Debris flow magnitudes and frequencies are compared across the Upper Colorado River valley to assess influences on debris flow occurrence and to evaluate valley geometry effects on sediment persistence. Dendrochronology, field mapping, and aerial photographic analysis are used to evaluate whether a 19th century earthen, water-conveyance ditch has altered the regime of debris flow occurrence in the Colorado River headwaters. Identifying any shifts in disturbance processes or changes in magnitudes and frequencies of occurrence is fundamental to establishing the historical range of variability (HRV) at the site. We found no substantial difference in frequency of debris flows cataloged at eleven sites of deposition between the east (8) and west (11) sides of the Colorado River valley over the last century, but four of the five largest debris flows originated on the west side of the valley in association with the earthen ditch, while the fifth is on a steep hillslope of hydrothermally altered rock on the east side. These results suggest that the ditch has altered the regime of debris flow activity in the Colorado River headwaters as compared to HRV by increasing the frequency of debris flows large enough to reach the Colorado River valley. Valley confinement is a dominant control on response to debris flows, influencing volumes of aggradation and persistence of debris flow deposits. Large, frequent debris flows, exceeding HRV, create persistent effects due to valley geometry and geomorphic setting conducive to sediment storage that are easily delineated by valley confinement ratios which are useful to land managers.
","language":"English","publisher":"Springer","doi":"10.1007/s00267-016-0695-1","usgsCitation":"Grimsley, K.J., Rathburn, S.L., Friedman, J.M., and Mangano, J.F., 2016, Debris flow occurrence and sediment persistence, Upper Colorado River Valley, CO: Environmental Management, v. 58, no. 1, p. 76-92, https://doi.org/10.1007/s00267-016-0695-1.","productDescription":"17 p.","startPage":"76","endPage":"92","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061452","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":327105,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountain National Park, Upper Colorado River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.87833404541014,\n 40.4090520858275\n ],\n [\n -105.87833404541014,\n 40.50231368920858\n ],\n [\n -105.8144760131836,\n 40.50231368920858\n ],\n [\n -105.8144760131836,\n 40.4090520858275\n ],\n [\n -105.87833404541014,\n 40.4090520858275\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-08","publicationStatus":"PW","scienceBaseUri":"57b82db2e4b03fd6b7da3671","contributors":{"authors":[{"text":"Grimsley, Kyle J","contributorId":173887,"corporation":false,"usgs":false,"family":"Grimsley","given":"Kyle","email":"","middleInitial":"J","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646514,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rathburn, Sara L.","contributorId":140606,"corporation":false,"usgs":false,"family":"Rathburn","given":"Sara","email":"","middleInitial":"L.","affiliations":[{"id":13539,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":646515,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":646513,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mangano, Joseph F. 0000-0003-4213-8406 jmangano@usgs.gov","orcid":"https://orcid.org/0000-0003-4213-8406","contributorId":4722,"corporation":false,"usgs":true,"family":"Mangano","given":"Joseph","email":"jmangano@usgs.gov","middleInitial":"F.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":646520,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188535,"text":"70188535 - 2016 - Tomographic Rayleigh-wave group velocities in the Central Valley, California centered on the Sacramento/San Joaquin Delta","interactions":[],"lastModifiedDate":"2017-06-14T15:14:42","indexId":"70188535","displayToPublicDate":"2016-04-08T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Tomographic Rayleigh-wave group velocities in the Central Valley, California centered on the Sacramento/San Joaquin Delta","docAbstract":"If shaking from a local or regional earthquake in the San Francisco Bay region were to rupture levees in the Sacramento/San Joaquin Delta then brackish water from San Francisco Bay would contaminate the water in the Delta: the source of fresh water for about half of California. As a prelude to a full shear-wave velocity model that can be used in computer simulations and further seismic hazard analysis, we report on the use of ambient noise tomography to build a fundamental-mode, Rayleigh-wave group velocity model for the region around the Sacramento/San Joaquin Delta in the western Central Valley, California. Recordings from the vertical component of about 31 stations were processed to compute the spatial distribution of Rayleigh wave group velocities. Complex coherency between pairs of stations were stacked over 8 months to more than a year. Dispersion curves were determined from 4 to about 18 seconds. We calculated average group velocities for each period and inverted for deviations from the average for a matrix of cells that covered the study area. Smoothing using the first difference is applied. Cells of the model were about 5.6 km in either dimension. Checkerboard tests of resolution, which is dependent on station density, suggest that the resolving ability of the array is reasonably good within the middle of the array with resolution between 0.2 and 0.4 degrees. Overall, low velocities in the middle of each image reflect the deeper sedimentary syncline in the Central Valley. In detail, the model shows several centers of low velocity that may be associated with gross geologic features such as faulting along the western margin of the Central Valley, oil and gas reservoirs, and large cross cutting features like the Stockton arch. At shorter periods around 5.5s, the model’s western boundary between low and high velocities closely follows regional fault geometry and the edge of a residual isostatic gravity low. In the eastern part of the valley, the boundaries of the low velocity zone and gravity anomaly are better aligned at longer periods (around 10.5s) suggesting that the eastern edge of the gravity low is associated with deeper structure. There is a strong correspondence between a low in gravity near the Kirby Hills fault and low velocities from the ambient noise tomography. At longer periods, higher velocities creep in from the east and narrow the overall dimension defined by the lower velocities. Overall, there is a strong correspondence between the shape and location of low velocities in the Rayleigh wave velocity images, and geological and geophysical features.","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015JB012376","usgsCitation":"Fletcher, J.P., Erdem, J., Seats, K., and Lawrence, J., 2016, Tomographic Rayleigh-wave group velocities in the Central Valley, California centered on the Sacramento/San Joaquin Delta: Journal of Geophysical Research B: Solid Earth, v. 121, no. 4, p. 2429-2446, https://doi.org/10.1002/2015JB012376.","productDescription":"18 p. ","startPage":"2429","endPage":"2446","ipdsId":"IP-062590","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":471084,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012376","text":"Publisher Index Page"},{"id":342506,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley, Sacramento/San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.7117919921875,\n 39.89709437260048\n ],\n [\n -122.01416015625,\n 38.225235239076824\n ],\n [\n -121.4813232421875,\n 37.63163475580643\n ],\n [\n -121.00341796874999,\n 37.08585785263673\n ],\n [\n -120.67932128906249,\n 36.730079507078415\n ],\n [\n -118.55895996093749,\n 37.53150992479082\n ],\n [\n -118.7017822265625,\n 38.043765107439675\n ],\n [\n -119.08630371093749,\n 38.543869175876154\n ],\n [\n -119.80590820312499,\n 39.317300373271024\n ],\n [\n -120.2838134765625,\n 39.774769485295465\n ],\n [\n -120.948486328125,\n 40.29628651711716\n ],\n [\n -121.31103515625,\n 40.53050177574321\n ],\n [\n -121.97021484374999,\n 40.53050177574321\n ],\n [\n -122.398681640625,\n 40.40094763151963\n ],\n [\n -122.72277832031251,\n 40.225024210604964\n ],\n [\n -122.7117919921875,\n 39.89709437260048\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-08","publicationStatus":"PW","scienceBaseUri":"59424b39e4b0764e6c65dc30","contributors":{"authors":[{"text":"Fletcher, Jon Peter B. 0000-0001-8885-6177 jfletcher@usgs.gov","orcid":"https://orcid.org/0000-0001-8885-6177","contributorId":1216,"corporation":false,"usgs":true,"family":"Fletcher","given":"Jon","email":"jfletcher@usgs.gov","middleInitial":"Peter B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":698169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erdem, Jemile 0000-0003-2353-9431 jerdem@usgs.gov","orcid":"https://orcid.org/0000-0003-2353-9431","contributorId":127700,"corporation":false,"usgs":true,"family":"Erdem","given":"Jemile","email":"jerdem@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":698172,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seats, Kevin","contributorId":192927,"corporation":false,"usgs":false,"family":"Seats","given":"Kevin","email":"","affiliations":[],"preferred":false,"id":698170,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawrence, Jesse","contributorId":192928,"corporation":false,"usgs":false,"family":"Lawrence","given":"Jesse","affiliations":[],"preferred":false,"id":698171,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170081,"text":"sim3352 - 2016 - Potentiometric surfaces, summer 2013 and winter 2015, and select hydrographs for the Southern High Plains aquifer, Cannon Air Force Base, Curry County, New Mexico","interactions":[],"lastModifiedDate":"2016-04-11T08:44:03","indexId":"sim3352","displayToPublicDate":"2016-04-07T00:00:00","publicationYear":"2016","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":"3352","title":"Potentiometric surfaces, summer 2013 and winter 2015, and select hydrographs for the Southern High Plains aquifer, Cannon Air Force Base, Curry County, New Mexico","docAbstract":"Cannon Air Force Base (Cannon AFB) is located in the High Plains physiographic region of east-central New Mexico, about 5 miles west of Clovis, New Mexico. The area surrounding Cannon AFB is primarily used for agriculture, including irrigated cropland and dairies. The Southern High Plains aquifer is the principal source of water for Cannon AFB, for the nearby town of Clovis, and for local agriculture and dairies. The Southern High Plains aquifer in the vicinity of Cannon AFB consists of three subsurface geological formations: the Chinle Formation of Triassic age, the Ogallala Formation of Tertiary age, and the Blackwater Draw Formation of Quaternary age. The Ogallala Formation is the main water-yielding formation of the Southern High Plains aquifer. Groundwater-supplied, center-pivot irrigation dominates pumping from the Southern High Plains aquifer in the area surrounding Cannon AFB, where the irrigation season typically extends from early March through October. The U.S. Geological Survey has been monitoring groundwater levels in the vicinity of Cannon AFB since 1954 and has developed general potentiometric-surface maps that show groundwater flow from northwest to southeast in the study area. While previous potentiometric-surface maps show the general direction of groundwater flow, a denser well network is needed to show details of groundwater flow at a local scale. Groundwater levels were measured in 93 wells during summer 2013 and 100 wells during winter 2015.
The summer and winter potentiometric-surface maps display the presence of what is interpreted to be a groundwater trough trending from the northwest to the southeast through the study area. This groundwater trough may be the hydraulic expression of a Tertiary-age paleochannel. Groundwater north of the trough flows in a southerly direction into the trough, and groundwater south of the trough flows in an easterly direction into the trough.
During the 18-month period between summer 2013 and winter 2015, changes in groundwater levels ranged from a rise of 10.0 to a decline of 3.8 feet. The regions to the north and south of the groundwater trough contained the majority of the rises in groundwater levels, whereas the regions within the trough contained the majority of the declines in groundwater levels. In contrast, the long-term groundwater-level trend in wells with 20 to 60 years of record is a steady decline in average annual water levels, with declines ranging from 0.41 to 2.81 feet per year. Overall, the northwestern part of the study area exhibits the smallest average annual declines, while the southeastern part of the study area exhibits the largest average annual declines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3352","collaboration":"Prepared in cooperation with the Air Force Civil Engineer Center, San Antonio, Texas","usgsCitation":"Collison, Jake, 2016, Potentiometric surfaces, summer 2013 and winter 2015, and select hydrographs for the Southern High Plains aquifer, Cannon Air Force Base, Curry County, New Mexico: U.S. Geological Survey Scientific Investigations Map 3352, https://dx.doi.org/10.3133/sim3352.","productDescription":"2 Sheets: 22 x 30 inches","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069490","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":319842,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3352/sim3352.pdf","text":"Map","size":"3.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3352"},{"id":319796,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3352/coverthb.jpg"}],"country":"United States","state":"New Mexico","county":"Curry County","otherGeospatial":"Southern High Plains Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.1997299194336,\n 34.30799463462751\n ],\n [\n -103.20213317871094,\n 34.32472485669068\n ],\n [\n -103.20144653320312,\n 34.349105308603086\n ],\n [\n -103.20144653320312,\n 34.36922801491184\n ],\n [\n -103.20144653320312,\n 34.387079327815805\n ],\n [\n -103.20247650146484,\n 34.415406930259294\n ],\n [\n -103.20281982421875,\n 34.43239888779366\n ],\n [\n -103.20625305175781,\n 34.453350878522286\n ],\n [\n -103.2117462158203,\n 34.45844651413059\n ],\n [\n -103.22410583496094,\n 34.46552326993406\n ],\n [\n -103.23715209960938,\n 34.468353804284924\n ],\n [\n -103.25809478759764,\n 34.469769035464836\n ],\n [\n -103.27869415283203,\n 34.47203335543746\n ],\n [\n -103.29689025878905,\n 34.472599425831355\n ],\n [\n -103.32572937011719,\n 34.47458064197167\n ],\n [\n -103.34152221679688,\n 34.47797690306098\n ],\n [\n -103.36658477783203,\n 34.47882594673332\n ],\n [\n -103.38512420654297,\n 34.477693886583516\n ],\n [\n -103.4036636352539,\n 34.474014585017066\n ],\n [\n -103.40572357177734,\n 34.46920294587255\n ],\n [\n -103.41053009033203,\n 34.45788034775209\n ],\n [\n -103.41053009033203,\n 34.44259240448441\n ],\n [\n -103.41190338134766,\n 34.43466422118617\n ],\n [\n -103.41293334960938,\n 34.41795594404557\n ],\n [\n -103.41361999511719,\n 34.41059191445944\n ],\n [\n -103.41293334960938,\n 34.40237742424137\n ],\n [\n -103.41121673583984,\n 34.39387882699731\n ],\n [\n -103.40744018554688,\n 34.38311269824024\n ],\n [\n -103.4084701538086,\n 34.37206181069581\n ],\n [\n -103.40572357177734,\n 34.361292876752564\n ],\n [\n -103.39508056640625,\n 34.35193978491414\n ],\n [\n -103.3868408203125,\n 34.34627073645813\n ],\n [\n -103.38306427001952,\n 34.336348979311985\n ],\n [\n -103.38272094726561,\n 34.326426048404265\n ],\n [\n -103.37448120117188,\n 34.3207552752374\n ],\n [\n -103.3480453491211,\n 34.31678550602219\n ],\n [\n -103.31817626953125,\n 34.313382697259456\n ],\n [\n -103.2879638671875,\n 34.311397661782244\n ],\n [\n -103.26221466064453,\n 34.30969616544571\n ],\n [\n -103.23646545410156,\n 34.30997975056304\n ],\n [\n -103.21792602539062,\n 34.30714385628804\n ],\n [\n -103.1997299194336,\n 34.30799463462751\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
5338 Montgomery Blvd. NE, Suite 400
Albuquerque, NM 87109–1311
A large-scale study by the U.S. Geological Survey, in cooperation with the Montana Department of Transportation and the Montana Department of Natural Resources and Conservation, was done to investigate general patterns in peak-flow temporal trends and stationarity through water year 2011 for 24 long-term streamflow-gaging stations (hereinafter referred to as gaging stations) in Montana. Hereinafter, all years refer to water years; a water year is the 12-month period from October 1 through September 30 and is designated by the year in which it ends. The primary focus of the study was to identify general patterns in peak-flow temporal trends and stationarity that are relevant to application of peak-flow frequency analyses within a statewide gaging-station network.
\nTemporal trends were analyzed for two hydrologic variables: annual peak flow and peak-flow timing. Annual peak flow is the maximum instantaneous discharge, in cubic feet per second, recorded each year a gaging station was operated. Peak-flow timing is the day of the annual peak flow (hereinafter referred to as day of peak), recorded each year a gaging station was operated.
\nStudy results provide evidence that annual peak flow for most of the long-term gaging stations can be reasonably considered as stationary for application of peak-flow frequency analyses within a statewide gaging station network. Upward trends in annual peak flow during 1930–76 generally were stronger than downward trends during 1967–2011 for most long-term gaging stations. Statistical distributions of annual peak flow generally were similar among three summary time periods (1930–78, 1979–2011, and the entire period of record). However, for two low-elevation gaging stations in eastern Montana (Poplar River at international boundary [gaging station 06178000] and Powder River at Moorhead, Montana [gaging station 06324500]), substantial downward trends in annual peak flow during 1967–2011 were of similar or stronger magnitude than the upward trends during 1930–76, and the annual-peak-flow medians for 1979–2011 were substantially lower than the medians for the entire period of record.
\nFor peak-flow timing for most long-term gaging stations, differences in trends between 1930–76 and 1967–2011 are variable and not particularly strong. Statistical distributions generally are similar among the summary time periods. However, for two high-elevation gaging stations on a Missouri River headwater tributary (Gallatin River near Gallatin Gateway, Montana [gaging station 06043500] and Gallatin River at Logan, Montana [gaging station 06052500]) and for five high-elevation gaging stations in the Yellowstone River Basin (Yellowstone River at Corwin Springs, Montana [gaging station 06191500], Yellowstone River near Livingston, Montana [gaging station 06192500], Clarks Fork Yellowstone River near Belfry, Montana [gaging station 06207500], Clarks Fork Yellowstone River at Edgar, Montana [gaging station 06208500], and Yellowstone River at Billings, Montana [gaging station 06214500]) downward trends in peak-flow timing during 1967–2011 generally were stronger than upward trends during 1930–1976, and day-of-peak medians for 1979–2011 were considerably less than medians for 1930–78. The downward trends in peak-flow timing for 1967–2011 indicate that the timing of annual peak flows changed from later in the year to earlier in the year. For the seven high-elevation gaging stations, the mean change during 1967–2011 was about 13 days (range of 8 to 22 days).
\nFor most of the high-elevation gaging stations in the Missouri River headwaters, Yellowstone River Basin, and Columbia River Basin, there was general correspondence between trend patterns for annual peak flow and trend patterns for peak-flow timing; that is, during periods when there were upward trends in annual peak flow, there generally also were upward trends in peak-flow timing. Conversely, during periods when there were downward trends in annual peak flow, there generally also were downward trends in peak-flow timing.
\nThe two low-elevation gaging stations in eastern Montana (Poplar River at international boundary [gaging station 06178000] and Powder River at Moorhead, Montana [gaging station 06324500]) had considerable changes in annual-peakflow characteristics after the mid-1970s, which might provide evidence of potential nonstationarity in the peak-flow records. The two low-elevation gaging stations that have potential nonstationarity are located in drainage basins that are strongly affected by agricultural activities that potentially affect the hydrologic regimes. Primary agricultural activities that might alter natural hydrologic conditions include construction of small impoundments (primarily for stock-watering purposes) and irrigation diversions. Temporal variability in these activities might contribute to the potential nonstationarity issues. Changes in climatic characteristics after the mid-1970s also possibly contribute to the potential nonstationarity issues. Lack of considerable indication of potential nonstationarity in annual peak flow for the other long-term gaging stations in this study might indicate that climatic changes have been more pronounced with respect to effects on peak flows in low elevation areas in eastern Montana than in areas represented by the other long-term gaging stations. Another possibility is that climatic changes after the mid-1970s are exacerbated in low-elevation areas where small-impoundment development and potential effects of irrigation diversions might be more extensive.
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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
The Great Lakes Geologic Mapping Coalition (GLGMC), consisting of state geological surveys from all eight Great Lakes states, the Ontario Geological Survey, and the U.S. Geological Survey, was conceived out of a societal need for unbiased and scientifically defensible geologic information on the shallow subsurface, particularly the delineation, interpretation, and viability of groundwater resources. Only a small percentage (<10%) of the region had been mapped in the subsurface, and there was recognition that no single agency had the financial, intellectual, or physical resources to conduct such a massive geologic mapping effort at a detailed scale over a wide jurisdiction. The GLGMC provides a strategy for generating financial and stakeholder support for three-dimensional (3-D) geologic mapping, pooling of physical and personnel resources, and sharing of mapping and technological expertise to characterize the thick cover of glacial sediments. Since its inception in 1997, the GLGMC partners have conducted detailed surficial and 3-D geologic mapping within all jurisdictions, and concurrent significant scientific advancements have been made to increase understanding of the history and framework of geologic processes. More importantly, scientific information has been provided to public policymakers in understandable formats, emphasis has been placed on training early-career scientists in new mapping techniques and emerging technologies, and a successful model has been developed of state/provincial and federal collaboration focused on geologic mapping, as evidenced by this program's unprecedented and long-term successful experiment of 10 geological surveys working together to address common issues.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2016.2520(37)","usgsCitation":"Berg, R.C., Brown, S.E., Thomason, J.F., Hasenmueller, N.R., Letsinger, S.L., Kincare, K.A., Esch, J.M., Kehew, A.E., Thorleifson, H., Kozlowski, A., Bird, B.C., Pavey, R.R., Bajc, A.F., Burt, A.K., Fleeger, G.M., and Carson, E.C., 2016, A multiagency and multijurisdictional approach to mapping the glacial deposits of the Great Lakes region in three dimensions: GSA Special Papers, v. 520, p. 415-447, https://doi.org/10.1130/2016.2520(37).","productDescription":"33 p.","startPage":"415","endPage":"447","ipdsId":"IP-061912","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":342336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"520","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593bb3a4e4b0764e6c60e7c5","contributors":{"authors":[{"text":"Berg, Richard C.","contributorId":192821,"corporation":false,"usgs":false,"family":"Berg","given":"Richard","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":697783,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Steven E.","contributorId":192822,"corporation":false,"usgs":false,"family":"Brown","given":"Steven","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":697784,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomason, Jason F.","contributorId":192823,"corporation":false,"usgs":false,"family":"Thomason","given":"Jason","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":697785,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hasenmueller, Nancy R.","contributorId":192824,"corporation":false,"usgs":false,"family":"Hasenmueller","given":"Nancy","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":697786,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Letsinger, Sally L.","contributorId":192825,"corporation":false,"usgs":false,"family":"Letsinger","given":"Sally","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":697787,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kincare, Kevin A. 0000-0002-1050-3627 kkincare@usgs.gov","orcid":"https://orcid.org/0000-0002-1050-3627","contributorId":2106,"corporation":false,"usgs":true,"family":"Kincare","given":"Kevin","email":"kkincare@usgs.gov","middleInitial":"A.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":697782,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Esch, John M.","contributorId":192826,"corporation":false,"usgs":false,"family":"Esch","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":697788,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kehew, Alan E.","contributorId":192827,"corporation":false,"usgs":false,"family":"Kehew","given":"Alan","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":697789,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thorleifson, Harvey 0000-0001-7160-255X","orcid":"https://orcid.org/0000-0001-7160-255X","contributorId":192828,"corporation":false,"usgs":false,"family":"Thorleifson","given":"Harvey","email":"","affiliations":[{"id":38105,"text":"Minnesota Geological Survey","active":true,"usgs":false}],"preferred":false,"id":697790,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kozlowski, Andrew","contributorId":192829,"corporation":false,"usgs":false,"family":"Kozlowski","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":697791,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bird, Brian C.","contributorId":192830,"corporation":false,"usgs":false,"family":"Bird","given":"Brian","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":697792,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pavey, Richard R.","contributorId":192831,"corporation":false,"usgs":false,"family":"Pavey","given":"Richard","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":697793,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Bajc, Andy F.","contributorId":192832,"corporation":false,"usgs":false,"family":"Bajc","given":"Andy","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":697794,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Burt, Abigail K.","contributorId":192833,"corporation":false,"usgs":false,"family":"Burt","given":"Abigail","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":697795,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Fleeger, Gary M.","contributorId":192834,"corporation":false,"usgs":false,"family":"Fleeger","given":"Gary","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":697796,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Carson, Eric C.","contributorId":192835,"corporation":false,"usgs":false,"family":"Carson","given":"Eric","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":697797,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70173645,"text":"70173645 - 2016 - Population size and stopover duration estimation using mark–resight data and Bayesian analysis of a superpopulation model","interactions":[],"lastModifiedDate":"2023-03-30T15:33:58.035111","indexId":"70173645","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1039,"text":"Biometrics","active":true,"publicationSubtype":{"id":10}},"title":"Population size and stopover duration estimation using mark–resight data and Bayesian analysis of a superpopulation model","docAbstract":"We present a novel formulation of a mark–recapture–resight model that allows estimation of population size, stopover duration, and arrival and departure schedules at migration areas. Estimation is based on encounter histories of uniquely marked individuals and relative counts of marked and unmarked animals. We use a Bayesian analysis of a state–space formulation of the Jolly–Seber mark–recapture model, integrated with a binomial model for counts of unmarked animals, to derive estimates of population size and arrival and departure probabilities. We also provide a novel estimator for stopover duration that is derived from the latent state variable representing the interim between arrival and departure in the state–space model. We conduct a simulation study of field sampling protocols to understand the impact of superpopulation size, proportion marked, and number of animals sampled on bias and precision of estimates. Simulation results indicate that relative bias of estimates of the proportion of the population with marks was low for all sampling scenarios and never exceeded 2%. Our approach does not require enumeration of all unmarked animals detected or direct knowledge of the number of marked animals in the population at the time of the study. This provides flexibility and potential application in a variety of sampling situations (e.g., migratory birds, breeding seabirds, sea turtles, fish, pinnipeds, etc.). Application of the methods is demonstrated with data from a study of migratory sandpipers.
","language":"English","publisher":"The International Biometric Society","doi":"10.1111/biom.12393","usgsCitation":"Lyons, J., Kendall, W., Royle, J., Converse, S., Andres, B.A., and Buchanan, J.B., 2016, Population size and stopover duration estimation using mark–resight data and Bayesian analysis of a superpopulation model: Biometrics, v. 72, no. 1, p. 262-271, https://doi.org/10.1111/biom.12393.","productDescription":"10 p.","startPage":"262","endPage":"271","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044684","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":323259,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-08","publicationStatus":"PW","scienceBaseUri":"57594221e4b04f417c256939","contributors":{"authors":[{"text":"Lyons, James E.","contributorId":35461,"corporation":false,"usgs":true,"family":"Lyons","given":"James E.","affiliations":[],"preferred":false,"id":637860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891 wkendall@usgs.gov","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":166709,"corporation":false,"usgs":true,"family":"Kendall","given":"William L.","email":"wkendall@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":637450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":138865,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":637451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Converse, Sarah J.","contributorId":85716,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah J.","affiliations":[],"preferred":false,"id":637861,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andres, Brad A.","contributorId":68811,"corporation":false,"usgs":true,"family":"Andres","given":"Brad","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":637862,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Buchanan, Joseph B.","contributorId":171532,"corporation":false,"usgs":false,"family":"Buchanan","given":"Joseph","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":637863,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70186184,"text":"70186184 - 2016 - Late Holocene expansion of Ponderosa pine (Pinus ponderosa) in the Central Rocky Mountains, USA","interactions":[],"lastModifiedDate":"2017-03-31T10:27:47","indexId":"70186184","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Late Holocene expansion of Ponderosa pine (Pinus ponderosa) in the Central Rocky Mountains, USA","docAbstract":"\"Aim: Ponderosa pine (Pinus ponderosa) experienced one of the most extensive and rapid post-glacial plant migrations in western North America. We used plant macrofossils from woodrat (Neotoma) middens to reconstruct its spread in the Central Rocky Mountains, identify other vegetation changes coinciding with P. ponderosa expansion at the same sites, and relate P. ponderosa migrational history to both its modern phylogeography and to a parallel expansion by Utah juniper (Juniperus osteosperma).\nLocation: Central Rocky Mountains, Wyoming and Montana, and Black Hills, Wyoming and South Dakota, USA.\nMethods: Plant macrofossils were analyzed in 90 middens collected at 14 widely separated sites in the northern part of the range of P. ponderosa var. scopulorum. Middens with and without P. ponderosa were 14C dated to pinpoint time of appearance at each site. Sensitivity experiments using a bioclimatic model were used to evaluate potential climatic drivers of late Holocene expansion.\nResults: P. ponderosa colonized the Black Hills region by at least 3850 yr BP (all ages given in calendar years before present). It expanded into the eastern Bighorn Mountains of northern Wyoming by 2630 yr BP, quickly spreading north in the western Bighorns from 1400 to 1000 yr BP. Concurrent with the latter expansion, P. ponderosa spread c. ~350 km to the Little Belt and Big Belt Mountains in western Montana, establishing its northern limit and the modern introgression zone between var. scopulorum and var. ponderosa. Expansion in the Central Rockies of P. ponderosa involved two known haplotypes.\nMain conclusions: P. ponderosa expanded its range across large parts of northern Wyoming and central Montana during the late Holocene, probably in response to both northward and westward increases in summer temperature and rainfall. The underlying climatic driver may be the same as for the contemporaneous expansion of J. osteosperma, but will remain undetermined without focused development and integration of independent palaeoclimate records in the region.\"","language":"English","publisher":"Wiley","doi":"10.1111/jbi.12670","usgsCitation":"Norris, J.R., Betancourt, J.L., and Jackson, S., 2016, Late Holocene expansion of Ponderosa pine (Pinus ponderosa) in the Central Rocky Mountains, USA: Journal of Biogeography, v. 43, no. 4, p. 778-790, https://doi.org/10.1111/jbi.12670.","productDescription":"3 p.","startPage":"778","endPage":"790","ipdsId":"IP-065920","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":338933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":338875,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1111/jbi.12670/full"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, South Dakota, Oregon, Utah, Washington, Wyoming","otherGeospatial":"Central Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.150390625,\n 47.18971246448421\n ],\n [\n -104.501953125,\n 47.21956811231547\n ],\n [\n -112.2802734375,\n 49.06666839558117\n ],\n [\n -121.86035156249999,\n 49.009050809382046\n ],\n [\n -124.541015625,\n 47.635783590864854\n ],\n [\n -124.8486328125,\n 43.644025847699496\n ],\n [\n -123.22265625000001,\n 36.80928470205937\n ],\n [\n -116.93847656250001,\n 32.69486597787505\n ],\n [\n -115.09277343749999,\n 32.731840896865684\n ],\n [\n -103.095703125,\n 32.58384932565662\n ],\n [\n -103.271484375,\n 36.94989178681327\n ],\n [\n -101.162109375,\n 40.1452892956766\n ],\n [\n -101.29394531249999,\n 47.07012182383309\n ],\n [\n -104.150390625,\n 47.18971246448421\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-14","publicationStatus":"PW","scienceBaseUri":"58df6ac1e4b02ff32c6aea3d","contributors":{"authors":[{"text":"Norris, Jodi R.","contributorId":190196,"corporation":false,"usgs":false,"family":"Norris","given":"Jodi","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":687784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Betancourt, Julio L. 0000-0002-7165-0743 jlbetanc@usgs.gov","orcid":"https://orcid.org/0000-0002-7165-0743","contributorId":3376,"corporation":false,"usgs":true,"family":"Betancourt","given":"Julio","email":"jlbetanc@usgs.gov","middleInitial":"L.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":687783,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Stephen T.","contributorId":127411,"corporation":false,"usgs":false,"family":"Jackson","given":"Stephen T.","affiliations":[],"preferred":false,"id":687785,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70169968,"text":"70169968 - 2016 - DNA and dispersal models highlight constrained connectivity in a migratory marine megavertebrate","interactions":[],"lastModifiedDate":"2017-05-02T14:15:37","indexId":"70169968","displayToPublicDate":"2016-03-31T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"DNA and dispersal models highlight constrained connectivity in a migratory marine megavertebrate","docAbstract":"Population structure and spatial distribution are fundamentally important fields within ecology, evolution, and conservation biology. To investigate pan-Atlantic connectivity of globally endangered green turtles (Chelonia mydas) from two National Parks in Florida, USA, we applied a multidisciplinary approach comparing genetic analysis and ocean circulation modeling. The Everglades (EP) is a juvenile feeding ground, whereas the Dry Tortugas (DT) is used for courtship, breeding, and feeding by adults and juveniles. We sequenced two mitochondrial segments from 138 turtles sampled there from 2006-2015, and simulated oceanic transport to estimate their origins. Genetic and ocean connectivity data revealed northwestern Atlantic rookeries as the major natal sources, while southern and eastern Atlantic contributions were negligible. However, specific rookery estimates differed between genetic and ocean transport models. The combined analyses suggest that post-hatchling drift via ocean currents poorly explains the distribution of neritic juveniles and adults, but juvenile natal homing and population history likely play important roles. DT and EP were genetically similar to feeding grounds along the southern US coast, but highly differentiated from most other Atlantic groups. Despite expanded mitogenomic analysis and correspondingly increased ability to detect genetic variation, no significant differentiation between DT and EP, or among years, sexes or stages was observed. This first genetic analysis of a North Atlantic green turtle courtship area provides rare data supporting local movements and male philopatry. The study highlights the applications of multidisciplinary approaches for ecological research and conservation.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.02056","usgsCitation":"Naro-Maciel, E., Hart, K.M., Cruciata, R., and Putman, N.F., 2016, DNA and dispersal models highlight constrained connectivity in a migratory marine megavertebrate: Ecography, v. 40, no. 5, p. 586-597, https://doi.org/10.1111/ecog.02056.","productDescription":"12 p.","startPage":"586","endPage":"597","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068508","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":471111,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/ecog.02056","text":"External Repository"},{"id":319674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Dry Tortugas Park, Everglades Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.1005859375,\n 24.462150693715266\n ],\n [\n -83.1005859375,\n 24.77177232822881\n ],\n [\n -82.6171875,\n 24.77177232822881\n ],\n [\n -82.6171875,\n 24.462150693715266\n ],\n [\n -83.1005859375,\n 24.462150693715266\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.09283447265625,\n 25.120419105501256\n ],\n [\n -81.09283447265625,\n 25.564742726875785\n ],\n [\n -80.6341552734375,\n 25.564742726875785\n ],\n [\n -80.6341552734375,\n 25.120419105501256\n ],\n [\n -81.09283447265625,\n 25.120419105501256\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"5","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-23","publicationStatus":"PW","scienceBaseUri":"56fe3c2ce4b075ab2b2aa0aa","contributors":{"authors":[{"text":"Naro-Maciel, Eugenia","contributorId":138902,"corporation":false,"usgs":false,"family":"Naro-Maciel","given":"Eugenia","email":"","affiliations":[{"id":12576,"text":"College of Staten Island, Staten Island, New York","active":true,"usgs":false}],"preferred":false,"id":625738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Kristen M. 0000-0002-5257-7974 kristen_hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":1966,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","email":"kristen_hart@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":625737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cruciata, Rossana","contributorId":168380,"corporation":false,"usgs":false,"family":"Cruciata","given":"Rossana","email":"","affiliations":[{"id":25274,"text":"Biology Dept., College of Staten Island, City University of New York","active":true,"usgs":false}],"preferred":false,"id":625739,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Putman, Nathan Freeman","contributorId":145423,"corporation":false,"usgs":false,"family":"Putman","given":"Nathan","email":"","middleInitial":"Freeman","affiliations":[{"id":16119,"text":"National Marine Fisheries Service, Miami, FL","active":true,"usgs":false}],"preferred":false,"id":625740,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168800,"text":"sir20165026 - 2016 - Simulation of groundwater storage changes in the eastern Pasco Basin, Washington","interactions":[],"lastModifiedDate":"2019-07-22T14:07:29","indexId":"sir20165026","displayToPublicDate":"2016-03-29T18:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5026","title":"Simulation of groundwater storage changes in the eastern Pasco Basin, Washington","docAbstract":"The Miocene Columbia River Basalt Group and younger sedimentary deposits of lacustrine, fluvial, eolian, and cataclysmic-flood origins compose the aquifer system of the Pasco Basin in eastern Washington. Irrigation return flow and canal leakage from the Columbia Basin Project have caused groundwater levels to rise substantially in some areas, contributing to landslides along the Columbia River. Water resource managers are considering extraction of additional stored groundwater to supply increasing demand and possibly mitigate problems caused by the increased water levels. To help address these concerns, the transient groundwater model of the Pasco Basin documented in this report was developed to quantify the changes in groundwater flow and storage. The MODFLOW model uses a 1-kilometer finite-difference grid and is constrained by logs and water levels from 846 wells in the study area. Eight model layers represent five sedimentary hydrogeologic units and underlying basalt formations. Head‑dependent flux boundaries represent the Columbia and Snake Rivers to the west and south, respectively, underflow to and (or) from adjacent areas to the northeast, and discharge to agricultural drains, springs, and groundwater withdrawal wells. Specified flux boundaries represent recharge from infiltrated precipitation and anthropogenic sources, including irrigation return flow and leakage from water-distribution canals. The model was calibrated with the parameter‑estimation code PEST++ to groundwater levels measured from 1907 through 2013 and measured discharge to springs and estimated discharge to agricultural drains. Increased recharge since pre-development resulted in a 6.8 million acre-feet increase in storage in the 508-14 administrative area of the Pasco Basin. Four groundwater-management scenarios simulate the 7-year drawdown resulting from withdrawals in different locations. Withdrawals of 2 million gallons per day (Mgal/d) from a hypothetical well field in the upper Ringold Formation along the Columbia River could generate 30–70 feet of drawdown, which may reduce landslide susceptibility along the White Bluffs. Drawdowns resulting from a 1 Mgal/d withdrawal from wells screened in either Pasco gravels, upper Ringold Formation, or both Ringold Formation and underlying basalt are simulated in the other three scenarios, and differ because of the contrasting hydraulic conductivities within the screened intervals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165026","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Heywood, C.E., Kahle, S.C., Olsen, T.D., Patterson, J.D., and Burns, Erick, 2016, Simulation of groundwater storage changes in the eastern Pasco Basin, Washington: U.S. Geological Survey Scientific Investigations Report 2016–5026, 44 p., 1 pl., https://dx.doi.org/10.3133/sir20165026.","productDescription":"Report: viii, 44 p.; Plate; Table","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069891","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":319591,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5026/sir20165026.pdf","text":"Report","size":"13.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5026"},{"id":319590,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5026/coverthb.jpg"},{"id":319592,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5026/sir20165026_plate01.pdf","text":"Plate 1","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5026 Plate 1"},{"id":319593,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5026/sir20165026_table07.xlsx","text":"Table 7","size":"96 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5026 Table 7"}],"country":"United States","state":"Washington","county":"Adams County, Franklin County, Grant County","otherGeospatial":"Pasco Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.9981689453125,\n 47.00273390667881\n ],\n [\n -118.49304199218749,\n 46.998987638154624\n ],\n [\n -118.50952148437499,\n 46.195042108660154\n ],\n [\n -118.7347412109375,\n 46.09228143052649\n ],\n [\n -118.9434814453125,\n 46.00459325574482\n ],\n [\n -119.344482421875,\n 46.00840867976965\n ],\n [\n -119.59716796875,\n 46.038922598236\n ],\n [\n -119.73999023437499,\n 46.33555079758302\n ],\n [\n -119.9871826171875,\n 46.49082901981415\n ],\n [\n -119.9981689453125,\n 47.00273390667881\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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The U.S. Geological Survey (USGS) has produced a 1-year seismic hazard forecast for 2016 for the Central and Eastern United States (CEUS) that includes contributions from both induced and natural earthquakes. The model assumes that earthquake rates calculated from several different time windows will remain relatively stationary and can be used to forecast earthquake hazard and damage intensity for the year 2016. This assessment is the first step in developing an operational earthquake forecast for the CEUS, and the analysis could be revised with updated seismicity and model parameters. Consensus input models consider alternative earthquake catalog durations, smoothing parameters, maximum magnitudes, and ground motion estimates, and represent uncertainties in earthquake occurrence and diversity of opinion in the science community. Ground shaking seismic hazard for 1-percent probability of exceedance in 1 year reaches 0.6 g (as a fraction of standard gravity [g]) in northern Oklahoma and southern Kansas, and about 0.2 g in the Raton Basin of Colorado and New Mexico, in central Arkansas, and in north-central Texas near Dallas. Near some areas of active induced earthquakes, hazard is higher than in the 2014 USGS National Seismic Hazard Model (NHSM) by more than a factor of 3; the 2014 NHSM did not consider induced earthquakes. In some areas, previously observed induced earthquakes have stopped, so the seismic hazard reverts back to the 2014 NSHM. Increased seismic activity, whether defined as induced or natural, produces high hazard. Conversion of ground shaking to seismic intensity indicates that some places in Oklahoma, Kansas, Colorado, New Mexico, Texas, and Arkansas may experience damage if the induced seismicity continues unabated. The chance of having Modified Mercalli Intensity (MMI) VI or greater (damaging earthquake shaking) is 5–12 percent per year in north-central Oklahoma and southern Kansas, similar to the chance of damage caused by natural earthquakes at sites in parts of California.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161035","usgsCitation":"Petersen, M.D., Mueller, C.S., Moschetti, M.P., Hoover, S.M., Llenos, A.L., Ellsworth, W.L., Michael, A.J., Rubinstein, J.L., McGarr, A.F., and Rukstales, K.S., 2016, 2016 One-year seismic hazard forecast for the Central and Eastern United States from induced and natural earthquakes: U.S. Geological Survey Open-File Report 2016–1035, 52 p., https://dx.doi.org/10.3133/ofr20161035.","productDescription":"v, 52 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073237","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":438626,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95M7TNI","text":"USGS data release","linkHelpText":"Data Release for the 2016 One-Year Seismic Hazard Forecast for the Central and Eastern United States from Induced and Natural 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U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225-0046
http://earthquake.usgs.gov/hazards/
","tableOfContents":"In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within the limit of California’s State Waters. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data, acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow subsurface geology.
The Offshore of Santa Cruz map area is located in central California, on the Pacific Coast about 98 km south of San Francisco. The city of Santa Cruz (population, about 63,000), the largest incorporated city in the map area and the county seat of Santa Cruz County, lies on uplifted marine terraces between the shoreline and the northwest-trending Santa Cruz Mountains, part of California’s Coast Ranges. All of California’s State Waters in the map area is part of the Monterey Bay National Marine Sanctuary.
The map area is cut by an offshore section of the San Gregorio Fault Zone, and it lies about 20 kilometers southwest of the San Andreas Fault Zone. Regional folding and uplift along the coast has been attributed to a westward bend in the San Andreas Fault Zone and to right-lateral movement along the San Gregorio Fault Zone. Most of the coastal zone is characterized by low, rocky cliffs and sparse, small pocket beaches backed by low, terraced hills. Point Santa Cruz, which forms the north edge of Monterey Bay, provides protection for the beaches in the easternmost part of the map area by sheltering them from the predominantly northwesterly waves.
The shelf in the map area is underlain by variable amounts (0 to 25 m) of upper Quaternary shelf, estuarine, and fluvial sediments deposited as sea level fluctuated in the late Pleistocene. The inner shelf is characterized by bedrock outcrops that have local thin sediment cover, the result of regional uplift, high wave energy, and limited sediment supply. The midshelf occupies part of an extensive, shore-parallel mud belt. The thickest sediment deposits, inferred to consist mainly of lowstand nearshore deposits, are found in the southeastern and northwestern parts of the map area.
Coastal sediment transport in the map area is characterized by northwest-to-southeast littoral transport of sediment that is derived mainly from ephemeral streams in the Santa Cruz Mountains and also from local coastal-bluff erosion. During the last approximately 300 years, as much as 18 million cubic yards (14 million cubic meters) of sand-sized sediment has been eroded from the area between Año Nuevo Island and Point Año Nuevo and transported south; this mass of eroded sand is now enriching beaches in the map area. Sediment transport is within the Santa Cruz littoral cell, which terminates in the submarine Monterey Canyon.
Benthic species observed in the Offshore of Santa Cruz map area are natives of the cold-temperate biogeographic zone that is called either the “Oregonian province” or the “northern California ecoregion.” This biogeographic province is maintained by the long-term stability of the southward-flowing California Current, the eastern limb of the North Pacific subtropical gyre that flows from southern British Columbia to Baja California. At its midpoint off central California, the California Current transports subarctic surface (0–500 m deep) waters southward, about 150 to 1,300 km from shore. Seasonal northwesterly winds that are, in part, responsible for the California Current, generate coastal upwelling. The south end of the Oregonian province is at Point Conception (about 300 km south of the map area), although its associated phylogeographic group of marine fauna may extend beyond to the area offshore of Los Angeles in southern California. The ocean off of central California has experienced a warming over the last 50 years that is driving an ecosystem shift away from the productive subarctic regime towards a depopulated subtropical environment.
Biological productivity resulting from coastal upwelling supports populations of Sooty Shearwater, Western Gull, Common Murre, Cassin’s Auklet, and many other less populous bird species. In addition, an observable recovery of Humpback and Blue Whales has occurred in the area; both species are dependent on coastal upwelling to provide nutrients. The large extent of exposed inner shelf bedrock supports large forests of “bull kelp,” which is well adapted for high-wave-energy environments. The kelp beds are the northernmost known habitat for the population of southern sea otters. Common fish species found in the kelp beds and rocky reefs include lingcod and various species of rockfish and greenling.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161024","usgsCitation":"Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., Sliter, R.W., Maier, K.L., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series — Offshore of Santa Cruz, California: U.S. Geological Survey Open-File Report 2016–1024, pamphlet 40 p., 10 sheets, scale 1:24,000, https://dx.doi.org/10.3133/ofr20161024.","productDescription":"Pamphlet: iv, 40 p.; 10 Sheets: 60.0 x 36.0 inches or smaller; Data Catalog; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-058743","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":438627,"rank":21,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TM785G","text":"USGS data release","linkHelpText":"California State Waters Map Series Data Catalog--Offshore of Santa Cruz, California"},{"id":318983,"rank":19,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20161025","text":"Open-File Report 2016–1025","linkHelpText":"California State Waters Map Series—Offshore of Aptos, California, by Guy R. 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Gary Greene, and Mercedes D. Erdey"},{"id":318968,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet6.pdf","text":"Sheet 6","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 6 PDF","linkHelpText":"Ground-Truth Studies, Offshore of Santa Cruz Map Area, California By Nadine E. Golden, Guy R. Cochrane, and Lisa M. Krigsman"},{"id":318967,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet5.pdf","text":"Sheet 5","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 5 PDF","linkHelpText":"Seafloor Character, Offshore of Santa Cruz Map Area, California By Mercedes D. Erdey and Guy R. Cochrane"},{"id":318966,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet4.pdf","text":"Sheet 4","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 4 PDF","linkHelpText":"Data Integration and Visualization, Offshore of Santa Cruz Map Area, California By Peter Dartnell"},{"id":318965,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet3.pdf","text":"Sheet 3","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 3 PDF","linkHelpText":"Acoustic Backscatter, Offshore of Santa Cruz Map Area, California By Peter Dartnell, Andrew C. Ritchie, and David P. 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Sliter"},{"id":399098,"rank":20,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_104092.htm"},{"id":318976,"rank":12,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1024/coverthb.jpg"},{"id":318964,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 2 PDF","linkHelpText":"Shaded-Relief Bathymetry, Offshore of Santa Cruz Map Area, California By Peter Dartnell, Andrew C. Ritchie, and David P. Finlayson"},{"id":318963,"rank":1,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 1 PDF","linkHelpText":"Colored Shaded-Relief Bathymetry, Offshore of Santa Cruz Map Area, California By Peter Dartnell, Andrew C. Ritchie, and David P. Finlayson"},{"id":318979,"rank":15,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/781/","text":"Data Series 781","linkHelpText":"California State Waters Map Series Data Catalog"},{"id":318972,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet10.pdf","text":"Sheet 10","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 10 PDF","linkHelpText":"Offshore and Onshore Geology and Geomorphology, Offshore of Santa Cruz Map Area, California By Samuel Y. Johnson, Stephen R. Hartwell, and Clifton W. Davenport"},{"id":318980,"rank":16,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/sim/3306/","text":"Scientific Investigations Map 3306","linkHelpText":"California State Waters Map Series—Offshore of San Gregorio, California, by Guy R. Cochrane and others."}],"country":"United States","state":"California","city":"Santa Cruz","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.1997,\n 36.8425\n ],\n [\n -122.0333,\n 36.8425\n ],\n [\n -122.0333,\n 37.0033\n ],\n [\n -122.1997,\n 37.0033\n ],\n [\n -122.1997,\n 36.8425\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
http://walrus.wr.usgs.gov/
In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar bathymetric data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow subsurface geology.
\nThe Offshore of Aptos map area is located on the Pacific Coast, on the north side of Monterey Bay, about 105 km southeast of San Francisco. The largest incorporated city in the map area, Capitola, and numerous unincorporated towns including Aptos, lie on uplifted marine terraces between the shoreline, and the northwest-trending Santa Cruz Mountains, part of California’s Coast Ranges. The map area includes the northernmost part of Santa Cruz Harbor, and Moss Landing Harbor is located about 10 km south of the map area. The offshore part of the map area is entirely within California’s State Waters and is also part of the Monterey Bay National Marine Sanctuary. In the southern part of the map area, the Soquel Canyon State Marine Conservation Area extends eastward from the limit of California’s State Waters across Soquel Canyon.
\nThe Offshore of Aptos map area is on the western margin of North American Plate—the only continental margin in the world delineated largely by transform faults. The San Andreas Fault Zone cuts through the Santa Cruz Mountains just 3.5 km northeast of the map area. The San Gregorio Fault Zone, another major plate-boundary structure, cuts through Monterey Canyon about 13 km southwest of the map area. Ongoing deformation associated with and between these major fault zones has uplifted the Santa Cruz Mountains and formed well-developed sets of marine terraces that characterize almost the entire coastal zone of the map area.
\nThe offshore part of the map area consists of relatively flat and shallow continental shelf which is underlain by variable amounts (0 to 30 m) of upper Quaternary shelf, estuarine, and fluvial sediments deposited as sea level fluctuated in the late Pleistocene. Along the southern edge of the map area, the shelf is incised by the head of Soquel Canyon, a northeast-trending tributary to the west-trending Monterey Canyon. During the last sea-level lowstand (the Last Glacial Maximum [LGM], about 21,000 years ago) Soquel Creek flowed through a paleochannel across the emergent shelf into the head of Soquel Canyon. This canyon was disconnected from its onshore watershed during the post-LGM sea-level rise of about 125 m; the abandoned paleochannel was subsequently filled with marine sediment. Coastal sediment in the Offshore of Aptos map area is supplied by coastal watersheds and bluff erosion. Sediment transport in this part of the Santa Cruz littoral cell is primarily from the northwest to the southeast and terminates in the submarine Monterey Canyon. Longshore drift is impeded by jetties at Santa Cruz Harbor, resulting in high beach erosion rates east of the harbor. Sediment dredged from the harbor mouth (estimated 300,000 yds3/yr) is currently being used to nourish beaches directly to the east. Farther downcoast, the rapidly eroding beach at Capitola was stabilized by construction of an about 75-m-long groin.
\nThis part of central California is exposed to large North Pacific swells from the northwest throughout the year. North Pacific swell heights range from 2 to 10 meters, with larger swells occurring from October to May. During El Niño-Southern Oscillation (ENSO) events, winter storms track farther south than they do in normal (non-ENSO) years, thereby impacting the map area more frequently and with waves of larger heights. Bedrock exposed in coastal cliffs is relatively erosion-resistant, and significant erosional events primarily are restricted to storm-wave activity that also erodes the overlying unconsolidated marine-terrace sediments.
\nThe Offshore of Aptos map area lies within the cold-temperate biogeographic zone that is called either the “Oregonian” province or the “northern California ecoregion.” This biogeographic province is maintained by the long-term stability of the southward-flowing California Current, the eastern limb of the North Pacific subtropical gyre that flows from southern British Columbia to Baja California. At its midpoint off central California, the California Current transports subarctic surface (0–500 m deep) waters southward, about 150 to 1,300 km from shore. Seasonal northwesterly winds that are, in part, responsible for the California Current, generate coastal upwelling. The south end of the Oregonian province is at Point Conception (about 310 km southeast of the map area), although its associated phylogeographic group of marine fauna may extend beyond to the area offshore of Los Angeles in southern California. The ocean off of central California has experienced a warming over the last 50 years that is driving an ecosystem shift away from the productive subarctic regime towards a depopulated subtropical environment.
\nSeafloor habitats in the Offshore of Aptos map area lie within the “Shelf” Megahabitat class and include patchy rocky habitats, gravel-rich scour depressions, minor bedrock habitat, and predominantly sandy inner shelf. Sandy shelf habitats grade offshore to mud-dominated habitats on the midshelf in deeper water. Biological productivity resulting from coastal upwelling supports populations of Sooty Shearwater, Western Gull, Common Murre, Cassin’s Auklet, and many other less populous bird species. In addition, an observable recovery of Humpback and Blue Whales has occurred in the area; both species are dependent on coastal upwelling to provide nutrients. California sea lions and Pacific harbor seals are abundant in the map area. Common bottlenose dolphins are often observed very close to shore and in the surf zone. The large extent of exposed inner shelf bedrock supports large forests of “bull kelp,” which is well adapted for high-wave-energy environments. The kelp beds are the northernmost known habitat for the population of southern sea otters. Common fish species found in the kelp beds and rocky reefs include blue rockfish, black rockfish, olive rockfish, kelp rockfish, gopher rockfish, black-and-yellow rockfish, painted greenling, kelp greenling, and lingcod.
\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20161025","usgsCitation":"Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, R.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series — Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, pamphlet 43 p., 10 sheets, scale 1:24,000, https://dx.doi.org/10.3133/ofr20161025.","productDescription":"Pamphlet: iv, 43 p.; 10 Sheets: 55.0 x 36.0 inches or smaller; Data Catalog; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059274","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":438628,"rank":21,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7K35RQB","text":"USGS data release","linkHelpText":"California State Waters Map Series Data Catalog--Offshore of Aptos, California"},{"id":318989,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20151191","text":"Open-File Report 2015–1191,","linkHelpText":"California State Waters Map Series—Offshore of Scott Creek, California, by Guy R. 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Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
http://walrus.wr.usgs.gov/
Arctic ecosystems are changing at an unprecedented rate. How Arctic species are able to respond to such environmental change is partially dependent on the connections between local and broadly distributed populations. For species like the Long-tailed Duck (Clangula hyemalis), we have limited telemetry and band-recovery information from which to infer population structure and migratory connectivity; however, genetic analyses can offer additional insights. To examine population structure in the Long-tailed Duck, we characterized variation at mtDNA control region and microsatellite loci among four breeding areas in Alaska, Canada, and Russia. We observed significant differences in the variance of mtDNA haplotype frequencies between the Yukon-Kuskokwim Delta (YKD) and the three Arctic locations (Arctic Coastal Plain in Alaska, eastern Siberia, and central Canadian Arctic). However, like most sea duck genetic assessments, our study found no evidence of population structure based on autosomal microsatellite loci. Long-tailed Ducks use multiple wintering areas where pair formation occurs with some populations using both the Pacific and Atlantic Oceans. This situation provides a greater opportunity for admixture across breeding locales, which would likely homogenize the nuclear genome even in the presence of female philopatry. The observed mtDNA differentiation was largely due to the presence of two divergent clades: (A) a clade showing signs of admixture among all breeding locales and (B) a clade primarily composed of YKD samples. We hypothesize that the pattern of mtDNA differentiation reflects some degree of philopatry to the YKD and isolation of two refugial populations with subsequent expansion and admixture. We recommend additional genetic assessments throughout the circumpolar range of Long-tailed Ducks to further quantify aspects of genetic diversity and migratory connectivity in this species.
","language":"English","publisher":"Arctic Institute of North America","publisherLocation":"Montreal","doi":"10.14430/arctic4548","usgsCitation":"Wilson, R.E., Gust, J., Petersen, M.R., and Talbot, S.L., 2016, Spatial genetic structure of Long-tailed Ducks (Clangula hyemalis) among Alaskan, Canadian, and Russian breeding populations: Arctic, v. 69, no. 1, p. 65-78, https://doi.org/10.14430/arctic4548.","productDescription":"Article: 14 p.; Data Release","startPage":"65","endPage":"78","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058269","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":438629,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7HX19RJ","text":"USGS data release","linkHelpText":"Long-tailed Duck (Clangula hyemalis) Microsatellite DNA Data, Alaska, Canada, Russia, 1994-2002"},{"id":319212,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335474,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7HX19RJ"}],"country":"Canada, Russia, United States","otherGeospatial":"Arctic Coastal Plain, central Canadian Arctic, eastern Siberia, Yukon-Kuskokwim Delta","volume":"69","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-01","publicationStatus":"PW","scienceBaseUri":"56f3be4fe4b0f59b85e02f08","contributors":{"authors":[{"text":"Wilson, Robert E. 0000-0003-1800-0183 rewilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":5718,"corporation":false,"usgs":true,"family":"Wilson","given":"Robert","email":"rewilson@usgs.gov","middleInitial":"E.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":623264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gust, J. R.","contributorId":167730,"corporation":false,"usgs":false,"family":"Gust","given":"J. R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":623267,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Petersen, Margaret R. 0000-0001-6082-3189 mrpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-6082-3189","contributorId":167729,"corporation":false,"usgs":true,"family":"Petersen","given":"Margaret","email":"mrpetersen@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":623265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":623266,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170288,"text":"70170288 - 2016 - Quasi-extinction risk and population targets for the Eastern, migratory population of monarch butterflies (Danaus plexippus)","interactions":[],"lastModifiedDate":"2017-02-13T14:20:40","indexId":"70170288","displayToPublicDate":"2016-03-21T01:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Quasi-extinction risk and population targets for the Eastern, migratory population of monarch butterflies (Danaus plexippus)","docAbstract":"The Eastern, migratory population of monarch butterflies (Danaus plexippus), an iconic North American insect, has declined by ~80% over the last decade. The monarch’s multi-generational migration between overwintering grounds in central Mexico and the summer breeding grounds in the northern U.S. and southern Canada is celebrated in all three countries and creates shared management responsibilities across North America. Here we present a novel Bayesian multivariate auto-regressive state-space model to assess quasi-extinction risk and aid in the establishment of a target population size for monarch conservation planning. We find that, given a range of plausible quasi-extinction thresholds, the population has a substantial probability of quasi-extinction, from 11–57% over 20 years, although uncertainty in these estimates is large. Exceptionally high population stochasticity, declining numbers, and a small current population size act in concert to drive this risk. An approximately 5-fold increase of the monarch population size (relative to the winter of 2014–15) is necessary to halve the current risk of quasi-extinction across all thresholds considered. Conserving the monarch migration thus requires active management to reverse population declines, and the establishment of an ambitious target population size goal to buffer against future environmentally driven variability.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/srep23265","usgsCitation":"Semmens, B.X., Semmens, D.J., Thogmartin, W.E., Wiederholt, R., Lopez-Hoffman, L., Diffendorfer, J., Pleasants, J., Oberhauser, K.S., and Taylor, O.R., 2016, Quasi-extinction risk and population targets for the Eastern, migratory population of monarch butterflies (Danaus plexippus): Scientific Reports, v. 6, Article number 23265; 7 p., https://doi.org/10.1038/srep23265.","productDescription":"Article number 23265; 7 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066835","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":471134,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/srep23265","text":"Publisher Index Page"},{"id":320161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-21","publicationStatus":"PW","scienceBaseUri":"571756e6e4b0ef3b7caa62a9","contributors":{"authors":[{"text":"Semmens, Brice X.","contributorId":149775,"corporation":false,"usgs":false,"family":"Semmens","given":"Brice","email":"","middleInitial":"X.","affiliations":[{"id":17820,"text":"Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":626766,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Semmens, Darius J. 0000-0001-7924-6529 dsemmens@usgs.gov","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":1714,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius","email":"dsemmens@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":626765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":626767,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiederholt, Ruscena","contributorId":149125,"corporation":false,"usgs":false,"family":"Wiederholt","given":"Ruscena","affiliations":[{"id":17653,"text":"School of Natural Resources & the Environment, The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":626768,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lopez-Hoffman, Laura","contributorId":149127,"corporation":false,"usgs":false,"family":"Lopez-Hoffman","given":"Laura","affiliations":[{"id":17654,"text":"School of Natural Resources & the Environment and Udall Center for Studies in Public Policy, The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":626769,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":626770,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pleasants, John M.","contributorId":168616,"corporation":false,"usgs":false,"family":"Pleasants","given":"John M.","affiliations":[{"id":25341,"text":"Department of Ecology, Evolution, and Organismal Biology, Iowa State University","active":true,"usgs":false}],"preferred":false,"id":626771,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oberhauser, Karen S.","contributorId":27737,"corporation":false,"usgs":true,"family":"Oberhauser","given":"Karen","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":626772,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Taylor, Orley R.","contributorId":168617,"corporation":false,"usgs":false,"family":"Taylor","given":"Orley","email":"","middleInitial":"R.","affiliations":[{"id":25342,"text":"Department of Ecology and Evolutionary Biology, University of Kansas","active":true,"usgs":false}],"preferred":false,"id":626773,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70209112,"text":"70209112 - 2016 - Report A: Fish and habitat assessment in Rock Creek, Klickitat County, Washington, June 2013-December 2015","interactions":[],"lastModifiedDate":"2020-03-18T06:43:44","indexId":"70209112","displayToPublicDate":"2016-03-17T07:26:46","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Report A: Fish and habitat assessment in Rock Creek, Klickitat County, Washington, June 2013-December 2015","docAbstract":"The U.S. Geological Survey (USGS) and the Yakama Nation have collaborated in the Rock Creek subbasin, southeastern, Washington since 2009 to assess steelhead (Oncorynchus mykiss) populations and habitat conditions. Rock Creek, flows south to the Columbia River at river kilometer (rkm) 368. During 2015, a habitat survey was conducted, and monitoring of Passive Integrated Transponder (PIT)-tagged salmonids in the Rock Creek subbasin continued. Two multiplexing PIT-tag interrogation systems (PTISs) were installed in Rock Creek in the fall of 2009 to evaluate timing and degree of salmonid movement, smolting success, stray rates, and other life history attributes. These have been monitored every year since, during the spring, fall, and winter months. Returning adult steelhead detection histories are summarized from past Rock Creek PIT-tagging efforts. Detection histories and detection efficiencies were used to estimate a smolt-to-adult return rate (SAR) for Rock Creek PIT-tagged steelhead (tagged from 2009 to 2012) that ranged from 2.2% to 5.5%. Additionally, a SAR was also estimated for Rock Creek PIT-tagged steelhead returning to Bonneville Dam, Columbia River (rkm 235). The SAR rate to Bonneville Dam was always higher (2.4% to 10.4%), indicating straying of adults to other sites for spawning potentially further upstream or in other tributaries, or pre-spawn mortality. Twenty-two Rock Creek PIT-tagged steelhead were detected returning to Rock Creek and 35 were detected at Bonneville Dam from past tagging efforts (2009 – 2012). Monitoring of the Rock Creek PTISs [Rock Creek Lower (RCL) and Rock Creek Squaw (RCS)] provide evidence for PIT-tagged salmonid use from fish tagged outside of Rock Creek subbasin (out-of-basin) origins. A total of 82 out-of-basin PIT-tagged fish have been detected at the Rock Creek PTISs since installation.\n\nThe habitat survey was conducted in Rock Creek from September 16 to October 7, 2015. The survey started at rkm 2 and continued upstream to rkm 29.3, and included portions of the major tributaries, ranging from 1 to 9 rkm survey length upstream from their confluence. During the survey, we measured the lengths of all dry and non-pool wet sections, and for pools: the length, wetted width, average residual depth, maximum residual depth, and temperature. During the 2015 survey of Rock Creek, 38% of the river between rkm 2 and 29 was classified as dry, with a higher relative proportion (57%) of dry being in the lower river section (rkm 2-13) than the upper river section (33%, rkm 14-22). This was higher than during survey years 2010 to 2012, which ranged from 29% to 43% in the lower river. As a result of the increase in dry area the percent of non-pool wet habitat was less (22%) than previous years (range 34% to 43%), as well as the percent of pools (21%). However, more river kilometers were surveyed in the lower river section (rkm 2-13) than previous years. For the 2015 survey length (rkm 2 to 29), 19% was classified as pools and 43% was non-pool wet. This work informs potential restoration actions by identifying the persistent pools across years and in years of low water flow (i.e., 2015). This work also provides a baseline to evaluate effectiveness of future restoration actions. Potential restoration actions could include headwater and upland restoration to improve base flows and pool habitat enhancement, through increased structure and vegetation plantings for increased cover.\n\nThe Rock Creek steelhead population remains to be an important cultural resource for the Rock Creek Band of the Yakama Nation Tribe. The Rock Creek steelhead population is part of the Cascades Eastern Slope major population group (MPG), one of four MPGs contributing to the Middle Columbia steelhead distinct population segment (DPS). The National Marine Fisheries Service recovery plan for Rock Creek (NMFS 2009a) identifies an overall biological recovery goal for Rock Creek steelhead to contribute to recovery of the Mid-Columbia D","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Rock Creek Fish and Habitat Assessment for Prioritization of Restoration and Protection Actions","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Bonneville Power Administration","collaboration":"Bonneville Power Administration; Yakama Nation","usgsCitation":"Hardiman, J.M., and Harvey, E., 2016, Report A: Fish and habitat assessment in Rock Creek, Klickitat County, Washington, June 2013-December 2015, 41 p.","productDescription":"41 p.","startPage":"A-1","endPage":"A-41","ipdsId":"IP-087695","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373308,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P160751"}],"country":"United States","state":"Washington","county":"Klickitat 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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hardiman, Jill M. 0000-0002-3661-9695 jhardiman@usgs.gov","orcid":"https://orcid.org/0000-0002-3661-9695","contributorId":2672,"corporation":false,"usgs":true,"family":"Hardiman","given":"Jill","email":"jhardiman@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":784965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Elaine","contributorId":203907,"corporation":false,"usgs":false,"family":"Harvey","given":"Elaine","email":"","affiliations":[{"id":36750,"text":"Yakama Nation Fisheries, 4 Bickleton Hwy, Goldendale, WA 98620","active":true,"usgs":false}],"preferred":false,"id":784966,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70168418,"text":"sir20165021 - 2016 - Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10","interactions":[],"lastModifiedDate":"2016-03-18T08:13:50","indexId":"sir20165021","displayToPublicDate":"2016-03-17T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5021","title":"Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10","docAbstract":"The Albuquerque area of New Mexico has two principal sources of water: (1) groundwater from the Santa Fe Group aquifer system, and (2) surface water from the Rio Grande. From 1960 to 2002, pumping from the Santa Fe Group aquifer system caused groundwater levels to decline more than 120 feet while water-level declines along the Rio Grande in Albuquerque were generally less than 40 feet. These differences in water-level declines in the Albuquerque area have resulted in a great deal of interest in quantifying the river-aquifer interaction associated with the Rio Grande.
In 2003, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, acting as fiscal agent for the Middle Rio Grande Endangered Species Collaborative Program, and the U.S. Army Corps of Engineers, began a study to characterize the hydrogeology of the Rio Grande inner valley alluvial aquifer in the Albuquerque area of New Mexico. The study provides hydrologic data in order to enhance the understanding of rates of water leakage from the Rio Grande to the alluvial aquifer, groundwater flow through the aquifer, and discharge of water from the aquifer to riverside drains. The study area extends about 20 miles along the Rio Grande in the Albuquerque area. Piezometers and surface-water gages were installed in paired transects at eight locations. Nested piezometers, completed at various depths in the alluvial aquifer, and surface-water gages, installed in the Rio Grande and riverside drains, were instrumented with pressure transducers. Water-level and water-temperature data were collected from 2009 to 2010.
Water levels from the piezometers indicated that groundwater movement was usually away from the river towards the riverside drains. Annual mean horizontal groundwater gradients in the inner valley alluvial aquifer ranged from 0.0024 (I-25 East) to 0.0144 (Pajarito East). The median hydraulic conductivity values of the inner valley alluvial aquifer, determined from slug tests, ranged from 30 feet per day (ft/d) (Montaño) to 120 ft/d (Central) for paired transects, with a median hydraulic conductivity for all transects of 50 ft/d. Daily mean groundwater fluxes from the river through the inner valley alluvial aquifer computed using Darcy’s Law and the slug test results ranged from about 0.01 ft/d (Montaño West) to between 1.0 and 2.0 ft/d (Central East). Median annual groundwater fluxes from the river through the inner valley alluvial aquifer determined using the Suzuki-Stallman method was greatest at Alameda East (0.50 ft/d) and lowest at Alameda West (0.25 ft/d). The results from both methods agreed reasonably well.
Seepage investigations conducted by measuring discharge in the east and west riverside drains provided information for computing changes in flow within the drains and for evaluating results from Darcy’s Law and Suzuki-Stallman method flux calculations. Discharge measured in the east riverside drain between the Barelas Bridge and the I-25 bridge indicated that the flow in the east riverside drain increased by an average of 56.5 cubic feet per day per linear foot (ft3/d/ft) of drain. Discharge measured in the west riverside drain between the Central bridge and the I-25 bridge indicated that flow increased between west drain miles 0 and 4, an average of 53.8 ft3/d/ft of drain, and that flow increased between west drain miles 7 and 10, an average of 44.9 ft3/d/ft of drain. In comparison to the seepage measurement results, the groundwater fluxes from the river through the inner valley alluvial aquifer calculated from Darcy’s Law (qslug) and by the Suzuki-Stallman method (qheat) would account for 20–36 percent or 53–95 percent, respectively, of the total flow in the east riverside drain and 22–31 percent or 19–26 percent, respectively, of the total flow in the west drain. These results indicate that the drains likely also receive water from outside the inner valley.
The spatial variability of horizontal hydraulic gradients and groundwater fluxes can be primarily attributed to variability in the distances between the river and riverside drains throughout the study area and geologic heterogeneities in the alluvial aquifer. Temporal variability in the water levels, which control the horizontal hydraulic gradients and fluxes between the Rio Grande and the riverside drains, can be primarily attributed to seasonal fluctuations in river stage and irrigation practices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165021","collaboration":"Prepared in cooperation with Bureau of Reclamation acting as fiscal agent for the Middle Rio Grande Endangered Species Collaborative Program","usgsCitation":"Rankin, D.R., Oelsner, G.P., McCoy, K.J., Moret, G.J.M., Worthington, J.A., and Bandy-Baldwin, K.M., 2016, Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10: U.S. Geological Survey Scientific Investigations Report 2016–5021, 89 p., https://dx.doi.org/10.3133/sir20165021.","productDescription":"viii, 89 p.","numberOfPages":"101","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-033038","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":318926,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5021/sir20165021.pdf","text":"Report","size":"5.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5021"},{"id":318925,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5021/coverthb.jpg"}],"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 -106.7266845703125,\n 34.95011635301367\n ],\n [\n -106.7266845703125,\n 35.290468565908775\n ],\n [\n -106.56051635742188,\n 35.290468565908775\n ],\n [\n -106.56051635742188,\n 34.95011635301367\n ],\n [\n -106.7266845703125,\n 34.95011635301367\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
5338 Montogmery Blvd., NE Suite 400
Albuquerque, NM 87109–1311
—The Southern Mule Deer is a mobile but non-migratory large mammal found throughout southern California and is a covered species in the San Diego Multi-Species Conservation Plan. We assessed deer movement and population connectivity across California State Route 67 and two smaller roads in eastern San Diego County using non-invasive genetic sampling. We collected deer scat pellets between April and November 2015, and genotyped pellets at 15 microsatellites and a sex determination marker. We successfully genotyped 71 unique individuals from throughout the study area and detected nine recapture events. Recaptures were generally found close to original capture locations (within 1.5 km). We did not detect recaptures across roads; however, pedigree analysis detected 21 first order relative pairs, of which approximately 20% were found across State Route 67. Exact tests comparing allele frequencies between groups of individuals in pre-defined geographic clusters detected significant genetic differentiation across State Route 67. In contrast, the assignment-based algorithm of STRUCTURE supported a single genetic cluster across the study area. Our data suggest that State Route 67 may reduce, but does not preclude, movement and gene flow of Southern Mule Deer.
","language":"English","publisher":"The Journal of the Western Section of The Wildlife Society","publisherLocation":"Albany, CA","usgsCitation":"Mitelberg, A., and Vandergast, A.G., 2016, Non-invasive genetic sampling of Southern Mule Deer (Odocoileus hemionus fuliginatus) reveals limited movement across California State Route 67 in San Diego County: Western Wildlife, v. 3, p. 8-18.","productDescription":"11 p.","startPage":"8","endPage":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-076907","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":325878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":325873,"type":{"id":15,"text":"Index Page"},"url":"https://wwjournal.org/index.php/current-volume"}],"country":"United States","state":"California","city":"San Diego","volume":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a072b9e4b060ce18fb2dda","contributors":{"authors":[{"text":"Mitelberg, Anna amitelberg@usgs.gov","contributorId":173293,"corporation":false,"usgs":true,"family":"Mitelberg","given":"Anna","email":"amitelberg@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":644117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571 avandergast@usgs.gov","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":3963,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"avandergast@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":644116,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70168374,"text":"sir20165024 - 2016 - Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012","interactions":[],"lastModifiedDate":"2022-09-15T18:41:32.475293","indexId":"sir20165024","displayToPublicDate":"2016-03-16T14:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5024","title":"Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012","docAbstract":"Estimates of the magnitude and frequency of floods are needed across Alaska for engineering design of transportation and water-conveyance structures, flood-insurance studies, flood-plain management, and other water-resource purposes. This report updates methods for estimating flood magnitude and frequency in Alaska and conterminous basins in Canada. Annual peak-flow data through water year 2012 were compiled from 387 streamgages on unregulated streams with at least 10 years of record. Flood-frequency estimates were computed for each streamgage using the Expected Moments Algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows. A multiple Grubbs-Beck test was used to identify potentially influential low floods in the time series of peak flows for censoring in the flood frequency analysis.
For two new regional skew areas, flood-frequency estimates using station skew were computed for stations with at least 25 years of record for use in a Bayesian least-squares regression analysis to determine a regional skew value. The consideration of basin characteristics as explanatory variables for regional skew resulted in improvements in precision too small to warrant the additional model complexity, and a constant model was adopted. Regional Skew Area 1 in eastern-central Alaska had a regional skew of 0.54 and an average variance of prediction of 0.45, corresponding to an effective record length of 22 years. Regional Skew Area 2, encompassing coastal areas bordering the Gulf of Alaska, had a regional skew of 0.18 and an average variance of prediction of 0.12, corresponding to an effective record length of 59 years. Station flood-frequency estimates for study sites in regional skew areas were then recomputed using a weighted skew incorporating the station skew and regional skew. In a new regional skew exclusion area outside the regional skew areas, the density of long-record streamgages was too sparse for regional analysis and station skew was used for all estimates. Final station flood frequency estimates for all study streamgages are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities.
Regional multiple-regression analysis was used to produce equations for estimating flood frequency statistics from explanatory basin characteristics. Basin characteristics, including physical and climatic variables, were updated for all study streamgages using a geographical information system and geospatial source data. Screening for similar-sized nested basins eliminated hydrologically redundant sites, and screening for eligibility for analysis of explanatory variables eliminated regulated peaks, outburst peaks, and sites with indeterminate basin characteristics. An ordinary least‑squares regression used flood-frequency statistics and basin characteristics for 341 streamgages (284 in Alaska and 57 in Canada) to determine the most suitable combination of basin characteristics for a flood-frequency regression model and to explore regional grouping of streamgages for explaining variability in flood-frequency statistics across the study area. The most suitable model for explaining flood frequency used drainage area and mean annual precipitation as explanatory variables for the entire study area as a region. Final regression equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability discharge in Alaska and conterminous basins in Canada were developed using a generalized least-squares regression. The average standard error of prediction for the regression equations for the various annual exceedance probabilities ranged from 69 to 82 percent, and the pseudo-coefficient of determination (pseudo-R2) ranged from 85 to 91 percent.
The regional regression equations from this study were incorporated into the U.S. Geological Survey StreamStats program for a limited area of the State—the Cook Inlet Basin. StreamStats is a national web-based geographic information system application that facilitates retrieval of streamflow statistics and associated information. StreamStats retrieves published data for gaged sites and, for user-selected ungaged sites, delineates drainage areas from topographic and hydrographic data, computes basin characteristics, and computes flood frequency estimates using the regional regression equations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165024","collaboration":"Prepared in cooperation with the Alaska Department of Transportation and Public Facilities, Alaska Department of Natural Resources, and U.S. Army Corps of Engineers","usgsCitation":"Curran, J.H., Barth, N.A., Veilleux, A.G., and Ourso, R.T., 2016, Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012: U.S. Geological Survey Scientific Investigations Report 2016–5024, 47 p., https://dx.doi.org/10.3133/sir20165024.","productDescription":"Report: vi, 47 p.; 3 Tables; 1 Appendix; Companion File; Database","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2011-10-01","ipdsId":"IP-068358","costCenters":[{"id":114,"text":"Alaska Science 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2011-2021"},{"id":438632,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JEL0UU","text":"USGS data release","linkHelpText":"Flood Frequency Data and 2020 Observed Flood Probability for Selected Streamgages in the Fortymile River Basin, Alaska, 1911-2020"},{"id":318921,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_table09.xlsx","text":"Table 9","size":"78 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":318920,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_appendixa.xlsx","text":"Appendix A","size":"99 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":318919,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_table04.xlsx","text":"Table 4","size":"88 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U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508-4560
http://alaska.usgs.gov
The Pliocene Model Intercomparison Project (PlioMIP) is a co-ordinated international climate modelling initiative to study and understand climate and environments of the Late Pliocene, as well as their potential relevance in the context of future climate change. PlioMIP examines the consistency of model predictions in simulating Pliocene climate and their ability to reproduce climate signals preserved by geological climate archives. Here we provide a description of the aim and objectives of the next phase of the model intercomparison project (PlioMIP Phase 2), and we present the experimental design and boundary conditions that will be utilized for climate model experiments in Phase 2.
\nFollowing on from PlioMIP Phase 1, Phase 2 will continue to be a mechanism for sampling structural uncertainty within climate models. However, Phase 1 demonstrated the requirement to better understand boundary condition uncertainties as well as uncertainty in the methodologies used for data–model comparison. Therefore, our strategy for Phase 2 is to utilize state-of-the-art boundary conditions that have emerged over the last 5 years. These include a new palaeogeographic reconstruction, detailing ocean bathymetry and land–ice surface topography. The ice surface topography is built upon the lessons learned from offline ice sheet modelling studies. Land surface cover has been enhanced by recent additions of Pliocene soils and lakes. Atmospheric reconstructions of palaeo-CO2 are emerging on orbital timescales, and these are also incorporated into PlioMIP Phase 2. New records of surface and sea surface temperature change are being produced that will be more temporally consistent with the boundary conditions and forcings used within models.
\nFinally we have designed a suite of prioritized experiments that tackle issues surrounding the basic understanding of the Pliocene and its relevance in the context of future climate change in a discrete way.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/cp-12-663-2016","usgsCitation":"Haywood, A.M., Dowsett, H.J., Dolan, A.M., Rowley, D., Abe-Ouchi, A., Otto-Bliesner, B., Chandler, M.A., Hunter, S.J., Lunt, D.J., Pound, M., and Salzmann, U., 2016, The Pliocene Model Intercomparison Project (PlioMIP) Phase 2: Scientific objectives and experimental design: Climate of the Past, v. 12, no. 3, p. 663-675, https://doi.org/10.5194/cp-12-663-2016.","productDescription":"13 p.","startPage":"663","endPage":"675","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060058","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":471149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-12-663-2016","text":"Publisher Index Page"},{"id":325565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-16","publicationStatus":"PW","scienceBaseUri":"5793444de4b0eb1ce79e8c1d","contributors":{"authors":[{"text":"Haywood, Alan M.","contributorId":86663,"corporation":false,"usgs":true,"family":"Haywood","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":643340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dowsett, Harry J. 0000-0003-1983-7524 hdowsett@usgs.gov","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":949,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"hdowsett@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":643339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dolan, Aisling M.","contributorId":30117,"corporation":false,"usgs":true,"family":"Dolan","given":"Aisling","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":643341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rowley, David","contributorId":173099,"corporation":false,"usgs":false,"family":"Rowley","given":"David","email":"","affiliations":[{"id":12621,"text":"University of Chicago and University of South Florida","active":true,"usgs":false}],"preferred":false,"id":643342,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Abe-Ouchi, Ayako","contributorId":94942,"corporation":false,"usgs":true,"family":"Abe-Ouchi","given":"Ayako","email":"","affiliations":[],"preferred":false,"id":643343,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Otto-Bliesner, Bette","contributorId":58171,"corporation":false,"usgs":true,"family":"Otto-Bliesner","given":"Bette","affiliations":[],"preferred":false,"id":643344,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chandler, Mark A.","contributorId":101768,"corporation":false,"usgs":true,"family":"Chandler","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":643345,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunter, Stephen J.","contributorId":55711,"corporation":false,"usgs":true,"family":"Hunter","given":"Stephen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":643346,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lunt, Daniel J.","contributorId":101168,"corporation":false,"usgs":true,"family":"Lunt","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":643347,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pound, Matthew","contributorId":173100,"corporation":false,"usgs":false,"family":"Pound","given":"Matthew","email":"","affiliations":[{"id":18103,"text":"Northumbria University, Newcastle Upon Tyne, UK","active":true,"usgs":false}],"preferred":false,"id":643357,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Salzmann, Ulrich","contributorId":173101,"corporation":false,"usgs":false,"family":"Salzmann","given":"Ulrich","email":"","affiliations":[{"id":18103,"text":"Northumbria University, Newcastle Upon Tyne, UK","active":true,"usgs":false}],"preferred":false,"id":643358,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70164510,"text":"sir20165022 - 2016 - Potential effects of alterations to the hydrologic system on the distribution of salinity in the Biscayne aquifer in Broward County, Florida","interactions":[],"lastModifiedDate":"2019-12-30T14:41:27","indexId":"sir20165022","displayToPublicDate":"2016-03-15T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5022","title":"Potential effects of alterations to the hydrologic system on the distribution of salinity in the Biscayne aquifer in Broward County, Florida","docAbstract":"To address concerns about the effects of water-resource management practices and rising sea level on saltwater intrusion, the U.S. Geological Survey in cooperation with the Broward County Environmental Planning and Community Resilience Division, initiated a study to examine causes of saltwater intrusion and predict the effects of future alterations to the hydrologic system on salinity distribution in eastern Broward County, Florida. A three-dimensional, variable-density solute-transport model was calibrated to conditions from 1970 to 2012, the period for which data are most complete and reliable, and was used to simulate historical conditions from 1950 to 2012. These types of models are typically difficult to calibrate by matching to observed groundwater salinities because of spatial variability in aquifer properties that are unknown, and natural and anthropogenic processes that are complex and unknown; therefore, the primary goal was to reproduce major trends and locally generalized distributions of salinity in the Biscayne aquifer. The methods used in this study are relatively new, and results will provide transferable techniques for protecting groundwater resources and maximizing groundwater availability in coastal areas. The model was used to (1) evaluate the sensitivity of the salinity distribution in groundwater to sea-level rise and groundwater pumping, and (2) simulate the potential effects of increases in pumping, variable rates of sea-level rise, movement of a salinity control structure, and use of drainage recharge wells on the future distribution of salinity in the aquifer.
\nResults from the simulation of historical conditions indicate that the model generally represents the observed greater westward extent of elevated salinity in the central part of the intruded area relative to the northern and southernmost parts of the intruded area. Results of sensitivity testing indicate that the extent of elevated salinity is most sensitive to pumping in areas where the source of saltwater is largely offshore, from the Atlantic Ocean, and is most sensitive to sea-level rise in areas where the source of salinity is downward leakage of brackish water from canals.
\nSimulations of future scenarios indicate that increases in pumping near the existing interface may cause the interface to advance and decreases in pumping may cause it to retreat. Climatic effects, such as periods of prolonged drought or high precipitation, may augment or counteract long-term effects of changes in pumping on aquifer salinity at well fields. With increasing rates of sea-level rise, the freshwater-saltwater interface advances progressively inland, and flow-averaged salinities at well fields near the existing interface increase commensurately. Hypothetical southeastward (downstream) re-positioning of the existing G–54 salinity-control structure may prevent the interface from moving northwestward along and near the North New River canal, but beneficial effects are localized. Implementation of freshwater recharge wells in the city of Hallandale Beach may also have only a localized freshening effect in the aquifer and little appreciable effect on the freshwater-saltwater interface or on concentrations of salinity at well fields.
\nModel accuracy and use are limited by uncertainty in the physical properties and boundary conditions of the system, uncertainty in historical and future conditions, and generalizations made in the mathematical relationships used to describe the physical processes of groundwater flow and transport. Because of these limitations, model results should be considered in relative rather than absolute terms. Nonetheless, model results do provide useful information on the relative scale of response of the system to changes in pumping distribution, sea-level rise, and mitigation activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165022","collaboration":"Prepared in cooperation with the Broward County Environmental Planning and Community Resilience Division","usgsCitation":"Hughes, J.D., Sifuentes, D.F., and White, J.T., 2016, Potential effects of alterations to the hydrologic system on the distribution of salinity in the Biscayne aquifer in Broward County, Florida: U.S. Geological Survey Scientific Investigations Report 2016–5022, 114 p., https://dx.doi.org/10.3133/sir20165022.","productDescription":"Report: x, 114 p.; Data Release","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-056536","costCenters":[{"id":269,"text":"FLWSC-Ft. 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Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
http://fl.water.usgs.gov/
The U.S. Geological Survey (USGS) collected data and simulated groundwater flow to increase understanding of the hydrology and the effects of drainage alterations on the water table in the vicinity of Great Marsh, near Beverly Shores and Town of Pines, Indiana. Prior land-management practices have modified drainage and caused changes in the distribution of open water, streams and ditches, and groundwater abundance and flow paths.
\nCollected hydrologic data indicate that the majority of water entering Great Marsh flows from the southern dune ridge beneath Town of Pines, Indiana. Groundwater flow is intercepted by Brown Ditch in the eastern portion of the study area and Derby Ditch in the western portion of the study area. A smaller amount of groundwater from the northern dune ridge beneath Beverly Shores also contributed water to Great Marsh. Continuous groundwater-level data collected indicate that the predominant north-south groundwater-flow gradients vary during the course of the year due to increased levels of precipitation or during periods of drainage obstructions. Continuous surface-water discharge and surface-water elevation were measured at three USGS streamgages, one each on Brown, Kintzele and Derby Ditches. The monthly mean discharge statistics indicate that during the period of record— June 2012 to September 2013—streamflow in Kintzele Ditch was lowest during July 2012 and highest during April 2013. In Derby Ditch, streamflow also was lowest during July 2012 and highest during April 2013.
\nPeriods of relatively high and low groundwater levels during August 1982, March 2013, and April 2014 were examined and simulated by using MODFLOW and companion software. Results from the simulation of conditions during March 2013 include that nearly 100 percent of all water entering the area simulating Town of Pines is from recharge. Of all the water simulated to enter the eastern and western portions of Great Marsh, nearly 20 and 18 percent, respectively, flows from Town of Pines to the western and eastern portions of Great Marsh. The dune ridges beneath Town of Pines and to a lesser extent beneath Beverly Shores are a major source of recharge to the surficial aquifer and Great Marsh.
\nResults from the simulation of the conditions of April 2014 include that, despite increases in the amount of water entering Great Marsh due to a beaver-dam-modified hydrologic condition, there is still virtually zero simulated groundwater flow from Great Marsh to Town of Pines. The volume of water simulated to be entering the zone representing Beverly Shores decreased by 0.43 cubic foot per second from the results of the March 2013 simulation. This simulated difference in water budgets can be attributed to increased simulated recharge in Great Marsh and Town of Pines. Effects of the inclusion of the beaver dam included the increase of the simulated water table and simulated inundated area upstream of the beaver dam due to the effects of ponding surface water.
\nResults from the simulation scenario that includes six proposed pool-riffle control structures in Brown Ditch under the hydrologic conditions of March 2013 indicate areas inundated by water are larger, including areas just to the north of the entrance of Brown Ditch into Great Marsh, and areas north of the confluence of Brown and Kintzele Ditches.
\nResults from the scenario simulating the increase of the Lake Michigan water level to the historical high of May 31, 1998, showed inundated areas of Great Marsh south of Beverly Shores enlarged on both sides of Lakeshore County Road with the greatest enlargement simulated to be southeast of the intersection of Lakeshore County Road and Beverly Drive. For the scenario simulating the decrease of the Lake Michigan water level to the historical low of December 23, 2007, results show little change from the original March 2013 inundated area.
\nThe results of this study can be used by water-resource managers to understand how surrounding ditches affect water levels in Great Marsh and other inland wetlands and residential areas. The groundwater model developed can be applied to answer questions about how alterations to the drainage system in the area affects water levels in the public and residential areas surrounding Great Marsh. The modeling methods developed in this study provide a template for other studies of groundwater flow and groundwater/surface-water interactions within the shallow surficial aquifer in northern Indiana, and in similar hydrologic settings that include surficial sand aquifers in coastal areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155141","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Lampe, D.C., 2015, Hydrologic data and groundwater-flow simulations in the Brown Ditch Watershed, Indiana Dunes National Lakeshore, near Beverly Shores and Town of Pines, Indiana: U.S. Geological Survey Scientific Investigations Report 2015– 5141, 97 p., https://dx.doi.org/10.3133/sir20155141.","productDescription":"xi, 97 p.","numberOfPages":"116","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-055857","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":318807,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5141/coverthb.jpg"},{"id":318808,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5141/sir20155141.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5141"}],"country":"United States","state":"Indiana","otherGeospatial":"Brown Ditch Watershed, Indiana Dunes National Lakeshore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.1,\n 41.65\n ],\n [\n -87.1,\n 41.73\n ],\n [\n -86.9,\n 41.73\n ],\n [\n -86.9,\n 41.65\n ],\n [\n -87.1,\n 41.65\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
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http://in.water.usgs.gov/
U.S. Geological Survey National Oil and Gas Assessments (NOGA) of Albian aged clastic reservoirs in the U.S. Gulf Coast region indicate a relatively low prospectivity for undiscovered hydrocarbon resources due to high levels of past production and exploration. Evaluation of two assessment units (AUs), (1) the Albian Clastic AU 50490125, and (2) the Updip Albian Clastic AU 50490126, were based on a geologic model incorporating consideration of source rock, thermal maturity, migration, events timing, depositional environments, reservoir rock characteristics, and production analyses built on well and field-level production histories. The Albian Clastic AU is a mature conventional hydrocarbon prospect with undiscovered accumulations probably restricted to small faulted and salt-associated structural traps that could be revealed using high resolution subsurface imaging and from targeting structures at increased drilling depths that were unproductive at shallower intervals. Mean undiscovered accumulation volumes from the probabilistic assessment are 37 million barrels of oil (MMBO), 152 billion cubic feet of gas (BCFG), and 4 million barrels of natural gas liquids (MMBNGL). Limited exploration of the Updip Albian Clastic AU reflects a paucity of hydrocarbon discoveries updip of the periphery fault zones in the northern Gulf Coastal region. Restricted migration across fault zones is a major factor behind the small discovered fields and estimation of undiscovered resources in the AU. Mean undiscovered accumulation volumes from the probabilistic assessment are 1 MMBO and 5 BCFG for the Updip Albian Clastic AU.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161026","usgsCitation":"Merrill, M.D., 2016, Geologic assessment of undiscovered oil and gas resources in the Albian clastic and updip Albian clastic assessment units, U.S. Gulf Coast region: U.S. Geological Survey Open-File Report 2016–1026, 31 p., https://dx.doi.org/10.3133/ofr20161026.","productDescription":"Report: v, 27; Appendixes 1-2","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-038743","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":318712,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1026//ofr20161026.pdf","text":"Report","size":"2.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1026"},{"id":318714,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1026/ofr20161026_appendix2.pdf","text":"Appendix 2 - 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http://energy.usgs.gov/
http://energy.usgs.gov/GeneralInfo/
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A 15-year estimated Trophic State Index (eTSI) for Michigan inland lakes is available, and it spans seven datasets, each representing 1 to 3 years of data from 1999 to 2013. On average, 3,000 inland lake eTSI values are represented in each of the datasets by a process that relates field-measured Secchi-disk transparency (SDT) to Landsat satellite imagery to provide eTSI values for unsampled inland lakes. The correlation between eTSI values and field-measured Trophic State Index (TSI) values from SDT was strong as shown by R2 values from 0.71 to 0.83. Mean eTSI values ranged from 42.7 to 46.8 units, which when converted to estimated SDT (eSDT) ranged from 8.9 to 12.5 feet for the datasets. Most eTSI values for Michigan inland lakes are in the mesotrophic TSI class. The Environmental Protection Agency (EPA) Level III Ecoregions were used to illustrate and compare the spatial distribution of eTSI classes for Michigan inland lakes. Lakes in the Northern Lakes and Forests, North Central Hardwood Forests, and Southern Michigan/Northern Indiana Drift Plains ecoregions are predominantly in the mesotrophic TSI class. The Huron/Erie Lake Plains and Eastern Corn Belt Plains ecoregions, had predominantly eutrophic class lakes and also the highest percent of hypereutrophic lakes than other ecoregions in the State. Data from multiple sampling programs—including data collected by volunteers with the Cooperative Lakes Monitoring Program (CLMP) through the Michigan Department of Environmental Quality (MDEQ), and the 2007 National Lakes Assessment (NLA)—were compiled to compare the distribution of lake TSI classes between each program. The seven eTSI datasets are available for viewing and download with eSDT from the Michigan Lake Water Clarity Interactive Map Viewer at http://mi.water.usgs.gov/projects/RemoteSensing/index.html.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165023","collaboration":"Prepared in cooperation with the Michigan Department of Environmental Quality","usgsCitation":"Fuller, L.M., and Jodoin, R.S., 2016, Estimation of a Trophic State Index for selected inland lakes in Michigan, 1999–2013: U.S. Geological Survey Scientific Investigations Report 2016–5023, 16 p., https://dx.doi.org/10.3133/sir20165023.","productDescription":"vii, 16 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-067016","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":318806,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5023/sir20165023.pdf","text":"Report","size":"1.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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\"}}]}","contact":"Director, Michigan Water Science Center
U.S. Geological Survey
6520 Mercantile Way, Suite 5
Lansing, MI 48911-5991
http://mi.water.usgs.gov/
The production and accessibility of geospatial information including Earth observation is changing greatly both technically and in terms of human participation. Advances in technology have changed the way that geospatial data are produced and accessed, resulting in more efficient processes and greater accessibility than ever before. Improved technology has also created opportunities for increased participation in the gathering and interpretation of data through crowdsourcing and citizen science efforts. Increased accessibility has resulted in greater participation in the use of data as prices for Government-produced data have fallen and barriers to access have been reduced.
\nThe increase in participation in the production and in the use of data, defined as data democracy for this workshop, are having great impacts on economics and more generally on society.
\nThere is also a strong drive by governments around the world, as shown by the G8 Declaration in June 2013, to make public sector information and scientific data more widely accessible. These are respectively termed “open data” and “open research data.”
\nThis report summarizes discussion at the Workshop on Assessing the Impact and Value of Open Geospatial Information held at George Washington University in Washington, D.C. in October 2014. Workshop participants examined the consequences of expanding data democracy with a focus on its socioeconomic impacts. Evaluations were presented of state-of-the-art methods to assess these socioeconomic impacts, which included position papers and remarks by discussants. The workshop included discussions about the following topics: (1) increased and expanded information sources; (2) societal impacts, including approaches to economics assessments; (3) constraints to open access, including the demands for return on investment, specifications of intellectual property rights, and privacy issues; and (4) learning from the experiences of other data-rich domains, such as environmental management, internet businesses, health, and transportation.
\nThe workshop was a working meeting with strong participant engagement, leading to recommendations for action. The meeting included five topic-driven sessions and keynote presentations. Precirculated position papers for each panel session facilitated preparation and remarks by discussants. After the position papers are updated following the discussants’ remarks, it is planned to submit them for publication.
\nThe workshop included 68 participants coming from international organizations, the U.S. public and private sectors, nongovernmental organizations, and academia. Participants included policy makers and analysts, financial analysts, economists, information scientists, geospatial practitioners, and other discipline experts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161036","collaboration":"Prepared in cooperation with the Socioeconomic Benefits Community","usgsCitation":"Pearlman, Francoise, Pearlman, Jay, Bernknopf, Richard, Coote, Andrew, Craglia, Massimo, Friedl, Lawrence, Gallo, Jason, Hertzfeld, Henry, Jolly, Claire, Macauley, Molly, Shapiro, Carl, and Smart, Alan, 2016, Assessing the socioeconomic impact and value of open geospatial information: U.S. Geological Survey Open-File Report 2016–1036, 36 p., https://dx.doi.org/10.3133/ofr20161036.","productDescription":"vi, 36 p.","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":318802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1036/coverthb.jpg"},{"id":318803,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1036/ofr20161036.pdf","text":"Report","size":"5.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1036"}],"otherGeospatial":"Global","contact":"U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
http://www.usgs.gov
The Niobrara River is an important and valuable economic and ecological resource in northern Nebraska that supports ecotourism, recreational boating, wildlife, fisheries, agriculture, and hydroelectric power. Because of its uniquely rich resources, a 122-kilometer reach of the Niobrara River was designated as a National Scenic River in 1991, which has been jointly managed by the U.S. Fish and Wildlife Service and National Park Service. To assess how the remarkable qualities of the National Scenic River may change if consumptive uses of water are increased above current levels, the U.S. Geological Survey, in cooperation with the National Park Service, initiated an investigation of how stream-channel morphology might be affected by potential decreases in summer streamflows. The study included a 65-kilometer segment in the wide, braided eastern stretch of the Niobrara National Scenic River that provides important nesting habitat for migratory bird species of concern to the Nation.
\nThe study focused on three river segments, separated at the confluences with two tributaries, Plum Creek and Long Pine Creek. With an overall temporal scope of 1988–2010 that includes a short interval preceding and a long interval following the Niobrara National Scenic River Designation Act of 1991, the study analyzed five separate time periods: 1988–93, 1994–99, 2000–3, 2004–6, and 2007–10, each of which ended with a year in which aerial photography coverage was available.
\nStreamflow duration was analyzed for one streamgage upstream from the study area and two streamgages on tributary streams within the study area. Summer streamflows (July, August, and September) were targeted for analysis because median flows of the Niobrara River are lowest during those 3 months. In addition, peak flows during the study period were used to estimate bankfull discharge, which is one determinant of channel dimensions.
\nChanges in channel morphology were examined using aerial photographs from 1993, 1999, 2003, 2006, and 2010 to measure channel width, area of islands, and incipient flood-plain surfaces, and to compute the braided index. Channel metrics were computed for each photography year and summarized by river segment. Additionally, at fixed-location cross sections, photography analysis identified localized geomorphic change to infer processes. Accuracy of geomorphic feature classification was estimated and the root-mean-square difference (RMSD) between aerial photographs was calculated to determine associated errors in channel metric calculations. The horizontal accuracy of boundaries delineated in the classification was estimated as 5 meters (m) for boundaries based on 1993 aerial photography and 4 m for all other aerial photography. The RMSD between aerial photography years ranged from 3.04 m to 4.16 m.
\nThe largest measurable changes in channel metrics were measured between 1993 and 1999 and between 1999 and 2003. Between 1993 and 1999, average total channel width increased by 9 m (3 percent) and 14 m (5 percent) in segments 2 and 3, respectively; average active channel width increased by 13 m (5 percent) in segment 3 and decreased by 6 m (4 percent) in segment 1; and incipient flood-plain-surface area increased by 40, 44, and 33 percent in segments 1, 2, and 3, respectively. Changes in channel metrics between 1999 and 2003 included a decrease in average total channel width of 14 m (5 percent) in segment 2; a decrease in active channel widths of 8 m (3 percent) and 6 m (2 percent) in segments 2 and 3, respectively; and an increase of 5 m (3 percent) in segment 1. Incipient flood-plain areas decreased by 22 and 33 percent in segments 1 and 2, respectively, and increased by 42 percent in segment 3.
\nLarge changes were measured between 1993 and 1999, and between 1999 and 2003, at many of the fixed-location cross sections. Large changes (that is, greater than 25 percent) in total channel width were measured in all three segments between 1993 and 1999 and again between 1999 and 2003; large increases were dominant between 1993 and 1999 and large decreases were dominant between 1999 and 2003. Segment 1 was the most susceptible to localized changes as there was only one period (between 2003 and 2006) in which the active channel width largely changed in fewer than 10 percent of the cross sections.
\nChanges in channel metrics generally corresponded to changes in streamflow conditions, but other than changes in incipient flood-plain area, these changes were small and were not measured in all three segments simultaneously. Increases in total channel width (except in segment 1) and incipient flood-plain area between 1993 and 1999 corresponded to increases in streamflow. Channel narrowing (except in segment 1) between 1999 and 2003 corresponded to lower summer streamflows and extended durations of very low summer streamflow. Although the pattern of low summer streamflow and extended durations of very low summer streamflow continued during the 2004–6 period and at the beginning of the 2007–10 period, no further narrowing was measured. Consistent tributary summer inflows help to explain the resistance of segments 2 and 3 to further narrowing. Because segment 1 is already much narrower than segments 2 and 3, its average current velocity is likely to be swifter and, therefore, competent to offset further effects of the processes that led to its narrowness.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165004","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Schaepe, N.J., Alexander, J.S., and Folz-Donahue, Kiernan, 2016, Effects of streamflows on stream-channel morphology in the eastern Niobrara National Scenic River, Nebraska, 1988–2010: U.S. Geological Survey Scientific Investigations Report 2016–5004, 30 p., https://dx.doi.org/10.3133/sir20165004.","productDescription":"vi, 30 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063713","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":318685,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5004/sir20165004.pdf","text":"Report","size":"2.13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5004"},{"id":318684,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5004/coverthb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Niobrara National Scenic River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.5523681640625,\n 42.706659563510385\n ],\n [\n -100.5523681640625,\n 42.956422511073335\n ],\n [\n -99.3218994140625,\n 42.956422511073335\n ],\n [\n -99.3218994140625,\n 42.706659563510385\n ],\n [\n -100.5523681640625,\n 42.706659563510385\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, Nebraska 68512