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
Worldwide populations of freshwater eels have declined with one of the contributing causes related to mortality during passage through hydropower turbines. An inherent trade‐off underlies turbine management where the competing demand for more hydropower comes at the expense of eel survival. A win–win solution exists when an option performs better on all competing demands compared to other options. A predictive model for eel migration based on a recent telemetry study was used to develop decision rules for turbine management in the Shenandoah River system. The performance of alternative decision rules was compared to the status quo policy to search for win–win solutions. Decision rules were defined by the probability of eel movement and were evaluated by the probabilities of false positive and false negative errors. The exact value of the cut‐off probability used in the decision rule will need to be determined through negotiation between stakeholders, but a range of cut‐off probabilities resulted in a win–win situation with both reduced eel mortality and increased turbine operation relative to the current shutdown strategy. Monitoring the implementation is needed to evaluate and update the predictive model and to refine the decision rule. Although the decision is framed for the Shenandoah River system, the analytical approach could be used to develop decision rules for turbine shutdown policy in other areas.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3182","usgsCitation":"Smith, D.R., Paul L. Fackler, Eyler, S.M., Villegas, L., and Welsh, S., 2017, Optimization of decision rules for hydroelectric operation to reduce both eel mortality and unnecessary turbine shutdown: A search for a win-win solution: Rivers Research and Applications, v. 33, no. 8, p. 1279-1285, https://doi.org/10.1002/rra.3182.","productDescription":"7 p.","startPage":"1279","endPage":"1285","ipdsId":"IP-084849","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":377523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia, West Virginia","otherGeospatial":"Shenandoah watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.75,\n 38.86751337001198\n ],\n [\n -78.8763427734375,\n 38.974357249228206\n ],\n [\n -79.07684326171875,\n 38.739088441876866\n ],\n [\n -79.29931640625,\n 38.41271038284709\n ],\n [\n -79.4586181640625,\n 38.16911413556086\n ],\n [\n -79.25537109375,\n 38.07620357665235\n ],\n [\n -78.70330810546875,\n 38.8504034216919\n ],\n [\n -78.75,\n 38.86751337001198\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"33","issue":"8","noUsgsAuthors":false,"publicationDate":"2017-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":796346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paul L. Fackler","contributorId":238522,"corporation":false,"usgs":false,"family":"Paul L. Fackler","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":796347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eyler, Sheila M.","contributorId":238523,"corporation":false,"usgs":false,"family":"Eyler","given":"Sheila","email":"","middleInitial":"M.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":796348,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Villegas, Laura","contributorId":238524,"corporation":false,"usgs":false,"family":"Villegas","given":"Laura","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":796349,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":796350,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70206544,"text":"70206544 - 2017 - Hydrologic impacts of changes in climate and glacier extent in the Gulf of Alaska watershed","interactions":[],"lastModifiedDate":"2019-11-08T09:46:41","indexId":"70206544","displayToPublicDate":"2017-07-20T09:39:21","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic impacts of changes in climate and glacier extent in the Gulf of Alaska watershed","docAbstract":"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":70189067,"text":"sir20175056 - 2017 - Water-quality models to assess algal community dynamics, water quality, and fish habitat suitability for two agricultural land-use dominated lakes in Minnesota, 2014","interactions":[],"lastModifiedDate":"2017-07-21T10:09:46","indexId":"sir20175056","displayToPublicDate":"2017-07-20T00:00:00","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":"2017-5056","title":"Water-quality models to assess algal community dynamics, water quality, and fish habitat suitability for two agricultural land-use dominated lakes in Minnesota, 2014","docAbstract":"Fish habitat can degrade in many lakes due to summer blue-green algal blooms. Predictive models are needed to better manage and mitigate loss of fish habitat due to these changes. The U.S. Geological Survey (USGS), in cooperation with the Minnesota Department of Natural Resources, developed predictive water-quality models for two agricultural land-use dominated lakes in Minnesota—Madison Lake and Pearl Lake, which are part of Minnesota’s sentinel lakes monitoring program—to assess algal community dynamics, water quality, and fish habitat suitability of these two lakes under recent (2014) meteorological conditions. The interaction of basin processes to these two lakes, through the delivery of nutrient loads, were simulated using CE-QUAL-W2, a carbon-based, laterally averaged, two-dimensional water-quality model that predicts distribution of temperature and oxygen from interactions between nutrient cycling, primary production, and trophic dynamics.
The CE-QUAL-W2 models successfully predicted water temperature and dissolved oxygen on the basis of the two metrics of mean absolute error and root mean square error. For Madison Lake, the mean absolute error and root mean square error were 0.53 and 0.68 degree Celsius, respectively, for the vertical temperature profile comparisons; for Pearl Lake, the mean absolute error and root mean square error were 0.71 and 0.95 degree Celsius, respectively, for the vertical temperature profile comparisons. Temperature and dissolved oxygen were key metrics for calibration targets. These calibrated lake models also simulated algal community dynamics and water quality. The model simulations presented potential explanations for persistently large total phosphorus concentrations in Madison Lake, key differences in nutrient concentrations between these lakes, and summer blue-green algal bloom persistence.
Fish habitat suitability simulations for cool-water and warm-water fish indicated that, in general, both lakes contained a large proportion of good-growth habitat and a sustained period of optimal growth habitat in the summer, without any periods of lethal oxythermal habitat. For Madison and Pearl Lakes, examples of important cool-water fish, particularly game fish, include northern pike (Esox lucius), walleye (Sander vitreus), and black crappie (Pomoxis nigromaculatus); examples of important warm-water fish include bluegill (Lepomis macrochirus), largemouth bass (Micropterus salmoides), and smallmouth bass (Micropterus dolomieu). Sensitivity analyses were completed to understand lake response effects through the use of controlled departures on certain calibrated model parameters and input nutrient loads. These sensitivity analyses also operated as land-use change scenarios because alterations in agricultural practices, for example, could potentially increase or decrease nutrient loads.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175056","collaboration":"Prepared in cooperation with the Minnesota Department of Natural Resources","usgsCitation":"Smith, E.A., Kiesling, R.L., and Ziegeweid, J.R., 2017, Water-quality models to assess algal community dynamics, water quality, and fish habitat suitability for two agricultural land-use dominated lakes in Minnesota, 2014: U.S. Geological Survey Scientific Investigations Report 2017–5056, 65 p., https://doi.org/10.3133/sir20175056.","productDescription":"x, 65 p.","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-079529","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":344145,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5056/sir20175056.pdf","text":"Report","size":"2.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
The five saltiest springs in the Sierra Nevada in California are found between 38.5° and 38.8° N. latitude, on the South Fork American River; on Caples Creek, a tributary of the Silver Fork American River; and on the North Fork Mokelumne River. The springs issue from Cretaceous granitic rocks in the bottoms of these major canyons, between 1,200- and 2,200-m elevation. All of these springs were well known to Native Americans, who excavated meter-sized basins in the granitic rock, within which they produced salt by evaporation near at least four of the five spring sites. The spring waters are dominated by Cl, Na, and Ca; are enriched relative to seawater in Ca, Li, and As; and are depleted in SO4, Mg, and K. Tritium analyses indicate that the spring waters have had little interaction with rainfall since about 1954. The waters are apparently an old groundwater of meteoric origin that resided at depth before moving up along fractures to the surface of the exhumed granitic rocks. However, along the way these waters incorporated salts from depth, the origin of which could have been either from marine sedimentary rocks intruded by the granitic magmas or from fluid inclusions in the granitic rocks. Prolonged storage at depth fostered water-rock interactions that undoubtedly modified the fluid compositions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175053","usgsCitation":"Moore, J.G., Diggles, M.F., Evans, W.C., and Klemic, K., 2017, The saltiest springs in the Sierra Nevada, California: U.S. Geological Survey Scientific Investigations Report 2017–5053, 21 p., 2 appendixes, https://doi.org/10.3133/sir20175053.","productDescription":"v, 21 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-079045","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":344092,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5053/coverthb.jpg"},{"id":344093,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5053/sir20175053.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5053"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.5,\n 39.06\n ],\n [\n -120,\n 39.06\n ],\n [\n -120,\n 38.416667\n ],\n [\n -120.5,\n 38.416667\n ],\n [\n -120.5,\n 39.06\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
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":70189636,"text":"70189636 - 2017 - Alternative rupture-scaling relationships for subduction interface and other offshore environments","interactions":[],"lastModifiedDate":"2017-07-19T08:19:16","indexId":"70189636","displayToPublicDate":"2017-07-19T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Alternative rupture-scaling relationships for subduction interface and other offshore environments","docAbstract":"Alternative fault-rupture-scaling relationships are developed for Mw 7.1–\n9.5 subduction interface earthquakes using a new database of consistently derived finitefault\nrupture models from teleseismic inversion. Scaling relationships are derived for\nrupture area, rupture length, rupture width, maximum slip, and average slip. These relationships\napply width saturation for large-magnitude interface earthquakes (approximately\nMw >8:6) for which the physical characteristics of subduction zones limit the\ndepth extent of seismogenic rupture, and consequently, the down-dip limit of strong\nground motion generation. On average, the down-dip rupture width for interface earthquakes\nsaturates near 200 km (196 km on average). Accordingly, the reinterpretation of\nrupture-area scaling for subduction interface earthquakes through the use of a bilinear\nscaling model suggests that rupture asperity area is less well correlated with magnitude\nfor earthquakes Mw >8:6. Consequently, the size of great-magnitude earthquakes appears\nto be more strongly controlled by the average slip across asperities.\nThe sensitivity of the interface scaling relationships is evaluated against geographic\nregion (or subduction zone) and average dip along the rupture interface to\nassess the need for correction factors. Although regional perturbations in fault-rupture\nscaling could be identified, statistical significance analyses suggest there is little\nrationale for implementing regional correction factors based on the limited number\nof interface rupture models available for each region.\nFault-rupture-scaling relationships are also developed for intraslab (within the\nsubducting slab), extensional outer-rise and offshore strike-slip environments. For\nthese environments, the rupture width and area scaling properties yield smaller dimensions\nthan interface ruptures for the corresponding magnitude. However, average and\nmaximum slip metrics yield larger values than interface events. These observations\nreflect both the narrower fault widths and higher stress drops in these faulting environments.\nAlthough expressing significantly different rupture-scaling properties from\nearthquakes in subduction environments, the characteristics of offshore strike-slip\nearthquake ruptures compare similarly to commonly used rupture-scaling relationships\nfor onshore strike-slip earthquakes.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160255","usgsCitation":"Allen, T., and Hayes, G.P., 2017, Alternative rupture-scaling relationships for subduction interface and other offshore environments: Bulletin of the Seismological Society of America, v. 107, no. 3, p. 1240-1253, https://doi.org/10.1785/0120160255.","productDescription":"14 p.","startPage":"1240","endPage":"1253","ipdsId":"IP-083339","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":344007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-21","publicationStatus":"PW","scienceBaseUri":"59706fb0e4b0d1f9f065a869","contributors":{"authors":[{"text":"Allen, Trevor I.","contributorId":138667,"corporation":false,"usgs":false,"family":"Allen","given":"Trevor","middleInitial":"I.","affiliations":[{"id":6672,"text":"former: USGS Southwest Biological Science Center, Colorado Plateau Research Station, Flagstaff, AZ. Current address: TN-SCORE, Univ of Tennessee, Knoxville, TN, e-mail: jennen@gmail.com","active":true,"usgs":false}],"preferred":false,"id":705525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705526,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70189588,"text":"70189588 - 2017 - Storage filters upland suspended sediment signals delivered from watersheds","interactions":[],"lastModifiedDate":"2017-07-18T09:27:14","indexId":"70189588","displayToPublicDate":"2017-07-18T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Storage filters upland suspended sediment signals delivered from watersheds","docAbstract":"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":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","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":70189007,"text":"sir20175062B - 2017 - Using CO2 Prophet to estimate recovery factors for carbon dioxide enhanced oil recovery","interactions":[{"subject":{"id":70189007,"text":"sir20175062B - 2017 - Using CO2 Prophet to estimate recovery factors for carbon dioxide enhanced oil recovery","indexId":"sir20175062B","publicationYear":"2017","noYear":false,"chapter":"B","displayTitle":"Using CO2 Prophet to estimate recovery factors for carbon dioxide enhanced oil recovery","title":"Using CO2 Prophet 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":"2017-07-17T14:13:22","indexId":"sir20175062B","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":"B","displayTitle":"Using CO2 Prophet to estimate recovery factors for carbon dioxide enhanced oil recovery","title":"Using CO2 Prophet to estimate recovery factors for carbon dioxide enhanced oil recovery","docAbstract":"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
The need to increase the efficiency of oil recovery and environmental concerns are bringing to prominence the use of carbon dioxide (CO2) as a tertiary recovery agent. Assessment of the impact of flooding with CO2 all eligible reservoirs in the United States not yet undergoing enhanced oil recovery (EOR) requires making the best possible use of the experience gained in 40 years of applications. Review of the publicly available literature has located relevant CO2-EOR information for 53 units (fields, reservoirs, pilot areas) in the United States and 17 abroad.
As the world simultaneously faces an increasing concentration of CO2 in the atmosphere and a higher demand for fossil fuels, the CO2-EOR process continues to gain popularity for its efficiency as a tertiary recovery agent and for the potential for having some CO2 trapped in the subsurface as an unintended consequence of the enhanced production (Advanced Resources International and Melzer Consulting, 2009). More extensive application of CO2-EOR worldwide, however, is not making it significantly easier to predict the exact outcome of the CO2 flooding in new reservoirs. The standard approach to examine and manage risks is to analyze the intended target by conducting laboratory work, running simulation models, and, finally, gaining field experience with a pilot test. This approach, though, is not always possible. For example, assessment of the potential of CO2-EOR at the national level in a vast country such as the United States requires making forecasts based on information already available.
Although many studies are proprietary, the published literature has provided reviews of CO2-EOR projects. Yet, there is always interest in updating reports and analyzing the information under new perspectives. Brock and Bryan (1989) described results obtained during the earlier days of CO2-EOR from 1972 to 1987. Most of the recovery predictions, however, were based on intended injections of 30 percent the size of the reservoir’s hydrocarbon pore volume (HCPV), and the predictions in most cases badly missed the actual recoveries because of the embryonic state of tertiary recovery in general and CO2 flooding in particular at the time. Brock and Bryan (1989), for example, reported for the Weber Sandstone in the Rangely oil field in Colorado, an expected recovery of 7.5 percent of the original oil in place (OOIP) after injecting a volume of CO2 equivalent to 30 percent of the HCPV, but Clark (2012) reported that after injecting a volume of CO2 equivalent to 46 percent of the HCPV, the actual recovery was 4.8 percent of the OOIP. Decades later, the numbers by Brock and Bryan (1989) continue to be cited as part of expanded reviews, such as the one by Kuuskraa and Koperna (2006). Other comprehensive reviews including recovery factors are those of Christensen and others (2001) and Lake and Walsh (2008). The Oil and Gas Journal (O&GJ) periodically reports on active CO2-EOR operations worldwide, but those releases do not include recovery factors. The monograph by Jarrell and others (2002) remains the most technically comprehensive publication on CO2 flooding, but it does not cover recovery factors either.
This chapter is a review of the literature found in a search for information about CO2-EOR. It has been prepared as part of a project by the U.S. Geological Survey (USGS) to assess the incremental oil production that would be technically feasible by CO2 flooding of all suitable oil reservoirs in the country not yet undergoing tertiary recovery.
","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, Virginia","doi":"10.3133/sir20175062D","usgsCitation":"Olea, R.A., 2017, Carbon dioxide enhanced oil recovery performance according to the literature, chap. D 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. D1–D21, https://doi.org/10.3133/sir20175062D.","productDescription":"iii, 21 p.","numberOfPages":"25","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":343121,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5062/d/coverthb.jpg"},{"id":343122,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5062/d/sir20175062_chapd.pdf","text":"Report","size":"570 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5062D"}],"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
We produce a one‐year 2017 seismic‐hazard forecast for the central and eastern United States from induced and natural earthquakes that updates the 2016 one‐year forecast; this map is intended to provide information to the public and to facilitate the development of induced seismicity forecasting models, methods, and data. The 2017 hazard model applies the same methodology and input logic tree as the 2016 forecast, but with an updated earthquake catalog. We also evaluate the 2016 seismic‐hazard forecast to improve future assessments. The 2016 forecast indicated high seismic hazard (greater than 1% probability of potentially damaging ground shaking in one year) in five focus areas: Oklahoma–Kansas, the Raton basin (Colorado/New Mexico border), north Texas, north Arkansas, and the New Madrid Seismic Zone. During 2016, several damaging induced earthquakes occurred in Oklahoma within the highest hazard region of the 2016 forecast; all of the 21 moment magnitude (M) ≥4 and 3 M≥5 earthquakes occurred within the highest hazard area in the 2016 forecast. Outside the Oklahoma–Kansas focus area, two earthquakes with M≥4 occurred near Trinidad, Colorado (in the Raton basin focus area), but no earthquakes with M≥2.7 were observed in the north Texas or north Arkansas focus areas. Several observations of damaging ground‐shaking levels were also recorded in the highest hazard region of Oklahoma. The 2017 forecasted seismic rates are lower in regions of induced activity due to lower rates of earthquakes in 2016 compared with 2015, which may be related to decreased wastewater injection caused by regulatory actions or by a decrease in unconventional oil and gas production. Nevertheless, the 2017 forecasted hazard is still significantly elevated in Oklahoma compared to the hazard calculated from seismicity before 2009.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220170005","usgsCitation":"Petersen, M.D., Mueller, C., Moschetti, M.P., Hoover, S.M., Shumway, A., McNamara, D.E., Williams, R., Llenos, A.L., Ellsworth, W., Rubinstein, J.L., McGarr, A.F., and Rukstales, K.S., 2017, 2017 One‐year seismic‐hazard forecast for the central and eastern United States from induced and natural earthquakes: Seismological Research Letters, v. 88, no. 3, p. 772-783, https://doi.org/10.1785/0220170005.","productDescription":"12 p.","startPage":"772","endPage":"783","ipdsId":"IP-083989","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":438266,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KP80B9","text":"USGS data release","linkHelpText":"Earthquake catalogs for the 2017 Central and Eastern U.S. short-term seismic hazard model"},{"id":438265,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RV0KWR","text":"USGS data release","linkHelpText":"2017 One-Year Seismic Hazard Forecast for the Central and Eastern United States from Induced and Natural Earthquakes"},{"id":343937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"88","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-01","publicationStatus":"PW","scienceBaseUri":"596dcca1e4b0d1f9f062754e","contributors":{"authors":[{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mueller, Charles 0000-0002-1868-9710 cmueller@usgs.gov","orcid":"https://orcid.org/0000-0002-1868-9710","contributorId":140380,"corporation":false,"usgs":true,"family":"Mueller","given":"Charles","email":"cmueller@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hoover, Susan M. 0000-0002-8682-6668 shoover@usgs.gov","orcid":"https://orcid.org/0000-0002-8682-6668","contributorId":5715,"corporation":false,"usgs":true,"family":"Hoover","given":"Susan","email":"shoover@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705087,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shumway, Allison 0000-0003-1142-7141 ashumway@usgs.gov","orcid":"https://orcid.org/0000-0003-1142-7141","contributorId":147862,"corporation":false,"usgs":true,"family":"Shumway","given":"Allison","email":"ashumway@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705088,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705089,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Williams, Robert 0000-0002-2973-8493 rawilliams@usgs.gov","orcid":"https://orcid.org/0000-0002-2973-8493","contributorId":140741,"corporation":false,"usgs":true,"family":"Williams","given":"Robert","email":"rawilliams@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705090,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Llenos, Andrea L. 0000-0002-4088-6737 allenos@usgs.gov","orcid":"https://orcid.org/0000-0002-4088-6737","contributorId":4455,"corporation":false,"usgs":true,"family":"Llenos","given":"Andrea","email":"allenos@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705091,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ellsworth, William L. 0000-0001-8378-4979","orcid":"https://orcid.org/0000-0001-8378-4979","contributorId":194691,"corporation":false,"usgs":true,"family":"Ellsworth","given":"William L.","affiliations":[],"preferred":false,"id":705092,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rubinstein, Justin L. 0000-0003-1274-6785 jrubinstein@usgs.gov","orcid":"https://orcid.org/0000-0003-1274-6785","contributorId":2404,"corporation":false,"usgs":true,"family":"Rubinstein","given":"Justin","email":"jrubinstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705093,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McGarr, Arthur F. 0000-0001-9769-4093 mcgarr@usgs.gov","orcid":"https://orcid.org/0000-0001-9769-4093","contributorId":3178,"corporation":false,"usgs":true,"family":"McGarr","given":"Arthur","email":"mcgarr@usgs.gov","middleInitial":"F.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705094,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rukstales, Kenneth S. 0000-0003-2818-078X rukstales@usgs.gov","orcid":"https://orcid.org/0000-0003-2818-078X","contributorId":775,"corporation":false,"usgs":true,"family":"Rukstales","given":"Kenneth","email":"rukstales@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705095,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70194723,"text":"70194723 - 2017 - A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing","interactions":[],"lastModifiedDate":"2017-12-15T10:18:37","indexId":"70194723","displayToPublicDate":"2017-07-17T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing","docAbstract":"Cyanobacterial harmful algal blooms (CyanoHAB) are thought to be increasing globally over the past few decades, but relatively little quantitative information is available about the spatial extent of blooms. Satellite remote sensing provides a potential technology for identifying cyanoHABs in multiple water bodies and across geo-political boundaries. An assessment method was developed using MEdium Resolution Imaging Spectrometer (MERIS) imagery to quantify cyanoHAB surface area extent, transferable to different spatial areas, in Florida, Ohio, and California for the test period of 2008 to 2012. Temporal assessment was used to evaluate changes in satellite resolvable inland waterbodies for each state of interest. To further assess cyanoHAB risk within the states, the World Health Organization’s (WHO) recreational guidance level thresholds were used to categorize surface area of cyanoHABs into three risk categories: low, moderate, and high-risk bloom area. Results showed that in Florida, the area of cyanoHABs increased largely due to observed increases in high-risk bloom area. California exhibited a slight decrease in cyanoHAB extent, primarily attributed to decreases in Northern California. In Ohio (excluding Lake Erie), little change in cyanoHAB surface area was observed. This study uses satellite remote sensing to quantify changes in inland cyanoHAB surface area across numerous water bodies within an entire state. The temporal assessment method developed here will be relevant into the future as it is transferable to the Ocean Land Colour Instrument (OLCI) on Sentinel-3A/3B missions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2017.06.001","usgsCitation":"Urquhart, E.A., Schaeffer, B.A., Stumpf, R.P., Loftin, K.A., and Werdell, P.J., 2017, A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing: Harmful Algae, v. 67, p. 144-152, https://doi.org/10.1016/j.hal.2017.06.001.","productDescription":"9 p.","startPage":"144","endPage":"152","ipdsId":"IP-087775","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":469677,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2017.06.001","text":"Publisher Index Page"},{"id":349988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Florida, Ohio","volume":"67","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb81e4b06e28e9c23148","contributors":{"authors":[{"text":"Urquhart, Erin A.","contributorId":201327,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":725009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaeffer, Blake A.","contributorId":201328,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":725010,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stumpf, Richard P.","contributorId":201329,"corporation":false,"usgs":false,"family":"Stumpf","given":"Richard","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":725011,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Keith A. 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":868,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":725008,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Werdell, P. Jeremy","contributorId":201330,"corporation":false,"usgs":false,"family":"Werdell","given":"P.","email":"","middleInitial":"Jeremy","affiliations":[],"preferred":false,"id":725012,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189541,"text":"70189541 - 2017 - Research, monitoring, and evaluation of emerging issues and measures to recover the Snake River Fall Chinook Salmon ESU, 1/1/2016 - 12/31/2016","interactions":[],"lastModifiedDate":"2017-07-16T10:08:23","indexId":"70189541","displayToPublicDate":"2017-07-16T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Research, monitoring, and evaluation of emerging issues and measures to recover the Snake River Fall Chinook Salmon ESU, 1/1/2016 - 12/31/2016","docAbstract":"The portion of the Snake River fall Chinook Salmon Oncorhynchus tshawytscha ESU that spawns upstream of Lower Granite Dam transitioned from low to high abundance during 1992–2016 in association with U.S. Endangered Species Act recovery efforts and other federally mandated actions. This annual report focuses on (1) numeric and habitat use responses by natural- and hatchery-origin spawners, (2) phenotypic and numeric responses by natural-origin juveniles, and (3) predator responses in the Snake River upper and lower reaches as abundance of adult and juvenile fall Chinook Salmon increased. Spawners have located and used most of the available spawning habitat and that habitat is gradually approaching redd capacity. Timing of spawning and fry emergence has been relatively stable; whereas the timing of parr dispersal from riverine rearing habitat into Lower Granite Reservoir has become earlier as apparent abundance of juveniles has increased. Growth rate (g/d) and dispersal size of parr also declined as apparent abundance of juveniles increased. Passage timing of smolts from the two Snake River reaches has become earlier and downstream movement rate faster as estimated abundance of fall Chinook Salmon smolts in Lower Granite Reservoir has increased. In 2016, we described estimated the consumption rate and loss of subyearlings by Smallmouth Bass before, during, and after four hatchery releases. Before releases, Smallmouth Bass consumption rates of subyearling was low (0–0.36 fish/bass/d), but the day after the releases consumption rates reached as high as 1.6 fish/bass/d. Bass consumption in the upper portion of Hells Canyon was high for about 1–2 d before returning to pre-release levels, but in the lower river consumption rates were reduced but took longer to return to pre-release levels. We estimated that most of the subyearlings consumed by bass were of hatchery origin. Smallmouth Bass predation on subyearlings is intense following a hatchery release, but the predation pressure is relatively short-lived as subyearlings quickly disperse downstream. This information will allow us to better estimate subyearling loss to predation from our past efforts at time intervals less than 2 weeks. These findings coupled with stock-recruitment analyses presented in this report provide evidence for density-dependence in the Snake River reaches and in Lower Granite Reservoir that was influenced by the expansion of the recovery program. The long-term goal is to use the information covered here in a comprehensive modeling effort to conduct action effectiveness and uncertainty research and to inform Fish Population, Hydrosystem, Harvest, Hatchery, and Predation and Invasive Species Management RM&E.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Connor, W.P., Mullins, F.L., Tiffan, K.F., Plumb, J.M., Perry, R.W., Erhardt, J.M., Hemingway, R.J., Bickford, B.K., and Rhodes, T.N., 2017, Research, monitoring, and evaluation of emerging issues and measures to recover the Snake River Fall Chinook Salmon ESU, 1/1/2016 - 12/31/2016, 67 p.","productDescription":"67 p.","ipdsId":"IP-085073","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":343912,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":343904,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P154616"}],"country":"United States","otherGeospatial":"Snake River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.35546875000001,\n 44.715513732021336\n ],\n [\n -114.6478271484375,\n 44.715513732021336\n ],\n [\n -114.6478271484375,\n 47.10378387099161\n ],\n [\n -119.35546875000001,\n 47.10378387099161\n ],\n [\n -119.35546875000001,\n 44.715513732021336\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"596c7b6ee4b0d1f9f0615dc9","contributors":{"authors":[{"text":"Connor, William P.","contributorId":107589,"corporation":false,"usgs":false,"family":"Connor","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":16677,"text":"U.S. Fish and Wildlife Service, Idaho Fishery Resource Office, 276 Dworshak Complex Drive, Orofino, ID 83544","active":true,"usgs":false}],"preferred":false,"id":705121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mullins, Frank L.","contributorId":146343,"corporation":false,"usgs":false,"family":"Mullins","given":"Frank","email":"","middleInitial":"L.","affiliations":[{"id":16677,"text":"U.S. Fish and Wildlife Service, Idaho Fishery Resource Office, 276 Dworshak Complex Drive, Orofino, ID 83544","active":true,"usgs":false}],"preferred":false,"id":705122,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plumb, John M. 0000-0003-4255-1612 jplumb@usgs.gov","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":3569,"corporation":false,"usgs":true,"family":"Plumb","given":"John","email":"jplumb@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705123,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705124,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Erhardt, John M. 0000-0002-5170-285X jerhardt@usgs.gov","orcid":"https://orcid.org/0000-0002-5170-285X","contributorId":5380,"corporation":false,"usgs":true,"family":"Erhardt","given":"John","email":"jerhardt@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705125,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hemingway, Rulon J. 0000-0001-8143-0325 rhemingway@usgs.gov","orcid":"https://orcid.org/0000-0001-8143-0325","contributorId":194697,"corporation":false,"usgs":true,"family":"Hemingway","given":"Rulon","email":"rhemingway@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":705127,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bickford, Brad K. 0000-0003-3756-6588 bbickford@usgs.gov","orcid":"https://orcid.org/0000-0003-3756-6588","contributorId":140889,"corporation":false,"usgs":true,"family":"Bickford","given":"Brad","email":"bbickford@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705128,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rhodes, Tobyn N. 0000-0002-4023-4827 trhodes@usgs.gov","orcid":"https://orcid.org/0000-0002-4023-4827","contributorId":140890,"corporation":false,"usgs":true,"family":"Rhodes","given":"Tobyn","email":"trhodes@usgs.gov","middleInitial":"N.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":705129,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70187748,"text":"tm6F1 - 2017 - Coding conventions and principles for a National Land-Change Modeling Framework","interactions":[],"lastModifiedDate":"2017-07-17T10:33:31","indexId":"tm6F1","displayToPublicDate":"2017-07-14T14:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-F1","title":"Coding conventions and principles for a National Land-Change Modeling Framework","docAbstract":"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
In intermittent rivers and ephemeral streams (hereafter, IRES), hydrological connectivity mediated by either flowing or nonflowing water extends along three spatial dimensions—longitudinal, lateral, and vertical—and varies over time. Flow intermittence disrupts this connectivity, operating through complex hydrological transitions (e.g., between flowing and nonflowing phases). These transitions occur concurrently and interact along all three spatial dimensions, primarily driven by flow regime and catchment geomorphology, modified by human activities. Longitudinally, streamflow cessation and drying interrupt hydrological connectivity, contributing to physicochemical patchiness, habitat isolation, and fragmentation of metapopulations and metacommunities. Laterally, hydrological connectivity established during overbank flows is lost when water levels fall, reducing water-mediated transfers of energy, materials, and organisms from the floodplain and riparian zone. Vertically, flow cessation impairs exchange of surface and shallow groundwater, severely altering hydrological, chemical, and microbial gradients within the sediments. Concurrent interactions and physical discontinuities in hydrological connectivity along these three dimensions produce complex mosaics of physicochemical patches at different scales whose boundaries fluctuate over time in response to the flow regime. This complex patchiness underpins the characteristic physical, chemical, and biological diversity at multiple scales along longitudinal, lateral, and vertical hydrological dimensions in IRES.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Intermittent rivers and ephemeral streams: Ecology and management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-803835-2.00004-8","usgsCitation":"Boulton, A.J., Rolls, R.J., Jaeger, K.L., and Datry, T., 2017, Hydrological connectivity in intermittent rivers and ephemeral streams, chap. 2.3 of Intermittent rivers and ephemeral streams: Ecology and management, p. 79-108, https://doi.org/10.1016/B978-0-12-803835-2.00004-8.","productDescription":"30 p.","startPage":"79","endPage":"108","ipdsId":"IP-121921","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":383279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boulton, Andrew J. 0000-0001-7393-2800","orcid":"https://orcid.org/0000-0001-7393-2800","contributorId":251647,"corporation":false,"usgs":false,"family":"Boulton","given":"Andrew","email":"","middleInitial":"J.","affiliations":[{"id":50368,"text":"University of New England, Armidale, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":810279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rolls, Robert J. 0000-0002-0402-411X","orcid":"https://orcid.org/0000-0002-0402-411X","contributorId":251648,"corporation":false,"usgs":false,"family":"Rolls","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":50369,"text":"University of Canberra: Canberra, Australian Capital Territory, AU","active":true,"usgs":false}],"preferred":false,"id":810280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810281,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Datry, Thibault 0000-0003-1390-6736","orcid":"https://orcid.org/0000-0003-1390-6736","contributorId":225166,"corporation":false,"usgs":false,"family":"Datry","given":"Thibault","email":"","affiliations":[{"id":41062,"text":"Centre de Lyon-Villeurbanne, 69626 Villeurbanne CEDEX, France","active":true,"usgs":false}],"preferred":false,"id":810282,"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
Of the primary responses to contemporary climate change – “move, adapt, acclimate, or die” – that are available to organisms, “acclimate” may be effectively achieved through behavioral modification. Behavioral flexibility allows animals to rapidly cope with changing environmental conditions, and behavior represents an important component of a species’ adaptive capacity in the face of climate change. However, there is currently a lack of knowledge about the limits or constraints on behavioral responses to changing conditions. Here, we characterize the contexts in which organisms respond to climate variability through behavior. First, we quantify patterns in behavioral responses across taxa with respect to timescales, climatic stimuli, life-history traits, and ecology. Next, we identify existing knowledge gaps, research biases, and other challenges. Finally, we discuss how conservation practitioners and resource managers can incorporate an improved understanding of behavioral flexibility into natural resource management and policy decisions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.1502","usgsCitation":"Beever, E., Hall, L., Varner, J., Loosen, A.E., Dunham, J.B., Gahl, M.K., Smith, F.A., and Lawler, J.J., 2017, Behavioral flexibility as a mechanism for coping with climate change: Frontiers in Ecology and the Environment, v. 15, no. 6, p. 299-308, https://doi.org/10.1002/fee.1502.","productDescription":"10 p.","startPage":"299","endPage":"308","ipdsId":"IP-069304","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":343832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-10","publicationStatus":"PW","scienceBaseUri":"59688699e4b0d1f9f05f594a","contributors":{"authors":[{"text":"Beever, Erik A. 0000-0002-9369-486X ebeever@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-486X","contributorId":147685,"corporation":false,"usgs":true,"family":"Beever","given":"Erik A.","email":"ebeever@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"preferred":true,"id":704895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, L. 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Embere","affiliations":[],"preferred":false,"id":704896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Varner, Johanna","contributorId":147700,"corporation":false,"usgs":false,"family":"Varner","given":"Johanna","email":"","affiliations":[{"id":16911,"text":"Dept. of Biology, University of Utah, Salt Lake City, UT","active":true,"usgs":false}],"preferred":false,"id":704897,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loosen, Anne E.","contributorId":194655,"corporation":false,"usgs":false,"family":"Loosen","given":"Anne","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":704898,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":704899,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gahl, Megan K.","contributorId":194656,"corporation":false,"usgs":false,"family":"Gahl","given":"Megan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":704900,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Felisa A.","contributorId":194657,"corporation":false,"usgs":false,"family":"Smith","given":"Felisa","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":704901,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lawler, Joshua J.","contributorId":73327,"corporation":false,"usgs":false,"family":"Lawler","given":"Joshua","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":704902,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70189480,"text":"70189480 - 2017 - Improved efficiency of maximum likelihood analysis of time series with temporally correlated errors","interactions":[],"lastModifiedDate":"2017-07-13T15:08:25","indexId":"70189480","displayToPublicDate":"2017-07-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2303,"text":"Journal of Geodesy","active":true,"publicationSubtype":{"id":10}},"title":"Improved efficiency of maximum likelihood analysis of time series with temporally correlated errors","docAbstract":"Most time series of geophysical phenomena have temporally correlated errors. From these measurements, various parameters are estimated. For instance, from geodetic measurements of positions, the rates and changes in rates are often estimated and are used to model tectonic processes. Along with the estimates of the size of the parameters, the error in these parameters needs to be assessed. If temporal correlations are not taken into account, or each observation is assumed to be independent, it is likely that any estimate of the error of these parameters will be too low and the estimated value of the parameter will be biased. Inclusion of better estimates of uncertainties is limited by several factors, including selection of the correct model for the background noise and the computational requirements to estimate the parameters of the selected noise model for cases where there are numerous observations. Here, I address the second problem of computational efficiency using maximum likelihood estimates (MLE). Most geophysical time series have background noise processes that can be represented as a combination of white and power-law noise,
Deepwater sculpin are important in oligotrophic lakes as one of the few fishes that use deep profundal habitats and link invertebrates in those habitats to piscivores. In Lake Ontario the species was once abundant, however drastic declines in the mid-1900s led some to suggest the species had been extirpated and ultimately led Canadian and U.S. agencies to elevate the species' conservation status. Following two decades of surveys with no captures, deepwater sculpin were first caught in low numbers in 1996 and by the early 2000s there were indications of population recovery. We updated the status of Lake Ontario deepwater sculpin through 2016 to inform resource management and conservation. Our data set was comprised of 8431 bottom trawls sampled from 1996 to 2016, in U.S. and Canadian waters spanning depths from 5 to 225 m. Annual density estimates generally increased from 1996 through 2016, and an exponential model estimated the rate of population increase was ~ 59% per year. The mean total length and the proportion of fish greater than the estimated length at maturation (~ 116 mm) generally increased until a peak in 2013. In addition, the mean length of all deepwater sculpin captured in a trawl significantly increased with depth. Across all years examined, deepwater sculpin densities generally increased with depth, increasing sharply at depths > 150 m. Bottom trawl observations suggest the Lake Ontario deepwater sculpin population has recovered and current densities and biomass densities may now be similar to the other Great Lakes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2016.12.011","usgsCitation":"Weidel, B., Walsh, M., Connerton, M., Lantry, B.F., Lantry, J.R., Holden, J.P., Yuille, M.J., and Hoyle, J., 2017, Deepwater sculpin status and recovery in Lake Ontario: Journal of Great Lakes Research, v. 43, no. 5, p. 854-862, https://doi.org/10.1016/j.jglr.2016.12.011.","productDescription":"9 p.","startPage":"854","endPage":"862","ipdsId":"IP-082229","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469680,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2016.12.011","text":"Publisher Index Page"},{"id":343808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.91455078125,\n 43.14909399920127\n ],\n [\n -76.025390625,\n 43.14909399920127\n ],\n [\n -76.025390625,\n 44.276671273775186\n ],\n [\n -79.91455078125,\n 44.276671273775186\n ],\n [\n -79.91455078125,\n 43.14909399920127\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5968869be4b0d1f9f05f5955","contributors":{"authors":[{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":704844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walsh, Maureen 0000-0001-7846-5025 mwalsh@usgs.gov","orcid":"https://orcid.org/0000-0001-7846-5025","contributorId":3659,"corporation":false,"usgs":true,"family":"Walsh","given":"Maureen","email":"mwalsh@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":704845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Connerton, Michael J.","contributorId":190416,"corporation":false,"usgs":false,"family":"Connerton","given":"Michael J.","affiliations":[],"preferred":false,"id":704846,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lantry, Brian F. 0000-0001-8797-3910 bflantry@usgs.gov","orcid":"https://orcid.org/0000-0001-8797-3910","contributorId":3435,"corporation":false,"usgs":true,"family":"Lantry","given":"Brian","email":"bflantry@usgs.gov","middleInitial":"F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":704847,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lantry, Jana R.","contributorId":28495,"corporation":false,"usgs":false,"family":"Lantry","given":"Jana","email":"","middleInitial":"R.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":704848,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holden, Jeremy P.","contributorId":190415,"corporation":false,"usgs":false,"family":"Holden","given":"Jeremy","email":"","middleInitial":"P.","affiliations":[{"id":16762,"text":"Ontario Ministry of Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":704849,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yuille, Michael J.","contributorId":194647,"corporation":false,"usgs":false,"family":"Yuille","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":704850,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":" Hoyle, James A.","contributorId":141108,"corporation":false,"usgs":false,"family":" Hoyle","given":"James A.","affiliations":[{"id":6780,"text":"Ontario Ministry of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":704851,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}