Visual cover class data were collected on over 80 species across 30 permanent sampling frames in sagebrush steppe vegetation communities in Grand Teton National Park from 2012 to 2018. In this report, temporal and spatial patterns in species composition were assessed and used to inform potential sampling strategies for future monitoring. Specifically, the viability of a reduction in sampling effort was evaluated based on the similarity in species composition within each frame over time and among frames within each year.
Using distance-based ordination techniques, we found little to no evidence of differences in species composition within each frame over time. Furthermore, there was little evidence of heterogeneity in species composition among frames within each year, though there was some evidence of differences in composition between the two principle sagebrush community types (sagebrush dry shrubland and sagebrush-bitterbrush) aggregated across frames. Based on these results, we propose that a reduction in sampling effort is viable and suggest a new monitoring schedule.
","language":"English","publisher":"National Park Service","usgsCitation":"Stratton, C., Hoegh, A., Irvine, K., Legg, K., McCloskey, K., Shanahan, E.K., Tercek, M., and Thoma, D., 2019, Assessing spatial and temporal patterns in sagebrush steppe vegetation communities 2012-2018: Grand Teton National Park: Natural Resource Report NPS/GRYN/NRR-2019/2020, v, 24 p.","productDescription":"v, 24 p.","ipdsId":"IP-108962","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":389548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389547,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://irma.nps.gov/DataStore/DownloadFile/631136"}],"country":"United States","state":"Wyoming","otherGeospatial":"Grand Teton National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.91522216796875,\n 43.530629170442424\n ],\n [\n -110.41259765625,\n 43.530629170442424\n ],\n [\n -110.41259765625,\n 44.10139306449849\n ],\n [\n -110.91522216796875,\n 44.10139306449849\n ],\n [\n -110.91522216796875,\n 43.530629170442424\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stratton, Christian","contributorId":265905,"corporation":false,"usgs":false,"family":"Stratton","given":"Christian","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":823693,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoegh, Andrew","contributorId":265906,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":823694,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":265898,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":823695,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Legg, Kristin","contributorId":265907,"corporation":false,"usgs":false,"family":"Legg","given":"Kristin","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":823696,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCloskey, Kelly","contributorId":265908,"corporation":false,"usgs":false,"family":"McCloskey","given":"Kelly","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":823697,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shanahan, Erin K.","contributorId":265909,"corporation":false,"usgs":false,"family":"Shanahan","given":"Erin","email":"","middleInitial":"K.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":823698,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tercek, Mike","contributorId":265910,"corporation":false,"usgs":false,"family":"Tercek","given":"Mike","affiliations":[{"id":54820,"text":"Walking Shadow Ecology","active":true,"usgs":false}],"preferred":false,"id":823699,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thoma, David","contributorId":265911,"corporation":false,"usgs":false,"family":"Thoma","given":"David","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":823700,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70259333,"text":"70259333 - 2019 - Monitoring, forecasting collapse events, and mapping pyroclastic deposits at Sinabung volcano with satellite imagery","interactions":[],"lastModifiedDate":"2024-10-04T13:56:02.834055","indexId":"70259333","displayToPublicDate":"2019-10-01T08:47:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring, forecasting collapse events, and mapping pyroclastic deposits at Sinabung volcano with satellite imagery","docAbstract":"During the ongoing (2013–present) eruption of Sinabung volcano, north Sumatra, we have routinely used a variety of satellite remote sensing data to observe and forecast lava dome and lava flow collapse events, to map the resulting pyroclastic deposits, and to estimate effusion rates. In this paper, we focus on the first two years of the current eruption (September 2013–December 2015), and we summarize major events in 2016. We divide the eruption into 5 major phases: 1) phreatomagmatic (July 2013–18 December 2013), 2) first dome growth and collapse (18 December 2013–10 January 2014), 3) lava-flow (10 January 2014–mid-September 2014), 4) second lava dome and collapse (mid-September 2014–July 2015), 5) lava dome collapse and ash explosion phase (August 2015–present). Throughout the eruption, remotely sensed information has been instrumental in assessing the stability of the lava dome and flow and to forecast collapse events that produce pyroclastic density currents (PDCs: block-and-ash flows, co-ignimbrite surges, and blasts). Forecasts based on remote sensing data in combination with seismic, geodetic and gas-monitoring data have also helped inform decisions related to alert levels and evacuations. Relatively unusual aspects of the Sinabung eruption include the transition from dome to flow morphology (phase 2 to phase 3 transition) and the frequent occurrence during phase 3 of collapses from the lava flow-front and flow-margins—collapses that produced extensive pyroclastic density currents. By analogy to the well-known “Merapi type” collapses and pyroclastic deposits, we propose that lava flow-front and flow-margin collapses with associated PDCs be known as “Sinabung type.” Although detailed study of deposits has not been possible due to continuing hazards, our observations suggest that the transition from lava dome to lava flow and the occurrence of flow-front and flow-margin collapses reflect a particular combination of lava viscosity and steepness of slope. Our observations also show clear evidence of at least one slope-parallel high-velocity and dilute PDC (a “blast”) that emanated from a lava-margin collapse site 500 m downslope from the vent. This 1 February 2014 blast downed and singed a forest out to at least 3.9 km from the collapse site and killed 16 people. We also use a combination of field and remotely sensed data to map the distribution of Sinabung deposits. We estimate eruptive volumes and extrusion rates by combining sequential measurements of lava surface and pyroclastic flow areas with thickness estimates derived from simple geometric assumptions, oblique photographs and Digital Elevation Models (DEMs) derived from remotely sensed data. Our estimates of short-term effusion rates vary widely on a daily to weekly basis, from <1 to >20 m3 s−1. In a few cases, periods of increased extrusion precede lava flow-front collapses by a few days to a week, suggesting delays in transmittance of effusion pulses as lava moves from vent to flow front. We find that, as of 1 January 2016, the total area of deposits is 107 m2, and their approximate deposit volume is about 0.3 km3, equivalent to 0.2 km3 Dense Rock Equivalent (DRE). We anticipate that our deposit maps will be valuable in the future as a framework for the study of the magmatic and textural evolution of eruptive products through time.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2018.05.012","usgsCitation":"Pallister, J.S., Wessels, R., Griswold, J.P., McCausland, W.A., Kartadinata, N., Gunawan, H., Budianto, A., and Primulyana, S., 2019, Monitoring, forecasting collapse events, and mapping pyroclastic deposits at Sinabung volcano with satellite imagery: Journal of Volcanology and Geothermal Research, v. 382, p. 149-163, https://doi.org/10.1016/j.jvolgeores.2018.05.012.","productDescription":"15 p.","startPage":"149","endPage":"163","ipdsId":"IP-077545","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467319,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2018.05.012","text":"Publisher Index Page"},{"id":462593,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Sinabung volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 97.99743834932508,\n 3.449644799978927\n ],\n [\n 97.99743834932508,\n 2.1401582830417425\n ],\n [\n 99.24416582769311,\n 2.1401582830417425\n ],\n [\n 99.24416582769311,\n 3.449644799978927\n ],\n [\n 97.99743834932508,\n 3.449644799978927\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"382","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pallister, John S. 0000-0002-2041-2147 jpallist@usgs.gov","orcid":"https://orcid.org/0000-0002-2041-2147","contributorId":2024,"corporation":false,"usgs":true,"family":"Pallister","given":"John","email":"jpallist@usgs.gov","middleInitial":"S.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":914964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wessels, Rick 0000-0001-9711-6402 rwessels@usgs.gov","orcid":"https://orcid.org/0000-0001-9711-6402","contributorId":198602,"corporation":false,"usgs":true,"family":"Wessels","given":"Rick","email":"rwessels@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":914965,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Griswold, Julia P. 0000-0001-5597-5030 griswold@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-5030","contributorId":202823,"corporation":false,"usgs":true,"family":"Griswold","given":"Julia","email":"griswold@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":914966,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCausland, Wendy A. 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":204380,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":914967,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kartadinata, Nugraha","contributorId":344903,"corporation":false,"usgs":false,"family":"Kartadinata","given":"Nugraha","email":"","affiliations":[{"id":37068,"text":"CVGHM","active":true,"usgs":false}],"preferred":false,"id":914968,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gunawan, Hendra","contributorId":344904,"corporation":false,"usgs":false,"family":"Gunawan","given":"Hendra","affiliations":[{"id":37068,"text":"CVGHM","active":true,"usgs":false}],"preferred":false,"id":914969,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Budianto, Agus","contributorId":344905,"corporation":false,"usgs":false,"family":"Budianto","given":"Agus","affiliations":[{"id":37068,"text":"CVGHM","active":true,"usgs":false}],"preferred":false,"id":914970,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Primulyana, Sofyan","contributorId":344906,"corporation":false,"usgs":false,"family":"Primulyana","given":"Sofyan","affiliations":[{"id":37068,"text":"CVGHM","active":true,"usgs":false}],"preferred":false,"id":914971,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70206799,"text":"70206799 - 2019 - Preface: Marine gas hydrate reservoir systems along the eastern continental margin of India: Results of the National Gas Hydrate Program Expedition 02","interactions":[],"lastModifiedDate":"2019-11-26T06:30:15","indexId":"70206799","displayToPublicDate":"2019-10-01T08:31:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2382,"text":"Journal of Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Preface: Marine gas hydrate reservoir systems along the eastern continental margin of India: Results of the National Gas Hydrate Program Expedition 02","docAbstract":"The 2015 India National Gas Hydrate Program (NGHP) Expedition 02 was conducted to obtain new information on the occurrence of gas hydrate systems and to advance the understanding of geologic controls on the formation of gas hydrate accumulations in the offshore of India. The ultimate goal of the NGHP effort is to assess the energy resource potential of marine gas hydrates in India. The Guest Editors for this special thematic issue of the Journal of Marine and Petroleum Geology (JMPG) have worked with more than 100 scientists and engineers to prepare the 45 individual reports included in this special issue to address the operational and technical contributions of the NGHP-02 Expedition. This thematic issue on natural gas hydrates in the offshore of India is built on the foundation of five previous JMPG thematic issues that focused on the scientific results of other marine gas hydrate expeditions. These foundational contributions include: (1) the “Scientific Results of the 2005 USDOE-Chevron JIP Drilling for Methane Hydrates Objectives in the Gulf of Mexico” (Volume 25, Issue 9, November 2008), (2) the “Resource and Hazard Implications of Gas Hydrates in the Northern Gulf of Mexico: Results of the 2009 Joint Industry Project Leg II Drilling Expedition” (Volume 34, Issue 1, June 2012), (3) the “Scientific Results of the Second Gas Hydrate Drilling Expedition in the Ulleung Basin (UBGH2), East Sea of Korea” (Volume 47, November 2013), (4) “Geologic Implications of Gas Hydrates in the Offshore of India: Results of the National Gas Hydrate Program Expedition 01” (Volume 58, Part A, December 2014), and (5) “Gas Hydrate Drilling in Eastern Nankai” (Volume 66, Part 2, September 2015). The NGHP-02 Expedition was conducted from 03-March- 2015 to 28-July- 2015 off the eastern coast of India. The first two months of the expedition were dedicated to establishing 25 new research sites that featured the drilling of a dedicated downhole logging hole at each site. The next three months of the NGHP-02 Expedition were dedicated to sediment coring and other downhole measurement operations at 10 of the sites established during the expedition’s first phase. The results of downhole logging, coring and formation pressure testing operations during NGHP-02 have confirmed the presence of large, highly concentrated gas hydrate accumulations in coarse-grained, sand-rich depositional systems throughout large portions of the Krishna-Godavari Basin. Post expedition research and reporting efforts included collaborative analysis of the unprecedented number of shipboard acquired data sets and core samples obtained during NGHP-02. The presentation of the scientific results of the NGHP-02 Expedition has culminated in the publication of the NGHP Expedition 02 Scientific Results Volume, which is represented by this Special Issue of the Journal of Marine and Petroleum Geology. This Special Issue features a series of four reports that summarize the operational and scientific results of NGHP-02 Expedition that are presented in the 41 technical reports included this Special Issue. The first summary report, “India National Gas Hydrate Program Expedition 02: Operational and Technical Summary,” focuses on reviewing the tools and operational procedures for the NGHP-02 Expedition that led to the acquisition of an unprecedented amount of high-quality downhole logging and core data from numerous pore-filling, fracture-filling, and sediment-displacement type gas hydrate occurrences. The summary report titled “India National Gas Hydrate Program Expedition 02 Summary of Scientific Results: Gas Hydrate Systems Along the Eastern Continental Margin of India” documents gas hydrate occurrences discovered during the NGHP-02 Expedition and examines geologic controls on the gas hydrate systems along the Eastern Continental Margin of India.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2019.03.005","usgsCitation":"Collett, T.S., Kumar, P., Boswell, R., and Waite, W., 2019, Preface: Marine gas hydrate reservoir systems along the eastern continental margin of India: Results of the National Gas Hydrate Program Expedition 02: Journal of Marine and Petroleum Geology, v. 108, p. 1-2, https://doi.org/10.1016/j.marpetgeo.2019.03.005.","productDescription":"2 p.","startPage":"1","endPage":"2","ipdsId":"IP-106500","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":369451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[77.83745,35.49401],[78.91227,34.32194],[78.81109,33.5062],[79.20889,32.99439],[79.17613,32.48378],[78.45845,32.61816],[78.73889,31.51591],[79.72137,30.88271],[81.11126,30.18348],[80.47672,29.72987],[80.08842,28.79447],[81.0572,28.4161],[81.99999,27.92548],[83.30425,27.36451],[84.67502,27.2349],[85.25178,26.7262],[86.02439,26.63098],[87.22747,26.3979],[88.06024,26.41462],[88.1748,26.81041],[88.04313,27.44582],[88.12044,27.87654],[88.73033,28.08686],[88.81425,27.29932],[88.83564,27.09897],[89.74453,26.7194],[90.37327,26.87572],[91.21751,26.80865],[92.03348,26.83831],[92.10371,27.45261],[91.69666,27.77174],[92.50312,27.89688],[93.41335,28.64063],[94.56599,29.27744],[95.4048,29.03172],[96.11768,29.4528],[96.58659,28.83098],[96.24883,28.41103],[97.32711,28.26158],[97.40256,27.88254],[97.05199,27.69906],[97.134,27.08377],[96.41937,27.26459],[95.12477,26.57357],[95.15515,26.00131],[94.60325,25.1625],[94.55266,24.67524],[94.10674,23.85074],[93.32519,24.07856],[93.28633,23.04366],[93.06029,22.70311],[93.16613,22.27846],[92.67272,22.04124],[92.14603,23.6275],[91.86993,23.62435],[91.70648,22.98526],[91.15896,23.50353],[91.46773,24.07264],[91.91509,24.13041],[92.3762,24.97669],[91.7996,25.14743],[90.87221,25.1326],[89.92069,25.26975],[89.83248,25.96508],[89.35509,26.01441],[88.56305,26.44653],[88.20979,25.76807],[88.93155,25.23869],[88.30637,24.86608],[88.08442,24.50166],[88.69994,24.23371],[88.52977,23.63114],[88.87631,22.87915],[89.03196,22.05571],[88.88877,21.69059],[88.2085,21.70317],[86.9757,21.49556],[87.03317,20.74331],[86.49935,20.15164],[85.06027,19.47858],[83.94101,18.30201],[83.18922,17.67122],[82.19279,17.01664],[82.19124,16.55666],[81.69272,16.31022],[80.792,15.95197],[80.3249,15.89918],[80.02507,15.13641],[80.23327,13.83577],[80.28629,13.00626],[79.86255,12.05622],[79.858,10.35728],[79.34051,10.30885],[78.88535,9.54614],[79.18972,9.21654],[78.27794,8.93305],[77.94117,8.25296],[77.5399,7.96553],[76.59298,8.89928],[76.13006,10.29963],[75.74647,11.30825],[75.3961,11.78125],[74.86482,12.74194],[74.61672,13.99258],[74.44386,14.61722],[73.5342,15.99065],[73.11991,17.92857],[72.82091,19.20823],[72.82448,20.4195],[72.63053,21.35601],[71.17527,20.75744],[70.47046,20.87733],[69.16413,22.0893],[69.64493,22.45077],[69.3496,22.84318],[68.17665,23.69197],[68.8426,24.35913],[71.04324,24.35652],[70.8447,25.2151],[70.28287,25.72223],[70.16893,26.49187],[69.51439,26.94097],[70.6165,27.9892],[71.77767,27.91318],[72.82375,28.96159],[73.45064,29.97641],[74.42138,30.97981],[74.40593,31.69264],[75.25864,32.27111],[74.45156,32.7649],[74.10429,33.44147],[73.74995,34.3177],[74.2402,34.74889],[75.75706,34.50492],[76.87172,34.65354],[77.83745,35.49401]]]},\"properties\":{\"name\":\"India\"}}]}","volume":"108","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":775779,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kumar, Pushpendra","contributorId":220793,"corporation":false,"usgs":false,"family":"Kumar","given":"Pushpendra","email":"","affiliations":[{"id":40268,"text":"Oil and Natural Gas Corporation, Panvel, Navi Mumbai, India","active":true,"usgs":false}],"preferred":false,"id":775780,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boswell, Ray","contributorId":173139,"corporation":false,"usgs":false,"family":"Boswell","given":"Ray","email":"","affiliations":[{"id":17887,"text":"National Energy Technology Laboratory, Department of Energy","active":true,"usgs":false}],"preferred":false,"id":775781,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waite, William 0000-0003-9857-8074 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-9857-8074","contributorId":220794,"corporation":false,"usgs":true,"family":"Waite","given":"William","email":"wwaite@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":775782,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206800,"text":"70206800 - 2019 - Formation pressure and fluid flow measurements in marine gas hydrate reservoirs, NGHP-02 expedition, offshore India","interactions":[],"lastModifiedDate":"2019-11-22T08:28:35","indexId":"70206800","displayToPublicDate":"2019-10-01T08:26:04","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2382,"text":"Journal of Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Formation pressure and fluid flow measurements in marine gas hydrate reservoirs, NGHP-02 expedition, offshore India","docAbstract":"Open Hole Modular Dynamic Testing (MDT) measurements were conducted in a gas hydrate-bearing sand-rich reservoir offshore India during the National Gas Hydrate Program 02 (NGHP-02) Expedition. The primary goal of this test was to obtain effective reservoir petrophysical properties in the presence of gas hydrates. The test plan included a series of pre-hydrate dissociation flow and build-up (shut-in) tests, and an attempt to dissociate gas hydrate in a sand-rich reservoir by depressurization to collect formation fluid samples and to further characterize in situ gas hydrate stability conditions. Schlumberger’s wireline MDT tool was used in a dual-packer configuration to isolate the formation being tested.\n\nThis paper presents the results of the open hole MDT measurements that were conducted in Hole NGHP-02-23-C in Krishna-Godavari Basin at a water depth of 2553.5 m. The MDT dual packer test was conducted in 27 cm (10.63 in) diameter open hole section of the borehole within a 1-m interval isolated between two inflatable packers with the midpoint of the test interval at 2853.0 meter below rig floor (mbrf) (271.0 meter below sea floor (mbsf)). This was the first gas hydrate MDT test ever conducted in ultradeep water to characterize a gas hydrate reservoir system. Pre-hydrate dissociation testing was performed with a drawdown period (depressurization) of 20 minutes followed by a build-up (shut-in) of 20 minutes. The measured formation pressure was 4090.7 psia and the formation fluid (i.e., water) mobility was calculated at 1.98 mD/cP. During the second dissociation phase of the same test a maximum pressure drawdown of 840 psia was achieved; however, falling short of the 1120 psia drawdown required for dissociation. During the dissociation test the flow line pressure stabilized at a flowing pressure of 3250 psia which can be attributed to the relatively high mobility of the free water phase in the hydrate-bearing reservoir. Good quality formation pressure and near well bore mobility data was acquired that yielded a “high confidence” reservoir effective radial permeability-thickness product of 0.2 mD.m (or a horizontal effective permeability of 0.1 mD assuming a reservoir thicknees of 1.8 m) using pressure transient analysis (radial flow regime was achieved) despite unstable borehole conditions and complex operations in these shallow unconsolidated sedimentary sections.","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2018.11.035","usgsCitation":"Kumar, P., Collett, T.S., Yadav, U., and Singh, J., 2019, Formation pressure and fluid flow measurements in marine gas hydrate reservoirs, NGHP-02 expedition, offshore India: Journal of Marine and Petroleum Geology, v. 108, p. 609-618, https://doi.org/10.1016/j.marpetgeo.2018.11.035.","productDescription":"10 p.","startPage":"609","endPage":"618","ipdsId":"IP-103515","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":459669,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1636237","text":"Publisher Index Page"},{"id":369450,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[77.83745,35.49401],[78.91227,34.32194],[78.81109,33.5062],[79.20889,32.99439],[79.17613,32.48378],[78.45845,32.61816],[78.73889,31.51591],[79.72137,30.88271],[81.11126,30.18348],[80.47672,29.72987],[80.08842,28.79447],[81.0572,28.4161],[81.99999,27.92548],[83.30425,27.36451],[84.67502,27.2349],[85.25178,26.7262],[86.02439,26.63098],[87.22747,26.3979],[88.06024,26.41462],[88.1748,26.81041],[88.04313,27.44582],[88.12044,27.87654],[88.73033,28.08686],[88.81425,27.29932],[88.83564,27.09897],[89.74453,26.7194],[90.37327,26.87572],[91.21751,26.80865],[92.03348,26.83831],[92.10371,27.45261],[91.69666,27.77174],[92.50312,27.89688],[93.41335,28.64063],[94.56599,29.27744],[95.4048,29.03172],[96.11768,29.4528],[96.58659,28.83098],[96.24883,28.41103],[97.32711,28.26158],[97.40256,27.88254],[97.05199,27.69906],[97.134,27.08377],[96.41937,27.26459],[95.12477,26.57357],[95.15515,26.00131],[94.60325,25.1625],[94.55266,24.67524],[94.10674,23.85074],[93.32519,24.07856],[93.28633,23.04366],[93.06029,22.70311],[93.16613,22.27846],[92.67272,22.04124],[92.14603,23.6275],[91.86993,23.62435],[91.70648,22.98526],[91.15896,23.50353],[91.46773,24.07264],[91.91509,24.13041],[92.3762,24.97669],[91.7996,25.14743],[90.87221,25.1326],[89.92069,25.26975],[89.83248,25.96508],[89.35509,26.01441],[88.56305,26.44653],[88.20979,25.76807],[88.93155,25.23869],[88.30637,24.86608],[88.08442,24.50166],[88.69994,24.23371],[88.52977,23.63114],[88.87631,22.87915],[89.03196,22.05571],[88.88877,21.69059],[88.2085,21.70317],[86.9757,21.49556],[87.03317,20.74331],[86.49935,20.15164],[85.06027,19.47858],[83.94101,18.30201],[83.18922,17.67122],[82.19279,17.01664],[82.19124,16.55666],[81.69272,16.31022],[80.792,15.95197],[80.3249,15.89918],[80.02507,15.13641],[80.23327,13.83577],[80.28629,13.00626],[79.86255,12.05622],[79.858,10.35728],[79.34051,10.30885],[78.88535,9.54614],[79.18972,9.21654],[78.27794,8.93305],[77.94117,8.25296],[77.5399,7.96553],[76.59298,8.89928],[76.13006,10.29963],[75.74647,11.30825],[75.3961,11.78125],[74.86482,12.74194],[74.61672,13.99258],[74.44386,14.61722],[73.5342,15.99065],[73.11991,17.92857],[72.82091,19.20823],[72.82448,20.4195],[72.63053,21.35601],[71.17527,20.75744],[70.47046,20.87733],[69.16413,22.0893],[69.64493,22.45077],[69.3496,22.84318],[68.17665,23.69197],[68.8426,24.35913],[71.04324,24.35652],[70.8447,25.2151],[70.28287,25.72223],[70.16893,26.49187],[69.51439,26.94097],[70.6165,27.9892],[71.77767,27.91318],[72.82375,28.96159],[73.45064,29.97641],[74.42138,30.97981],[74.40593,31.69264],[75.25864,32.27111],[74.45156,32.7649],[74.10429,33.44147],[73.74995,34.3177],[74.2402,34.74889],[75.75706,34.50492],[76.87172,34.65354],[77.83745,35.49401]]]},\"properties\":{\"name\":\"India\"}}]}","volume":"108","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kumar, Pushpendra","contributorId":212239,"corporation":false,"usgs":false,"family":"Kumar","given":"Pushpendra","affiliations":[{"id":38465,"text":"Oil and Natural Gas Corp. Panvel, Navi Mumbai, India","active":true,"usgs":false}],"preferred":false,"id":775823,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":775824,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yadav, U.S.","contributorId":127763,"corporation":false,"usgs":false,"family":"Yadav","given":"U.S.","email":"","affiliations":[{"id":7141,"text":"Oil and Natural Gas Corporation Ltd, KDM Institute of Petroleum Exploration, India","active":true,"usgs":false}],"preferred":false,"id":775825,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Singh, Juli","contributorId":220817,"corporation":false,"usgs":false,"family":"Singh","given":"Juli","email":"","affiliations":[],"preferred":false,"id":775826,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206802,"text":"70206802 - 2019 - India National Gas Hydrate Program Expedition-02: Operational and technical summary","interactions":[],"lastModifiedDate":"2019-11-22T08:25:02","indexId":"70206802","displayToPublicDate":"2019-10-01T08:23:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2382,"text":"Journal of Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"India National Gas Hydrate Program Expedition-02: Operational and technical summary","docAbstract":"The India National Gas Hydrate Program is being steered by the government of India's Ministry of Petroleum and Natural Gas (MoPNG) with participation of Directorate General of Hydrocarbons (DGH), Oil and Natural Gas Corporation Limited (ONGC), and the National Oil Companies and Research Institutes of India. The India National Gas Hydrate Program Expedition 01 (NGHP-01) established the presence of gas hydrate in the Krishna Godavari (KG) and Mahanadi Basins and in the offshore area of the Andaman Sea Basin. However, the gas hydrates discovered during NGHP-01 were mainly distributed as fracture-filling material in fine-grained clay-rich sediments. The India National Gas Hydrate Program Expedition 02 (NGHP-02) was carried out with an objective to discover gas hydrate in sand-rich sediment along the eastern offshore margin of India. ONGC planned and executed NGHP-02 on the behalf of the MoPNG.\n\nNGHP-02 started on March 3, 2015 and was completed on July 28, 2015 (total 147 days) using the Japanese scientific Drilling Vessel Chikyu (D/V Chikyu). During NGHP-02, 42 holes at 25 sites were drilled, cored, and/or surveyed with downhole logging tools. These sites were located in four areas along the eastern margin of India and formally named Area A (Mahanadi Basin, three sites), Area B (northern part of the KG-Basin, twelve sites), Area C (central part of the KG-Basin, six sites), and Area E (southern part to the KG-Basin, four sites). All 25 sites established during NGHP-02 were first drilled and logged with logging-while-drilling (LWD) tools and an additional 17 holes were then drilled and/or cored with conventional coring tools (HPCS/ESCS) or pressure coring tools (PCTB). Wireline logging was conducted in 10 holes and formation tests using a dual packer Modular Formation Dynamics Tester (MDT) tool were carried out in two holes.\n\nThe onboard science team used the laboratory facilities on the D/V Chikyu to examine and analyse the physical properties, geochemistry, and sedimentology of all the cores collected during the expedition. Core samples were also analysed in additional post-expedition shore-based studies conducted in numerous domestic and international gas hydrate research laboratories. The NGHP-02 sediment cores were archived at the National Gas Hydrate Core Repository in Mumbai (India), which is associated with the ONGC Gas Hydrate Research and Technology Centre (GHRTC). The necessary data for characterizing the occurrence of gas hydrate, such as interstitial water chlorinities, core-derived gas chemistry, physical and sedimentological properties, thermal images of the recovered cores, pressure core and downhole measured logging data (LWD and/or conventional wireline log data), were obtained from most of the drill sites established during NGHP-02. Almost all the drill sites yielded evidence for the occurrence of gas hydrate; however, the inferred in situ concentration of gas hydrate varied substantially from site to site. For the most part, the interpretation of downhole logging data, core thermal images, interstitial water analyses, and pressure core images from the sites established during NGHP-02 indicate that the occurrence of concentrated gas hydrate is mostly associated with coarser grained (sand-rich) sediments. This paper presents the operational and technical summary of NGHP-02.\n\nNGHP-02 started on March 3, 2015 and was completed on July 28, 2015 (total 147 days) using the Japanese scientific Drilling Vessel Chikyu (D/V Chikyu). During NGHP-02, 42 holes at 25 sites were drilled, cored, and/or surveyed with downhole logging tools. These sites were located in four areas along the eastern margin of India and formally named Area A (Mahanadi Basin, three sites), Area B (northern part of the KG-Basin, twelve sites), Area C (central part of the KG-Basin, six sites), and Area E (southern part to the KG-Basin, four sites). All 25 sites established during NGHP-02 were first drilled and logged with logging-while-drilling (LWD) to","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2018.11.021","usgsCitation":"Kumar, P., Collett, T.S., K. M. Shukla, Yadav, U.S., Lall, M.V., and Krishna Vishwanath, 2019, India National Gas Hydrate Program Expedition-02: Operational and technical summary: Journal of Marine and Petroleum Geology, v. 108, p. 3-38, https://doi.org/10.1016/j.marpetgeo.2018.11.021.","productDescription":"36 p.","startPage":"3","endPage":"38","ipdsId":"IP-103513","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":459672,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1636323","text":"Publisher Index Page"},{"id":369449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[77.83745,35.49401],[78.91227,34.32194],[78.81109,33.5062],[79.20889,32.99439],[79.17613,32.48378],[78.45845,32.61816],[78.73889,31.51591],[79.72137,30.88271],[81.11126,30.18348],[80.47672,29.72987],[80.08842,28.79447],[81.0572,28.4161],[81.99999,27.92548],[83.30425,27.36451],[84.67502,27.2349],[85.25178,26.7262],[86.02439,26.63098],[87.22747,26.3979],[88.06024,26.41462],[88.1748,26.81041],[88.04313,27.44582],[88.12044,27.87654],[88.73033,28.08686],[88.81425,27.29932],[88.83564,27.09897],[89.74453,26.7194],[90.37327,26.87572],[91.21751,26.80865],[92.03348,26.83831],[92.10371,27.45261],[91.69666,27.77174],[92.50312,27.89688],[93.41335,28.64063],[94.56599,29.27744],[95.4048,29.03172],[96.11768,29.4528],[96.58659,28.83098],[96.24883,28.41103],[97.32711,28.26158],[97.40256,27.88254],[97.05199,27.69906],[97.134,27.08377],[96.41937,27.26459],[95.12477,26.57357],[95.15515,26.00131],[94.60325,25.1625],[94.55266,24.67524],[94.10674,23.85074],[93.32519,24.07856],[93.28633,23.04366],[93.06029,22.70311],[93.16613,22.27846],[92.67272,22.04124],[92.14603,23.6275],[91.86993,23.62435],[91.70648,22.98526],[91.15896,23.50353],[91.46773,24.07264],[91.91509,24.13041],[92.3762,24.97669],[91.7996,25.14743],[90.87221,25.1326],[89.92069,25.26975],[89.83248,25.96508],[89.35509,26.01441],[88.56305,26.44653],[88.20979,25.76807],[88.93155,25.23869],[88.30637,24.86608],[88.08442,24.50166],[88.69994,24.23371],[88.52977,23.63114],[88.87631,22.87915],[89.03196,22.05571],[88.88877,21.69059],[88.2085,21.70317],[86.9757,21.49556],[87.03317,20.74331],[86.49935,20.15164],[85.06027,19.47858],[83.94101,18.30201],[83.18922,17.67122],[82.19279,17.01664],[82.19124,16.55666],[81.69272,16.31022],[80.792,15.95197],[80.3249,15.89918],[80.02507,15.13641],[80.23327,13.83577],[80.28629,13.00626],[79.86255,12.05622],[79.858,10.35728],[79.34051,10.30885],[78.88535,9.54614],[79.18972,9.21654],[78.27794,8.93305],[77.94117,8.25296],[77.5399,7.96553],[76.59298,8.89928],[76.13006,10.29963],[75.74647,11.30825],[75.3961,11.78125],[74.86482,12.74194],[74.61672,13.99258],[74.44386,14.61722],[73.5342,15.99065],[73.11991,17.92857],[72.82091,19.20823],[72.82448,20.4195],[72.63053,21.35601],[71.17527,20.75744],[70.47046,20.87733],[69.16413,22.0893],[69.64493,22.45077],[69.3496,22.84318],[68.17665,23.69197],[68.8426,24.35913],[71.04324,24.35652],[70.8447,25.2151],[70.28287,25.72223],[70.16893,26.49187],[69.51439,26.94097],[70.6165,27.9892],[71.77767,27.91318],[72.82375,28.96159],[73.45064,29.97641],[74.42138,30.97981],[74.40593,31.69264],[75.25864,32.27111],[74.45156,32.7649],[74.10429,33.44147],[73.74995,34.3177],[74.2402,34.74889],[75.75706,34.50492],[76.87172,34.65354],[77.83745,35.49401]]]},\"properties\":{\"name\":\"India\"}}]}","volume":"108","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kumar, Pushpendra","contributorId":220793,"corporation":false,"usgs":false,"family":"Kumar","given":"Pushpendra","email":"","affiliations":[{"id":40268,"text":"Oil and Natural Gas Corporation, Panvel, Navi Mumbai, India","active":true,"usgs":false}],"preferred":false,"id":775784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":775783,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"K. M. Shukla","contributorId":220795,"corporation":false,"usgs":false,"family":"K. M. Shukla","affiliations":[{"id":40269,"text":"Oil and Natural Gas Corporation Ltd","active":true,"usgs":false}],"preferred":false,"id":775785,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yadav, U. S.","contributorId":220796,"corporation":false,"usgs":false,"family":"Yadav","given":"U.","email":"","middleInitial":"S.","affiliations":[{"id":40270,"text":"Oil and Natural Gas Corporation Ltd.","active":true,"usgs":false}],"preferred":false,"id":775786,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lall, M. V.","contributorId":220797,"corporation":false,"usgs":false,"family":"Lall","given":"M.","email":"","middleInitial":"V.","affiliations":[{"id":40270,"text":"Oil and Natural Gas Corporation Ltd.","active":true,"usgs":false}],"preferred":false,"id":775787,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Krishna Vishwanath","contributorId":220798,"corporation":false,"usgs":false,"family":"Krishna Vishwanath","affiliations":[{"id":40271,"text":"Directorate General of Hydrocarbons","active":true,"usgs":false}],"preferred":false,"id":775788,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70206403,"text":"70206403 - 2019 - Ensembles of ETAS models provide optimal operational earthquake forecasting during swarms: Insights from the 2015 San Ramon, California swarm","interactions":[],"lastModifiedDate":"2019-12-03T10:01:11","indexId":"70206403","displayToPublicDate":"2019-10-01T06:48:33","publicationYear":"2019","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":"Ensembles of ETAS models provide optimal operational earthquake forecasting during swarms: Insights from the 2015 San Ramon, California swarm","docAbstract":"Earthquake swarms, typically modeled as time-varying changes in background seismicity that are driven by external processes such as fluid flow or aseismic creep, present challenges for operational earthquake forecasting. While the time decay of aftershock sequences can be estimated with the modified Omori law, it is difficult to forecast the temporal behavior of seismicity rates during a swarm. To explore these issues, we apply the Epidemic-Type Aftershock Sequence (ETAS) model (Ogata, JASA, 1988) to the 2015 San Ramon, California swarm, which lasted several weeks and had almost 100 2≤M≤3.6 earthquakes. We develop 3-day forecasts during the swarm based on an ETAS model fit to all prior seismicity in the region as well as an ETAS model fit only to previous swarms in the region, which is better at capturing the higher background rate during the swarm. We also explore forecasts where the background rate is updated periodically during the swarm using data over different lookback windows and find that generally these models perform better than the models where the background rate is fixed. Finally, we construct ensemble forecasts by combining the different models weighted according to their performance. The ensemble forecasts outperform all of the individual models and allow us to avoid making arbitrary choices at the outset of a swarm as to which single model will perform the best.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120190020","usgsCitation":"Llenos, A.L., and Michael, A.J., 2019, Ensembles of ETAS models provide optimal operational earthquake forecasting during swarms: Insights from the 2015 San Ramon, California swarm: Bulletin of the Seismological Society of America, v. 109, no. 6, p. 2145-2158, https://doi.org/10.1785/0120190020.","productDescription":"14 p.","startPage":"2145","endPage":"2158","ipdsId":"IP-101090","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":368858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Contra Costa County","city":"San 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and gas development","docAbstract":"An analytical framework was designed to estimate water use associated with continuous oil and gas (COG) development in support of the U.S. Geological Survey Water Availability and Use Science Program. This framework was developed to better understand the relation between the production of COG resources for energy and the amount of water needed to sustain this type of energy development in the United States. The total mean undiscovered, technically recoverable volume of COG has increased, highlighting the continued need to develop approaches to better characterize water use associated with COG development.
The analytical framework can be used to estimate water use associated with COG development for three water-use components—direct, indirect, and ancillary water use—that are related to the life cycle of COG development. Direct water use is defined as water used in a wellbore to complete a well, including the water used for drilling, cementing, stimulating, and maintaining the well during production. Indirect water use is the water used at or near the well site, including water used for dust abatement, for cleaning equipment, and for crew and staff use. Ancillary water use is all other water used during the life cycle of COG development that is not categorized as direct or indirect, such as additional local or regional water use resulting from a change (for example, population) related to COG development. The analytical framework includes the data inputs, the processes involved in estimating the water-use coefficients and analyzing their uncertainties, and the outputs. The analytical framework was developed as an R script, which contains the statistical models used to estimate water-use components.
The availability of data across COG reservoirs in the United States is variable and presents challenges for estimating water use for extracting COG from their reservoirs; thus, the R script can be modified for the types of data available within a COG reservoir, the extent and resolution of data available for each water-use component, and the desired output of the water-use assessment. The script was written so that the units of the data in the script were standardized. Water-use estimates were simulated for the mean and 10th, 50th, and 90th percentiles of the data distributions. Uncertainties were quantified with confidence intervals for the estimated coefficients. Uncertainty for estimated or simulated data can be calculated with the R script by providing a range of representative values that are within the appropriate confidence intervals of the mean of the data.
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New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE, Suite B
Albuquerque, NM 87113
This study explores the feasibility and utility of integrating environmental DNA (eDNA) assessments of species occurrences into the United States (U.S.) Geological Survey’s national streamgage network. We used an existing network of five gages in southwest Idaho to explore the type of information that could be gained as well as the associated costs and limitations. Hydrologic technicians were trained in eDNA sampling protocols and they collected samples during routine monthly visits to streamgages over an entire water year (2016). We analyzed the eDNA in the filtered water samples to determine the presence of two fish species: bull trout and rainbow trout. We then modeled the spatiotemporal distribution of each species using discharge and temperature data. To assess the influence of the spatial distribution of the gages on the biological information obtained, we also collected eDNA samples from locations between the gages three times during the water year. We found eDNA monitoring at the five gages provided meaningful information about the distribution of both species, especially when detection probabilities accounted for variations in temperature and discharge. Sampling between the gages provided additional information about bull trout distribution — the rarer of the two species. Our study suggests the integration of eDNA sampling into a streamgage network is feasible and could provide a novel and powerful source of biological information for riverine ecosystems in the U.S.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12800","usgsCitation":"Pilliod, D.S., Laramie, M., McCoy, D., and Maclean, S., 2019, Integration of eDNA-based biological monitoring within the US Geological Survey’s national streamgage network: Journal of the American Water Resources Association, v. 55, no. 6, p. 1505-1518, https://doi.org/10.1111/1752-1688.12800.","productDescription":"14 p.","startPage":"1505","endPage":"1518","numberOfPages":"14","ipdsId":"IP-104039","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":459688,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12800","text":"Publisher Index Page"},{"id":367934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Nebraska ","otherGeospatial":"Bruneau–Jarbidge Rivers watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.26281738281249,\n 41.32732632036622\n ],\n [\n -114.87854003906249,\n 41.32732632036622\n ],\n [\n -114.87854003906249,\n 42.46399280017058\n ],\n [\n -116.26281738281249,\n 42.46399280017058\n ],\n [\n -116.26281738281249,\n 41.32732632036622\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":216342,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":772287,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laramie, Matthew 0000-0001-7820-2583 mlaramie@usgs.gov","orcid":"https://orcid.org/0000-0001-7820-2583","contributorId":152532,"corporation":false,"usgs":true,"family":"Laramie","given":"Matthew","email":"mlaramie@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":772288,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCoy, Dorene","contributorId":219452,"corporation":false,"usgs":false,"family":"McCoy","given":"Dorene","email":"","affiliations":[{"id":39997,"text":"Idaho Water Science Center (retired)","active":true,"usgs":false}],"preferred":false,"id":772289,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maclean, Scott","contributorId":219453,"corporation":false,"usgs":false,"family":"Maclean","given":"Scott","email":"","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":772290,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205851,"text":"70205851 - 2019 - A fuzzy logic approach for estimating recovery factors of miscible CO2-EOR projects in the United States","interactions":[],"lastModifiedDate":"2019-10-08T12:35:44","indexId":"70205851","displayToPublicDate":"2019-09-30T12:34:24","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2419,"text":"Journal of Petroleum Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"A fuzzy logic approach for estimating recovery factors of miscible CO2-EOR projects in the United States","docAbstract":"\"Recovery factor (RF) is one of the most fundamental parameters that define engineering and economical success of any operational phase in oil and gas production. The effectiveness of the operation, e.g. CO2-EOR (enhanced oil recovery with carbon dioxide injection), is usually defined by multiplying the resultant recovery factor by the original oil in place. Moreover, investment decisions for such engineering projects are also performed based on predicted recovery factors. Despite its importance, though, it is not easy to predict recovery factors as they are affected by many factors including the type of the recovery process, reservoir type, fluid properties, reservoir heterogeneity, depth, thickness, to name a few. The usual method of estimating recovery factors is laboratory experiments or numerical modeling, each of which has their own limitations due to data requirements, boundary conditions and scale effects.\nIn this work, a fuzzy inference system approach has been adopted to predict miscible CO2-EOR recovery factors of the major field applications in the United States with the premise that it can be used as a guidance tool for making decisions based on different inputs. The fuzzy system was build using a Mamdani-type fuzzy logic inference engine, and by using reservoir data compiled from different sources as inputs and recovery factors gathered from a literature survey. Due to the limited number of field cases that could be used for this purpose, 24 sets of applications were included in the study. Selected input variables were water saturation after waterflood (Sorw), well spacing, porosity, permeability, depth, net pay thickness, initial pressure, API gravity of oil, hydrocarbon pore volume CO2 injected, and reservoir lithology. The type of membership functions were decided based on the system’s predictive performance. The model showed reasonable predictive capability for the field observations of recovery factor despite the complexity of this parameter. In addition, since the fuzzy solution was multi-dimensional due to multiple inputs, system behavior was used to demonstrate response of miscible CO2-EOR recovery factor to different inputs.\n\"","language":"English","publisher":"Elsevier","doi":"10.1016/j.petrol.2019.106533","usgsCitation":"Karacan, C.O., 2019, A fuzzy logic approach for estimating recovery factors of miscible CO2-EOR projects in the United States: Journal of Petroleum Science and Engineering, v. 184, 106533, https://doi.org/10.1016/j.petrol.2019.106533.","productDescription":"106533","ipdsId":"IP-103343","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":368100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":368097,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0920410519309544"}],"volume":"184","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":772619,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70206034,"text":"70206034 - 2019 - DNA Sequencing confirms Tundra Bean Goose (Anser serrirostris serrirostris) occurrence in the Mississippi Alluvial Valley in Arkansas, USA","interactions":[],"lastModifiedDate":"2019-10-21T06:41:10","indexId":"70206034","displayToPublicDate":"2019-09-30T12:15:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"DNA Sequencing confirms Tundra Bean Goose (Anser serrirostris serrirostris) occurrence in the Mississippi Alluvial Valley in Arkansas, USA","docAbstract":"—First sighting records of rare occurrences may become increasingly important for recognizing changes in distribution, changes in migratory strategies, or increases in hybridization. We focumented the first record of a Tundra Bean Goose in the Mississippi Alluvial Valley, the outlet and historic floodplain for much of North America and one of the most important waterfowl wintering areas on the continent. We also document the first genetically confirmed record in the contiguous USA. Bean Goose (Anser fabalis and A. serrirostris) occurrences in North America\nare rare, especially outside of Alaska. On 24 January 2018, a Tundra Bean Goose (A. s. serrirostris) was harvested by a hunter in a winter-flooded rice field in Desha County, Arkansas, USA, near Dumas. The goose was mixed with a flock of 50 Greater White-Fronted Geese (A. albifrons). Because this individual was legally, albeit accidentally shot, we had the rare and exciting opportunity to obtain morphometric measurements and biological samples. As a result, we were able to verify the species and subspecies through genetic and morphological analysis. We determined the goose was an adult female Tundra Bean Goose, and mitochondrial DNA control region sequence data indicated this specimen was the subspecies A. s. serrirostris.","language":"English","publisher":"BioOne","doi":"10.1675/063.042.0310","collaboration":"None","usgsCitation":"Osborne, D.C., Wilson, R.E., Carlson, L., Sonsthagen, S.A., and Talbot, S.L., 2019, DNA Sequencing confirms Tundra Bean Goose (Anser serrirostris serrirostris) occurrence in the Mississippi Alluvial Valley in Arkansas, USA: Waterbirds, v. 42, no. 3, p. 333-342, https://doi.org/10.1675/063.042.0310.","productDescription":"10 p.","startPage":"333","endPage":"342","ipdsId":"IP-101771","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":368391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas 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\"}}]}","volume":"42","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Osborne, Douglas C.","contributorId":219861,"corporation":false,"usgs":false,"family":"Osborne","given":"Douglas","email":"","middleInitial":"C.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":773375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Robert E. 0000-0003-1800-0183 rewilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":5718,"corporation":false,"usgs":true,"family":"Wilson","given":"Robert","email":"rewilson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":773372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carlson, Lindsay","contributorId":214924,"corporation":false,"usgs":false,"family":"Carlson","given":"Lindsay","email":"","affiliations":[{"id":39139,"text":"Utah State University and the Ecology Center","active":true,"usgs":false}],"preferred":false,"id":773376,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":773373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":773374,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70207121,"text":"70207121 - 2019 - Value of migratory bird recreation at the Bosque del Apache National Wildlife Refuge in New Mexico","interactions":[],"lastModifiedDate":"2019-12-09T12:10:18","indexId":"70207121","displayToPublicDate":"2019-09-30T12:10:09","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5899,"text":"Western Economics Forum","active":true,"publicationSubtype":{"id":10}},"title":"Value of migratory bird recreation at the Bosque del Apache National Wildlife Refuge in New Mexico","docAbstract":"Each fall, thousands of Rocky Mountain Sandhill Cranes and other migratory birds congregate at the Bosque del Apache National Wildlife Refuge in New Mexico’s Rio Grande Valley in search of wintering habitat. As such, this refuge is known as one of the premier destinations for bird viewing and photography in the United States. Using contingent valuation data, this case study quantifies the value associated with migratory bird recreation at this refuge to be \\$7.5 million in 2010. It is estimated that this annual value increased by more than \\$6.4 million in 2017 due to growth in annual refuge visitation.
","language":"English","publisher":"Western Agricultural Economics Association","usgsCitation":"Huber, C., and Sexton, N., 2019, Value of migratory bird recreation at the Bosque del Apache National Wildlife Refuge in New Mexico: Western Economics Forum, v. 17, no. 2, p. 52-62.","productDescription":"11 p.","startPage":"52","endPage":"62","ipdsId":"IP-068398","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":370108,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":370074,"type":{"id":15,"text":"Index Page"},"url":"https://www.waeaonline.org/publications/western-economics-forum"}],"country":"United States","state":"New Mexico","otherGeospatial":"Bosque del Apache National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.86676025390625,\n 33.64720713369293\n ],\n [\n -106.73492431640624,\n 33.87212589943945\n ],\n [\n -106.99996948242186,\n 33.87782681357735\n ],\n [\n -106.86676025390625,\n 33.64720713369293\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Huber, Christopher 0000-0001-8446-8134 chuber@usgs.gov","orcid":"https://orcid.org/0000-0001-8446-8134","contributorId":127600,"corporation":false,"usgs":true,"family":"Huber","given":"Christopher","email":"chuber@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":776965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sexton, Natalie","contributorId":103320,"corporation":false,"usgs":true,"family":"Sexton","given":"Natalie","affiliations":[],"preferred":false,"id":776966,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205617,"text":"sir20195089 - 2019 - Status of groundwater-level altitudes and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston region, Texas, 2019","interactions":[],"lastModifiedDate":"2019-09-30T14:06:15","indexId":"sir20195089","displayToPublicDate":"2019-09-30T11:08:27","publicationYear":"2019","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":"2019-5089","displayTitle":"Status of Groundwater-Level Altitudes and Long-Term Groundwater-Level Changes in the Chicot, Evangeline, and Jasper Aquifers, Houston-Galveston Region, Texas, 2019","title":"Status of groundwater-level altitudes and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston region, Texas, 2019","docAbstract":"Since the early 1900s, most of the groundwater withdrawals in the Houston-Galveston region, Texas, have been from the three primary aquifers that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers. Withdrawals from these aquifers are used for municipal supply, commercial and industrial use, and irrigation. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, is one in an annual series of reports depicting the status of groundwater-level altitudes and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers in the Houston-Galveston region. This report contains regional-scale maps depicting approximate 2019 groundwater-level altitudes (represented by measurements made during December 2018 through March 2019) and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers.
In 2019, groundwater-level-altitude contours for the Chicot aquifer ranged from 200 feet (ft) below the North American Vertical Datum of 1988 (hereinafter referred to as “datum”) to 200 ft above datum. The 1977–2019 groundwater-level-change contours for the Chicot aquifer depict a large area of decline in groundwater-level altitudes (100 ft) in northwestern Harris County. The largest rise in groundwater-level altitudes in the Chicot aquifer from 1977 to 2019 (200 ft) was in southeastern Harris County.
In 2019, groundwater-level-altitude contours for the Evangeline aquifer ranged from 300 ft below datum to 200 ft above datum. The 1977–2019 groundwater-level-change contours for the Evangeline aquifer depict broad areas where groundwater-level altitudes either declined or rose. The largest decline in groundwater-level altitudes (280 ft) was in southern Montgomery and northern Harris Counties. The largest rise in groundwater-level altitudes in the Evangeline aquifer from 1977 to 2019 (240 ft) was in southeastern Harris County.
In 2019, groundwater-level-altitude contours for the Jasper aquifer ranged from 200 ft below datum to 250 ft above datum. The 2000–19 groundwater-level-change contours for the Jasper aquifer depict groundwater-level declines throughout most of the study area where groundwater-level-altitude data from the Jasper aquifer were collected, with the largest decline (200 ft) in southern Montgomery County.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195089","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District","usgsCitation":"Braun, C.L., Ramage, J.K., and Shah, S.D., 2019, Status of groundwater-level altitudes and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston region, Texas, 2019: U.S. Geological Survey Scientific Investigations Report 2019–5089, 18 p., https://doi.org/10.3133/sir20195089.","productDescription":"Report: vi, 18 p.; 2 Data Releases","onlineOnly":"N","ipdsId":"IP-108529 ","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":367799,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LKT49P","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Depth to Groundwater Measured from Wells Completed in the Chicot, Evangeline, and Jasper Aquifers, Houston-Galveston Region, Texas, 2019"},{"id":367796,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5089/coverthb.jpg"},{"id":367797,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5089/sir20195089.pdf","text":"Report","size":"14.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5089"},{"id":367798,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/doi:10.5066/P91CKWVC","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Groundwater-level altitudes and long-term groundwater-level Changes in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston Region, Texas, 2019"}],"country":"United States","state":"Texas","otherGeospatial":"Chicot Aquifer, Evangeline Aquifer, Jasper Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.13336181640625,\n 29.03215782622282\n ],\n [\n -94.2022705078125,\n 29.57345707301757\n ],\n [\n -94.9822998046875,\n 30.398937557618677\n ],\n [\n -95.635986328125,\n 30.836214626064844\n ],\n [\n -95.888671875,\n 30.850363469502362\n ],\n [\n -96.61102294921875,\n 30.14512718337613\n ],\n [\n -96.27319335937499,\n 29.506549442788593\n ],\n [\n -95.130615234375,\n 29.020149792758527\n ],\n [\n -95.13336181640625,\n 29.03215782622282\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Most geothermal resources in the Great Basin region of the western USA are blind, and thus the discovery of new commercial-grade systems requires synthesis of favorable characteristics for geothermal activity. The geothermal play fairway concept involves integration of multiple parameters indicative of geothermal activity to identify promising areas for new development. This project integrated multiple datasets to apply the play fairway concept and assess geothermal potential in a large region of the Great Basin in Nevada. It is therefore referred to as the Nevada play fairway project. This project was a strong collaborative effort between several organizations, led by the Nevada Bureau of Mines and Geology at the University of Nevada, Reno, but with key support from the U.S. Geological Survey, ATLAS Geosciences, Inc,, Hi-Q Geophysical, Inc., Lawrence Berkeley National Laboratory, Utah Geological Survey, and Innovative Geothermal Ltd. In Budget Period 1 of this project, available data for nine geologic, geochemical, and geophysical parameters were initially synthesized to produce a new detailed geothermal potential map of 96,000 km2 from west-central to eastern Nevada (Figure 1). These parameters were grouped into subsets and individually weighted (Figure 2) to delineate rankings for local permeability, intermediate permeability, regional permeability, and thermal potential, which collectively defined geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). This initial work was aimed at reducing the risks in regional exploration and therefore facilitating discovery of new commercial-grade systems in blind settings, as well as in areas with surface expressions of geothermal activity. Budget Period 2 of the project involved detailed analysis of some of the most promising areas identified in Phase 1. Twenty-four highly prospective areas, including both known undeveloped systems and previously undiscovered potential blind systems, were identified for further analysis (Figures 3 and 4). After reconnaissance of these areas, five of the most promising sites were selected for detailed studies. Multiple techniques were employed in the detailed studies, including geologic mapping, shallow temperature surveys, gravity surveys, Lidar, geochemical studies, seismic reflection analysis, and 3D modeling. The goal of the detailed studies was to identify specific areas with the highest likelihood for high permeability and thermal fluids, such that drill sites could be targeted. Three main sets of predictive maps were generated for each detailed study area: 1) play fairway maps, 2) play fairway error maps, and 3) direct evidence maps. Local- and intermediate-scale permeability models were revised to reflect results of the detailed geologic, geophysical, and geochemical analyses. Budget Period 3 of the project involved more detailed geophysical analyses and temperature-gradient (TG) drilling in southeastern Gabbs Valley and northern Granite Springs Valley (Figure 4), deemed the two most promising sites, with the goal of providing preliminary validation of the play fairway methodology. In southeastern Gabbs Valley, the collocation of a favorable structural setting (displacement transfer zone and fault intersections), Quaternary faults, intersecting and terminating gravity gradients, magnetic low, shallow (2 m) temperature anomaly, low resistivity anomaly, and promising geothermometry from nearby water wells provided evidence for a blind system. Drilling of six TG holes defines an apparent geothermal system at this locality with temperatures as high as 124°C at 152 m. This system is blind, with no surface hot springs, fumaroles, or paleo-geothermal deposits. For northern Granite Springs Valley, a favorable structural setting (termination of a major Quaternary normal fault), terminating gravity gradient, magnetic gradient, newly discovered sinter deposits, nearby warm water wells, previously drilled TG holes in the vicinity, and promising geothermometry suggest a hidden system. Drilling of six new TG holes yields temperatures of ~96°C at ~250 m, suggesting the presence of a geothermal system. Major lessons learned in the course of this project include: 1) initially identified sites commonly include multiple favorable structural settings at a finer scale; 2) promising sites in Cenozoic basins cannot be recognized without detailed geophysical surveys; and 3) play fairway analysis should be refined as the exploration program vectors into the most promising sites and finer-scale data are acquired. In addition to producing copious amounts of data, this project resulted in 16 published papers, 10 abstracts, more than 40 presentations across the U.S. and abroad (including several keynote addresses), 2 Masters theses, and 7 media reports.
Electricity from wind energy is a major contributor to the strategy to reduce greenhouse gas emissions from fossil fuel use and thus reduce the negative impacts of climate change. Wind energy, like all power sources, can have adverse impacts on wildlife. After nearly 25 years of focused research, these impacts are much better understood, although uncertainty remains. In this report, we summarize positive impacts of replacing fossil fuels with wind energy, while describing what we have learned and what remains uncertain about negative ecological impacts of the construction and operation of land-based and offshore wind energy on wildlife and wildlife habitat in the U.S. Finally, we propose research on ways to minimize these impacts. TO SUMMARIZE: 1 Environmental and other benefits of wind energy include near-zero greenhouse gas emissions, reductions of other common air pollutants, and little or no water use associated with producing electricity from wind energy. Various scenarios for meeting U.S. carbon emission reduction goals indicate that a four- to five-fold expansion of land-based wind energy from the current 97 gigawatts (GW) by the year 2050 is needed to minimize temperature increases and reduce the risk of climate change to people and wildlife. 2 Collision fatalities of birds and bats are the most visible and measurable impacts of wind energy production. Current estimates suggest most bird species, especially songbirds, are at low risk of population-level impacts. Raptors as a group appear more vulnerable to collisions. Population-level impacts on migratory tree bats are a concern, and better information on population sizes is needed to evaluate potential impacts to these species. Although recorded fatalities of cave-dwelling bat species are typically low at most wind energy facilities, additional mortality from collisions is a concern given major declines in these species due to white-nose syndrome (WNS). Assessments of regional and cumulative fatality impacts for birds and bats have been hampered by the lack of data from areas with a high proportion of the nation’s installed wind energy capacity. Efforts to expand data accessibility from all regions are underway, and this greater access to data along with improvements in statistical estimators should lead to improved impact assessments. 3 Habitat impacts of wind energy development are difficult to assess. An individual wind energy facility may encompass thousands of acres, but only a small percentage of the landscape within the project area is directly transformed. If a project is sited in previously undisturbed habitat, there is concern for indirect impacts, such as displacement of sensitive species. Studies to date indicate displacement of some species, but the long-term population impacts are unknown. 4 Offshore wind energy development in the U.S. is just beginning. Studies at offshore wind facilities in Europe indicate some bird and marine mammal species are displaced from project areas, but substantial uncertainty exists regarding the individual or population-level impacts of this displacement. Bird and bat collisions with offshore turbines are thought to be less common than at terrestrial facilities, but currently the tools to measure fatalities at offshore wind energy facilities are not available. The wind energy industry, state and federal agencies, conservation groups, academia, and scientific organizations have collaborated for nearly 25 years to conduct the research needed to improve our understanding of risk to wildlife and to avoid and minimize that risk. Efforts to reduce the uncertainty about wildlife risk must keep up withthe pace and scale of the need to reduce carbon emissions. This will require focusing our research priorities and increasing the rate at which we incorporate research results into the development and validation of best practices for siting and operating wind energy facilities.
","language":"English","publisher":"Ecological Society of America","usgsCitation":"Allison, T., Diffendorfer, J., Baerwald, E., Julie Beston, David Drake, Hale, A., Cris Hein, Huso, M.M., Loss, S., Lovich, J.E., Strickland, D., Kate Williams, and Virginia Winder, 2019, Impacts to wildlife of wind energy siting and operation in the United States: Issues in Ecology, no. 21, p. 1-24.","productDescription":"24 p.","startPage":"1","endPage":"24","ipdsId":"IP-082737","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":368388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":368387,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.esa.org/wp-content/uploads/2019/09/Issues-in-Ecology_Fall-2019.pdf"}],"issue":"21","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Allison, Taber","contributorId":215930,"corporation":false,"usgs":false,"family":"Allison","given":"Taber","email":"","affiliations":[{"id":39329,"text":"American Wind Wildlife Institute","active":true,"usgs":false}],"preferred":false,"id":773360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":773359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baerwald, Erin","contributorId":219854,"corporation":false,"usgs":false,"family":"Baerwald","given":"Erin","email":"","affiliations":[{"id":16660,"text":"University of Calgary","active":true,"usgs":false}],"preferred":false,"id":773361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Julie Beston","contributorId":174816,"corporation":false,"usgs":false,"family":"Julie Beston","affiliations":[{"id":27515,"text":"UW Stout","active":true,"usgs":false}],"preferred":false,"id":773362,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"David Drake","contributorId":219855,"corporation":false,"usgs":false,"family":"David Drake","affiliations":[{"id":29843,"text":"Univ of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":773363,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hale, Amanda","contributorId":219856,"corporation":false,"usgs":false,"family":"Hale","given":"Amanda","email":"","affiliations":[{"id":25471,"text":"Texas Christian University","active":true,"usgs":false}],"preferred":false,"id":773364,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cris Hein","contributorId":219857,"corporation":false,"usgs":false,"family":"Cris Hein","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":773365,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Huso, Manuela M. 0000-0003-4687-6625 mhuso@usgs.gov","orcid":"https://orcid.org/0000-0003-4687-6625","contributorId":150012,"corporation":false,"usgs":true,"family":"Huso","given":"Manuela","email":"mhuso@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":773366,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Loss, Scott","contributorId":167554,"corporation":false,"usgs":false,"family":"Loss","given":"Scott","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":773367,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":773368,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Strickland, Dale","contributorId":219858,"corporation":false,"usgs":false,"family":"Strickland","given":"Dale","email":"","affiliations":[{"id":40080,"text":"WEST Inc","active":true,"usgs":false}],"preferred":false,"id":773369,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kate Williams","contributorId":219859,"corporation":false,"usgs":false,"family":"Kate Williams","affiliations":[{"id":40081,"text":"BRI","active":true,"usgs":false}],"preferred":false,"id":773370,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Virginia Winder","contributorId":219860,"corporation":false,"usgs":false,"family":"Virginia Winder","affiliations":[{"id":40082,"text":"Benedictine University","active":true,"usgs":false}],"preferred":false,"id":773371,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70205375,"text":"ofr20191106 - 2019 - Characterization and load estimation of polychlorinated biphenyls (PCBs) from selected Rio Grande tributary stormwater channels in the Albuquerque urbanized area, New Mexico, 2017–18","interactions":[],"lastModifiedDate":"2019-09-30T10:05:38","indexId":"ofr20191106","displayToPublicDate":"2019-09-27T17:45:38","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1106","displayTitle":"Characterization and Load Estimation of Polychlorinated Biphenyls (PCBs) From Selected Rio Grande Tributary Stormwater Channels in the Albuquerque Urbanized Area, New Mexico, 2017–18","title":"Characterization and load estimation of polychlorinated biphenyls (PCBs) from selected Rio Grande tributary stormwater channels in the Albuquerque urbanized area, New Mexico, 2017–18","docAbstract":"In cooperation with the New Mexico County of Bernalillo, the U.S. Geological Survey characterized potential polychlorinated biphenyl (PCB) concentration and estimated loading into the Rio Grande from watersheds that are under the county’s jurisdiction. Water and sediment samples were collected in 2017–18 from six sites within four stormwater drainage basins in the Albuquerque, New Mexico, urbanized area for the analysis of PCB congeners and other water-quality constituents during dry and wet seasons. Also, the rainfall-runoff model Arid Lands Hydrologic Model (AHYMO) was used to estimate stormwater discharge at the two sample collection sites not affected by pump station operation. Along with the PCB analysis, the discharge data were used to estimate total PCB stormflow event loads for eight events in these urban Rio Grande tributaries. PCBs were detected in 34 of 36 water samples at concentrations as high as 65.8 nanograms per liter and in 12 of 13 sediment samples at concentrations as high as 163,000 nanograms per kilogram dry weight. Six of the 36 water samples exceeded the New Mexico surface-water quality standard for protection of wildlife habitat and aquatic life of 14 nanograms per liter for PCBs. None of the water samples exceeded the U.S. Environmental Protection Agency’s National Pollutant Discharge Elimination System permit level limit of 200 nanograms per liter for PCBs in stormwater systems discharging into the Rio Grande. PCB concentrations in water samples in this study were not linearly related to antecedent precipitation or measured water-quality parameters, but PCB concentrations had a statistically significant positive Kendall’s tau correlation with total suspended solids for water samples and with total organic carbon for sediment samples. The PCB congener profiles indicate that sources to stormwater drainage basins in Bernalillo County originate both from legacy sources, such as Aroclors (for example, in landfills and old building materials), and from current-use sources, such as yellow pigments (for example, in printed materials and packaging in urban litter or refuse). Total PCB stormflow event loads were calculated with average potential minimum and maximum event loads of 0.73 and 4.32 milligrams per storm event, respectively, at the Adobe Acres pump station site and 56.78 and 315.13 milligrams per storm event at the Sanchez Farms inflow at Albuquerque, N. Mex., site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191106","collaboration":"Prepared in cooperation with Bernalillo County","usgsCitation":"Shephard, Z.M., Conn, K.E., Beisner, K.R., Jornigan, A.D., and Bryant, C.F., 2019, Characterization and load estimation of polychlorinated biphenyls (PCBs) from selected Rio Grande tributary stormwater channels in the Albuquerque urbanized area, New Mexico, 2017–18: U.S. Geological Survey Open-File Report 2019–1106, 48 p., https://doi.org/10.3133/of20191106.","productDescription":"x, 48 p.","numberOfPages":"61","onlineOnly":"Y","ipdsId":"IP-109136","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":367784,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1106/coverthb.jpg"},{"id":367785,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1106/ofr20191106.pdf","size":"4.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1106"}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Rio Grande","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8255615234375,\n 34.9371707067839\n ],\n [\n -106.48223876953125,\n 34.9371707067839\n ],\n [\n -106.48223876953125,\n 35.20579439829525\n ],\n [\n -106.8255615234375,\n 35.20579439829525\n ],\n [\n -106.8255615234375,\n 34.9371707067839\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
6700 Edith Blvd.
Albuquerque, NM 87113
Eastern Energy Resources Science Center
12201 Sunrise Valley Drive
956 National Center
Reston, VA 20192
Hydrous pyrolysis was applied to four low-maturity aliquots from the Utica, Excello, Monterey, and Niobrara Shale Formations in North America to create artificial maturation sequences, which could be used to study the impact of maturation on geochemical and microstructural properties. Modified Rock-Eval pyrolysis, reflectance, organic petrology, and Fourier transform infrared spectroscopy (FTIR) were employed to analyze their geochemical properties, while gas adsorption (CO2 and N2) was used to characterize their pore structures (pores < 200 nm). Organic petrography using white and blue light (fluorescence) before and after hydrous pyrolysis showed that amorphous organic matter cracked into solid bitumen, oil, and gas during hydrous pyrolysis. A reduction of the CH2/CH3 ratio in hydrous pyrolysis residues was observed from FTIR analysis. Rock-Eval pyrolysis showed that kerogens in the four samples were dissimilar, and hydrous pyrolysis residues showed smaller hydrogen index and Sh2 values than starting materials. Results from CO2 and N2 gas adsorption analysis showed that pore structures (micropore volume, micropore surface area, meso-macropore volume, and meso-macropore surface area) changed significantly during hydrous pyrolysis. However, changes in pore structure were dissimilar among the four samples, which was attributed to different activation energies of organic matter. A thermodynamic fractal model showed a decrease in fractal dimensions of Utica, Monterey, and Excello after hydrous pyrolysis, indicating a decrease in surface roughness. The pore size heterogeneity in the Utica sample increased as hydrous pyrolysis temperature increased, whereas the pore size heterogeneity distributions in the Monterey and Excello decreased based on the N2 adsorption data.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.energyfuels.9b02389","usgsCitation":"Liu, K., Ostadhassan, M., Hackley, P.C., Gentzis, T., Zou, J., Yuan, Y., Carvajal-Ortiz, H., Rezaee, R., and Bubach, B., 2019, Experimental study on the impact of thermal maturity on shale microstructures using hydrous pyrolysis: Energy & Fuels, v. 33, no. 10, p. 9702-9719, https://doi.org/10.1021/acs.energyfuels.9b02389.","productDescription":"18 p.","startPage":"9702","endPage":"9719","ipdsId":"IP-103911","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":379391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"10","noUsgsAuthors":false,"publicationDate":"2019-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Kouqi","contributorId":243145,"corporation":false,"usgs":false,"family":"Liu","given":"Kouqi","email":"","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":801610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ostadhassan, M.","contributorId":243146,"corporation":false,"usgs":false,"family":"Ostadhassan","given":"M.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":801611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":801612,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gentzis, T.","contributorId":243147,"corporation":false,"usgs":false,"family":"Gentzis","given":"T.","affiliations":[{"id":39779,"text":"Core Laboratories","active":true,"usgs":false}],"preferred":false,"id":801613,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zou, J.","contributorId":243148,"corporation":false,"usgs":false,"family":"Zou","given":"J.","affiliations":[{"id":48648,"text":"Department of Petroleum Engineering, Curtin University","active":true,"usgs":false}],"preferred":false,"id":801614,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yuan, Y.","contributorId":243149,"corporation":false,"usgs":false,"family":"Yuan","given":"Y.","affiliations":[{"id":48648,"text":"Department of Petroleum Engineering, Curtin University","active":true,"usgs":false}],"preferred":false,"id":801615,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carvajal-Ortiz, H.","contributorId":243150,"corporation":false,"usgs":false,"family":"Carvajal-Ortiz","given":"H.","affiliations":[{"id":39779,"text":"Core Laboratories","active":true,"usgs":false}],"preferred":false,"id":801616,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rezaee, R.","contributorId":243151,"corporation":false,"usgs":false,"family":"Rezaee","given":"R.","email":"","affiliations":[{"id":48648,"text":"Department of Petroleum Engineering, Curtin University","active":true,"usgs":false}],"preferred":false,"id":801617,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bubach, B.","contributorId":243152,"corporation":false,"usgs":false,"family":"Bubach","given":"B.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":801618,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70205500,"text":"sim3438 - 2019 - Map of the approximate inland extent of saltwater at the base of the Biscayne aquifer in Miami-Dade County, Florida, 2018","interactions":[],"lastModifiedDate":"2019-09-26T08:02:35","indexId":"sim3438","displayToPublicDate":"2019-09-25T14:58:55","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3438","displayTitle":"Map of the Approximate Inland Extent of Saltwater at the Base of the Biscayne Aquifer in Miami-Dade County, Florida, 2018","title":"Map of the approximate inland extent of saltwater at the base of the Biscayne aquifer in Miami-Dade County, Florida, 2018","docAbstract":"The inland extent of saltwater at the base of the Biscayne aquifer in eastern Miami-Dade County, Florida, was mapped in 2011, and it was mapped in the Model Land Area in 2016. The saltwater interface has continued to move inland in some areas and is now near several active well fields. An updated approximation of the inland extent of saltwater has been created by using data collected during March 8–December 13, 2018, from 111 monitoring wells open to the Biscayne aquifer near its base. Chloride concentrations in water samples from the monitoring wells and bulk conductivity from geophysical logs and measurements of the specific conductance of groundwater were used to approximate the position of the isochlor representing a chloride concentration of 1,000 milligrams per liter (mg/L) at the base of the Biscayne aquifer.
An average rate of saltwater interface movement of about 102 meters per year in the Model Land Area along SW 360 Street was estimated from the approximated dates of arrival of the 250-, 500-, and 1,000-mg/L isochlors at wells TPGW-7L (2013–2014) and ACI-MW-05-FS (2017–2018). This estimate assumes that the interface is traveling in a path parallel to an imaginary line connecting the two monitoring wells.
Of the 111 wells from which data were used, 80 wells have open intervals of ≤ 4 meters, 20 of the wells have open intervals that range from 4.3 to 39.6 meters, and the lengths of the open intervals could not be determined in 11 wells. Studies have shown that long open intervals might allow water from various depths to mix under ambient or pumped conditions, which in turn could alter the maximum chloride concentration sampled in the well, or it might change the depth at which the maximum specific conductance is measured within a well, relative to its depth in the aquifer. The approximation of the inland extent of the saltwater interface and the estimated rate of movement of the interface are dependent on the quality of existing data. Improved estimates could be obtained by installing uniformly designed monitoring wells in systematic transects extending landward of the advancing saltwater interface. To achieve this goal, Miami-Dade County and some other organizations are routinely adding new monitoring wells with short open intervals and replacing poorly designed or positioned monitoring wells to improve spatial coverage of the network.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3438","collaboration":"Prepared in cooperation with Miami-Dade County","usgsCitation":"Prinos, S.T., 2019, Map of the approximate inland extent of saltwater at the base of the Biscayne aquifer in Miami-Dade County, Florida, 2018: U.S. Geological Survey Scientific Investigations Map 3438, 10-p. pamphlet, 1 sheet, https://doi.org/10.3133/sim3438.","productDescription":"Pamphlet: vii, 10 p.; 1 Plate: 35.8 x 46.0 inches; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-107371","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":367675,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3438/sim3438.pdf","text":"Sheet","size":"898 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3438"},{"id":367674,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3438/coverthb3.jpg"},{"id":367676,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3438/sim3438_pamphlet.pdf","text":"Pamphlet","size":"857 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3438 Pamphlet"},{"id":367677,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZIC1O4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data Pertaining to Mapping the Approximate Inland Extent of Saltwater at the Base of the Biscayne Aquifer in Miami-Dade County, Florida, 2018"}],"country":"United States","state":"Florida","county":"Miami-Dade County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.8538818359375,\n 25.095548539604252\n ],\n [\n -79.9969482421875,\n 25.095548539604252\n ],\n [\n -79.9969482421875,\n 26.892679095908164\n ],\n [\n -80.8538818359375,\n 26.892679095908164\n ],\n [\n -80.8538818359375,\n 25.095548539604252\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The U.S. Geological Survey publishes information on the mass, or load, of water-quality constituents transported through rivers and streams sampled as part of the operation of the National Water Quality Network (NWQN). This study evaluates methods for computing annual water-quality loads, specifically with respect to procedures currently (2019) used at sites in the NWQN. Near-daily datasets of chloride, total nitrogen, nitrate plus nitrite, total phosphorus, and suspended sediment were subset to determine the accuracy of various load-estimation methods, including linear interpolation, ratio estimators, and linear and weighted-regression methods. Water-quality loads are computed under different sampling strategies and at multiple sampling sites to provide a more complete evaluation of load-estimation methods.
Estimation methods were less accurate when computing loads at annual rather than decadal time steps. Depending on the water-quality constituent, annual loads were within comparable accuracy thresholds 21 to 64 percent of the time relative to decadal loads. The accuracy of annual load estimates varied among water-quality constituents, sampling strategies, sampling sites, and estimation methods. Methods were most accurate when estimating chloride and decreased in accuracy when estimating total nitrogen, nitrate plus nitrite, total phosphorus, and suspended-sediment loads. Estimation methods were most likely to compute accurate annual loads when samples were collected frequently (26 samples per year) and when sampling strategies targeted high-flow conditions. For a given water-quality constituent, estimation accuracy differed substantially among sampling sites; estimates were more likely to be accurate at large rivers with less variability in concentration and (or) discharge conditions and were less likely to be accurate at smaller stream sites with more variable streamflow and (or) water-quality concentrations.
The Weighted Regressions on Time, Discharge, and Season method with Kalman filtering (WRTDS_K) generally produced the most accurate annual load estimates among sampling sites and water-quality constituents. Although WRTDS_K was the most accurate generally, every estimation method evaluated had the potential to produce accurate (and inaccurate) load estimates depending on the site, constituent, and water year. Linear interpolation and ratio estimators that used samples exclusively from the year being estimated were among the best performing methods for total nitrogen and nitrate plus nitrite loads but were among the least accurate when estimating annual total phosphorus and suspended-sediment loads. Ratio estimation that considered samples from previous years and stratified based on streamflow conditions produced among the most accurate total phosphorus estimates but was among the least accurate for other constituents. Regression-based methods that assumed linear or quadratic relations among the logarithm of water-quality concentrations and streamflow conditions were among the least accurate methods generally, whereas regression-based methods that considered cubic relations among the logarithm of concentration and streamflow and the Weighted Regressions on Time, Discharge, and Season (WRTDS) method were typically more accurate. Methods that adjusted daily estimates computed from regression or weighted-regression methods based on departures from sampled values, such as WRTDS_K and the composite method, improved estimate accuracy for most sites and constituents, but especially for chloride, total nitrogen, nitrate plus nitrite, and suspended-sediment estimates.
Investigation of the underlying causes of estimation method bias indicated that sites and years with more variability in concentration and loading conditions, higher slopes in the relation of the logarithm of concentration and discharge, and sampling plans that underrepresented high-flow conditions generally led to less accurate load estimates. Finally, because all methods indicated the capacity to produce biased load estimates, additional work is needed to identify the capacity of new technologies, such as continuous water-quality sensors, to improve the accuracy of annual or shorter term load estimates. Based on findings in this report, the NWQN will continue to publish water-quality loads using LOADEST-based methods that consider multiple transformations of streamflow, as well as season, time, and variables indicative of historical streamflow conditions to maintain consistent methods for stakeholders. However, the NWQN also plans to begin publishing annual load estimates using the WRTDS_K method in 2020 because this method was determined to be the most accurate for a given site, constituent, and water year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195084","usgsCitation":"Lee, C.J., Hirsch, R.M., and Crawford, C.G., 2019, An evaluation of methods for computing annual water-quality loads: U.S. Geological Survey Scientific Investigations Report 2019–5084, 59 p., https://doi.org/10.3133/sir20195084.","productDescription":"Report: x, 59 p.; Appendix Figures 3–7; Data Release","startPage":"1-84","numberOfPages":"74","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103673","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":367653,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BK91LN","text":"USGS data release","linkHelpText":"Supplementary data used to evaluate methods for computing annual water-quality loads, 1948–2016"},{"id":367650,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5084/coverthb.jpg"},{"id":367651,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5084/sir20195084.pdf","text":"Report","size":"3.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5084"},{"id":367652,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5084/downloads","text":"Appendix figures 3–7","description":"SIR 2019–5084 Appendix Figures 3–7"}],"contact":"Chief, National Water-Quality Assessment Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, VA 20192