevin","email":"","affiliations":[{"id":17853,"text":"New England Reptile Distributors","active":true,"usgs":false}],"preferred":false,"id":579991,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Blehert, David S. 0000-0002-1065-9760 dblehert@usgs.gov","orcid":"https://orcid.org/0000-0002-1065-9760","contributorId":1816,"corporation":false,"usgs":true,"family":"Blehert","given":"David S.","email":"dblehert@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":579992,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70159236,"text":"ofr20151191 - 2015 - California State Waters map series — Offshore of Scott Creek, California","interactions":[],"lastModifiedDate":"2022-04-18T21:30:15.119324","indexId":"ofr20151191","displayToPublicDate":"2015-11-17T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1191","title":"California State Waters map series — Offshore of Scott Creek, California","docAbstract":"In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within the 3-nautical-mile limit of California’s State Waters. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data, acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow subsurface geology.
\nThe Offshore of Scott Creek map area is located in central California, on the Pacific Coast about 65 km south of San Francisco and 12 km northwest of Santa Cruz. The onshore part of the map area is sparsely populated; the only cultural center is Davenport, a small community with a population of less than 500. The hilly coastal area is virtually undeveloped, and a large percentage of coastal land is incorporated in open-space trusts. Agricultural land is almost entirely limited to coastal areas between the shoreline and the northwest-trending Santa Cruz Mountains, on Pleistocene alluvial fan deposits and the lowest emergent marine terrace. The Santa Cruz Mountains are part of the northwest-trending Coast Ranges that run roughly parallel to the San Andreas Fault Zone.
\nThe map area is cut by the San Gregorio Fault Zone, and it lies a few kilometers southwest of the San Andreas Fault Zone. Regional folding and uplift along the coast has been attributed to a westward bend in the San Andreas Fault Zone and also to right-lateral movement along the San Gregorio Fault Zone. The irregular coastal geomorphology of this area, which consists of low, rocky cliffs and sparse, small pocket beaches backed by low, terraced hills, is partly attributable to this ongoing deformation.
\nThe shelf in the map area is underlain by variable amounts (0 to 25 m) of upper Quaternary shelf, nearshore, and fluvial sediments deposited as sea level fluctuated in the late Pleistocene. The northernmost part of the map area is characterized by the presence of uplifted bedrock that has been linked to a local transpressional zone in the San Gregorio Fault Zone. This uplift, coupled with high wave energy, has resulted in little or no sediment cover in this area where exposures of bedrock are present at water depths of as much as 45 m. The thickest deposits of sediment lie offshore of both Davenport and the mouth of Waddell Creek.
\nCoastal sediment transport in the map area is characterized by north-to-south littoral transport of sediment that is derived mainly from streams in the Santa Cruz Mountains and also from local coastal erosion. Shoreline-change studies indicate long-term erosion; within the region between San Francisco and Davenport, the highest long- and short-term coastal-erosion rates occur north of the map area, just north of Point Año Nuevo. During the last approximately 300 years, as much as 18 million cubic yards (14 million cubic meters) of sand-sized sediment has been eroded from the area between Año Nuevo Island and Point Año Nuevo and transported south. Once widened by this pulse of eroded sediment, beaches in the map area are now narrowing as the tail end of this mass of sand progresses farther south.
\nThe Offshore of Scott Creek map area lies within the cold-temperate biogeographic zone that is called either the “Oregonian province” or the “northern California ecoregion.” This biogeographic province is maintained by the long-term stability of the southward-flowing California Current, the eastern limb of the North Pacific subtropical gyre that flows from southern British Columbia to Baja California. At its midpoint off central California, the California Current transports subarctic surface (0–500 m deep) waters southward, about 150 to 1,300 km from shore. Seasonal northwesterly winds that are, in part, responsible for the California Current, generate coastal upwelling. The south end of the Oregonian province is at Point Conception (about 320 km south of the map area), although its associated phylogeographic group of marine fauna may extend beyond to the area offshore of Los Angeles in southern California. The ocean off of central California has experienced a warming over the last 50 years that is driving an ecosystem shift away from the productive subarctic regime towards a depopulated subtropical environment.
\nSeafloor habitats in the Offshore of Scott Creek map area, which lie within the Shelf (continental shelf) megahabitat, range from significant rocky outcrops that support kelp-forest communities nearshore to rocky-reef communities in deeper water. Biological productivity resulting from coastal upwelling supports populations of Sooty Shearwater, Western Gull, Common Murre, Cassin’s Auklet, and many other less populous bird species. In addition, an observable recovery of Humpback and Blue Whales has occurred in the area; both species are dependent on coastal upwelling to provide nutrients. The large extent of exposed inner shelf bedrock supports large forests of “bull kelp,” which is well adapted for high-wave-energy environments. The kelp beds are the northernmost known habitat for the population of southern sea otters. Common fish species found in the kelp beds and rocky reefs include lingcod and various species of rockfish and greenling.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151191","usgsCitation":"Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series — Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015–1191, pamphlet 40 p., 10 sheets, scale 1:24,000, https://dx.doi.org/10.3133/ofr20151191.","productDescription":"Pamphlet: iv, 40 p.; 10 Sheets: 51 x 36 inches or less; Dataset; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-058155","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":438667,"rank":20,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CJ8BJW","text":"USGS data release","linkHelpText":"California State Waters Map Series Data Catalog--Offshore of Scott Creek, California"},{"id":310185,"rank":18,"type":{"id":28,"text":"Dataset"},"url":"https://dx.doi.org/10.5066/F7CJ8BJW","text":"Data Catalog","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1191 Data Catalog","linkHelpText":"The GIS data layers for this map are accessible from “Data Catalog—Offshore of Scott Creek, California,” which is part of California State Waters Map Series Data Catalog. Each GIS data file is listed with a brief description, a small image, and links to the metadata files and the downloadable data files."},{"id":310184,"rank":17,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_metadata.html","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1191 Metadata"},{"id":310104,"rank":15,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet10.pdf","text":"Sheet 10","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 10","linkHelpText":"Offshore and Onshore Geology and Geomorphology, Offshore of Scott Creek Map Area, California By Stephen R. Hartwell, Samuel Y. Johnson, and Clifton W. Davenport"},{"id":310103,"rank":14,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet9.pdf","text":"Sheet 9","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 9","linkHelpText":"Local (Offshore of Scott Creek Map Area) and Regional (Offshore from Pigeon Point to Southern Monterey Bay) Shallow-Subsurface Geology and Structure, California By Samuel Y. Johnson, Stephen R. Hartwell, Janet T. Watt, Ray W. Sliter, and Katherine L. Maier"},{"id":310102,"rank":13,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet8.pdf","text":"Sheet 8","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 8","linkHelpText":"Seismic-Reflection Profiles, Offshore of Scott Creek Map Area, California by Samuel Y. Johnson, Stephen R. Hartwell, and Ray W. Sliter"},{"id":310101,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet7.pdf","text":"Sheet 7","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 7","linkHelpText":"Potential Marine Benthic Habitats, Offshore of Scott Creek Map Area, California By Charles A. Endris, H. Gary Greene, Bryan E. Dieter, and Mercedes D. Erdey"},{"id":310093,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/of/2014/1214/","text":"Open-File Report 2014–1214","description":"Open-File Report 2014–1214","linkHelpText":"California State Waters Map Series—Offshore of Half Moon Bay, California, by Guy R. Cochrane and others"},{"id":399010,"rank":19,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_103676.htm"},{"id":310168,"rank":16,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Pamphlet"},{"id":310100,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet6.pdf","text":"Sheet 6","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 6","linkHelpText":"Ground-Truth Studies, Offshore of Scott Creek Map Area, California By Nadine E. Golden, Guy R. Cochrane, and Lisa M. Krigsman"},{"id":310099,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet5.pdf","text":"Sheet 5","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 5","linkHelpText":"Seafloor Character, Offshore of Scott Creek Map Area, California By Mercedes D. Erdey and Guy R. Cochrane"},{"id":310098,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet4.pdf","text":"Sheet 4","linkFileType":{"id":1,"text":"pdf"},"description":"OFR2015-1191 Sheet 4","linkHelpText":"Data Integration and Visualization, Offshore of Scott Creek Map Area, California By Peter Dartnell"},{"id":310097,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet3.pdf","text":"Sheet 3","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 3","linkHelpText":"Acoustic Backscatter, Offshore of Scott Creek Map Area, California By Peter Dartnell, Andrew C. Ritchie, David P. Finlayson, and Rikk G. Kvitek"},{"id":310096,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 2","linkHelpText":"Shaded-Relief Bathymetry, Offshore of Scott Creek Map Area, California By Peter Dartnell, Andrew C. Ritchie, David P. Finlayson, and Rikk G. Kvitek"},{"id":310095,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2015/1191/ofr20151191_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1191 Sheet 1","linkHelpText":"Colored Shaded-Relief Bathymetry, Offshore of Scott Creek Map Area, California By Peter Dartnell, Andrew C. Ritchie, David P. Finlayson, and Rikk G. Kvitek"},{"id":310094,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/of/2014/1260/","text":"Open-File Report 2014–1260","description":"Open-File Report 2014–1260","linkHelpText":"California State Waters Map Series—Offshore of Pacifica, California, by Brian D. Edwards and others."},{"id":310092,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/sim/3306/","text":"Scientific Investigations Map 3306","description":"Scientific Investigations Map 3306","linkHelpText":"California State Waters Map Series—Offshore of San Gregorio, California, by Guy R. Cochrane and others."},{"id":310091,"rank":2,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/781/","text":"Data Series 781","description":"Data Series 781","linkHelpText":"California State Waters Map Series Data Catalog"},{"id":310090,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1191/coverthb.jpg"}],"scale":"24000","country":"United States","state":"California","otherGeospatial":"Scott Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.3867,\n 36.9428\n ],\n [\n -122.1881,\n 36.9428\n ],\n [\n -122.1881,\n 37.1022\n ],\n [\n -122.3867,\n 37.1022\n ],\n [\n -122.3867,\n 36.9428\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
http://walrus.wr.usgs.gov/
The Black Rail is the smallest member of the avian family Rallidae and has a wide-ranging but highly scattered distribution throughout the New World. Of five subspecies, two occur in North America—the Eastern Black Rail (L.j. jamaicensis) and the California Black Rail (L.j. coturniculus). Throughout its range, the Black Rail is a secretive inhabitant of tidal and freshwater wetlands and rarely ventures out from the cover of dense marsh vegetation. It is more likely to be heard than seen; spontaneous vocalizations tend to be concentrated in the nesting season and are much less common during the rest of the year.
","largerWorkTitle":"The Baylands and climate change: What can we do? Baylands Ecosystem Habitat Goals science update 2015","language":"English","publisher":"California State Coastal Conservancy","collaboration":"Prepared by the San Franciso Bay Area Wetlands Ecosystem Goals Project","usgsCitation":"Evens, J.G., and Thorne, K.M., 2015, Case Study, California Black Rail (Laterallus jamaicensis corturniculus): Science Foundation Chapter 5, Appendix 5.1 in The Baylands and climate change: What can we do?, 13 p.","productDescription":"13 p.","onlineOnly":"Y","ipdsId":"IP-060996","costCenters":[{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":331322,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":331321,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://baylandsgoals.org/science-update-2016/"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.10729980468749,\n 37.28716518793858\n ],\n [\n -123.10729980468749,\n 38.68122173079789\n ],\n [\n -121.53625488281249,\n 38.68122173079789\n ],\n [\n -121.53625488281249,\n 37.28716518793858\n ],\n [\n -123.10729980468749,\n 37.28716518793858\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583ff350e4b04fc80e43726a","contributors":{"authors":[{"text":"Evens, Jules G.","contributorId":12966,"corporation":false,"usgs":true,"family":"Evens","given":"Jules","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":578414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thorne, Karen M. 0000-0002-1381-0657 kthorne@usgs.gov","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":4191,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen","email":"kthorne@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":578413,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159874,"text":"70159874 - 2015 - Comment on \"Donders, T.H. 2014. Middle Holocene humidity increase in Florida: climate or sea-level? Quaternary Science Reviews 103:170-174.\"","interactions":[],"lastModifiedDate":"2015-12-07T13:59:57","indexId":"70159874","displayToPublicDate":"2015-11-15T01:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Comment on \"Donders, T.H. 2014. Middle Holocene humidity increase in Florida: climate or sea-level? Quaternary Science Reviews 103:170-174.\"","docAbstract":"Donders (2014) has recently proposed that the climate of Florida became progressively wetter over the past 5000 years in response to a marked strengthening of the El Niño regime. This reconstruction is largely based on a re-analysis of pollen records from regions north of Lake Okeechobee (Fig. 1) using a new set of pollen transfer functions. Donders concluded that a latitudinal gradient in precipitation prevailed across Florida since the mid Holocene, but the overall trend was toward progressively wetter conditions from 5000 cal BP to the present.
\nDonders (2014) also proposed that this climatic trend extended across South Florida despite contrary paleo-records from the Everglades. In particular he singled out the Northeast Shark River Slough (NESRS) record of Glaser et al. (2013) as an atypical local signal of paleo-environmental change that was biased by a misinterpretation of the ecology of pine and Amaranthaceae (Amaranth family). In response to this direct critique of our paleo-environmental interpretation, we wish to point out that:
\nForecasting volcanic activity relies fundamentally on tracking magma pressure through the use of proxies, such as ground surface deformation and earthquake rates. Lava lakes at open-vent basaltic volcanoes provide a window into the uppermost magma system for gauging reservoir pressure changes more directly. At Kīlauea Volcano (Hawaiʻi, USA) the surface height of the summit lava lake in Halemaʻumaʻu Crater fluctuates with surface deformation over short (hours to days) and long (weeks to months) time scales. This correlation implies that the lake behaves as a simple piezometer of the subsurface magma reservoir. Changes in lava level and summit deformation scale with (and shortly precede) changes in eruption rate from Kīlauea's East Rift Zone, indicating that summit lava level can be used for short-term forecasting of rift zone activity and associated hazards at Kīlauea.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G36896.1","usgsCitation":"Patrick, M.R., Anderson, K.R., Poland, M., Orr, T., and Swanson, D., 2015, Lava lake level as a gauge of magma reservoir pressure and eruptive hazard: Geology, v. 43, no. 9, p. 831-834, https://doi.org/10.1130/G36896.1.","productDescription":"5 p.","startPage":"831","endPage":"834","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063784","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":311187,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.2141571044922,\n 19.254756553409987\n ],\n [\n -155.2141571044922,\n 19.419325579756944\n ],\n [\n -154.96421813964844,\n 19.419325579756944\n ],\n [\n -154.96421813964844,\n 19.254756553409987\n ],\n [\n -155.2141571044922,\n 19.254756553409987\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-05","publicationStatus":"PW","scienceBaseUri":"564466a7e4b0aafbcd01854b","contributors":{"authors":[{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":579631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":579632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":635,"corporation":false,"usgs":true,"family":"Poland","given":"Michael P.","email":"mpoland@usgs.gov","affiliations":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"preferred":false,"id":579633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Orr, Tim R. torr@usgs.gov","contributorId":3766,"corporation":false,"usgs":true,"family":"Orr","given":"Tim R.","email":"torr@usgs.gov","affiliations":[],"preferred":false,"id":579634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Swanson, Donald A. donswan@usgs.gov","contributorId":149804,"corporation":false,"usgs":true,"family":"Swanson","given":"Donald A.","email":"donswan@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":579635,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70156833,"text":"ofr20151168 - 2015 - Groundwater quality in the Chemung River, Eastern Lake Ontario, and Lower Hudson River Basins, New York, 2013","interactions":[],"lastModifiedDate":"2015-11-10T12:38:32","indexId":"ofr20151168","displayToPublicDate":"2015-11-10T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1168","title":"Groundwater quality in the Chemung River, Eastern Lake Ontario, and Lower Hudson River Basins, New York, 2013","docAbstract":"In a study conducted by the U.S. Geological Survey (USGS) in cooperation with the New York State Department of Environmental Conservation, water samples were collected from 4 production wells and 4 domestic wells in the Chemung River Basin, 8 production wells and 7 domestic wells in the Eastern Lake Ontario Basin, and 12 production wells and 13 domestic wells in the Lower Hudson River Basin (south of the Federal Lock and Dam at Troy) in New York. All samples were collected in June, July, and August 2013 to characterize groundwater quality in these basins. The samples were collected and processed using standard USGS procedures and were analyzed for 148 physiochemical properties and constituents, including dissolved gases, major ions, nutrients, trace elements, pesticides, volatile organic compounds, radionuclides, and indicator bacteria.
\nThe Chemung River Basin study area covers 1,744 square miles in south-central New York and encompasses the part of the Chemung River Basin that lies within New York. Two of the wells sampled in the Chemung River Basin are completed in sand and gravel, and 6 are completed in bedrock. Groundwater in the Chemung River Basin was generally of good quality, although properties and concentrations of some constituents—sodium, arsenic, aluminum, iron, manganese, radon-222, total coliform bacteria, and Escherichia coli bacteria—equaled or exceeded primary, secondary, or proposed drinking-water standards. The constituent most frequently detected in concentrations exceeding drinking-water standards (six of eight samples) was radon-222.
\nThe Eastern Lake Ontario Basin study area covers 3,225 square miles in north-central New York. The Eastern Lake Ontario Basin (between the Oswego River Basin and the St. Lawrence River Basin) includes the Mid-Northern Lake Ontario Basin, the Black River Basin, and the Chaumont River-Perch River Basin. Five of the wells sampled in the Eastern Lake Ontario Basin are completed in sand and gravel, and 10 are completed in bedrock. Groundwater in the Eastern Lake Ontario Basin was generally of good quality, although properties and concentrations of some constituents—color, pH, sodium, dissolved solids, fluoride, iron, manganese, uranium, gross-α radioactivity, radon-222, total coliform bacteria, and fecal coliform bacteria—equaled or exceeded primary, secondary, or proposed drinking-water standards. The constituent most frequently detected in concentrations exceeding drinking-water standards (10 of 15 samples) was radon-222.
\nThe Lower Hudson River Basin study area covers 5,607 square miles and encompasses the part of the Lower Hudson River Basin that lies within New York plus the parts of the Housatonic, Hackensack, Bronx, and Saugatuck River Basins that are in New York. Twelve of the wells sampled in the Lower Hudson River Basin are completed in sand-and-gravel deposits, and 13 are completed in bedrock. Groundwater in the Lower Hudson River Basin was generally of good quality, although properties and concentrations of some constituents—pH, sodium, chloride, dissolved solids, arsenic, aluminum, iron, manganese, radon-222, total coliform bacteria, fecal coliform bacteria, Escherichia coli bacteria, and heterotrophic plate count—equaled or exceeded primary, secondary, or proposed drinking-water standards. The constituent most frequently detected in concentrations exceeding drinking-water standards (20 of 25 samples) was radon-222.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151168","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Scott, T.-M., Nystrom, E.A., and Reddy, J.E., 2015, Groundwater quality in the Chemung River, eastern Lake Ontario, and lower Hudson River Basins, New York, 2013: U.S. Geological Survey Open-File Report 2015–1168, 41 p., appendixes, https://dx.doi.org/10.3133/ofr20151168.","productDescription":"Report: viii, 39 p.; Appendixes: 1-2","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2013-01-01","temporalEnd":"2013-12-31","ipdsId":"IP-061358","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":310960,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1168/ofr20151168.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1168"},{"id":310961,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1168/appendix/ofr20151168_appendix1.xlsx","text":"Appendix 1","size":"113 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2015-1168","linkHelpText":"Results of Water-Sample Analyses, 2013"},{"id":310962,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1168/appendix/ofr20151168_appendix2.xlsx","text":"Appendix 2","size":"58.5 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2015-1168","linkHelpText":"Results of Water-Sample Analyses, 2008 and 2013"},{"id":310959,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1168/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Chemung River Basin, Eastern Lake Ontario Basin, Lower Hudson River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.6845703125,\n 43.30119623257966\n ],\n [\n -76.6845703125,\n 44.41024041296011\n ],\n [\n -73.76220703125,\n 44.41024041296011\n ],\n [\n -73.76220703125,\n 43.30119623257966\n ],\n [\n -76.6845703125,\n 43.30119623257966\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.7227783203125,\n 42.00032514831621\n ],\n [\n -77.7227783203125,\n 42.44778143462245\n ],\n [\n -76.4263916015625,\n 42.44778143462245\n ],\n [\n -76.4263916015625,\n 42.00032514831621\n ],\n [\n -77.7227783203125,\n 42.00032514831621\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.817138671875,\n 40.81796653313175\n ],\n [\n -73.641357421875,\n 41.0130657870063\n ],\n [\n -73.7127685546875,\n 41.10005163093046\n ],\n [\n -73.4600830078125,\n 41.20758898181025\n ],\n [\n -73.5479736328125,\n 41.29844430929419\n ],\n [\n -73.27880859375,\n 42.742978093466434\n ],\n [\n -74.2291259765625,\n 42.90011265525328\n ],\n [\n -74.72351074218749,\n 41.364441530542244\n ],\n [\n -73.916015625,\n 41.000629848685385\n ],\n [\n -74.036865234375,\n 40.713955826286046\n ],\n [\n -73.817138671875,\n 40.81796653313175\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
Information requests:
(518) 285-5602
or visit our Web site at:
http://ny.water.usgs.gov
Harmful cyanobacterial “algal” blooms (cyanoHABs) and associated toxins, such as microcystin, are a major water-quality issue for Lake Erie and inland lakes in Ohio. Predicting when and where a bloom may occur is important to protect the public that uses and consumes a water resource; however, predictions are complicated and likely site specific because of the many factors affecting toxin production. Monitoring for a variety of environmental and water-quality factors, for concentrations of cyanobacteria by molecular methods, and for algal pigments such as chlorophyll and phycocyanin by using optical sensors may provide data that can be used to predict the occurrence of cyanoHABs.
\nTo test these monitoring approaches, water-quality samples were collected at Ohio recreational sites during May–November in 2013 and 2014. In 2013, samples were collected monthly at eight sites at eight lakes to facilitate an initial assessment and select sites for more intensive sampling during 2014. In 2014, samples were collected approximately weekly at five sites at three lakes. Physical water-quality parameters were measured at the time of sampling. Composite samples were preserved and analyzed for dissolved and total nutrients, toxins, phytoplankton abundance and biovolume, and cyanobacterial genes by molecular methods. Molecular assays were done to enumerate (1) general cyanobacteria, (2) general Microcystis and Dolichospermum (Anabaena), (3) mcyE genes forMicrocystis, Dolichospermum (Anabaena), and Planktothrix targeting deoxyribonucleic acid (DNA), and (4) mcyE transcripts for Microcystis, Dolichospermum (Anabaena), and Planktothrix targeting ribonucleic acid (RNA).The DNA assays for the mcyE gene provide data on cyanobacteria that have the potential to produce microcystin, whereas the RNA assays provide data on cyanobacteria that are actively transcribing the toxin gene. Environmental data were obtained from available online sources. Quality-control (QC) samples were collected and analyzed for all constituents to characterize bias and variability; however, QC data for molecular assays were examined in more detail than for the other constituents. The QC data for molecular assays suggested that sampling variability and qPCR variability were small in comparison with the combined variability associated with sample filtering, extraction and purification, and the matrix itself.
\nA total of 46 water-quality samples were collected during 2013 at 8 beach sites—Buck Creek, Buckeye Crystal, Deer Creek, Harsha Main, Maumee Bay State Park (MBSP) Inland (negative control site), MBSP Lake Erie, Port Clinton, and Sandusky Bay. Microcystin was detected in 67–100 percent of samples at all sites except for MBSP Inland, where microcystin was detected in only 20 percent of samples. Microcystin concentrations ranged from <0.10 to 48 micrograms per liter (µ/L), with the widest range found at MBSP Lake Erie and the highest concentrations found at Buckeye Crystal. Saxitoxin was detected in five samples, and cylindrospermopsin was not detected in any samples.
\nA total of 65 water-quality samples were collected during 2014 at 5 sites on 3 lakes—Buckeye Fairfield and Onion Island, Harsha Main and Campers, and MBSP Lake Erie beach. Four of the sites were bathing beaches and one site, Onion Island, was an offshore boater swim area. Concentrations of microcystin ranged from <0.10 to 240 µ/L and, as in 2013, the widest range was found at MBSP Lake Erie. At Buckeye Lake, microcystin concentrations were consistently high (greater than 20 µ/L), ranging from 23 to 81 µ/L. At Harsha Main and Campers, microcystin concentrations ranged from <0.10 to 15 µ/L. Saxitoxin was detected in four samples collected at MBSP Lake Erie. Throughout the 2014 season, the cyanobacterial community, as determined by molecular and microscopy methods, and the dominance associated with the highest microcystin concentrations were unique to individual lakes. At Buckeye Lake, Planktothrix dominated the cyanobacterial community throughout the season and Planktothrix DNA and RNA were found in 100 percent of samples; Microcystis mcyE DNA was found in low concentrations. At Harsha Lake, Dolichospermum and Microcystis were a substantial percentage of the community from late May through August, and the highest microcystin concentrations occurred in June and July. At MBSP Lake Erie, Microcystis generally dominated from mid-July through early November, and the highest microcystin concentrations occurred in August.
\nSpearman’s correlation coefficient (rho) was computed to determine the relations between environmental and water-quality factors and microcystin concentrations at four sites—Buckeye Fairfield, Buckeye Onion Island, Harsha Main, and MBSP Lake Erie. Factors were evaluated for use as potential independent variables in two types of predictive models—daily and long-term models. Easily or continuously measured water-quality factors and available environmental data are used for daily predictions that do not require a site visit. Data from factors used in daily predictions and results from samples collected and analyzed in a laboratory are used for long-term predictions (a few days to several weeks). A few statistically significant correlations (p ≤ 0.05) between microcystin concentrations and factors for both daily and long-term predictions were found at Buckeye Onion Island, and many were found at Harsha Main and MBSP Lake Erie. There were only a few statistically significant factors for daily predictions at Buckeye Fairfield, likely because of the lack of variability in microcystin concentrations. Among factors for daily predictions, phycocyanin had the highest Spearman’s correlation to microcystin concentrations (rho = 0.79 to 0.93) at all sites except for Buckeye Fairfield. Turbidity, pH, algae category, and Secchi depth were significantly correlated to microcystin concentrations at Harsha Main and MBSP Lake Erie. Algae categories were observational categories from 0 (none) to 4 (extreme). Several discharge variables (Maumee River at Waterville, river mouth is approximately 3.5 miles from the beach) at MBSP Lake Erie were promising environmental factors for daily predictions. In addition to discrete water-quality measurements recorded at Harsha Main at the time of sampling, many manipulated measurements (factors derived from mathematical manipulation of time-series data) available from a nearby continuous monitor were strongly correlated to microcystin concentrations; the highest correlation was found for the relation between microcystin concentrations and the antecedent 7-day average phycocyanin (rho = 0.98). For long-term predictions, the most highly correlated molecular assays were Planktothrix mcyE DNA at Buckeye Onion Island and Microcystis mcyE DNA at Harsha Main and MBSP Lake Erie. Concentrations of several nutrient constituents were significantly correlated to microcystin concentrations including total nitrogen at Buckeye Onion Island, ammonia and nitrate plus nitrite (both negatively correlated) at Harsha Main and MBSP Lake Erie, and total phosphorus at MBSP Lake Erie.
\nThe results of this study showed that water-quality and environmental variables are promising for use in site-specific daily or long-term predictive models. In order to develop more accurate models to predict toxin concentrations at freshwater lake sites, data need to be collected more frequently and for consecutive days in future studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155120","collaboration":"Prepared in cooperation with the Ohio Water Development Authority","usgsCitation":"Francy, D.S., Graham, J.L., Stelzer, E.A., Ecker, C.D., Brady, A.M.G., Struffolino, Pamela, and Loftin, K.A., 2015, Water quality, cyanobacteria, and environmental factors and their relations to microcystin concentrations for use in predictive models at Ohio Lake Erie and inland lake recreational sites, 2013–14: U.S. Geological Survey Scientific Investigations Report 2015–5120, 58 p., https://dx.doi.org/10.3133/sir20155120.","productDescription":"Report: vii, 58 p.; Appendix","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2013-05-01","temporalEnd":"2014-11-01","ipdsId":"IP-064699","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":310974,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5120/sir20155120.pdf","text":"Report","size":"9.41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":310973,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5120/coverthb.jpg"},{"id":310975,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5120/sir20155120_appendix2_phytoplanktondata.xlsx","text":"Appendix 2","size":"181 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 2","linkHelpText":"Phytoplankton abundance and community composition at Ohio recreational lake sites, 2013–14."}],"country":"United States","state":"Ohio","otherGeospatial":"Buck Creek State Park, Buckeye Lake State Park, Deer Creek State Park, East Fork State Park, Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.638916015625,\n 41.413895564677304\n ],\n [\n -83.638916015625,\n 41.7672146942102\n ],\n [\n -82.6556396484375,\n 41.7672146942102\n ],\n [\n -82.6556396484375,\n 41.413895564677304\n ],\n [\n -83.638916015625,\n 41.413895564677304\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.54440307617188,\n 39.89498716884207\n ],\n [\n -82.54440307617188,\n 39.94712141785606\n ],\n [\n -82.40432739257812,\n 39.94712141785606\n ],\n [\n -82.40432739257812,\n 39.89498716884207\n ],\n [\n -82.54440307617188,\n 39.89498716884207\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.17449951171875,\n 38.983965305617666\n ],\n [\n -84.17449951171875,\n 39.0533181067413\n ],\n [\n -84.05982971191406,\n 39.0533181067413\n ],\n [\n -84.05982971191406,\n 38.983965305617666\n ],\n [\n -84.17449951171875,\n 38.983965305617666\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.7546157836914,\n 39.94212035820183\n ],\n [\n -83.7546157836914,\n 39.99369266969988\n ],\n [\n -83.70586395263672,\n 39.99369266969988\n ],\n [\n -83.70586395263672,\n 39.94212035820183\n ],\n [\n -83.7546157836914,\n 39.94212035820183\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.27104568481445,\n 39.59748778978444\n ],\n [\n -83.27104568481445,\n 39.64535376010791\n ],\n [\n -83.21285247802734,\n 39.64535376010791\n ],\n [\n -83.21285247802734,\n 39.59748778978444\n ],\n [\n -83.27104568481445,\n 39.59748778978444\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio Water Science Center
U.S. Geological Survey
6480 Doubletree Ave
Columbus, OH 43229-1111
http://oh.water.usgs.gov/
Seismicity rates have increased sharply since 2009 in the central and eastern United States, with especially high rates of activity in the state of Oklahoma. Growing evidence indicates that many of these events are induced, primarily by injection of wastewater in deep disposal wells. The upsurge in activity has raised two questions: What is the background rate of tectonic earthquakes in Oklahoma? How much has the rate varied throughout historical and early instrumental times? In this article, we show that (1) seismicity rates since 2009 surpass previously observed rates throughout the twentieth century; (2) several lines of evidence suggest that most of the significant earthquakes in Oklahoma during the twentieth century were likely induced by oil production activities, as they exhibit statistically significant temporal and spatial correspondence with disposal wells, and intensity measurements for the 1952 El Reno earthquake and possibly the 1956 Tulsa County earthquake follow the pattern observed in other induced earthquakes; and (3) there is evidence for a low level of tectonic seismicity in southeastern Oklahoma associated with the Ouachita structural belt. The 22 October 1882 Choctaw Nation earthquake, for which we estimate Mw 4.8, occurred in this zone.
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Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":579029,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70158960,"text":"sir20155146 - 2015 - Climate and streamflow characteristics for selected streamgages in eastern South Dakota, water years 1945–2013","interactions":[],"lastModifiedDate":"2017-10-12T20:01:45","indexId":"sir20155146","displayToPublicDate":"2015-11-02T01:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5146","title":"Climate and streamflow characteristics for selected streamgages in eastern South Dakota, water years 1945–2013","docAbstract":"Upward trends in precipitation and streamflow have been observed in the northeastern Missouri River Basin during the past century, including the area of eastern South Dakota. Some of the identified upward trends were anomalously large relative to surrounding parts of the northern Great Plains. Forcing factors for streamflow trends in eastern South Dakota are not well understood, and it is not known whether streamflow trends are driven primarily by climatic changes or various land-use changes. Understanding the effects that climate (specifically precipitation and temperature) has on streamflow characteristics within a region will help to better understand additional factors such as land-use alterations that may affect the hydrology of the region. To aid in this understanding, a study was completed by the U.S. Geological Survey, in cooperation with the East Dakota Water Development District and James River Water Development District, to assess trends in climate and streamflow characteristics at 10 selected streamgages in eastern South Dakota for water years (WYs) 1945–2013 (69 years) and WYs 1980–2013 (34 years). A WY is the 12-month period, October 1 through September 30, and is designated by the calendar year in which it ends. One streamgage is on the Whetstone River, a tributary to the Minnesota River, and the other streamgages are in the James, Big Sioux, and Vermillion River Basins. The watersheds for two of the James River streamgages extend into North Dakota, and parts of the watersheds for two of the Big Sioux River streamgages extend into Minnesota and Iowa. The objectives of this study were to document trends in streamflow and precipitation in these watersheds, and characterize the residual streamflow variability that might be attributed to factors other than precipitation. Residuals were computed as the departure from a locally-weighted scatterplot smoothing (LOWESS) model. Significance of trends was based on the Mann-Kendall nonparametric test at a 0.10 significance level.
\nOf the 10 streamgages selected, only the Elm River at Westport (in the upper part of James River Basin) did not have a significant upward trend in annual mean streamflow for WYs 1945–2013, whereas only one-half of the streamgages had significant upward trends in annual mean streamflow for WYs 1980–2013. Mean and 7-day minimum streamflows also had upward trends for the spring runoff period (March–May) for most of the streamgages during WYs 1945–2013 and for one streamgage during WYs 1980–2013. Magnitudes of increases in streamflow were as great as 30 cubic feet per second per year for the streamgage on the James River near Scotland during WYs 1980–2013.
\nPrecipitation trends for WYs 1945–2013 were not necessarily significant for the watersheds of streamgages with a significant streamflow trend. Annual total precipitation had a significant upward trend for the watersheds of 4 of the 10 streamgages during WYs 1945–2013 and no significant trends for WYs 1980–2013. The most widespread precipitation increase was for September–November, with significant upward trends for the watersheds of 8 of the 10 streamgages during WYs 1945–2013; however, no trends in September– November precipitation were significant for WYs 1980–2013. The greatest magnitude of increase in precipitation was for the December–May season during WYs 1980–2013, which had a mean increase of 0.106 inch per year in the watersheds of streamgages with significant trends.
\nThe correlation between streamflow and precipitation metrics was low as indicated by the mean coefficient of determination (R2) of 0.18 for all pairs considered. The highest locally-weighed scatterplot smoothing (LOWESS) correlation was between annual precipitation (by water year) and annual mean streamflow (by water year), which had a mean R2 of 0.47 for all streamgages and was as high as 0.72 for one streamgage. The correlation between annual precipitation and March–May mean streamflow had a mean R2 of 0.33 for all streamgages and was as high as 0.52 for one streamgage. Other metrics had R2 values for LOWESS correlations that were less than 0.3 and were not further considered for analyses of residuals. For annual precipitation as a predictor of annual mean flow, precipitation-removed streamflow had significant upward trends during WYs 1945–2013 for one-half of the streamgages. Upward trends in residual annual mean streamflow were indicated for the Whetstone River and lower part of the Big Sioux River Basin, indicating that other factors are contributors to streamflow variability during WYs 1945–2013. In contrast, most of the streamgages in the James and Vermillion River Basins had no trends in residual annual mean streamflow, indicating that streamflow trends can be explained primarily by precipitation. Precipitation-removed streamflow had fewer trends during the more recent analysis period of WYs 1980–2013 than WYs 1945–2013 for all streamgages in eastern South Dakota. Upward trends in residuals for March– May mean streamflow were indicated for Skunk Creek at Sioux Falls and the Big Sioux River at Akron, but trends in residuals were not significant at the remaining streamgages.
\nFor the streamgages with significant trends in residual streamflow (such as the streamgage on the Whetstone River and streamgages in the Big Sioux River Basin), land-use changes likely are minor factors, with the main factors probably being changes in the timing and frequency of large precipitation events and persistently wetter antecedent conditions. Changes in the relation between precipitation and streamflow since 1945 were evident when considering the runoff efficiency of the watershed. For example, the streamflow response to annual precipitation of 25 inches for the James River near Scotland increased from approximately 1,000 cubic feet per second for WYs 1945–1990 to about 2,500 cubic feet per second for WYs 1991–2013. The importance of antecedent conditions on annual mean streamflow also was indicated by the significance of the multiple linear regression coefficients of annual mean streamflow and precipitation from preceding water years for all but one streamgage. In addition, rising groundwater levels are present in wells in eastern South Dakota, particularly since the 1980s.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155146","collaboration":"Prepared in cooperation with the East Dakota Water Development District and James River Water Development District","usgsCitation":"Hoogestraat, G.K., and Stamm, J.F., 2015, Climate and streamflow characteristics for selected streamgages in eastern\nSouth Dakota, water years 1945–2013: U.S. Geological Survey Scientific Investigations Report 2015–5146, 35 p., with\nappendix, https://dx.doi.org/10.3133/sir20155146.","productDescription":"Report: v, 35 p.; Appendix","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"1944-10-01","temporalEnd":"2013-09-30","ipdsId":"IP-066397","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":310790,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5146/sir20155146.pdf","text":"Report","size":"3.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5146"},{"id":310792,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5146/appendix.xlsx","text":"Appendix","size":"94 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5146 Appendix"},{"id":310789,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5146/coverthb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Big Sioux River basin, James River basin, Minnesota River basin, Vermillion River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.8876953125,\n 46.649436163350245\n ],\n [\n -100.17333984375,\n 45.98169518512228\n ],\n [\n -100.2392578125,\n 44.94924926661153\n ],\n [\n -98.8330078125,\n 43.27720532212024\n ],\n [\n -98.59130859375,\n 43.004647127794435\n ],\n [\n -97.88818359375,\n 42.76314586689494\n ],\n [\n -97.44873046875,\n 42.827638636242284\n ],\n [\n -96.45996093749999,\n 42.553080288955826\n ],\n [\n -96.45996093749999,\n 42.956422511073335\n ],\n [\n -95.77880859375,\n 43.42100882994726\n ],\n [\n -95.95458984375,\n 44.11914151643737\n ],\n [\n -96.96533203125,\n 44.793530904744074\n ],\n [\n -96.591796875,\n 45.336701909968106\n ],\n [\n -96.83349609375,\n 45.61403741135093\n ],\n [\n -97.3828125,\n 45.55252525134013\n ],\n [\n -97.91015624999999,\n 45.72152152227954\n ],\n [\n -98.06396484375,\n 46.07323062540838\n ],\n [\n -98.4814453125,\n 46.30140615437332\n ],\n [\n -98.7890625,\n 46.6795944656402\n ],\n [\n -99.25048828124999,\n 46.76996843356982\n ],\n [\n -99.8876953125,\n 46.649436163350245\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Dakota Water Science Center
U.S. Geological Survey
1608 Mountain View Road
Rapid City, South Dakota 57702
http://sd.water.usgs.gov/
Shale is now commonly exploited as a hydrocarbon resource. Due to the high degree of geochemical and petrophysical heterogeneity both between shale reservoirs and within a single reservoir, there is a growing need to find more efficient methods of extracting petroleum compounds (crude oil, natural gas, bitumen) from potential source rocks. In this study, supercritical carbon dioxide (CO2) was used to extract n-aliphatic hydrocarbons from ground samples of Marcellus shale. Samples were collected from vertically drilled wells in central and western Pennsylvania, USA, with total organic carbon (TOC) content ranging from 1.5 to 6.2 wt %. Extraction temperature and pressure conditions (80°C and 21.7 MPa, respectively) were chosen to represent approximate in situ reservoir conditions at sample depth (1920–2280 m). Hydrocarbon yield was evaluated as a function of sample matrix particle size (sieve size) over the following size ranges: 1000–500 μm, 250–125 μm, and 63–25 μm. Several methods of shale characterization including Rock-Eval II pyrolysis, organic petrography, Brunauer–Emmett–Teller surface area, and X-ray diffraction analyses were also performed to better understand potential controls on extraction yields. Despite high sample thermal maturity, results show that supercritical CO2 can liberate diesel-range (n-C11 through n-C21) n-aliphatic hydrocarbons. The total quantity of extracted, resolvable n-aliphatic hydrocarbons ranges from approximately 0.3 to 12 mg of hydrocarbon per gram of TOC. Sieve size does have an effect on extraction yield, with highest recovery from the 250–125 μm size fraction. However, the significance of this effect is limited, likely due to the low size ranges of the extracted shale particles. Additional trends in hydrocarbon yield are observed among all samples, regardless of sieve size: 1) yield increases as a function of specific surface area (r2 = 0.78); and 2) both yield and surface area increase with increasing TOC content (r2 = 0.97 and 0.86, respectively). Given that supercritical CO2 is able to mobilize residual organic matter present in overmature shales, this study contributes to a better understanding of the extent and potential factors affecting the extraction process.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.energyfuels.5b02059","usgsCitation":"Botterell, P.J., Candela, P.A., Zhu, W., and Kaufman, A.J., 2015, Extraction of hydrocarbons from high-maturity Marcellus Shale using supercritical carbon dioxide: Energy & Fuels, v. 29, no. 12, p. 7897-7909, https://doi.org/10.1021/acs.energyfuels.5b02059.","productDescription":"13 p.","startPage":"7897","endPage":"7909","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066645","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":312576,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Central and Western Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.518798828125,\n 41.95949009892465\n ],\n [\n -80.52978515625,\n 39.740986355883564\n ],\n [\n -77.2998046875,\n 39.73253798438173\n ],\n [\n -77.27783203125,\n 42.00848901572399\n ],\n [\n -79.749755859375,\n 42.00848901572399\n ],\n [\n -79.771728515625,\n 42.285437007491545\n ],\n [\n -80.518798828125,\n 41.95949009892465\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"12","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-20","publicationStatus":"PW","scienceBaseUri":"567930c6e4b0da412f4fb55f","chorus":{"doi":"10.1021/acs.energyfuels.5b02059","url":"http://dx.doi.org/10.1021/acs.energyfuels.5b02059","publisher":"American Chemical Society (ACS)","authors":"Jarboe Palma J., Candela Philip A., Zhu Wenlu, Kaufman Alan J.","journalName":"Energy & Fuels","publicationDate":"12/17/2015"},"contributors":{"authors":[{"text":"Botterell, Palma J. 0000-0001-7140-0915 pjarboe@usgs.gov","orcid":"https://orcid.org/0000-0001-7140-0915","contributorId":5805,"corporation":false,"usgs":true,"family":"Botterell","given":"Palma","email":"pjarboe@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":582557,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Candela, Philip A.","contributorId":150643,"corporation":false,"usgs":false,"family":"Candela","given":"Philip","email":"","middleInitial":"A.","affiliations":[{"id":18057,"text":"Department of Geology, University of Maryland, College Park, Maryland","active":true,"usgs":false}],"preferred":false,"id":582558,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Wenlu","contributorId":150644,"corporation":false,"usgs":false,"family":"Zhu","given":"Wenlu","email":"","affiliations":[{"id":18057,"text":"Department of Geology, University of Maryland, College Park, Maryland","active":true,"usgs":false}],"preferred":false,"id":582559,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kaufman, Alan J.","contributorId":150645,"corporation":false,"usgs":false,"family":"Kaufman","given":"Alan","email":"","middleInitial":"J.","affiliations":[{"id":18058,"text":"Department of Geology, and Earth System Science Interdisciplinary Center. University of Maryland, College Park, Maryland","active":true,"usgs":false}],"preferred":false,"id":582560,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70186185,"text":"70186185 - 2015 - Trends and natural variability of North American spring onset as evaluated by a new gridded dataset of spring indices","interactions":[],"lastModifiedDate":"2017-03-31T10:20:53","indexId":"70186185","displayToPublicDate":"2015-11-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2216,"text":"Journal of Climate","active":true,"publicationSubtype":{"id":10}},"title":"Trends and natural variability of North American spring onset as evaluated by a new gridded dataset of spring indices","docAbstract":"Climate change is expected to modify the timing of seasonal transitions this century, impacting wildlife migrations, ecosystem function, and agricultural activity. Tracking seasonal transitions in a consistent manner across space and through time requires indices that can be used for monitoring and managing biophysical and ecological systems during the coming decades. Here a new gridded dataset of spring indices is described and used to understand interannual, decadal, and secular trends across the coterminous United States. This dataset is derived from daily interpolated meteorological data, and the results are compared with historical station data to ensure the trends and variations are robust. Regional trends in the first leaf index range from 20.8 to 21.6 days decade21, while first bloom index trends are between20.4 and 21.2 for most regions. However, these trends are modulated by interannual to multidecadal variations, which are substantial throughout the regions considered here. These findings emphasize the important role large-scale climate modes of variability play in modulating spring onset on interannual to multidecadal time scales. Finally, there is some potential for successful subseasonal forecasts of spring onset, as indices from most regions are significantly correlated with antecedent large-scale modes of variability.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JCLI-D-14-00736.1","usgsCitation":"Ault, T.R., Schwartz, M.D., Zurita-Milla, R., Weltzin, J.F., and Betancourt, J.L., 2015, Trends and natural variability of North American spring onset as evaluated by a new gridded dataset of spring indices: Journal of Climate, v. 28, no. 21, p. 8363-8378, https://doi.org/10.1175/JCLI-D-14-00736.1.","productDescription":"15 p.","startPage":"8363","endPage":"8378","ipdsId":"IP-064784","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":471671,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research.utwente.nl/en/publications/0858f753-4773-4800-b11e-86d3513ced55","text":"External Repository"},{"id":338921,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"21","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-30","publicationStatus":"PW","scienceBaseUri":"58df6ac2e4b02ff32c6aea43","contributors":{"authors":[{"text":"Ault, Toby R.","contributorId":146164,"corporation":false,"usgs":false,"family":"Ault","given":"Toby","email":"","middleInitial":"R.","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":687787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwartz, Mark D.","contributorId":175228,"corporation":false,"usgs":false,"family":"Schwartz","given":"Mark","email":"","middleInitial":"D.","affiliations":[{"id":18038,"text":"University of Wisconsin, Milwaukee","active":true,"usgs":false}],"preferred":false,"id":687788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zurita-Milla, Raul","contributorId":146213,"corporation":false,"usgs":false,"family":"Zurita-Milla","given":"Raul","email":"","affiliations":[{"id":16630,"text":"Department of Geo-Information Processing, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":687789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weltzin, Jake F. 0000-0001-8641-6645 jweltzin@usgs.gov","orcid":"https://orcid.org/0000-0001-8641-6645","contributorId":189061,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake","email":"jweltzin@usgs.gov","middleInitial":"F.","affiliations":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"preferred":false,"id":687790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Betancourt, Julio L. 0000-0002-7165-0743 jlbetanc@usgs.gov","orcid":"https://orcid.org/0000-0002-7165-0743","contributorId":3376,"corporation":false,"usgs":true,"family":"Betancourt","given":"Julio","email":"jlbetanc@usgs.gov","middleInitial":"L.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":687786,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70161997,"text":"70161997 - 2015 - Imaging the magmatic system of Mono Basin, California with magnetotellurics in three--dimensions","interactions":[],"lastModifiedDate":"2016-01-13T09:58:17","indexId":"70161997","displayToPublicDate":"2015-11-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Imaging the magmatic system of Mono Basin, California with magnetotellurics in three--dimensions","docAbstract":"A three–dimensional (3D) electrical resistivity model of Mono Basin in eastern California unveils a complex subsurface filled with zones of partial melt, fluid–filled fracture networks, cold plutons, and regional faults. In 2013, 62 broadband magnetotelluric (MT) stations were collected in an array around southeastern Mono Basin from which a 3D electrical resistivity model was created with a resolvable depth of 35 km. Multiple robust electrical resistivity features were found that correlate with existing geophysical observations. The most robust features are two 300 ± 50 km3 near-vertical conductive bodies (3–10 Ω·m) that underlie the southeast and north-eastern margin of Mono Craters below 10 km depth. These features are interpreted as magmatic crystal–melt mush zones of 15 ± 5% interstitial melt surrounded by hydrothermal fluids and are likely sources for Holocene eruptions. Two conductive east–dipping structures appear to connect each magma source region to the surface. A conductive arc–like structure (< 0.9 Ω·m) links the northernmost mush column at 10 km depth to just below vents near Panum Crater, where the high conductivity suggests the presence of hydrothermal fluids. The connection from the southernmost mush column at 10 km depth to below South Coulée is less obvious with higher resistivity (200 Ω·m) suggestive of a cooled connection. A third, less constrained conductive feature (4–10 Ω·m) 15 km deep extending to 35 km is located west of Mono Craters near the eastern front of the Sierra Nevada escarpment, and is coincident with a zone of sporadic, long–period earthquakes that are characteristic of a fluid-filled (magmatic or metamorphic) fracture network. A resistive feature (103–105 Ω·m) located under Aeolian Buttes contains a deep root down to 25 km. The eastern edge of this resistor appears to structurally control the arcuate shape of Mono Craters. These observations have been combined to form a new conceptual model of the magmatic system beneath Mono Craters to a depth of 30 km.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015JB012071","usgsCitation":"Peacock, J.R., Mangan, M.T., McPhee, D., and Ponce, D.A., 2015, Imaging the magmatic system of Mono Basin, California with magnetotellurics in three--dimensions: Journal of Geophysical Research, v. 120, no. 11, p. 7273-7289, https://doi.org/10.1002/2015JB012071.","productDescription":"17 p.","startPage":"7273","endPage":"7289","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064799","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":471679,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012071","text":"Publisher Index Page"},{"id":314262,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mono Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.6136474609375,\n 37.89219554724437\n ],\n [\n -119.6136474609375,\n 38.39764411353181\n ],\n [\n -118.60290527343749,\n 38.39764411353181\n ],\n [\n -118.60290527343749,\n 37.89219554724437\n ],\n [\n -119.6136474609375,\n 37.89219554724437\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","issue":"11","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-07","publicationStatus":"PW","scienceBaseUri":"5697833ce4b039675d00a6e7","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224 jpeacock@usgs.gov","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":4996,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared","email":"jpeacock@usgs.gov","middleInitial":"R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":588286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mangan, Margaret T. 0000-0002-5273-8053 mmangan@usgs.gov","orcid":"https://orcid.org/0000-0002-5273-8053","contributorId":3343,"corporation":false,"usgs":true,"family":"Mangan","given":"Margaret","email":"mmangan@usgs.gov","middleInitial":"T.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":588287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McPhee, Darcy 0000-0002-5177-3068 dmcphee@usgs.gov","orcid":"https://orcid.org/0000-0002-5177-3068","contributorId":2621,"corporation":false,"usgs":true,"family":"McPhee","given":"Darcy","email":"dmcphee@usgs.gov","affiliations":[{"id":412,"text":"National Cooperative Geologic Mapping Program","active":false,"usgs":true}],"preferred":true,"id":588288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ponce, David A. 0000-0003-4785-7354 ponce@usgs.gov","orcid":"https://orcid.org/0000-0003-4785-7354","contributorId":1049,"corporation":false,"usgs":true,"family":"Ponce","given":"David","email":"ponce@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":588289,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159630,"text":"70159630 - 2015 - Remote sensing to monitor cover crop adoption in southeastern Pennsylvania","interactions":[],"lastModifiedDate":"2015-11-13T16:07:41","indexId":"70159630","displayToPublicDate":"2015-11-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing to monitor cover crop adoption in southeastern Pennsylvania","docAbstract":"In the Chesapeake Bay Watershed, winter cereal cover crops are often planted in rotation with summer crops to reduce the loss of nutrients and sediment from agricultural systems. Cover crops can also improve soil health, control weeds and pests, supplement forage needs, and support resilient cropping systems. In southeastern Pennsylvania, cover crops can be successfully established following corn (Zea mays L.) silage harvest and are strongly promoted for use in this niche. They are also planted following corn grain, soybean (Glycine max L.), and vegetable harvest. In Pennsylvania, the use of winter cover crops for agricultural conservation has been supported through a combination of outreach, regulation, and incentives. On-farm implementation is thought to be increasing, but the actual extent of cover crops is not well quantified. Satellite imagery can be used to map green winter cover crop vegetation on agricultural fields and, when integrated with additional remote sensing data products, can be used to evaluate wintertime vegetative groundcover following specific summer crops. This study used Landsat and SPOT (System Probatoire d’ Observation de la Terre) satellite imagery, in combination with the USDA National Agricultural Statistics Service Cropland Data Layer, to evaluate the extent and amount of green wintertime vegetation on agricultural fields in four Pennsylvania counties (Berks, Lebanon, Lancaster, and York) from 2010 to 2013. In December of 2010, a windshield survey was conducted to collect baseline data on winter cover crop implementation, with particular focus on identifying corn harvested for silage (expected earlier harvest date and lower levels of crop residue), versus for grain (expected later harvest date and higher levels of crop residue). Satellite spectral indices were successfully used to detect both the amount of green vegetative groundcover and the amount of crop residue on the surveyed fields. Analysis of wintertime satellite imagery showed consistent increases in vegetative groundcover over the four-year study period and determined that trends did not result from annual weather variability, indicating that farmers are increasing adoption of practices such as cover cropping that promote wintertime vegetation. Between 2010 and 2013, the occurrence of wintertime vegetation on agricultural fields increased from 36% to 67% of corn fields in Berks County, from 53% to 75% in Lancaster County, from 42% to 65% in Lebanon County, and from 26% to 52% in York County. Apparently, efforts to promote cover crop use in the Chesapeake Bay Watershed have coincided with a rapid increase in the occurrence of wintertime vegetation following corn harvest in southeastern Pennsylvania. However, despite these increases, between 25% and 48% of corn fields remained without substantial green vegetation over the wintertime, indicating further opportunity for cover crop adoption.
","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.70.6.340","usgsCitation":"Hively, W., Duiker, S., Greg McCarty, and Prabhakara, K., 2015, Remote sensing to monitor cover crop adoption in southeastern Pennsylvania: Journal of Soil and Water Conservation, v. 70, no. 6, p. 340-352, https://doi.org/10.2489/jswc.70.6.340.","productDescription":"13 p.","startPage":"340","endPage":"352","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061440","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.70.6.340","text":"Publisher Index Page"},{"id":311303,"type":{"id":15,"text":"Index Page"},"url":"https://www.jswconline.org/content/70/6/340.full.pdf"},{"id":311321,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Southeastern and Central Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2176513671875,\n 39.73253798438173\n ],\n [\n -76.48681640625,\n 40.0360265298117\n ],\n [\n -76.22314453125,\n 40.12429084831405\n ],\n [\n -76.3275146484375,\n 40.32141999593439\n ],\n [\n -76.08032226562499,\n 40.35073056591789\n ],\n [\n -76.08032226562499,\n 40.32560799973207\n ],\n [\n -75.78369140625,\n 40.41767833585551\n ],\n [\n -75.5474853515625,\n 40.27533480732468\n ],\n [\n -75.860595703125,\n 39.757879992021756\n ],\n [\n -75.8660888671875,\n 39.72831341029745\n ],\n [\n -76.2176513671875,\n 39.73253798438173\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.9754638671875,\n 41.31907562295136\n ],\n [\n -77.9425048828125,\n 40.61812224225511\n ],\n [\n -77.0306396484375,\n 40.6723059714534\n ],\n [\n -77.0965576171875,\n 41.36031866306708\n ],\n [\n -77.9754638671875,\n 41.31907562295136\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"70","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-06","publicationStatus":"PW","scienceBaseUri":"564717d7e4b0e2669b313129","contributors":{"authors":[{"text":"Hively, Wells whively@usgs.gov","contributorId":149843,"corporation":false,"usgs":true,"family":"Hively","given":"Wells","email":"whively@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":579787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duiker, Sjoerd","contributorId":149844,"corporation":false,"usgs":false,"family":"Duiker","given":"Sjoerd","email":"","affiliations":[{"id":17838,"text":"Dep. of Crop and Soil Sciences, The Pennsylvania State University, 116 ASI Building, University Park, PA 16802-3504","active":true,"usgs":false}],"preferred":false,"id":579788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Greg McCarty","contributorId":149845,"corporation":false,"usgs":false,"family":"Greg McCarty","affiliations":[{"id":17839,"text":"USDA-Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Building 007 Room 104 BARC-West, 10300 Baltimore Avenue, Beltsville, MD","active":true,"usgs":false}],"preferred":false,"id":579789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prabhakara, Kusuma","contributorId":6313,"corporation":false,"usgs":true,"family":"Prabhakara","given":"Kusuma","email":"","affiliations":[],"preferred":false,"id":579790,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70158701,"text":"fs20153071 - 2015 - The Chesapeake Bay impact structure","interactions":[],"lastModifiedDate":"2015-11-02T10:16:42","indexId":"fs20153071","displayToPublicDate":"2015-10-28T03:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-3071","title":"The Chesapeake Bay impact structure","docAbstract":"About 35 million years ago, during late Eocene time, a 2-mile-wide asteroid or comet smashed into Earth in what is now the lower Chesapeake Bay in Virginia. The oceanic impact vaporized, melted, fractured, and (or) displaced the target rocks and sediments and sent billions of tons of water, sediments, and rocks into the air. Glassy particles of solidified melt rock rained down as far away as Texas and the Caribbean. Models suggest that even up to 50 miles away the velocity of the intensely hot air blast was greater than 1,500 miles per hour, and ground shaking was equivalent to an earthquake greater than magnitude 8.0 on the Richter scale. Large tsunamis affected most of the North Atlantic basin. The Chesapeake Bay impact structure is among the 20 largest known impact structures on Earth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153071","usgsCitation":"Powars, D.S., Edwards, L.E., Gohn, G.S., and Horton, J.W., Jr., 2015, The Chesapeake Bay impact structure: U.S. Geological Survey Fact Sheet 2015–3071, 2 p., https://dx.doi.org/10.3133/fs20153071.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069422","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":310712,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://dx.doi.org/10.3133/gip159","text":"General Information Product 159 - Bookmark","size":"348 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3071"},{"id":310711,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3071/fs20153071.pdf","text":"Report","size":"1.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3071"},{"id":310710,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3071/coverthb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.003173828125,\n 36.641977814705946\n ],\n [\n -77.003173828125,\n 37.79676317682161\n ],\n [\n -75.0531005859375,\n 37.79676317682161\n ],\n [\n -75.0531005859375,\n 36.641977814705946\n ],\n [\n -77.003173828125,\n 36.641977814705946\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Eastern Geology and Paleoclimate Science Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://geology.er.usgs.gov/egpsc
About 35 million years ago, a 2-mile-wide meteorite smashed into Earth in what is now the lower Chesapeake Bay in Virginia. The oceanic impact vaporized, melted, fractured, and displaced rocks and sediments and sent billions of tons of water, sediments, and rocks into the air. Glassy particles of solidified melt rock rained down as far away as Texas and the Caribbean. Large tsunamis affected most of the North Atlantic basin. The resulting impact structure is more than 53 miles wide and has a 23-mile-wide, filled central crater surrounded by collapsed sediments. Now buried by hundreds of feet of younger sediments, the Chesapeake Bay impact structure is among the 20 largest known impact structures on Earth.
\nSince its discovery in the early 1990s, scientists have conducted deep drilling and geophysical surveys of the impact structure to find out more about its size, composition, structure, age, and biological effects and to understand its lingering influences on the regional groundwater system. These efforts culminated in the drilling of a 1-mile-deep, continuously sampled corehole in 2005 by an international group of scientists and agencies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip159","issn":"2332–3531","isbn":"2332–354X","usgsCitation":"Powars, D.S., Edwards, L.E., Gohn, G.S., and Horton, J.W., Jr., 2015, Chesapeake Bay impact structure—A blast from the past: U.S. Geological Survey General Information Product 159, 2 p., https://dx.doi.org/10.3133/gip159.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-069162","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":310647,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0159/gip159.pdf","text":"Report","size":"346 KB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 159","linkHelpText":"Chesapeake Bay Impact Structure: A Blast from the Past - Bookmark"},{"id":310648,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://dx.doi.org/10.3133/fs20153071","text":"Fact Sheet 2015-3071","size":"1.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 159"},{"id":310646,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0159/coverthb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.003173828125,\n 36.641977814705946\n ],\n [\n -77.003173828125,\n 37.79676317682161\n ],\n [\n -75.0531005859375,\n 37.79676317682161\n ],\n [\n -75.0531005859375,\n 36.641977814705946\n ],\n [\n -77.003173828125,\n 36.641977814705946\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Eastern Geology and Paleoclimate Science Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://geology.er.usgs.gov/egps
A series of 10 digital flood-inundation maps were developed for a 3.3-mile reach of the North River in Colrain, Charlemont, and Shelburne, Massachusetts, by the U.S. Geological Survey in cooperation with the Federal Emergency Management Agency. The coverage of the maps extends from the confluence of the East and West Branch North Rivers to the Deerfield River. Peak-flow estimates at the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities were computed for the reach from updated flood-frequency analyses. These peak flows were routed through a one-dimensional step-backwater hydraulic model to obtain the corresponding peak water-surface elevations and to place the tropical storm Irene flood of August 28, 2011, into historical context. The hydraulic model was calibrated by using the current [2015] stage-discharge relation at the U.S. Geological Survey streamgage North River at Shattuckville, MA (station number 01169000), and from documented high-water marks from the tropical storm Irene flood, which had a peak flow with approximately a 0.2-percent annual exceedance probability.
\nA hydraulic model was used to compute water-surface profiles for 10 flood stages referenced to the streamgage and ranging from 6.6 feet (ft; 464.5 ft North American Vertical Datum of 1988 [which is approximately bankfull]) to 18.3 ft (476.2 ft North American Vertical Datum of 1988 [which is the stage of the 0.2-percent annual exceedance probability peak flow and exceeds the maximum recorded water level at the streamgage and the National Weather Service major flood stage of 13.0 ft]. The mapped stages of 6.6 to 18.3 ft were selected to match the stages of flows for bankfull; the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities; and an incremental stage of 17.0 ft. The simulated water-surface profiles were combined with a geographic information system digital elevation model derived from light detection and ranging (lidar) data with a 0.5-ft vertical accuracy to create a set of flood-inundation maps.
\nThe availability of the flood-inundation maps, combined with information regarding near-real-time stage from the U.S. Geological Survey North River at Shattuckville, MA streamgage can provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures, and postflood recovery efforts. The flood-inundation maps are nonregulatory, but provide Federal, State, and local agencies and the public with estimates of the potential extent of flooding during selected peak-flow events. Introduction
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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Or visit our Web site at
http://newengland.water.usgs.gov/