In October 2012, the U.S. Geological Survey (USGS) began measuring the concentration of the pesticide fipronil and three of its degradates (desulfinylfipronil, fipronil sulfide, and fipronil sulfone) by a new laboratory method using direct aqueous-injection liquid chromatography tandem mass spectrometry (DAI LC–MS/MS). This method replaced the previous method—in use since 2002—that used gas chromatography/mass spectrometry (GC/MS). The performance of the two methods is not comparable for fipronil and the three degradates. Concentrations of these four chemical compounds determined by the DAI LC–MS/MS method are substantially lower than the GC/MS method. A method was developed to correct for the difference in concentrations obtained by the two laboratory methods based on a methods comparison field study done in 2012. Environmental and field matrix spike samples to be analyzed by both methods from 48 stream sites from across the United States were sampled approximately three times each for this study. These data were used to develop a relation between the two laboratory methods for each compound using regression analysis. The relations were used to calibrate data obtained by the older method to the new method in order to remove any biases attributable to differences in the methods. The coefficients of the equations obtained from the regressions were used to calibrate over 16,600 observations of fipronil, as well as the three degradates determined by the GC/MS method retrieved from the USGS National Water Information System. The calibrated values were then compared to over 7,800 observations of fipronil and to the three degradates determined by the DAI LC–MS/MS method also retrieved from the National Water Information System. The original and calibrated values from the GC/MS method, along with measures of uncertainty in the calibrated values and the original values from the DAI LC–MS/MS method, are provided in an accompanying data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171056","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Crawford, C.G., and Martin, J.D., 2017, A method for addressing differences in concentrations of fipronil and three degradates obtained by two different laboratory methods: U.S. Geological Survey Open-File Report 2017–1056, 26 p., https://doi.org/10.3133/ofr20171056.","productDescription":"Report: vi, 26 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-085104","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":343953,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QC01QR","text":"USGS data release","description":"USGS data release","linkHelpText":"A Method for Addressing Differences in Concentrations of Fipronil and Three Degradates Obtained by Two Different Laboratory Methods"},{"id":342897,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1056/ofr20171056.pdf","text":"Report","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1056"},{"id":342896,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1056/coverthb.jpg"}],"contact":"National Water-Quality Assessment Project
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
In this study, we combined two 1 km actual evapotranspiration datasets (ET), one obtained from a root zone water balance model and another from an energy balance model, to partition annual ET into green (rainfall-based) and blue (surface water/groundwater) sources. Time series maps of green water ET (GWET) and blue water ET (BWET) are produced for the conterminous United States (CONUS) over 2001–2015. Our results indicate that average green and blue water for all land cover types in CONUS accounts for nearly 70% and 30% of the total ET, respectively. The ET in the eastern US arises mostly from GWET, and in the western US, it is mostly BWET. Analysis of the BWET in the 16 irrigated areas in CONUS revealed interesting results. While the magnitude of the BWET gradually showed a decline from west to east, the increase in coefficient of variation from west to east confirmed greater use of supplemental irrigation in the central and eastern US. We also established relationships between different hydro-climatology zones and their blue water requirements. This study provides insights on the relative contributions and the spatiotemporal dynamics of GWET and BWET, which could lead to improved water resources management.
Data from a long-term capture-recapture program were used to assess the status and dynamics of populations of two long-lived, federally endangered catostomids in Upper Klamath Lake, Oregon. Lost River suckers (LRS; Deltistes luxatus) and shortnose suckers (SNS; Chasmistes brevirostris) have been captured and tagged with passive integrated transponder (PIT) tags during their spawning migrations in each year since 1995. In addition, beginning in 2005, individuals that had been previously PIT-tagged were re-encountered on remote underwater antennas deployed throughout sucker spawning areas. Captures and remote encounters during the spawning season in spring 2015 were incorporated into capture-recapture analyses of population dynamics. Cormack-Jolly-Seber (CJS) open population capture-recapture models were used to estimate annual survival probabilities, and a reverse-time analog of the CJS model was used to estimate recruitment of new individuals into the spawning populations. In addition, data on the size composition of captured fish were examined to provide corroborating evidence of recruitment. Separate analyses were done for each species and also for each subpopulation of LRS. Shortnose suckers and one subpopulation of LRS migrate into tributary rivers to spawn, whereas the other LRS subpopulation spawns at groundwater upwelling areas along the eastern shoreline of the lake. Characteristics of the spawning migrations in 2015, such as the effects of temperature on the timing of the migrations, were similar to past years.
Capture-recapture analyses for the LRS subpopulation that spawns at the shoreline areas included encounter histories for 13,617 individuals, and analyses for the subpopulation that spawns in the rivers included 39,321 encounter histories. With a few exceptions, the survival of males and females in both subpopulations was high (greater than or equal to 0.86) between 1999 and 2013. Survival was notably lower for males from the rivers in 2000, 2006, and 2012. Survival probabilities were lower for males from the shoreline areas in 2002. Between 2001 and 2014, the abundance of males in the lakeshore spawning subpopulation decreased by at least 59 percent and the abundance of females decreased by at least 53 percent. By combining information from capture-recapture models and size composition data, we concluded that the abundance of both sexes in the river spawning subpopulation of LRS likely has decreased at rates similar to the rates for the lakeshore spawning subpopulation between 2002 and 2014. Capture-recapture analyses for SNS included encounter histories for 20,981 individuals. Most annual survival estimates between 2005 and 2009 were high (greater than 0.88), but both sexes of SNS experienced lower and more variable survival in 2001–04 and 2010–13. The best-case scenario for SNS, based on capture-recapture recruitment modeling, indicates that the abundance of males in the spawning population decreased by 77 percent and the abundance of females decreased by 74 percent between 2001 and 2014. Decreases in abundance for both sexes likely are greater than these estimates indicate. Despite relatively high survival in most years, we conclude that both species have experienced substantial decreases in the abundance of spawning adults because losses from mortality have not been balanced by recruitment of new individuals. The status of the endangered sucker populations in Upper Klamath Lake remains worrisome, especially for SNS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171059","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hewitt, D.A., Janney, E.C., Hayes, B.S., and Harris, A.C., 2017, Status and trends of adult Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) sucker populations in Upper Klamath Lake, Oregon, 2015: U.S. Geological Survey Open-File Report 2017–1059, 38 p., https://doi.org/10.3133/ofr20171059.","productDescription":"iv, 38 p.","onlineOnly":"Y","ipdsId":"IP-081967","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":344162,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1059/ofr20171059.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1059"},{"id":344161,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1059/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.17,\n 42.2\n ],\n [\n -121.75,\n 42.2\n ],\n [\n -121.75,\n 42.62\n ],\n [\n -122.17,\n 42.62\n ],\n [\n -122.17,\n 42.2\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
Because fast and efficient serial processing of raster-graphic images and other two-dimensional arrays is a requirement in land-change modeling and other applications, the effects of 10 factors on the runtimes for processing two-dimensional arrays with C and C++ are evaluated in a comparative factorial study. This study’s factors include the choice among three C or C++ source-code techniques for array processing; the choice of Microsoft Windows 7 or a Linux operating system; the choice of 4-byte or 8-byte array elements and indexes; and the choice of 32-bit or 64-bit memory addressing. This study demonstrates how programmer choices can reduce runtimes by 75 percent or more, even after compiler optimizations. Ten points of practical advice for faster processing of two-dimensional arrays are offered to C and C++ programmers. Further study and the development of a C and C++ software test suite are recommended.
Key words: array processing, C, C++, compiler, computational speed, land-change modeling, raster-graphic image, two-dimensional array, software efficiency
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section E: Evaluating and Improving Computational Performance in Book 7: Automated Data Processing and Computations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7E1","usgsCitation":"Donato, D.I., 2017, Efficient processing of two-dimensional arrays with C or C++: U.S. Geological Survey Techniques and Methods Report 7–E1, 58 pages, https://doi.org/10.3133/tm7E1.","productDescription":"Report: ix, 58 p.; Appendixes; Data Release","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066329","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":342931,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/07/e01/appendix/tm7e1_erc-appendix6.zip","text":"Appendix 6","size":"7.82 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Scripts and Code for Conducting Timing Tests on Windows"},{"id":342929,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/07/e01/appendix/tm7e1_erc-appendix4.zip","text":"Appendix 4","size":"11.4 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Source Code for C++ Test Programs"},{"id":342114,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/e01/tm7e1.pdf","text":"Report","size":"2.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-E1"},{"id":342930,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/07/e01/appendix/tm7e1_erc-appendix5.zip","text":"Appendix 5","size":"5.34 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Scripts and Code for Conducting Timing Tests on Linux"},{"id":342928,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/07/e01/appendix/tm7e1_erc-appendix3.zip","text":"Appendix 3","size":"11.3 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Source Code for C Test Programs"},{"id":342115,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7W66HZS","text":"USGS data release","description":"USGS data release","linkHelpText":"Runtimes for Tests of Array-Processing Speed: Factorial Tests Using C and C++ Under Windows and Linux"},{"id":342113,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/e01/coverthb2.jpg"}],"publicComments":"This report is Chapter 1 of Section E: Evaluating and Improving Computational Performance in Book 7: Automated Data Processing and Computations.","contact":"Director, Eastern Geographic Science Center
U.S. Geological Survey
521 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
High‐resolution regional‐scale hydrologic models were used to quantify the response of late 21st century runoff from the Gulf of Alaska (GOA) watershed to changes in regional climate and glacier extent. NCEP Climate Forecast System Reanalysis data were combined with five Coupled Model Intercomparison Project Phase 5 general circulation models (GCMs) for two representative concentration pathway (RCP) scenarios (4.5 and 8.5) to develop meteorological forcing for the period 2070–2099. A hypsographic model was used to estimate future glacier extent given assumed equilibrium line altitude (ELA) increases of 200 and 400 m. GCM predictions show an increase in annual precipitation of 12% for RCP 4.5 and 21% for RCP 8.5, and an increase in annual temperature of 2.5°C for RCP 4.5 and 4.3°C for RCP 8.5, averaged across the GOA. Scenarios with perturbed climate and glaciers predict annual GOA‐wide runoff to increase by 9% for RCP4.5/ELA200 case and 14% for the RCP8.5/ELA400 case. The glacier runoff decreased by 14% for RCP4.5/ELA200 and by 34% for the RCP8.5/ELA400 case. Intermodel variability in annual runoff was found to be approximately twice the variability in precipitation input. Additionally, there are significant changes in runoff partitioning and increases in snowpack runoff are dominated by increases in rain‐on‐snow events. We present results aggregated across the entire GOA and also for individual watersheds to illustrate the range in hydrologic regime changes and explore the sensitivities of these results by independently perturbing only climate forcings and only glacier cover.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016WR020033","usgsCitation":"Beamer, J., Hill, D., Mcgrath, D., Arendt, A.A., and Kienholz, C., 2017, Hydrologic impacts of changes in climate and glacier extent in the Gulf of Alaska watershed: Water Resources Research, v. 53, no. 9, p. 7502-7520, https://doi.org/10.1002/2016WR020033.","productDescription":"19 p.","startPage":"7502","endPage":"7520","ipdsId":"IP-081123","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":369083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia, Yukon","otherGeospatial":"Gulf of Alaska watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.65234374999997,\n 59.80063426102869\n ],\n [\n -134.560546875,\n 50.736455137010665\n ],\n [\n -123.662109375,\n 52.74959372674114\n ],\n [\n -137.197265625,\n 64.92354174306496\n ],\n [\n -163.65234374999997,\n 59.80063426102869\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"53","issue":"9","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Beamer, Jordan","contributorId":220414,"corporation":false,"usgs":false,"family":"Beamer","given":"Jordan","affiliations":[{"id":34888,"text":"Oregon Water Resources Department","active":true,"usgs":false}],"preferred":false,"id":774924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hill, Dave","contributorId":220415,"corporation":false,"usgs":false,"family":"Hill","given":"Dave","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":774925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mcgrath, Daniel 0000-0002-9462-6842 dmcgrath@usgs.gov","orcid":"https://orcid.org/0000-0002-9462-6842","contributorId":145635,"corporation":false,"usgs":true,"family":"Mcgrath","given":"Daniel","email":"dmcgrath@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":774923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arendt, Anthony A.","contributorId":200572,"corporation":false,"usgs":false,"family":"Arendt","given":"Anthony","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":774926,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kienholz, Christian","contributorId":220416,"corporation":false,"usgs":false,"family":"Kienholz","given":"Christian","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":774927,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188682,"text":"sir20175069 - 2017 - Physical characteristics of the lower San Joaquin River, California, in relation to white sturgeon spawning habitat, 2011–14","interactions":[],"lastModifiedDate":"2017-07-20T10:50:14","indexId":"sir20175069","displayToPublicDate":"2017-07-19T00:00:00","publicationYear":"2017","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":"2017-5069","title":"Physical characteristics of the lower San Joaquin River, California, in relation to white sturgeon spawning habitat, 2011–14","docAbstract":"The U.S. Fish and Wildlife Service confirmed that white sturgeon (Acipenser transmontanus) recently spawned in the lower San Joaquin River, California. Decreases in the San Francisco Bay estuary white sturgeon population have led to an increased effort to understand their migration behavior and habitat preferences. The preferred spawning habitat of other white sturgeon (for example, those in the Columbia and Klamath Rivers) is thought to be areas that have high water velocity, deep pools, and coarse bed material. Coarse bed material (pebbles and cobbles), in particular, is important for the survival of white sturgeon eggs and larvae. Knowledge of the physical characteristics of the lower San Joaquin River can be used to preserve sturgeon spawning habitat and lead to management decisions that could help increase the San Francisco Bay estuary white sturgeon population.
Between 2011 and 2014, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, assessed selected reaches and tributaries of the lower river in relation to sturgeon spawning habitat by (1) describing selected spawning reaches in terms of habitat-related physical characteristics (such as water depth and velocity, channel slope, and bed material) of the lower San Joaquin River between its confluences with the Stanislaus and Merced Rivers, (2) describing variations in these physical characteristics during wet and dry years, and (3) identifying potential reasons for these variations.
The lower San Joaquin River was divided into five study reaches. Although data were collected from all study reaches, three subreaches where the USFWS collected viable eggs at multiple sites in 2011–12 from Orestimba Creek to Sturgeon Bend were of special interest. Water depth and velocity were measured using two different approaches—channel cross sections and longitudinal profiles—and data were collected using an acoustic Doppler current profiler.
During the first year of data collection (water year 2011), runoff was greatest, and gaged streamflow, measured as discharge, peaked at 875 cubic meters per second in the lower San Joaquin River. Also during that year, water velocity was generally between 0.6 and 0.9 meters per second, and depth was typically between 2.5 and 4.5 meters, but water depth exceeded 6 meters in several pools. Water year 2011 was classified as a “wet” year. Later water years were classified as either “dry” (water year 2012) or “critical” (water years 2013 and 2014). During the drier years, water was shallower, and velocities were slower. The streambed aggraded in several areas during the study. At Sturgeon Bend, for example, which had the deepest pool measured in 2011 (maximum depth was 14 meters), about 8 meters of sediment was deposited by 2014.
The bed of the lower San Joaquin River was predominately sand, except in areas downstream from the mouth of Del Puerto Creek. A large amount of sand, gravel, and cobble was deposited at the mouth of Del Puerto Creek, and in the 9.5 kilometers downstream from the mouth of Del Puerto Creek, we encountered several gravel bars and patches of gravel-size (8–64 millimeters) bed material. Del Puerto and Orestimba Creeks drain from the Coast Ranges on the west side of the river. Only small quantities of gravel-size bed material were observed in the reach downstream from Orestimba Creek, indicating Orestimba Creek does not deliver much coarse sediment to the lower San Joaquin River. Del Puerto Creek appeared to be the primary source of gravels suitable for white sturgeon spawning in the lower San Joaquin River, and thus, it is important for the long-term spawning success of sturgeon in the San Joaquin River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175069","collaboration":"Prepared in cooperation with the United States Fish and Wildlife Service","usgsCitation":"Marineau, M.D., Wright, S.A., Whealdon-Haught, D.R., Kinzel, P.J., 2017, Physical characteristics of the lower San Joaquin River, California, in relation to white sturgeon spawning habitat, 2011–14: U.S. Geological Survey Scientific Investigation Report 2017–5069, 47 p., https://doi.org/10.3133/sir20175069.","productDescription":"vii, 47 p.","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-051877","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":438264,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7W66HWW","text":"USGS data release","linkHelpText":"Depth and Velocity Data in the Lower San Joaquin River, California, 2011-2014"},{"id":344080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5069/sir20175069.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5069"},{"id":344079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5069/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.794677734375,\n 41.95949009892467\n ],\n [\n -121.46484375,\n 41.376808565702355\n ],\n [\n -121.97021484374999,\n 41.64007838467894\n ],\n [\n -122.58544921875,\n 41.64007838467894\n ],\n [\n -123.00292968749999,\n 41.16211393939692\n ],\n [\n -122.71728515624999,\n 40.713955826286046\n ],\n [\n -123.50830078125,\n 40.51379915504413\n ],\n [\n -123.28857421875,\n 40.01078714046552\n ],\n [\n -122.80517578125,\n 39.7240885773337\n ],\n [\n -123.1787109375,\n 39.436192999314095\n ],\n [\n -122.607421875,\n 39.11301365149975\n ],\n [\n -121.39892578125,\n 37.07271048132943\n ],\n [\n -119.81689453125,\n 34.65128519895413\n ],\n [\n -118.740234375,\n 34.50655662164561\n ],\n [\n -117.42187500000001,\n 34.994003757575776\n ],\n [\n -118.16894531249999,\n 36.26199220445664\n ],\n [\n -119.520263671875,\n 37.86618078529668\n ],\n [\n -120.28930664062499,\n 39.38526381099774\n ],\n [\n -120.091552734375,\n 39.51251701659638\n ],\n [\n -120.47607421874999,\n 40.455307212131494\n ],\n [\n -120.157470703125,\n 41.15384235711447\n ],\n [\n -120.16845703125,\n 41.983994270935625\n ],\n [\n -120.794677734375,\n 41.95949009892467\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Groundwater has been the primary source of domestic, agricultural, and municipal water supplies in the southwestern Mojave Desert, California, since the early 1900s. Increased demands on water supplies have caused groundwater-level declines of more than 100 feet (ft) in some areas of this desert between the 1950s and the 1990s (Stamos and others, 2001; Sneed and others, 2003). These water-level declines have caused the aquifer system to compact, resulting in land subsidence. Differential land subsidence (subsidence occurring at different rates across the landscape) can alter surface drainage routes and damage surface and subsurface infrastructure. For example, fissuring across State Route 247 at Lucerne Lake has required repairs as has pipeline infrastructure near Troy Lake.
Land subsidence within the Mojave River and Morongo Groundwater Basins of the southwestern Mojave Desert has been evaluated using InSAR, ground-based measurements, geology, and analyses of water levels between 1992 and 2009 (years in which InSAR data were collected). The results of the analyses were published in three USGS reports— Sneed and others (2003), Stamos and others (2007), and Solt and Sneed (2014). Results from the latter two reports were integrated with results from other USGS/ MWA cooperative groundwater studies into the broader scoped USGS Mojave Groundwater Resources Web site (http://ca.water.usgs.gov/ mojave/). This fact sheet combines the detailed analyses from the three subsidence reports, distills them into a longer-term context, and provides an assessment of options for future monitoring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173053","usgsCitation":"Brandt, Justin, and Sneed, Michelle, 2017, Land subsidence in the southwestern Mojave Desert, California, 1992–2009: U.S. Geological Survey Fact Sheet 2017-3053, 6 p., https://doi.org/10.3133/fs20173053.","productDescription":"6 p. ","ipdsId":"IP-072664","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":344070,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3053/fs20173053.pdf","text":"Report","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3053"},{"id":344069,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3053/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.49053955078125,\n 34.04128062212254\n ],\n [\n -116.30126953125,\n 34.04128062212254\n ],\n [\n -116.30126953125,\n 35.099686964274724\n ],\n [\n -117.49053955078125,\n 35.099686964274724\n ],\n [\n -117.49053955078125,\n 34.04128062212254\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"U.S. Geological Survey
6000 J Street, Placer Hall,
California State University, Sacramento
Sacramento, CA 95819
https://ca.water.usgs.gov/mojave
The increasing demands on groundwater for water supply in desert areas in California and the western United States have resulted in the need to better understand groundwater sources, availability, and sustainability. This is true for a 650-square-mile area that encompasses the Antelope Valley, El Mirage Valley, and Upper Mojave River Valley groundwater basins, about 50 miles northeast of Los Angeles, California, in the western part of the Mojave Desert. These basins have been adjudicated to ensure that groundwater rights are allocated according to legal judgments. In an effort to assess if the boundary between the Antelope Valley and El Mirage Valley groundwater basins could be better defined, the U.S. Geological Survey began a cooperative study in 2014 with the Mojave Water Agency to better understand the hydrogeology in the area and investigate potential controls on groundwater flow and availability, including basement topography.
Recharge is sporadic and primarily from small ephemeral washes and streams that originate in the San Gabriel Mountains to the south; estimates range from about 400 to 1,940 acre-feet per year. Lateral underflow from adjacent basins has been considered minor in previous studies; underflow from the Antelope Valley to the El Mirage Valley groundwater basin has been estimated to be between 100 and 1,900 acre-feet per year. Groundwater discharge is primarily from pumping, mostly by municipal supply wells. Between October 2013 and September 2014, the municipal pumpage in the Antelope Valley and El Mirage Valley groundwater basins was reported to be about 800 and 2,080 acre-feet, respectively.
This study was motivated by the results from a previously completed regional gravity study, which suggested a northeast-trending subsurface basement ridge and saddle approximately 3.5 miles west of the boundary between the Antelope Valley and El Mirage Valley groundwater basins that might influence groundwater flow. To better define potential basement structures that could affect groundwater flow between the groundwater basins in the study area, gravity data were collected using more closely spaced measurements in September 2014. Groundwater-level data was gathered and collected from March 2014 through March 2015 to determine depth to water and direction of groundwater flow. The gravity and groundwater-level data showed that the saturated thickness of the alluvium was about 2,000 feet thick to the east and about 130 feet thick above the northward-trending basement ridge near Llano, California. Although it was uncertain whether the basement ridge affects the groundwater system, a potential barrier to groundwater flow could be created if the water table fell below the altitude of the basement ridge, effectively causing the area to the west of the basement ridge to become hydraulically isolated from the area to the east. In addition, the direction of regional-groundwater flow likely will be influenced by future changes in the number and distribution of pumping wells and the thickness of the saturated alluvium from which water is withdrawn. Three-dimensional animations were created to help visualize the relation between the basins’ basement topography and the groundwater system in the area. Further studies that could help to more accurately define the basins and evaluate the groundwater-flow system include exploratory drilling of multi-depth monitoring wells; collection of depth-dependent water-quality samples; and linking together existing, but separate, groundwater-flow models from the Antelope Valley and El Mirage Valley groundwater basins into a single, calibrated groundwater-flow model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175065","collaboration":"Prepared in cooperation with the Mojave Water Agency","usgsCitation":"Stamos, C.L., Christensen, A.H., and Langenheim, V.E., 2017, Preliminary hydrogeologic assessment near the boundary of the Antelope Valley and El Mirage Valley groundwater basins, California: U.S. Geological Survey Scientific Investigations Report 2017–5065, 44 p., https://doi.org/10.3133/sir20175065.","productDescription":"Report: vii, 44 p.; 2 Figures","onlineOnly":"Y","ipdsId":"IP-064470","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":343370,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5065/sir20175065_fig14_dewatering.mp4","text":"Figure 14.","size":"18 MB","description":"SIR 2017-5065 Animation","linkHelpText":"- Animation showing the potential dewatering of the saturated alluvium starting with the 2014–15 water-table altitude and assuming an incremental 16.4 feet (5 meter) drop per frame of the water table, near Piñon Hills, California."},{"id":343369,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5065/sir20175065_fig13_gravity.mp4","text":"Figure 13.","size":"11 MB","description":"SIR 2017-5065 Animation","linkHelpText":"- Animation showing the altitude of the top of the basement rocks based on the gravity data and altitude of the water table in 2014–15, near Piñon Hills, California. "},{"id":343217,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5065/sir20175065.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5065"},{"id":343216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5065/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Antelope Valley groundwater basin, El Mirage Valley groundwater basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.033333,\n 34.366667\n ],\n [\n -117.5,\n 34.366667\n ],\n [\n -117.5,\n 34.75\n ],\n [\n -118.033333,\n 34.75\n ],\n [\n -118.033333,\n 34.366667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Managers require accurate models to predict post-fire tree mortality to plan prescribed fire treatments and examine their effectiveness. Here we assess the performance of a common post-fire tree mortality model with an independent dataset of 11 tree species from 13 National Park Service units in the western USA. Overall model discrimination was generally strong, but performance varied considerably among species and sites. The model tended to have higher sensitivity (proportion of correctly classified dead trees) and lower specificity (proportion of correctly classified live trees) for many species, indicating an overestimation of mortality. Variation in model accuracy (percentage of live and dead trees correctly classified) among species was not related to sample size or percentage observed mortality. However, we observed a positive relationship between specificity and a species-specific bark thickness multiplier, indicating that overestimation was more common in thin-barked species. Accuracy was also quite low for thinner bark classes (<1 cm) for many species, leading to poorer model performance. Our results indicate that a common post-fire mortality model generally performs well across a range of species and sites; however, some thin-barked species and size classes would benefit from further refinement to improve model specificity.
","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/WF16081","collaboration":"NPS, FS, JFSP","usgsCitation":"Kane, J.M., van Mantgem, P.J., Lalemand, L., and Keifer, M., 2017, Higher sensitivity and lower specificity in post-fire mortality model validation of 11 western US tree species: International Journal of Wildland Fire, v. 26, no. 5, p. 444-454, https://doi.org/10.1071/WF16081.","productDescription":"11 p.","startPage":"444","endPage":"454","ipdsId":"IP-075011","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":344043,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"5","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59706faee4b0d1f9f065a85a","contributors":{"authors":[{"text":"Kane, Jeffrey M.","contributorId":181978,"corporation":false,"usgs":false,"family":"Kane","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":705587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422 pvanmantgem@usgs.gov","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":2838,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip","email":"pvanmantgem@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":705586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lalemand, Laura 0000-0001-8025-5975 llalemand@usgs.gov","orcid":"https://orcid.org/0000-0001-8025-5975","contributorId":174212,"corporation":false,"usgs":true,"family":"Lalemand","given":"Laura","email":"llalemand@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":705588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keifer, MaryBeth","contributorId":194887,"corporation":false,"usgs":false,"family":"Keifer","given":"MaryBeth","email":"","affiliations":[],"preferred":false,"id":705589,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189666,"text":"70189666 - 2017 - Inundation, vegetation, and sediment effects on litter decomposition in Pacific Coast tidal marshes","interactions":[],"lastModifiedDate":"2018-03-26T12:15:53","indexId":"70189666","displayToPublicDate":"2017-07-19T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Inundation, vegetation, and sediment effects on litter decomposition in Pacific Coast tidal marshes","docAbstract":"The cycling and sequestration of carbon are important ecosystem functions of estuarine wetlands that may be affected by climate change. We conducted experiments across a latitudinal and climate gradient of tidal marshes in the northeast Pacific to evaluate the effects of climate- and vegetation-related factors on litter decomposition. We manipulated tidal exposure and litter type in experimental mesocosms at two sites and used variation across marsh landscapes at seven sites to test for relationships between decomposition and marsh elevation, soil temperature, vegetation composition, litter quality, and sediment organic content. A greater than tenfold increase in manipulated tidal inundation resulted in small increases in decomposition of roots and rhizomes of two species, but no significant change in decay rates of shoots of three other species. In contrast, across the latitudinal gradient, decomposition rates of Salicornia pacifica litter were greater in high marsh than in low marsh. Rates were not correlated with sediment temperature or organic content, but were associated with plant assemblage structure including above-ground cover, species composition, and species richness. Decomposition rates also varied by litter type; at two sites in the Pacific Northwest, the grasses Deschampsia cespitosa and Distichlis spicata decomposed more slowly than the forb S. pacifica. Our data suggest that elevation gradients and vegetation structure in tidal marshes both affect rates of litter decay, potentially leading to complex spatial patterns in sediment carbon dynamics. Climate change may thus have direct effects on rates of decomposition through increased inundation from sea-level rise and indirect effects through changing plant community composition.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-017-0111-6","usgsCitation":"Janousek, C., Buffington, K., Guntenspergen, G.R., Thorne, K.M., Dugger, B., and Takekawa, J.Y., 2017, Inundation, vegetation, and sediment effects on litter decomposition in Pacific Coast tidal marshes: Ecosystems, v. 20, no. 7, p. 1296-1310, https://doi.org/10.1007/s10021-017-0111-6.","productDescription":"15 p.","startPage":"1296","endPage":"1310","ipdsId":"IP-082125","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":438262,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70P0X6C","text":"USGS data release","linkHelpText":"Decomposition of plant litter in Pacific coast tidal marshes, 2014-2015"},{"id":344068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126,\n 33\n ],\n [\n -115,\n 33\n ],\n [\n -115,\n 48\n ],\n [\n -126,\n 48\n ],\n [\n -126,\n 33\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"7","noUsgsAuthors":false,"publicationDate":"2017-02-03","publicationStatus":"PW","scienceBaseUri":"59706faee4b0d1f9f065a857","contributors":{"authors":[{"text":"Janousek, Christopher 0000-0003-2124-6715 cjanousek@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":150053,"corporation":false,"usgs":true,"family":"Janousek","given":"Christopher","email":"cjanousek@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":705682,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":705683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":705684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":705685,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dugger, Bruce D.","contributorId":81236,"corporation":false,"usgs":true,"family":"Dugger","given":"Bruce D.","affiliations":[],"preferred":false,"id":705686,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Takekawa, John Y. 0000-0003-0217-5907 john_takekawa@usgs.gov","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":176168,"corporation":false,"usgs":true,"family":"Takekawa","given":"John","email":"john_takekawa@usgs.gov","middleInitial":"Y.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":705687,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70189328,"text":"ofr20171015 - 2017 - National assessment of shoreline change—Summary statistics for updated vector shorelines and associated shoreline change data for the Gulf of Mexico and Southeast Atlantic coasts","interactions":[],"lastModifiedDate":"2017-07-19T08:25:32","indexId":"ofr20171015","displayToPublicDate":"2017-07-18T15:00:00","publicationYear":"2017","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":"2017-1015","title":"National assessment of shoreline change—Summary statistics for updated vector shorelines and associated shoreline change data for the Gulf of Mexico and Southeast Atlantic coasts","docAbstract":"Long-term rates of shoreline change for the Gulf of Mexico and Southeast Atlantic regions of the United States have been updated as part of the U.S. Geological Survey’s National Assessment of Shoreline Change project. Additional shoreline position data were used to compute rates where the previous rate-of-change assessment only included four shoreline positions at a given location. The long-term shoreline change rates also incorporate the proxy-datum bias correction to account for the unidirectional onshore bias of the proxy-based high water line shorelines relative to the datum-based mean high water shorelines. The calculation of uncertainty associated with the long-term average rates has also been updated to match refined methods used in other study regions of the National Assessment project. The average rates reported here have a reduced amount of uncertainty relative to those presented in the previous assessments for these two regions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171015","usgsCitation":"Himmelstoss, E.A., Kratzmann, M.G., and Thieler, E.R., 2017, National assessment of shoreline change—Summary statistics for updated vector shorelines and associated shoreline change data for the Gulf of Mexico and Southeast Atlantic coasts: U.S. Geological Survey Open-File Report 2017–1015, 8 p., https://doi.org/10.3133/ofr20171015.","productDescription":"Report: vi, 8 p.; 2 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-080390","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":343570,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1015/coverthb.jpg"},{"id":343571,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1015/ofr20171015.pdf","text":"Report","size":"567 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\"}}]}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543
Climate change, tectonics, and humans create long- and short-term temporal variations in the supply of suspended sediment to rivers. These signals, generated in upland erosional areas, are filtered by alluvial storage before reaching the basin outlet. We quantified this filter using a random walk model driven by sediment budget data, a power-law distributed probability density function (PDF) to determine how long sediment remains stored, and a constant downstream drift velocity during transport of 157 km/yr. For 25 km of transport, few particles are stored, and the median travel time is 0.2 yr. For 1000 km of transport, nearly all particles are stored, and the median travel time is 2.5 m.y. Both travel-time distributions are power laws. The 1000 km travel-time distribution was then used to filter sinusoidal input signals with periods of 10 yr and 104 yr. The 10 yr signal is delayed by 12.5 times its input period, damped by a factor of 380, and is output as a power law. The 104 yr signal is delayed by 0.15 times its input period, damped by a factor of 3, and the output signal retains its sinusoidal input form (but with a power-law “tail”). Delivery time scales for these two signals are controlled by storage; in-channel transport time is insignificant, and low-frequency signals are transmitted with greater fidelity than high-frequency signals. These signal modifications are essential to consider when evaluating watershed restoration schemes designed to control sediment loading, and where source-area geomorphic processes are inferred from the geologic record.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G38170.1","usgsCitation":"Pizzuto, J.E., Keeler, J., Skalak, K., and Karwan, D., 2017, Storage filters upland suspended sediment signals delivered from watersheds: Geology, v. 45, no. 2, p. 151-154, https://doi.org/10.1130/G38170.1.","productDescription":"4 p.","startPage":"151","endPage":"154","ipdsId":"IP-081208","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":343977,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-01","publicationStatus":"PW","scienceBaseUri":"596f1e1ee4b0d1f9f0640734","contributors":{"authors":[{"text":"Pizzuto, James E.","contributorId":49424,"corporation":false,"usgs":false,"family":"Pizzuto","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":13220,"text":"The Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":705310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keeler, Jeremy","contributorId":194778,"corporation":false,"usgs":false,"family":"Keeler","given":"Jeremy","email":"","affiliations":[],"preferred":false,"id":705311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skalak, Katherine 0000-0003-4122-1240 kskalak@usgs.gov","orcid":"https://orcid.org/0000-0003-4122-1240","contributorId":3990,"corporation":false,"usgs":true,"family":"Skalak","given":"Katherine","email":"kskalak@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":705309,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karwan, Diana","contributorId":194779,"corporation":false,"usgs":false,"family":"Karwan","given":"Diana","affiliations":[],"preferred":false,"id":705312,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189569,"text":"70189569 - 2017 - Limiting the effects of earthquakes on gravitational-wave interferometers","interactions":[],"lastModifiedDate":"2017-07-18T08:29:36","indexId":"70189569","displayToPublicDate":"2017-07-18T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5464,"text":"Classical and Quantum Gravity","active":true,"publicationSubtype":{"id":10}},"title":"Limiting the effects of earthquakes on gravitational-wave interferometers","docAbstract":"Ground-based gravitational wave interferometers such as the Laser Interferometer Gravitational-wave Observatory (LIGO) are susceptible to ground shaking from high-magnitude teleseismic events, which can interrupt their operation in science mode and significantly reduce their duty cycle. It can take several hours for a detector to stabilize enough to return to its nominal state for scientific observations. The down time can be reduced if advance warning of impending shaking is received and the impact is suppressed in the isolation system with the goal of maintaining stable operation even at the expense of increased instrumental noise. Here, we describe an early warning system for modern gravitational-wave observatories. The system relies on near real-time earthquake alerts provided by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). Preliminary low latency hypocenter and magnitude information is generally available in 5 to 20 min of a significant earthquake depending on its magnitude and location. The alerts are used to estimate arrival times and ground velocities at the gravitational-wave detectors. In general, 90% of the predictions for ground-motion amplitude are within a factor of 5 of measured values. The error in both arrival time and ground-motion prediction introduced by using preliminary, rather than final, hypocenter and magnitude information is minimal. By using a machine learning algorithm, we develop a prediction model that calculates the probability that a given earthquake will prevent a detector from taking data. Our initial results indicate that by using detector control configuration changes, we could prevent interruption of operation from 40 to 100 earthquake events in a 6-month time-period.
","language":"English","publisher":"Institute of Physics","doi":"10.1088/1361-6382/aa5a60","usgsCitation":"Coughlin, M., Earle, P.S., Harms, J., Biscans, S., Buchanan, C., Coughlin, E., Donovan, F., Fee, J., Gabbard, H., Guy, M.M., Mukund, N., and Perry, M., 2017, Limiting the effects of earthquakes on gravitational-wave interferometers: Classical and Quantum Gravity, v. 34, no. 4, Article 044004: 14 p., https://doi.org/10.1088/1361-6382/aa5a60.","productDescription":"Article 044004: 14 p.","ipdsId":"IP-083270","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":469676,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/1611.09812","text":"External Repository"},{"id":343966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-02","publicationStatus":"PW","scienceBaseUri":"596f1e20e4b0d1f9f0640744","contributors":{"authors":[{"text":"Coughlin, Michael","contributorId":194752,"corporation":false,"usgs":false,"family":"Coughlin","given":"Michael","email":"","affiliations":[],"preferred":false,"id":705250,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harms, Jan","contributorId":194753,"corporation":false,"usgs":false,"family":"Harms","given":"Jan","email":"","affiliations":[],"preferred":false,"id":705252,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Biscans, Sebastien","contributorId":194754,"corporation":false,"usgs":false,"family":"Biscans","given":"Sebastien","email":"","affiliations":[],"preferred":false,"id":705253,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buchanan, Christopher","contributorId":194755,"corporation":false,"usgs":false,"family":"Buchanan","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":705254,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coughlin, Eric","contributorId":194756,"corporation":false,"usgs":false,"family":"Coughlin","given":"Eric","email":"","affiliations":[],"preferred":false,"id":705255,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Donovan, Fred","contributorId":194757,"corporation":false,"usgs":false,"family":"Donovan","given":"Fred","email":"","affiliations":[],"preferred":false,"id":705256,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fee, Jeremy 0000-0002-6851-2796 jmfee@usgs.gov","orcid":"https://orcid.org/0000-0002-6851-2796","contributorId":194758,"corporation":false,"usgs":true,"family":"Fee","given":"Jeremy","email":"jmfee@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705257,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gabbard, Hunter","contributorId":194759,"corporation":false,"usgs":false,"family":"Gabbard","given":"Hunter","email":"","affiliations":[],"preferred":false,"id":705258,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Guy, Michelle M. 0000-0003-3450-4656 mguy@usgs.gov","orcid":"https://orcid.org/0000-0003-3450-4656","contributorId":173432,"corporation":false,"usgs":true,"family":"Guy","given":"Michelle","email":"mguy@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705259,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mukund, Nikhil","contributorId":194760,"corporation":false,"usgs":false,"family":"Mukund","given":"Nikhil","email":"","affiliations":[],"preferred":false,"id":705260,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Perry, Matthew","contributorId":194761,"corporation":false,"usgs":false,"family":"Perry","given":"Matthew","affiliations":[],"preferred":false,"id":705261,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70189586,"text":"fs20173054 - 2017 - Brackish groundwater and its potential to augment freshwater supplies","interactions":[],"lastModifiedDate":"2017-07-19T08:49:00","indexId":"fs20173054","displayToPublicDate":"2017-07-18T00:00:00","publicationYear":"2017","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":"2017-3054","title":"Brackish groundwater and its potential to augment freshwater supplies","docAbstract":"Secure, reliable, and sustainable water resources are fundamental to the Nation’s food production, energy independence, and ecological and human health and well-being. Indications are that at any given time, water resources are under stress in selected parts of the country. The large-scale development of groundwater resources has caused declines in the amount of groundwater in storage and declines in discharges to surface water bodies (Reilly and others, 2008). Water supply in some regions, particularly in arid and semiarid regions, is not adequate to meet demand, and severe drought intensifies the stresses affecting water resources (National Drought Mitigation Center, the U.S. Department of Agriculture, and the National Oceanic and Atmospheric Association, 2015). If these drought conditions continue, water shortages could adversely affect the human condition and threaten environmental flows necessary to maintain ecosystem health.
In support of the national census of water resources, the U.S. Geological Survey (USGS) completed the national brackish groundwater assessment to provide updated information about brackish groundwater as a potential resource to augment or replace freshwater supplies (Stanton and others, 2017). Study objectives were to consolidate available data into a comprehensive database of brackish groundwater resources in the United States and to produce a summary report highlighting the distribution, physical and chemical characteristics, and use of brackish groundwater resources. This assessment was authorized by section 9507 of the Omnibus Public Land Management Act of 2009 (42 U.S.C. 10367), passed by Congress in March 2009. Before this assessment, the last national brackish groundwater compilation was completed in the mid-1960s (Feth, 1965). Since that time, substantially more hydrologic and geochemical data have been collected and now can be used to improve the understanding of the Nation’s brackish groundwater resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173054","usgsCitation":"Stanton, J.S., and Dennehy, K.F., 2017, Brackish groundwater and its potential to augment freshwater supplies: U.S. Geological Survey Fact Sheet 2017–3054, 4 p., https://doi.org/10.3133/fs20173054.","productDescription":"Document: 4 p.; Companion File; Data Release","onlineOnly":"N","ipdsId":"IP-078564","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":343973,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3054/coverthb.jpg"},{"id":343974,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3054/fs20173054.pdf","text":"Fact Sheet","size":"2.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3054"},{"id":343975,"rank":3,"type":{"id":7,"text":"Companion 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2017.06.013","usgsCitation":"Ji, L., and Brown, J.F., 2017, Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics: International Journal of Applied Earth Observation and Geoinformation, v. 62, p. 215-223, https://doi.org/10.1016/j.jag.2017.06.013.","productDescription":"9 p.","startPage":"215","endPage":"223","ipdsId":"IP-081678","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469675,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2017.06.013","text":"Publisher Index Page"},{"id":343965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"596f1e1fe4b0d1f9f064073c","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":705288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":705289,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70189010,"text":"sir20175062C - 2017 - Application of decline curve analysis to estimate recovery factors for carbon dioxide enhanced oil recovery","interactions":[{"subject":{"id":70189010,"text":"sir20175062C - 2017 - Application of decline curve analysis to estimate recovery factors for carbon dioxide enhanced oil recovery","indexId":"sir20175062C","publicationYear":"2017","noYear":false,"chapter":"C","title":"Application of decline curve analysis to estimate recovery factors for carbon dioxide enhanced oil recovery"},"predicate":"IS_PART_OF","object":{"id":70188786,"text":"sir20175062 - 2017 - Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery","indexId":"sir20175062","publicationYear":"2017","noYear":false,"title":"Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery"},"id":1}],"isPartOf":{"id":70188786,"text":"sir20175062 - 2017 - Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery","indexId":"sir20175062","publicationYear":"2017","noYear":false,"title":"Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery"},"lastModifiedDate":"2017-07-17T13:33:47","indexId":"sir20175062C","displayToPublicDate":"2017-07-17T13:30:00","publicationYear":"2017","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":"2017-5062","chapter":"C","title":"Application of decline curve analysis to estimate recovery factors for carbon dioxide enhanced oil recovery","docAbstract":"In the decline curve analysis (DCA) method of estimating recoverable hydrocarbon volumes, the analyst uses historical production data from a well, lease, group of wells (or pattern), or reservoir and plots production rates against time or cumulative production for the analysis. The DCA of an individual well is founded on the same basis as the fluid-flow principles that are used for pressure-transient analysis of a single well in a reservoir domain and therefore can provide scientifically reasonable and accurate results. However, when used for a group of wells, a lease, or a reservoir, the DCA becomes more of an empirical method. Plots from the DCA reflect the reservoir response to the oil withdrawal (or production) under the prevailing operating and reservoir conditions, and they continue to be good tools for estimating recoverable hydrocarbon volumes and future production rates. For predicting the total recoverable hydrocarbon volume, the DCA results can help the analyst to evaluate the reservoir performance under any of the three phases of reservoir productive life—primary, secondary (waterflood), or tertiary (enhanced oil recovery) phases—so long as the historical production data are sufficient to establish decline trends at the end of the three phases.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery (Scientific Investigations Report 2017–5062)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175062C","usgsCitation":"Jahediesfanjani, Hossein, 2017, Application of decline curve analysis to estimate recovery factors for carbon dioxide enhanced oil recovery, chap. C of Verma, M.K., ed., Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery: U.S. Geological Survey Scientific Investigations Report 2017–5062, \np. C1–C20, https://doi.org/10.3133/sir20175062C.","productDescription":"iv, 20 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":343119,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5062/c/sir20175062_chapc.pdf","text":"Report","size":"1.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017=5062C"},{"id":343118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5062/c/coverthb.jpg"}],"contact":" Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The Oil and Gas Journal’s enhanced oil recovery (EOR) survey for 2014 (Koottungal, 2014) showed that gas injection is the most frequently applied method of EOR in the United States and that carbon dioxide (CO2 ) is the most commonly used injection fluid for miscible operations. The CO2-EOR process typically follows primary and secondary (waterflood) phases of oil reservoir development. The common objective of implementing a CO2-EOR program is to produce oil that remains after the economic limit of waterflood recovery is reached. Under conditions of miscibility or multicontact miscibility, the injected CO2 partitions between the gas and liquid CO2 phases, swells the oil, and reduces the viscosity of the residual oil so that the lighter fractions of the oil vaporize and mix with the CO2 gas phase (Teletzke and others, 2005). Miscibility occurs when the reservoir pressure is at least at the minimum miscibility pressure (MMP). The MMP depends, in turn, on oil composition, impurities of the CO2 injection stream, and reservoir temperature. At pressures below the MMP, component partitioning, oil swelling, and viscosity reduction occur, but the efficiency is increasingly reduced as the pressure falls farther below the MMP.
CO2-EOR processes are applied at the reservoir level, where a reservoir is defined as an underground formation containing an individual and separate pool of producible hydrocarbons that is confined by impermeable rock or water barriers and is characterized by a single natural pressure system. A field may consist of a single reservoir or multiple reservoirs that are not in communication but which may be associated with or related to a single structural or stratigraphic feature (U.S. Energy Information Administration [EIA], 2000).
The purpose of modeling the CO2-EOR process is discussed along with the potential CO2-EOR predictive models. The data demands of models and the scope of the assessments require tradeoffs between reservoir-specific data that can be assembled and simplifying assumptions that allow assignment of default values for some reservoir parameters. These issues are discussed in the context of the CO2 Prophet EOR model, and their resolution is demonstrated with the computation of recovery-factor estimates for CO2-EOR of 143 reservoirs in the Powder River Basin Province in southeastern Montana and northeastern Wyoming.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery (Scientific Investigations Report 2017–5062)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175062B","usgsCitation":"Attanasi, E.D., 2017, Using CO2 Prophet to estimate recovery factors for carbon dioxide enhanced oil recovery, chap. B of Verma, M.K., ed., Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery: U.S. Geological Survey Scientific Investigations Report 2017–5062, p. B1–B10, https://doi.org/10.3133/sir20175062B.","productDescription":"iii, 10 p.","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":343112,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5062/b/sir20175062_chapb.pdf","text":"Report","size":"377 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5062B"},{"id":343111,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5062/b/coverthb.jpg"}],"contact":" Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
In order to determine the hydrocarbon potential of oil reservoirs within the U.S. sedimentary basins for which the carbon dioxide enhanced oil recovery (CO2-EOR) process has been considered suitable, the CO2 Prophet model was chosen by the U.S. Geological Survey (USGS) to be the primary source for estimating recovery-factor values for individual reservoirs. The choice was made because of the model’s reliability and the ease with which it can be used to assess a large number of reservoirs. The other two approaches—the empirical decline curve analysis (DCA) method and a review of published literature on CO2-EOR projects—were deployed to verify the results of the CO2 Prophet model. This chapter discusses the results from CO2 Prophet (chapter B, by Emil D. Attanasi, this report) and compares them with results from decline curve analysis (chapter C, by Hossein Jahediesfanjani) and those reported in the literature for selected reservoirs with adequate data for analyses (chapter D, by Ricardo A. Olea).
To estimate the technically recoverable hydrocarbon potential for oil reservoirs where CO2-EOR has been applied, two of the three approaches—CO2 Prophet modeling and DCA—do not include analysis of economic factors, while the third approach—review of published literature—implicitly includes economics. For selected reservoirs, DCA has provided estimates of the technically recoverable hydrocarbon volumes, which, in combination with calculated amounts of original oil in place (OOIP), helped establish incremental CO2-EOR recovery factors for individual reservoirs.
The review of published technical papers and reports has provided substantial information on recovery factors for 70 CO2-EOR projects that are either commercially profitable or classified as pilot tests. When comparing the results, it is important to bear in mind the differences and limitations of these three approaches.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery (Scientific Investigations Report 2017–5062)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175062E","usgsCitation":"Verma, M.K., 2017, Summary of the analyses for recovery factors, chap. E of Verma, M.K., ed., Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery: U.S. Geological Survey Scientific Investigations Report 2017–5062, p. E1–E2, https://doi.org/10.3133/sir20175062E.","productDescription":"iii, 2 p.","numberOfPages":"6","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":343123,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5062/e/coverthb.jpg"},{"id":343124,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5062/e/sir20175062_chape.pdf","text":"Report","size":"198 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5062E"}],"contact":" Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey has developed a new global high-resolution hydrologic derivative database. Loosely modeled on the HYDRO1k database, this new database, entitled Hydrologic Derivatives for Modeling and Analysis, provides comprehensive and consistent global coverage of topographically derived raster layers (digital elevation model data, flow direction, flow accumulation, slope, and compound topographic index) and vector layers (streams and catchment boundaries). The coverage of the data is global, and the underlying digital elevation model is a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), GMTED2010 (Global Multi-resolution Terrain Elevation Data 2010), and the SRTM (Shuttle Radar Topography Mission). For most of the globe south of 60°N., the raster resolution of the data is 3 arc-seconds, corresponding to the resolution of the SRTM. For the areas north of 60°N., the resolution is 7.5 arc-seconds (the highest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30 arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information. This database is appropriate for use in continental-scale modeling efforts. The work described in this report was conducted by the U.S. Geological Survey in cooperation with the National Aeronautics and Space Administration Goddard Space Flight Center.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1053","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration Goddard Space Flight Center","usgsCitation":"Verdin, K.L., 2017, Hydrologic Derivatives for Modeling and Analysis—A new global high-resolution database: U.S. Geological Survey Data Series 1053, 16 p., https://doi.org/10.3133/ds1053.","productDescription":"Report: iv, 16 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-079740","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":343796,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1053/ds1053.pdf","text":"Report","size":"7.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1053"},{"id":343795,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1053/coverthb.jpg"},{"id":343823,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7S180ZP","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Hydrologic Derivatives for Modeling and Applications (HDMA) database"}],"contact":"Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
The U.S. Geological Survey (USGS), in cooperation with the U.S. Nuclear Regulatory Commission, has investigated statistical methods for probabilistic flood hazard assessment to provide guidance on very low annual exceedance probability (AEP) estimation of peak-streamflow frequency and the quantification of corresponding uncertainties using streamgage-specific data. The term “very low AEP” implies exceptionally rare events defined as those having AEPs less than about 0.001 (or 1 × 10–3 in scientific notation or for brevity 10–3). Such low AEPs are of great interest to those involved with peak-streamflow frequency analyses for critical infrastructure, such as nuclear power plants. Flood frequency analyses at streamgages are most commonly based on annual instantaneous peak streamflow data and a probability distribution fit to these data. The fitted distribution provides a means to extrapolate to very low AEPs. Within the United States, the Pearson type III probability distribution, when fit to the base-10 logarithms of streamflow, is widely used, but other distribution choices exist. The USGS-PeakFQ software, implementing the Pearson type III within the Federal agency guidelines of Bulletin 17B (method of moments) and updates to the expected moments algorithm (EMA), was specially adapted for an “Extended Output” user option to provide estimates at selected AEPs from 10–3 to 10–6. Parameter estimation methods, in addition to product moments and EMA, include L-moments, maximum likelihood, and maximum product of spacings (maximum spacing estimation). This study comprehensively investigates multiple distributions and parameter estimation methods for two USGS streamgages (01400500 Raritan River at Manville, New Jersey, and 01638500 Potomac River at Point of Rocks, Maryland). The results of this study specifically involve the four methods for parameter estimation and up to nine probability distributions, including the generalized extreme value, generalized log-normal, generalized Pareto, and Weibull. Uncertainties in streamflow estimates for corresponding AEP are depicted and quantified as two primary forms: quantile (aleatoric [random sampling] uncertainty) and distribution-choice (epistemic [model] uncertainty). Sampling uncertainties of a given distribution are relatively straightforward to compute from analytical or Monte Carlo-based approaches. Distribution-choice uncertainty stems from choices of potentially applicable probability distributions for which divergence among the choices increases as AEP decreases. Conventional goodness-of-fit statistics, such as Cramér-von Mises, and L-moment ratio diagrams are demonstrated in order to hone distribution choice. The results generally show that distribution choice uncertainty is larger than sampling uncertainty for very low AEP values.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175038","collaboration":"Prepared in cooperation with the U.S. Nuclear Regulatory Commission","usgsCitation":"Asquith, W.H., Kiang, J.E., and Cohn, T.A., 2017, Application of at-site peak-streamflow frequency analyses for very low annual exceedance probabilities: U.S. Geological Survey Scientific Investigation Report 2017–5038, 93 p., https://doi.org/10.3133/sir20175038.","productDescription":"ix, 93 p.","onlineOnly":"Y","ipdsId":"IP-079000","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":343747,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5038/coverthb.jpg"},{"id":343748,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5038/sir20175038.pdf","text":"Report","size":"6.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5038"}],"contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
This report establishes specific rules for writing computer source code for use with the National Land-Change Modeling Framework (NLCMF). These specific rules consist of conventions and principles for writing code primarily in the C and C++ programming languages. Collectively, these coding conventions and coding principles create an NLCMF programming style. In addition to detailed naming conventions, this report provides general coding conventions and principles intended to facilitate the development of high-performance software implemented with code that is extensible, flexible, and interoperable. Conventions for developing modular code are explained in general terms and also enabled and demonstrated through the appended templates for C++ base source-code and header files. The NLCMF limited-extern approach to module structure, code inclusion, and cross-module access to data is both explained in the text and then illustrated through the module templates. Advice on the use of global variables is provided.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section F: Land-change modeling and analysis in Book 6: Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6F1","usgsCitation":"Donato, D.I., 2017, Coding conventions and principles for a National Land-Change Modeling Framework: U.S. Geological Survey Techniques and Methods, book 6, chap. F1, 30 p., https://doi.org/10.3133/tm6F1.","productDescription":"iv, 30 p. ","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068071","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":343791,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/f01/coverthb.jpg"},{"id":343792,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/f01/tm6f1.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"TM 06-F1"}],"publicComments":"This report is Chapter 1 of Section F: Land-change modeling and analysis in Book 6: Modeling techniques.","contact":"Director, Eastern Geographic Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 521
Reston, VA 20192
Background: Passive integrated transponder (PIT) tags have been used to document and monitor the movement or behavior of numerous species of fishes. Data on short-term and long-term survival and tag retention are needed before initiating studies using PIT tags on a new species or life stage. We evaluated the survival and tag retention of 153 metamorphosing juvenile Sea Lamprey Petromyzon marinus tagged with 12 mm PIT tags on three occasions using a simple surgical procedure.
Results: Tag retention was 100% and 98.6% at 24 h and 28-105 d post-tagging. Of the lamprey that retained their tags, 87.3% had incisions sufficiently healed to prevent further loss. Survival was 100% and 92.7% at 24 h and 41-118 d post-tagging with no significant difference in survival between tagged and untagged control lamprey. Of the 11 lamprey that died, four had symptoms that indicated their death was directly related to tagging. Survival was positively correlated with Sea Lamprey length.
Conclusions: Given the overall high level of survival and tag retention in this study, future studies can utilize 12 mm PIT tags to monitor metamorphosing juvenile Sea Lamprey movement and migration patterns.
","language":"English","publisher":"BioMed Central","doi":"10.1186/s40317-017-0133-z","usgsCitation":"Simard, L.G., Sotola, V.A., Marsden, J., and Miehls, S.M., 2017, Assessment of PIT tag retention and post-tagging survival in metamorphosing juvenile Sea Lamprey: Animal Biotelemetry, v. 5, no. 18, p. 1-7, https://doi.org/10.1186/s40317-017-0133-z.","productDescription":"7 p. ","startPage":"1","endPage":"7","ipdsId":"IP-085186","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469678,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-017-0133-z","text":"Publisher Index Page"},{"id":344067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"18","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-14","publicationStatus":"PW","scienceBaseUri":"59706fb4e4b0d1f9f065a87c","contributors":{"authors":[{"text":"Simard, Lee G.","contributorId":194905,"corporation":false,"usgs":false,"family":"Simard","given":"Lee","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":705665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sotola, V. Alex","contributorId":194906,"corporation":false,"usgs":false,"family":"Sotola","given":"V.","email":"","middleInitial":"Alex","affiliations":[],"preferred":false,"id":705666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marsden, J. Ellen","contributorId":194907,"corporation":false,"usgs":false,"family":"Marsden","given":"J. Ellen","affiliations":[],"preferred":false,"id":705667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":705664,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188429,"text":"sir20175059 - 2017 - Estimation of salt loads for the Dolores River in the Paradox Valley, Colorado, 1980–2015","interactions":[],"lastModifiedDate":"2017-08-07T16:16:01","indexId":"sir20175059","displayToPublicDate":"2017-07-13T15:45:00","publicationYear":"2017","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":"2017-5059","title":"Estimation of salt loads for the Dolores River in the Paradox Valley, Colorado, 1980–2015","docAbstract":"Regression models that relate total dissolved solids (TDS) concentrations to specific conductance were used to estimate salt loads for two sites on the Dolores River in the Paradox Valley in western Colorado. The salt-load estimates will be used by the Bureau of Reclamation to evaluate salt loading to the river coming from the Paradox Valley and the effect of the Paradox Valley Unit (PVU), a project designed to reduce the salinity of the Colorado River. A second-order polynomial provided the best fit of the discrete data for both sites on the river. The largest bias occurred in samples with elevated sulfate concentrations (greater than 500 milligrams per liter), which were associated with short-duration runoff events in late summer and fall. Comparison of regression models from a period of time before operation began at the PVU and three periods after operation began suggests the relation between TDS and specific conductance has not changed over time. Net salt gain through the Paradox Valley was estimated as the TDS load at the downstream site minus the load at the upstream site. The mean annual salt gain was 137,900 tons per year prior to operation of the PVU (1980–1993) and 43,300 tons per year after the PVU began operation (1997–2015). The difference in annual salt gain in the river between the pre-PVU and post-PVU periods was 94,600 tons per year, which represents a nearly 70 percent reduction in salt loading to the river.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175059","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Mast, M.A., 2017, Estimation of salt loads for the Dolores River in the Paradox Valley, Colorado, 1980–2015: U.S. Geological Survey Scientific Investigations Report 2017–5059, 20 p., https://doi.org/10.3133/sir20175059.","productDescription":"v, 20 p.","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-079370","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":343666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5059/coverthb.jpg"},{"id":343668,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5059/sir20175059.pdf","text":"Report","size":"6.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5059"}],"country":"United States","state":"Colorado","otherGeospatial":"Dolores River, Paradox Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.907470703125,\n 38.30179226344099\n ],\n [\n -108.81906509399414,\n 38.30179226344099\n ],\n [\n -108.81906509399414,\n 38.36211833953394\n ],\n [\n -108.907470703125,\n 38.36211833953394\n ],\n [\n -108.907470703125,\n 38.30179226344099\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046