The Nooksack River is a dynamic gravel-bedded river in northwestern Washington, draining off Mount Baker and the North Cascades into Puget Sound. Working in cooperation with the Whatcom County Flood Control Zone District, the U.S. Geological Survey studied topographic, hydrologic, and climatic data for the Nooksack River basin to document recent changes in sediment storage, long-term bed elevation trends, rates of sediment transport, and factors influencing surficial drainage in order to support ongoing river management. Differences in elevations between topographic and bathymetric surveys in 2005/06 and 2013/15 indicate the active channel aggraded about 1–2 feet locally near the cities of Ferndale and Everson but was primarily stable between them. The active channel upstream of Nugent’s Corner generally incised. Total incision upstream of Nugent’s Corner to Glacier Creek generated 2.3 ± 1.7 million cubic yards of sediment from 2005/06 to 2013 and likely represented a significant source of coarse sediment to the lower mainstem river over that time.
Long-term records of local channel-bed elevation, derived from U.S. Geological Survey streamgage data, show bed-elevation changes of about 1–3 feet. The river bed at most streamgages exhibits long-term trends, with relatively consistent rates of change on the order of 1 foot per decade that persist years to decades. Lagged correlations in bed-elevation trends at all seven streamgages in the North Fork Nooksack and mainstem Nooksack suggest that decadal periods of persistent aggradation and incision originate in the North Fork and translate downstream. The channel-change signal propagates downstream 0.5–2.5 miles per year, with the rate of propagation scaling closely with channel slope. The pattern of incision and aggradation in the North Fork correlates with regional climate, where persistent incision follows extended cold and wet periods, and persistent aggradation follows extended warm and dry periods. Climate-driven variation in coarse-sediment delivery, primarily from the North Fork Nooksack, then appears to be a strong control on long-term vertical channel adjustments at sites downstream. The downstream-translating climate signal generated in the North Fork would account for recently observed aggradation at Everson and Ferndale but not the observed incision in unconfined reaches upstream of Nugent’s Corner from 2005–06 to 2013. This mismatch indicates that understanding how changes in sediment-supply influence those unconfined reaches remains a key uncertainty for predicting future channel change.
Continuous turbidity monitoring integrated with suspended sediment and limited bedload sampling were used to calculate annual sediment loads at five sites in the basin. The sediment load in the lower river at Ferndale ranged from 0.78 to 1.17 million tons per year and averaged 0.97 million tons per year for WYs 2012–17. Suspended sediment made up 93 percent of the load, and bedload made up 7 percent. Most of the fine sediment load of the lower river is supplied from headwaters of the North, Middle, and South Fork Nooksack basins, with relatively little net increase in fine sediment loads in the lower mainstem basin. The three forks supply approximately equal proportions of the lower-river fine sediment load. However, the glacially sourced North and Middle Fork Nooksack basins carry a notably sandier suspended-sediment load than the South Fork Nooksack.
A comparison of monthly streamflow and precipitation trends since 1981 indicate statistically significant increases in total spring precipitation and the number of spring days with measurable precipitation in much of the basin, as well as increases in mean spring river stage near Ferndale. Since no trends in mean spring discharge are observed, the trends in river stage are attributed primarily to observed changes in bed elevation. Changes in bed elevation and precipitation may then both have plausibly impacted field drainage in the lower river below Ferndale.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195008","collaboration":"Prepared in cooperation with the Whatcom County Flood Control Zone District","usgsCitation":"Anderson, S.W., Konrad, C.P., Grossman, E.E., and Curran, C.A., 2019, Sediment storage and transport in the Nooksack River basin, northwestern Washington, 2006–15: U.S. Geological Survey Scientific Investigations Report 2019-5008, 43 p., https://doi.org/10.3133/sir20195008.","productDescription":"vii, 43 p.","onlineOnly":"Y","ipdsId":"IP-097289","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":362810,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5008/sir20195008.pdf","text":"Report","size":"42.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5008"},{"id":362809,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5008/coverthb2.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nooksack River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.64312744140624,\n 48.499317631540286\n ],\n [\n -121.453857421875,\n 48.499317631540286\n ],\n [\n -121.453857421875,\n 48.9991410647952\n ],\n [\n -122.64312744140624,\n 48.9991410647952\n ],\n [\n -122.64312744140624,\n 48.499317631540286\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Mauna Loa, the largest volcano on Earth, has erupted 33 times since written descriptions became available in 1832. Some eruptions began with only brief seismic unrest, while others followed several months to a year of increased seismicity. Once underway, its eruptions can produce lava flows that may reach the sea in less than 24 hours, severing roads and utilities. For example, lava flows erupted from the Southwest Rift Zone (SWRZ) in 1950 advanced at an average rate of 9.3 km per hour, and all three lobes reached the ocean within approximately 24 hours (Finch and Macdonald, 1953). Near the eruptive vents, the flows must have traveled even faster. In terms of eruption frequency, pre-eruption warning, and rapid flow emplacement, Mauna Loa poses an enormous volcanic-hazard threat to the Island of Hawai‘i. Volcanic hazards on Mauna Loa may be anticipated, and risk substantially mitigated, by documenting the past activity to refine our knowledge of the hazards and by alerting the public and local government officials of our findings and their implications for hazards assessments and risk.
From the geologic record, we may deduce several generalized facts about the geologic history of the Northeast Rift Zone (NERZ). The middle to uppermost segments of the rift zone were more active in the past 4,000 years than the lower portion of the rift zone. This may be due to buttressing of the lower east rift zone by Mauna Kea and Kīlauea volcanoes. The historical flows that erupted on the north side of the rift zone advanced toward Hilo. This flank of the volcano may be more vulnerable to inundation. Lockwood (1990) noted that the vents of historical activity are migrating to the south. The volcano appears to have a self-regulating mechanism that evenly distributes long-term activity across its flanks. The geologic record also supports this notion; the time prior to the historical period (Age Group 1, pre-A.D. 1832 to 1,000 yrs B.P.; orange units) is dominated by activity on the south side of the NERZ.
Although most Mauna Loa eruptions begin in the summit area at the 12,000-ft elevation (Lockwood and Lipman, 1987), the central-southeast flank has not been the source of any activity. All flows originated from the summit or the upper reaches of the Northeast Rift Zone (NERZ) or the Southwest Rift Zone (SWRZ). The NERZ was the source of eight flank eruptions since 1843. The NERZ extends from the 13,680-ft-high summit towards Hilo (population ~60,000; second-largest city in State of Hawaii). The northern portion of the map area is built entirely on flows erupted from the NERZ. The SWRZ extends from the summit towards Kalae (South Point) at sea level. The southern portion of the map area is built entirely on flows erupted from the SWRZ.
The map area extends from the 10,350-ft elevation on Mauna Loa’s east flank toward the Hawaii Volcanoes National Park and the town of Volcano (population approx. 2,000) in the northeast. At the south boundary of the map area is the town of Pāhala (population approx. 900). This map includes areas adjacent to and downslope of the NERZ and regions east of and directly downslope of Moku‘āweoweo, Mauna Loa’s summit caldera.
The map encompasses 506 km2 of the southeast flank (fig. 1) of Mauna Loa from 10,350-ft elevation to sea level. The map of the central-southeast flank of Mauna Loa shows the distribution and relations of volcanic and surficial sedimentary deposits separated into 15 age groups ranging from a period greater than 50,000 yr B.P. to A.D. 1984. It incorporates previously reported work published in generalized small-scale maps (Lockwood and Lipman, 1987; Lockwood, 1995; Wolfe and Morris, 1996).
This map is the second in a series of five maps that will cover Mauna Loa volcano. See SIM 2932-A at https://doi.org/10.3133/sim2932A.
NOTE: Map sheet 1 contains lines and type with overprint. This feature may be turned on or off in the Adobe Acrobat page display preferences.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim2932B","usgsCitation":"Trusdell, F.A., and Lockwood, J.P., 2019, Geologic map of the central-southeast flank of Mauna Loa volcano, Island of Hawai‘i, Hawaii: U.S. Geological Survey Scientific Investigations Map 2932–B, scale 1:50,000, 2 sheets, pamphlet 23 p., https://doi.org/10.3133/sim2932B.","productDescription":"Pamphlet: iii, 23 p.; 2 Sheets: 33.94 x 39.27 inches and 39.59 x 29.91 inches; Chemical data table; Metadata; Read Me; Geospatial data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-011879","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":429220,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2932E","text":"Scientific Investigations Map 2932-E","linkHelpText":"- Geologic Map of the Northwest Flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii"},{"id":374330,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2932C","text":"Scientific Investigations Map 2932-C","linkHelpText":"- Geologic Map of the Southern Flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii"},{"id":362610,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2932A","text":"Scientific Investigations Map 2932-A","linkHelpText":"- Geologic Map of the Northeast Flank of Mauna Loa Volcano, Island of Hawai'i, Hawaii"},{"id":362608,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/2932/b/sim2932b_chemical_data_table.xlsx","text":"Chemical data table","size":"25 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":362607,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/2932/b/sim2932b_sheet2.pdf","text":"Sheet 2","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":362606,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/2932/b/sim2932b_sheet1.pdf","text":"Sheet 1","size":"7.1 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":362605,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/2932/b/sim2932b_pamphlet.pdf","text":"Pamphlet","size":"700 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":362604,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/2932/b/sim2932b_METADATA.zip","size":"400 KB","linkFileType":{"id":6,"text":"zip"}},{"id":362603,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/2932/b/sim2932b_DATABASE.zip","text":"Geospatial data","size":"6.5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":362590,"rank":2,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/2932/b/sim2932b_readme.docx","size":"2 KB docx"},{"id":362543,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/2932/b/coverthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Loa Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.5,\n 19.125\n ],\n [\n -155.125,\n 19.125\n ],\n [\n -155.125,\n 19.5\n ],\n [\n -155.5,\n 19.5\n ],\n [\n -155.5,\n 19.125\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact HVO
Volcano Science Center, Hawaiian Volcano Observatory
U.S. Geological Survey
The U.S. Geological Survey, in cooperation with the Kansas Department of Health and Environment (KDHE), completed a study to quantify the spatial and temporal variability of cyanobacterial blooms in Milford Lake, Kansas, over a range of environmental conditions at various time scales (hours to months). A better understanding of the spatial and temporal variability of cyanobacteria and microcystin will inform sampling and management strategies for Milford Lake and for other lakes with cyanobacterial harmful algal bloom (CyanoHAB) issues throughout the Nation. Spatial and temporal variability were assessed in the upstream one-third of Milford Lake (designated as “Zone C” by KDHE) during May through November 2016 using a combination of time-lapse photography, continuous water-quality monitors, discrete phytoplankton, chlorophyll, and microcystin samples, and spatially dense near-surface data. Combined, these data were used to characterize variability of cyanobacterial abundance, algal biomass, and microcystin concentrations in Zone C of Milford Lake before, during, and after cyanobacterial blooms in 2016.
Temporal patterns were evaluated during May through November 2016 using time-lapse photography at six locations in Zone C and at a single point location (the Wakefield site) using a combination of discrete and continuously measured water-quality data (including the cyanobacterial pigment phycocyanin). Based on time-lapse photography, CyanoHABs developed in Zone C of Milford Lake in early July and persisted through the end of November. Bloom accumulations at individual sites were dependent on wind direction. After a change in wind direction, it would take about 1 day for accumulations to become visible at different locations. During periods with low wind, accumulations were widespread and visible at all sites. Cyanobacteria were absent from the algal community at the Wakefield site in late May and were a minor component of the community in June; however, by mid-July the cyanobacteria were dominant and remained dominant until early November.
Chlorophyll and microcystin concentrations at the Wakefield site were estimated using sensor-measured phycocyanin based on regression models developed for Zone C. Regression-estimated concentrations likely are more indicative of seasonal patterns in algal biomass (as indicated by chlorophyll concentrations) and microcystin than discretely collected samples because regression-estimated data have a much higher temporal resolution. Based on regression estimates, algal biomass and microcystin concentrations at the Wakefield site steadily increased from May through August. After August, concentrations decreased but remained relatively high compared to May and June. Daily chlorophyll maxima were as much as 400 times higher than daily minima, and daily microcystin maxima were as many as several orders of magnitude higher than daily minima. The extreme variability in algal biomass and microcystin concentrations at the Wakefield site reflects the development and dissipation of blooms, as indicated by the time-lapse cameras.
Based on regression-estimated microcystin concentrations, the KDHE watch and warning thresholds for microcystin were exceeded during mid-June through late November. Exceedance of KDHE advisory thresholds often changed from no advisory to watch or warning over the course of the day because of the variability in algal biomass and microcystin concentrations caused by bloom development and dissipation. Continuous water-quality monitors may be useful in informing public-health decisions in lakes with variable CyanoHAB conditions; however, site-specific models need to be developed, and best practices for using continuous water-quality monitors to inform CyanoHAB management strategies need to be established.
Spatial data were collected on May 26, July 21, and September 15, 2016, using a combination of a boat-mounted array and discrete water-quality samples analyzed for phytoplankton community composition and chlorophyll and microcystin concentrations. Spatial patterns were described using regression-estimated chlorophyll and microcystin concentrations. During the May 26, 2016, spatial surveys, cyanobacterial abundances were relatively low throughout Zone C and did not exceed KDHE guidance values compared to spatial surveys on July 21 and September 15. Regression-estimated chlorophyll concentrations were indicative of higher algal biomass uplake in Zone C, and decreases in the downlake direction towards Zone B. Regression-estimated chlorophyll concentrations also were more variable uplake than downlake. Based on regression estimates, microcystin concentrations did not exceed KDHE guidance values anywhere in Zone C on May 26. Spatial patterns in microcystin throughout Zone C did not match patterns in regression-estimated chlorophyll concentrations, likely because the algal community was not dominated by cyanobacteria at most locations in May.
During the July 21, 2016, spatial surveys, cyanobacterial abundances in Zone C exceeded KDHE guidance values in 50 percent of samples. The algal community in Zone C was dominated by cyanobacteria at all locations except two, where cyanobacteria codominated with diatoms. Both locations where cyanobacteria and diatoms codominated were north of the causeway. Regression-estimated chlorophyll concentrations were indicative of higher algal biomass north of the causeway and on the eastern shore of Zone C. On July 21, algal biomass did not always decrease in the downlake direction. There was a decrease just south of the causeway but an increase shortly after with higher concentrations into Zone B. Spatial maps indicated changes in algal distribution at a 0.5-meter depth, with algae moving to the central part of the lake north of the causeway and along the eastern shore south of the causeway. Most regression-estimated microcystin concentrations on July 21 exceeded KDHE guidance values, reflecting the pervasive bloom conditions in Zone C during this period. Spatial patterns in regression-estimated microcystin concentrations throughout Zone C were similar to patterns seen in discrete samples and regression-estimated chlorophyll concentrations, with higher concentrations north of the causeway and on the east shore of Zone C.
During the September 15, 2016, spatial surveys, cyanobacterial abundances did not exceed KDHE guidance values. The algal community north of the causeway was dominated by diatoms. The algal community throughout the rest of Zone C was dominated by cyanobacteria. Of regression-estimated microcystin concentrations on September 15, 80 percent did not exceed KDHE guidance values. Spatial patterns indicated northward movement of the cyanobacterial bloom consistent with a wind shift noted the previous day. On September 14, winds were generally from the north to northwest, shifting to the south by September 15. There was a northward progression of chlorophyll and microcystin during the spatial surveys. These data, along with the camera data and spatial and wind data from May and July, indicate that wind can be a major driver of the spatial and temporal variability of cyanobacterial blooms in Milford Lake and likely plays a role in the extent and duration of near-shore accumulations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185166","collaboration":"Prepared in cooperation with the Kansas Department of Health and Environment","usgsCitation":"Foster, G.M., Graham, J.L., and King, L.R., 2019, Spatial and temporal variability of harmful algal blooms in Milford Lake, Kansas, May through November 2016: U.S. Geological Survey Scientific Investigations Report 2018–5166, 36 p., https://doi.org/10.3133/sir20185166.","productDescription":"Report: vi, 36 p.; Appendixes: 28 p.; Data Releases: 4","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-093516","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":361764,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78S4P4M","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality data from two sites on Milford Lake, Kansas, May 25–26, June 8–10, July 20–21, and September 14–15, 2016"},{"id":361765,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JH3KCV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Time-lapse photography of Milford Lake, Kansas, June through November 2016"},{"id":361760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5166/coverthb.jpg"},{"id":361763,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DJ5DVX","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Milford Lake, Kansas spatial water-quality data, May 26, June 9, July 14, July 21, and September 15, 2016"},{"id":361761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5166/sir20185166.PDF","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5166"},{"id":361762,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5166/sir20185166_appendixes.pdf","text":"Appendix 1 and 2","size":"571 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5166 Appendixes 1 and 2"},{"id":361766,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7513XFN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Phytoplankton data for Milford Lake, Kansas, May through November 2016"}],"country":"United States","state":"Kansas","otherGeospatial":"Milford Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.1630859375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 38.982897808179985\n ]\n ]\n ]\n }\n }\n ]\n}\n\n\n\n","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The arrival of space-based imaging radar as a revolutionary land-surface mapping and monitoring tool little more than a quarter century ago enabled a spate of innovative volcano research worldwide. Soon after launch of European Space Agency’s ERS-1 spacecraft in 1991, the U.S. Geological Survey began SAR and InSAR studies of volcanoes in the Aleutian and Cascades arcs, in Hawai’i, and elsewhere in the western U.S. including the Yellowstone and Long Valley calderas. This paper summarizes results of that effort and presents new findings concerning: (1) prevalence of volcano deformation in the Aleutian and Cascade arcs; (2) surface-change detection and hazard assessment during eruptions at Aleutian and Hawaiian volcanoes; (3) geodetic imaging of magma storage and transport systems in Hawai’i; and (4) deformation sources and processes at the Yellowstone and Long Valley calderas. Surface deformation caused by a variety of processes is common in arc settings and could easily escape detection without systematic InSAR surveillance. Space-based SAR imaging of active lava flows and domes in remote or heavily vegetated settings, including during periods of bad weather and darkness, extends land-based monitoring capabilities and improves hazards assessments. At Kīlauea Volcano, comprehensive SAR and InSAR observations identify multiple magma storage zones beneath the summit area and along the East Rift Zone, and illuminate magma transport pathways. The same approach at Yellowstone tracks the ascent of magmatic volatiles from a mid-crustal intrusion to shallow depth and relates that process to increased hydrothermal activity at the surface. Together with recent and planned launches of highly capable imaging-radar satellites, these findings support an optimistic outlook for near-real time surveillance of volcanoes at global scale in the coming decade.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2018.00249","usgsCitation":"Dzurisin, D., Lu, Z., Poland, M.P., and Wicks, C.W., 2019, Space-based imaging radar studies of U.S. volcanoes: Frontiers in Earth Science, v. 6, p. 1-15, https://doi.org/10.3389/feart.2018.00249.","productDescription":"Article 249; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-099931","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467929,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2018.00249","text":"Publisher Index Page"},{"id":361027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Dzurisin, Daniel 0000-0002-0138-5067 dzurisin@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-5067","contributorId":538,"corporation":false,"usgs":true,"family":"Dzurisin","given":"Daniel","email":"dzurisin@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":756676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Zhong","contributorId":202550,"corporation":false,"usgs":false,"family":"Lu","given":"Zhong","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":756679,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":756678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wicks, Charles W. Jr. 0000-0002-0809-1328 cwicks@usgs.gov","orcid":"https://orcid.org/0000-0002-0809-1328","contributorId":127701,"corporation":false,"usgs":true,"family":"Wicks","given":"Charles","suffix":"Jr.","email":"cwicks@usgs.gov","middleInitial":"W.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":756677,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201820,"text":"70201820 - 2019 - The formation of gullies on Mars today","interactions":[],"lastModifiedDate":"2019-01-31T12:45:02","indexId":"70201820","displayToPublicDate":"2019-01-31T12:44:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1791,"text":"Geological Society, London, Special Publications","active":true,"publicationSubtype":{"id":10}},"title":"The formation of gullies on Mars today","docAbstract":"A decade of high-resolution monitoring has revealed extensive activity in fresh Martian gullies. Flows within the gullies are diverse: they can be relatively light, neutral or dark, colourful or bland, and range from superficial deposits to 10 m-scale topographic changes. We observed erosion and transport of material within gullies, new terraces, freshly eroded channel segments, migrating sinuous curves, channel abandonment, and lobate deposits. We also observed early stages of gully initiation, demonstrating that these processes are not merely modifying pre-existing landforms. The timing of activity closely correlates with the presence of seasonal CO2 frost, so the current changes must be part of ongoing gully formation that is driven largely by its presence. We suggest that the cumulative effect of many flows erodes alcoves and channels, and builds lobate aprons, with no involvement of liquid water. Instead, flows may be fluidized by sublimation of entrained CO2 ice or other mechanisms. The frequent activity is likely to have erased any features dating from high-obliquity periods, so fresh gully geomorphology at middle and high latitudes is not evidence for past liquid water. CO2 ice-driven processes may have been important throughout Martian geological history and their deposits could exist in the rock record, perhaps resembling debris-flow sediments.
","language":"English","publisher":"Geological Society of London","doi":"10.1144/SP467.5","usgsCitation":"Dundas, C.M., McEwen, A.S., Diniega, S., Hansen, C.J., and McElwaince, J.N., 2019, The formation of gullies on Mars today: Geological Society, London, Special Publications, v. 467, p. 67-94, https://doi.org/10.1144/SP467.5.","productDescription":"28 p.","startPage":"67","endPage":"94","ipdsId":"IP-082415","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":467952,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/journal_contribution/The_formation_of_gullies_on_Mars_today/5624641","text":"External Repository"},{"id":360869,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"467","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Dundas, Colin M. 0000-0003-2343-7224 cdundas@usgs.gov","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":2937,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin","email":"cdundas@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":755475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McEwen, Alfred S.","contributorId":61657,"corporation":false,"usgs":false,"family":"McEwen","given":"Alfred","email":"","middleInitial":"S.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":755476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diniega, Serina","contributorId":212017,"corporation":false,"usgs":false,"family":"Diniega","given":"Serina","email":"","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":755477,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Candice J.","contributorId":70235,"corporation":false,"usgs":false,"family":"Hansen","given":"Candice","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":755478,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McElwaince, Jim N.","contributorId":212018,"corporation":false,"usgs":false,"family":"McElwaince","given":"Jim","email":"","middleInitial":"N.","affiliations":[{"id":38386,"text":"Durham University/PSI","active":true,"usgs":false}],"preferred":true,"id":755479,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204196,"text":"70204196 - 2018 - Drain tiles and groundwater resources: Understanding the relations","interactions":[],"lastModifiedDate":"2019-08-02T10:52:42","indexId":"70204196","displayToPublicDate":"2019-06-01T11:14:22","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Drain tiles and groundwater resources: Understanding the relations","docAbstract":"Executive Summary
Drainage for agricultural production over the past 150 years has been an integral component of human-driven change to Minnesota’s rural landscapes.
Benefits of drainage
Historically, poorly drained soils across much of the State would often remain saturated or flooded after spring snowmelt, preventing timely farm operations such as tilling and planting crops (Arneman, 1963). Installation of agricultural drainage, both surface ditches and subsurface drainage, accelerated transport of water off farm fields and imparted producers higher crop yields (Beauchamp, 1987; Stoner and others, 1993). Agricultural drainage offered many other benefits such as preventing crop drown out, aerating the soil profile for improved plant growth, limiting surface runoff and soil erosion, and allowing farmers better access to croplands (Fausey and others, 1987). Without agricultural drainage on much of Minnesota’s croplands, it would have been difficult to realize high enough crop yields to remain economically viable.
Environmental concerns
While drainage of Minnesota croplands provided the benefits mentioned above, several environmental concerns result. These include wetland loss, degradation of downstream water quality, and reduced [potential for] recharge.
Early agricultural drainage efforts (pre-20th century) led to the disappearance of much of Minnesota’s natural wetlands. Increased focus on preventing or mitigating wetland loss over the last 50 years has helped curtail further losses, even as agricultural drainage proceeds. Prior to establishment of Minnesota statehood, wetlands accounted for more than 10 million acres in Minnesota, including prairie wetlands, peatlands, and forest wetlands that comprised approximately 19 percent of the total land area (Palmer, 1915; King, 1980). In 2018, only half of Minnesota’s pre-settlement wetlands remain, mostly in parts of the State that have not experienced widespread drainage, such as northern Minnesota.
Water-quality monitoring has shown that agricultural drainage, in particular the practice of subsurface drainage, provides a direct flow path for nutrient (nitrogen and soluble phosphorus) losses to surface water resources. The negative consequences of agricultural drainage on surface water quality are well documented (for example, Dinnes and others, 2002; Kladivko and others, 2004; Richards and others, 2008; Rozemeijer and others, 2010; Schottler and others, 2013). Agricultural basins with a high percentage of agricultural drainage have been implicated as part of the cause of the Gulf of Mexico hypoxia zone due to excessive nitrogen export (Goolsby and Battaglin, 2001; Randall and Mulla, 2001).
The connection of hydrological effects of agricultural subsurface drainage on groundwater recharge and aquifers, on the other hand, has not been well-established. Agricultural subsurface drainage intercepts infiltrating water below croplands and directly discharges the water to nearby surface waters. However, the size of the water balance shift from drained water that would have evapotranspired or run off the land to drained water that would recharge underlying aquifers has been poorly characterized (Schuh, 2008).
Drain Tiles and Groundwater
Given the poor accounting of subsurface drainage effects on groundwater resources, the Minnesota Ground Water Association (MGWA) deemed it imperative that we document these effects so that groundwater resources in agricultural regions with substantial drainage can be effectively managed. This white paper documents the relations of drain tiles and groundwater resources and discusses the historical significance of agricultural drainage practices, the recognized positive benefits and potential negative consequences of agricultural drainage practices, and the gaps in understanding of the connections between agricultural drainage and groundwater resources.
The major messages emerged from the findings of this white paper are:
Sandbars of large sand-bedded rivers of the central United States serve important ecological functions to many species, including the endangered Interior Least Tern (Sternula antillarum, ILT). The ILT is a colonial bird that feeds on fish and nests primarily on riverine sandbars during its annual breeding season of around May through July, depending on region. During this time, ILTs require bare sand of sufficient elevation so as not to be inundated between nest initiation and fledging of hatchlings. Partly because of decreases in available sandbar habitat from river channelization and impoundment, ILTs were listed as endangered in 1985.
Sandbars used by ILTs in central United States rivers are highly dynamic and undergo substantive changes across a wide range of temporal and spatial scales. River hydrology is the primary driver of sandbar morphodynamics in these systems. Better characterization of sandbar area with time, accounting for varying flow regimes, allows for a better understanding of landscape-scale ecology for sandbar-dependent species such as the ILT. This work uses remote-sensing techniques to quantify sandbar area that may be used by ILTs at the land-scape scale and how it has changed with time. The assessment of landscape-scale trends in sandbar area with time requires datasets with high temporal resolution and long record periods covering large geographic areas. Evaluation of remotely sensed datasets requires consideration of river stage fluctuations. To make this assessment, we developed land-cover classification datasets within active channel masks using all available images from the Landsat Thematic Mapper series of satellites meeting cloud-free (40 percent or less) and ice-free criteria. Landsat imagery was selected because of its long record period, spatial coverage, and regular reimaging cycle, making it well suited to monitor ILT sandbar habitat with time. We also attributed each scene with discharge or stage using a new database integrating U.S. Geological Survey and U.S. Army Corps of Engineers river data with Landsat metadata. This report documents development of these riverine classification datasets with a focus on applicability to the ILT. This framework may be used to continue monitoring the ILT sandbar nesting habitat or to evaluate other aquatic and terrestrial species whose life cycles are related to sandbars and channel complexity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1098","collaboration":"Prepared in cooperation with the American Bird Conservancy","usgsCitation":"Bulliner, E.A., Elliott, C.M., Jacobson, R.B., and Lott, C., 2018, Interior Least Tern sandbar nesting habitat measurements from Landsat Thematic Mapper imagery: U.S. Geological Survey Data Series 1098, 32 p., https://doi. org/10.3133/ds1098. ","productDescription":"Report: v, 32 p.; Tables 9–12; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066937","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":360602,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/1098/ds1098_tables9-12.xlsx","text":"Tables 9–12","size":"28.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Tables 9–12"},{"id":360653,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CV4GNG","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Interior least tern sandbar nesting habitat measurements from Landsat TM imagery"},{"id":360600,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1098/coverthb.jpg"},{"id":360601,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1098/ds1098.pdf","text":"Report","size":"1.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1098"}],"contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
Freshwater streams in Connecticut are subject to many competing demands, including public water supply; agricultural, commercial, and industrial water use; and ecosystem and habitat needs. In recent years, drought has further stressed Connecticut’s water resources. To sustainably allocate and manage water resources among these competing uses, Federal, State, and local water-resource managers require data and modeling tools to estimate the water availability at a variety of temporal and spatial scales for planning purposes. The Connecticut Streamflow and Sustainable Water Use Estimator (CT SSWUE), developed by the U.S. Geological Survey in cooperation with the Connecticut Department of Energy and Environmental Protection, is a decision-support tool for estimating daily unaltered streamflow and sustainable water use at ungaged sites in Connecticut.
The CT SSWUE estimates unaltered daily mean streamflow and water-use-adjusted streamflow for the period from October 1, 1960, to September 30, 2015, and the monthly sustainable net withdrawal at ungaged sites in Connecticut. Unaltered streamflow is the estimated daily mean streamflow in a drainage basin in the absence of any water withdrawals or wastewater discharges and with minimal human development. Sustainable net withdrawal is the maximum net withdrawal (withdrawal minus wastewater discharges) that can be drawn from a basin without critically depleting the water available through natural streamflow patterns. Sustainable net withdrawal is defined for this study as the difference between the unaltered daily mean streamflow and a user-defined target minimum streamflow.
Weighted least squares and Tobit regression techniques were used to develop equations for estimating streamflow at ungaged sites at 19 streamflow quantiles with exceedance probabilities ranging from 0.005 to 99.995 percent. Regressions were based on streamflow quantiles and basin characteristics from 36 reference streamgages in and around Connecticut. Four basin characteristics—drainage area, mean of the soil permeability, mean of the average annual precipitation, and ratio of the length of streams that overlay sand and gravel deposits to the total length of streams in the basin—are used as explanatory variables in the equations. At an ungaged site, interpolation between the streamflow quantiles estimated from the regression equations produces a continuous flow-duration curve. A time series of daily mean streamflow at an ungaged site is then estimated by assuming that for each day, the streamflow quantile occurs on the same date at both a reference streamgage and the ungaged site.
In a remove-one cross validation, estimated unaltered daily mean streamflow agreed well with observed values at reference streamgages, with a few exceptions. Nash Sutcliffe efficiency ranged from −0.43 to 0.97 with a median value of 0.88. The normalized root-mean-square error ranged from 16.6 to 120.4 percent with a median value of 34.5 percent.
An empirical method for estimating 95-percent prediction intervals for unaltered daily and monthly mean streamflow was developed and tested by using the cross-validation data. Prediction intervals for unaltered daily mean streamflow at the cross-validation reference streamgages performed well in most cases. Gaged streamflow values from the cross-validation data fell within the prediction intervals a median 96.6 percent of the time for daily mean time series and 93.9 percent of the time for monthly mean time series.
The CT SSWUE computes water-use-adjusted streamflow using spatially referenced water-use information provided by the Connecticut Department of Energy and Environmental Protection. Available water-use information included permitted and registered water withdrawals and permitted wastewater discharges during 1998 to 2015 for the Thames River Basin and central coastal drainage basins. Water-use information was incorporated into the U.S. Geological Survey StreamStats web application for Connecticut and can be used for computing water-use-adjusted streamflow and sustainable net withdrawal at selected points of interest. Altered daily streamflow is computed by applying average daily withdrawals and wastewater discharges to the water balance equation. Average daily surface water withdrawals and wastewater discharges are applied directly to the daily water balance equation. Time-lagged alterations on streamflow from groundwater withdrawals or wastewater discharges are estimated by using a response-coefficient method developed from results of previously published, calibrated groundwater models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185135","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Levin, S.B., Olson, S.A., Nielsen, M.G., and Granato, G.E., 2018, The Connecticut Streamflow and Sustainable Water Use Estimator—A decision-support tool to estimate water availability at ungaged stream locations in Connecticut: U.S. Geological Survey Scientific Investigations Report 2018–5135, 34 p., https://doi.org/10.3133/sir20185135.","productDescription":"Report: vii, 34 p.; Table; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087738","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":360020,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5135/sir20185135.pdf","text":"Report","size":"8.92 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release","linkHelpText":"Connecticut Streamflow and Sustainable Water Use Estimator (CT SSWUE) Application Software "},{"id":360022,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2018/5135/sir20185135_table1.csv","text":"Tabel 1 - CSV","size":"10.7 KB","linkFileType":{"id":7,"text":"csv"}}],"country":"United 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The Piceance and Yellow Creek watersheds in Rio Blanco County, Colorado, are known to contain important energy resources (oil shale and natural gas) and mineral resources (nahcolite). The primary sources of fresh groundwater in the Piceance and Yellow Creek watersheds are bedrock aquifers in the Uinta and Green River Formations. The aquifers are divided into an upper and lower aquifer separated by a regionally extensive semiconfining layer. These aquifers provide water to streams and springs in the watersheds and are an important resource to people living and working in the area. Development of energy and mineral resources has the potential to affect the quality of groundwater in several ways. The Bureau of Land Management and the U.S. Geological Survey began groundwater monitoring in 2010 to characterize the groundwater quality and water-level elevations of shallow bedrock aquifers in the Piceance and Yellow Creek watersheds. The purpose of this report is to present ground-water chemistry and water-level elevations collected during 2013–16. Comparisons are made to data that were collected from the bedrock aquifers from 2010 to 2012 to identify the potential for changes in water quality and water-level elevations.
Appreciable changes in water-level elevations and hydraulic gradient were observed in early April 2015 in two wells completed in the upper and lower aquifers. The hydraulic gradient between the two wells was consistently downward from the upper aquifer to the lower aquifer during 2010–15; however, in early April 2015, the gradient changed from downward to upward between the two aquifers. Overall, water-level elevations declined by about 14 and 11 feet in the upper and lower aquifers, respectively, from 2013 to 2016. Previously published data estimated groundwater ages at 1,200 years old in the upper aquifer and 9,600 years old in the lower aquifer. These groundwater ages indicate that ground-water was recharged over thousands of years. With such long periods of time for aquifer recharge, declines in water-level elevation over short time steps (a few months) have important implications for sustainable management of this resource. Solution mining activities or drilling for oil and natural gas in the area could be related to the changes observed in water-level elevations in these wells; however, further investigation would be needed to evaluate causation.
Changes in major-ion chemistry were evaluated in the bedrock aquifer using time series plots of select major-ion data from 2010 to 2016. Major-ion chemistry was variable for a single well from 2010 to 2016 where alkalinity and sulfate were the most variable constituents. One possible explanation for the observed changes in major-ion chemistry may be that the sample depth for that well no longer represents the most appreciable flow in the borehole. On a larger scale, potential changes in flow within the borehole may indicate changes in the regional flow system. Methane and volatile organic compound concentrations were evaluated using a similar approach to that of major ions and had similar findings. Methane concentrations in wells sampled from 2010 to 2016 were generally constant. The only exception was observed at a single well where the range of methane concentrations was from 57.4 (2010) to 4.02 milligrams per liter (2013). This is the same well where changes in water-level elevation, hydraulic gradient, and major-ion chemistry were observed, providing multiple lines of evidence to indicate change in the bedrock aquifers. Sampling of a well located in an area with little energy development but where faults or fractures could provide a path for the migration of fluids indicate mixing of groundwater between the upper and lower aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185142","collaboration":"Prepared in cooperation with the Bureau of Land Management, White River Field Office","usgsCitation":"Thomas, J.C., and McMahon, P.B., 2018, Groundwater chemistry and water-level elevations in bedrock aquifers of the Piceance and Yellow Creek watersheds, Rio Blanco County, Colorado, 2013–16: U.S. Geological Survey Scientific Investigations Report 2018–5142, 26 p., https://doi.org/10.3133/sir20185142.","productDescription":"v, 26 p.","onlineOnly":"Y","ipdsId":"IP-093390","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":359632,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5142/coverthb.jpg"},{"id":359633,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5142/sir20185142.pdf","text":"Report","size":"13.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5142"}],"country":"United States","state":"Colorado","county":"Rio Blanco County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.75,\n 39.5\n ],\n [\n -107.75,\n 39.5\n ],\n [\n -107.75,\n 40.25\n ],\n [\n -108.75,\n 40.25\n ],\n [\n -108.75,\n 39.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
The rocks and landforms of the Fort Collins 30′ × 60′ 1:100,000-scale U.S. Geological Survey quadrangle reveals a particularly complete record of geologic history in the northern Front Range of Colorado. The Proterozoic basement rocks exposed in the core of the range preserve evidence of Paleoproterozoic marine sedimentation, volcanism, and regional soft-sediment deformation, followed by regional folding and gradational metamorphism. Mesoproterozoic time was marked by intrusion of the Berthoud Plutonic Suite into crust that was structurally neutral or moderately extending in an east-northeast direction.
Evidence of the late Paleozoic Anasazi uplift (Ancestral Rocky Mountains uplift) within the quadrangle is recorded by removal of Permian and older sediments and deposition of proximal Pennsylvanian and Permian strata unconformably onto the exhumed Proterozoic basement rocks. The Phanerozoic sediments indicate a steady progression of fluvial, eolian, and lacustrine environments throughout most of the Mesozoic Era which was a time of relatively slow sediment accumulation. Early Cretaceous time was marked by incursion of the Cretaceous Western Interior Seaway, a shallow-water marine embayment that persisted throughout the latter part of the Mesozoic Era. Sedimentation rates increased significantly in the latter part of this period during down-warping related to distant crustal loading by thrusting along the western continental margin.
With onset of the Laramide orogeny in latest Cretaceous time, mountain building resumed in this region. This deformation placed Proterozoic rock over Cretaceous and Paleocene strata along the western margin of the Front Range and Medicine Bow Mountains. Post-Laramide time was marked by a prolonged period of weathering, erosion, and planation of the basement-rock surface, extending perhaps into late Oligocene or early Miocene time.
Erosion on the eastern slope of the Front Range in late Paleogene to early Neogene time produced a broad, rolling surface surrounding residual highlands and east-trending fluvial channels filled with coarse, boulder gravel.
Significant global cooling during the Pliocene led to glaciation during the Quaternary. In the Rocky Mountain region, renewed uplift allowed erosion to accentuate the topographic relief across the high mountains of the map area and established the elevations necessary to trigger accumulation of persistent snow and ice. Mountain glaciers advanced and retreated during at least three glacial-interglacial cycles during the middle and late Pleistocene in this area.
Erosion continues to this day on the High Plains east of the mountain front, and progressive incision of the drainage is recorded by at least five major gravel-clad terrace and pediment surfaces along the major fluvial channels that connect to the South Platte River system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3399","usgsCitation":"Workman, J.B., Cole, J.C., Shroba, R.R., Kellogg, K.S., and Premo, W.R., 2018, Geologic map of the Fort Collins 30'×60' quadrangle, Larimer and Jackson Counties, Colorado, and Albany and Laramie Counties, Wyoming: U.S. Geological Survey Scientific Investigations Map 3399, pamphlet 83 p., scale 1:100,000, https://doi.org/10.3133/sim3399/.","productDescription":"Report: vii, 83 p.; 2 Maps: 59.0 x 38.5 inches; Data Release; Read Me","onlineOnly":"Y","ipdsId":"IP-078484","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":359260,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7G44PHV","text":"USGS data release","linkHelpText":"Data release for geologic map of the Fort Collins 30' x 60' quadrangle, Larimer and Jackson Counties, Colorado and Albany and Laramie Counties, Wyoming"},{"id":359258,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3399/sim3399_sheet_georeferenced.pdf","text":"Georeferenced Map","size":"59.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3399 Georeferenced Map"},{"id":359257,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3399/sim3399_sheet.pdf","text":"Map","size":"57.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3399 Map"},{"id":359259,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3399/sim3399_Readme.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3399 Read Me"},{"id":359255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3399/coverthb2.jpg"},{"id":359256,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3399/sim3399_pamphlet.pdf","text":"Report","size":"19.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3399 Pamphlet"}],"country":"United States","state":"Colorado, Wyoming","county":"Albany County, Jackson County, Laramie County, Larimer County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106,\n 40.5\n ],\n [\n -105,\n 40.5\n ],\n [\n -105,\n 41\n ],\n [\n -106,\n 41\n ],\n [\n -106,\n 40.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
The global‐scale degradation of coral reefs has reached a critical threshold wherein further declines threaten both ecological functionality and the persistence of reef structure. Geological records can provide valuable insights into the long‐term controls on reef development that may be key to solving the modern coral‐reef crisis. Our analyses of new and existing coral‐reef cores from throughout the Florida Keys reef tract (FKRT) revealed significant spatial and temporal variability in reef development during the Holocene. Whereas maximum Holocene reef thickness in the Dry Tortugas was comparable to elsewhere in the western Atlantic, most of Florida's reefs had relatively thin accumulations of Holocene reef framework. During periods of active reef development, average reef accretion rates were similar throughout the FKRT at ~3 m/ky. The spatial variability in reef thickness was instead driven by differences in the duration of reef development. Reef accretion declined significantly from ~6,000 years ago to present, and by ~3,000 years ago, the majority of the FKRT was geologically senescent. Although sea level influenced the development of Florida's reefs, it was not the ultimate driver of reef demise. Instead, we demonstrate that the timing of reef senescence was modulated by subregional hydrographic variability, and hypothesize that climatic cooling was the ultimate cause of reef shutdown. The senescence of the FKRT left the ecosystem balanced at a delicate tipping point at which a veneer of living coral was the only barrier to reef erosion. Modern climate change and other anthropogenic disturbances have now pushed many reefs past that critical threshold and into a novel ecosystem state, in which reef structures built over millennia could soon be lost. The dominant role of climate in the development of the FKRT over timescales of decades to millennia highlights the potential vulnerability of both geological and ecological reef processes to anthropogenic climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14389","usgsCitation":"Toth, L., Kuffner, I.B., Stathakopoulos, A., and Shinn, E.A., 2018, A 3,000‐year lag between the geological and ecological shutdown of Florida's coral reefs: Global Change Biology, v. 24, no. 11, p. 5471-5483, https://doi.org/10.1111/gcb.14389.","productDescription":"13 p.","startPage":"5471","endPage":"5483","ipdsId":"IP-095331","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":359758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"11","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-21","publicationStatus":"PW","scienceBaseUri":"5bffb75de4b0815414ca8e4a","contributors":{"authors":[{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":752435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":752436,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":752437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shinn, Eugene A.","contributorId":210858,"corporation":false,"usgs":false,"family":"Shinn","given":"Eugene","email":"","middleInitial":"A.","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":752438,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198509,"text":"sir20185106 - 2018 - Simulation of groundwater flow, 1895–2010, and effects of additional groundwater withdrawals on future stream base flow in the Elkhorn and Loup River Basins, central Nebraska—Phase three","interactions":[],"lastModifiedDate":"2018-10-02T10:59:41","indexId":"sir20185106","displayToPublicDate":"2018-10-01T11:33:36","publicationYear":"2018","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":"2018-5106","title":"Simulation of groundwater flow, 1895–2010, and effects of additional groundwater withdrawals on future stream base flow in the Elkhorn and Loup River Basins, central Nebraska—Phase three","docAbstract":"The U.S. Geological Survey, in cooperation with the Lewis and Clark, Lower Elkhorn, Lower Loup, Lower Platte North, Lower Niobrara, Middle Niobrara, Upper Elkhorn, and the Upper Loup Natural Resources Districts, designed a study to refine the spatial and temporal discretization of a previously modeled area. This updated study focused on a 30,000-square-mile area of the High Plains aquifer and constructed regional groundwater-flow models to evaluate the effects of groundwater withdrawal on stream base flow in the Elkhorn and Loup River Basins, Nebraska. The model was calibrated to match groundwater-level and base-flow data from the stream-aquifer system from pre-1940 through 2010 (including predevelopment [pre-1895], early development [1895–1940], and historical development [1940 through 2010] conditions) using an automated parameter-estimation method. The calibrated model then was used to simulate hypothetical development conditions (2011 through 2060). Predicted changes to stream base flow based on simulated changes to groundwater withdrawal will aid in developing strategies for management of hydrologically connected water supplies.
Additional wells were simulated throughout the model domain and pumped for 50 years to assess the effect of wells on aquifer depletions, including stream base flow. The percentage of withdrawal for each well after 50 years, which was compensated by aquifer reductions to stream base flow, storage, or evapotranspiration, was computed and mapped. These depletions are influenced by aquifer properties, time, and distance from the well. Stream base-flow depletion results showed that the closer the added well was to a stream, the greatest the effect on the stream base flow. Areas of stream base-flow depletion percentages greater than 80 percent were generally within 1 mile (mi) from the stream. The distance increased to 6 mi near the confluence of the Dismal and Middle Loup Rivers, and the North Loup and Calamus Rivers. The percentage of stream base-flow depletion decreased as the distance from the stream increased. Areas more than 10 mi from the stream generally had a stream base-flow depletion of 10 percent or less. Evapotranspiration depletion was largest in areas closest to streams, specifically in the Elkhorn River watershed. It was also larger in areas of interdunal wetlands within the Sand Hills. Evapotranspiration depletion was negligible in areas greater than 5 mi from a stream, with the exception of interdunal areas in Cherry, Grant, and Arthur Counties. The storage depletion percentage increased as the distance from a stream increased. Storage depletion was largest in areas between streams. Areas experiencing the smallest amount of storage depletion were adjacent to streams. Calibrated model outputs and streamflow depletion analysis are publicly available online.
Accuracy of the simulations is affected by input data limitations, system simplifications, assumptions, and resources available at the time of the simulation construction and calibration. Most of the important limitations relate either to data used as simulation inputs or to data used to estimate simulation inputs. Development of the regional simulations focused on generalized hydrogeologic characteristics within the study area and did not attempt to describe variations important to local-scale conditions. These simulations are most appropriate for analyzing groundwater-management scenarios for large areas and during long periods and are not suitable for analysis of small areas or short periods.
Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The 2014 update of the U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) for the conterminous United States (2014 NSHM; Petersen and others, 2014, 2015) included probabilistic ground motion maps for 2 percent and 10 percent probabilities of exceedance in 50 years, derived from seismic hazard curves for peak ground acceleration (PGA) and 0.2 and 1.0 second spectral accelerations (SAs) with 5 percent damping for the National Earthquake Hazards Reduction Program (NEHRP) site class boundary B/C (time-averaged shear wave velocity in the upper 30 meters [VS30]=760 meters per second [m/s]). We now provide uniform NEHRP site class maps for 2, 5, and 10 percent probabilities of exceedance in 50 years derived from hazard curves for additional spectral periods. For the central and eastern United States (CEUS) and western United States (WUS), hazard curves and maps for PGA, 0.1, 0.2, 0.3, 0.5, 1.0, and 2.0 second SAs are now available. The WUS additionally includes hazard curves and maps for 0.75, 3.0, 4.0, and 5.0 second SAs. The use of region-specific suites of weighted ground motion models (GMMs) in the 2014 NSHM precluded the calculation of ground motions for a uniform set of periods and site classes for the conterminous United States. At the time of the development of the 2014 NSHM, there was no consensus in the CEUS on an appropriate site-amplification model to use; therefore, we calculated hazard curves and maps for NEHRP site class A, for which most stable continental GMMs were originally developed, based on simulations for hard rock site conditions (VS30=2,000 m/s). In the WUS, however, the active crustal Next Generation Attenuation Relationships for the WUS (NGA-West2 GMMs) and subduction GMMs allow amplification of ground motions based on site class (defined by VS30); so we calculated hazard curves and maps for NEHRP site classes B (VS30=1,080 m/s), C (VS30=530 m/s), D (VS30=260 m/s), and E (VS30=150 m/s) and site class boundaries A/B (VS30=1,500 m/s), B/C (VS30=760 m/s), C/D (VS30=365 m/s), and D/E (VS30=185 m/s). The 2014 NSHM introduced a set of criteria for selecting GMMs for use in the NSHMs. When calculating additional period and site class maps, we verified whether the 2014 NSHM original suites of GMMs satisfied these ground motion selection criteria at all additional periods and site classes using GMM magnitude-distance scaling relation plots. Results of our analysis show that certain GMMs give unrealistic results at longer periods, distances, and softer soils in the WUS. In these rare instances, the GMM was removed from the original suite of GMMs (for all periods and site classes) and the weights of the remaining GMMs in the suite were renormalized. Ratio maps show these updated suites of weighted GMMs result in probabilistic ground motion changes of less than 10 percent in the WUS at PGA, as well as 0.2 and 1.0 second SAs, except in the Pacific Northwest, where differences as much as 20 percent are seen. Hazard curves and uniform hazard response spectra at test sites across the conterminous United States were produced to verify that results were reasonable. The additional period and site class maps, and the hazard curves from which they were derived, are available for download from the USGS ScienceBase Catalog.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181111","usgsCitation":"Shumway, A.M., Petersen, M.D., Powers, P.M., and Rezaeian, S., 2018, Additional period and site class maps for the 2014 National Seismic Hazard Model for the conterminous United States: U.S. Geological Survey Open-File Report 2018–1111, 46 p., https://doi.org/10.3133/ofr20181111.","productDescription":"Report: v, 46 p.; Data release","onlineOnly":"Y","ipdsId":"IP-098308","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":357217,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9I6BPX5","text":"USGS data release","linkHelpText":"Data Release for Additional Period and Site Class Maps for the 2014 National Seismic Hazard Model for the Conterminous United 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\"}}]}\n\n\n","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225
Meticulous field observations are a common underpinning of two landmark studies conducted by Don Swanson dealing with the rate at which magma is supplied to Kīlauea Volcano, Hawai‘i. The first combined effusion rate and ground deformation observations to show that the supply rate to Kīlauea was constant at ~0.11 km3/yr during three sustained eruptions from 1952 to 1971, a quiescent period at neighboring Mauna Loa volcano. This rate was also interpreted as the steady supply rate from the mantle to both volcanoes combined throughout historical time. The second breakthrough involved field evidence that activity at Kīlauea alternates between dominantly effusive and explosive styles over time scales of several centuries, and that the magma supply rate during explosive periods is only 1%-2% of the rate during effusive periods. For the historical period, several later studies concluded that the supply rate to Kīlauea has varied by as much as an order of magnitude, contrary to Swanson’s earlier suggestion. All such estimates are fraught with uncertainty, given the poorly known amount of magma stored within the volcano’s rift zones as a function of time—an enduring problem and active research topic. Nonetheless, Swanson’s original work remains an important touchstone that spurred many subsequent investigations and refinements. For example, there is strong evidence that Kīlauea experienced a surge in magma supply during 2003–2007 that exceeded the historical average by as much as a factor of two, and that the surge was followed by a comparable lull before the supply rate returned to “normal” by 2016. There is also evidence for supply-rate variations of similar magnitude during the latter part of the twentieth century and possibly earlier, subject to the aforementioned uncertainty in rift-zone storage. The extent to which variations in the magma supply to Kīlauea can be attributed to partitioning between Kīlauea and Mauna Loa, a long-debated topic, remains uncertain. Since Kilauea’s inception, the net magma supply to the volcano (and also to Lō‘ihi Seamount, since it began growing) has increased, while Mauna Loa’s growth rate has slowed, suggesting that the volcanoes compete for the same magma supply. However, geochemical differences between lavas erupted at Kīlauea and Mauna Loa indicate that they do not share a homogeneous mantle source or common lithospheric magma plumbing system. Both ideas might be correct; i.e., Kīlauea and Mauna Loa magmas may be sourced in differing portions of the same melt accumulation zone and ascend through different crustal pathways, but those pathways interact through stress or pressure changes that modulate the supply to each volcano. Currently, magma supply-rate estimates are facilitated by comprehensive imaging of surface deformation and topographic change coupled with measurements of gas emissions. Physics-based models are being developed within a probabilistic framework to provide rigorous estimates of model parameters, including magma supply rate, and their uncertainties. Further refinement will require intensive multiparameter observations of the entire magmatic system—from source to surface and above, and from the volcanoes’ summits to their submerged lower flanks—in order to account fully for a complex magma budget.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Field volcanology: A tribute to the distinguished career of Don Swanson","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2018.2538(12)","usgsCitation":"Dzurisin, D., and Poland, M.P., 2018, Magma supply to Kīlauea Volcano, Hawai‘i, from inception to now: Historical perspective, current state of knowledge, and future challenges: Geological Society of America Special Papers , v. 538, p. 275-295, https://doi.org/10.1130/2018.2538(12).","productDescription":"21 p.","startPage":"275","endPage":"295","ipdsId":"IP-087117","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":460853,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2018.2538(12)","text":"Publisher Index Page"},{"id":357767,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai‘i","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.35354614257812,\n 19.330582575049508\n ],\n [\n -155.15853881835938,\n 19.330582575049508\n ],\n [\n -155.15853881835938,\n 19.47500813674322\n ],\n [\n -155.35354614257812,\n 19.47500813674322\n ],\n [\n -155.35354614257812,\n 19.330582575049508\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"538","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02fa3e4b0fc368eb53947","contributors":{"authors":[{"text":"Dzurisin, Daniel 0000-0002-0138-5067 dzurisin@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-5067","contributorId":538,"corporation":false,"usgs":true,"family":"Dzurisin","given":"Daniel","email":"dzurisin@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":746253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":746254,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198576,"text":"70198576 - 2018 - Effect of spatial and temporal scale on simulated groundwater recharge investigations","interactions":[],"lastModifiedDate":"2018-08-10T11:25:15","indexId":"70198576","displayToPublicDate":"2018-08-10T11:21:51","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"Effect of spatial and temporal scale on simulated groundwater recharge investigations","docAbstract":"Hydrologic model input datasets such as climate, land use, elevation, soil, and geology information are available in a range of scales for use in water resources investigations. Smaller spatial and temporal scale input data allow groundwater recharge models to simulate more physically realistic processes and presumably result in more accurate estimates of groundwater recharge. Projected climate data are, therefore, often downscaled to smaller spatial and temporal scales for use in these models. It is unknown, however, if increasingly smaller-scale climate data produce substantially different simulated recharge results, either in magnitude or trend. Also, even if simulated recharge results are different at a higher space and time resolution, simulation at coarser resolution might be adequate to provide recharge information at decision scales (e.g., meeting Colorado River compact requirements on a ten-year moving average basis). Historical climate datasets at three spatial (∼800 m, ∼4 km, and ∼12 km) and two temporal (daily and monthly) scales were used in a Soil Water Balance (SWB) model of the upper Colorado River basin (UCRB) to simulate groundwater recharge over the water-year 1982–2014 time period. The magnitude of annual and moving ten-year annual average recharge results for daily climate data were within 5% and 7% of ∼4 km results for ∼800 m and ∼12 km climate data, respectively, with deviations from 1982 to 2014 means within 1% and 3% (median), respectively. Comparison of simulated recharge results using the coarsest spatial and temporal climate data with results from the finest scale data indicated similar small differences over ten-year moving annual averages, over water years, and during high recharge months. While differences in simulated groundwater recharge magnitude, which may be important for groundwater-flow simulations, were substantial during some seasonal comparisons, trends in recharge were almost identical across scales, leading to similar conclusions about change from “normal”. Considering the uncertainty inherent in projected climate data, coarser spatial and longer temporal scale input data may be sufficient for water resources managers who need to understand changes in trends in groundwater recharge over water-year or longer time periods.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.advwatres.2018.07.014","usgsCitation":"Tillman, F.D., Pruitt, T., and Gangopadhyay, S., 2018, Effect of spatial and temporal scale on simulated groundwater recharge investigations: Advances in Water Resources, v. 119, p. 257-270, https://doi.org/10.1016/j.advwatres.2018.07.014.","productDescription":"14 p.","startPage":"257","endPage":"270","ipdsId":"IP-087425","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":356384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.77490234375,\n 36.66841891894786\n ],\n [\n -105.62255859375,\n 36.66841891894786\n ],\n [\n -105.62255859375,\n 43.35713822211053\n ],\n [\n -111.77490234375,\n 43.35713822211053\n ],\n [\n -111.77490234375,\n 36.66841891894786\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"119","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc3c5e4b0f5d57878e8db","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":741994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pruitt, Tom 0000-0002-3543-1324","orcid":"https://orcid.org/0000-0002-3543-1324","contributorId":173440,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","email":"","affiliations":[{"id":27228,"text":"Reclamation","active":true,"usgs":false}],"preferred":false,"id":741996,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":741995,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228742,"text":"70228742 - 2018 - Paleoclimate Records: Providing context and understanding of current Arctic change","interactions":[],"lastModifiedDate":"2022-02-18T17:15:28.160551","indexId":"70228742","displayToPublicDate":"2018-08-01T10:40:53","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10118,"text":"Bulletin American Meteorological Society","active":true,"publicationSubtype":{"id":10}},"title":"Paleoclimate Records: Providing context and understanding of current Arctic change","docAbstract":"At present, the Arctic Ocean is experiencing changes in ocean surface temperature and sea ice extent that are unprecedented in the era of satellite observations, which extend from the 1980s to the present (see sections 5c,d). To provide context for current changes, scientists turn to paleoclimate records to document and study anthropogenic influence and natural decadal and multidecadal climate variability in the Arctic system. Paleoceanographic records extend limited Arctic instrumental measurements back in time and are central to improving our understanding of climate dynamics and the predictive capability of climate models. By comparing paleoceanographic records with modern observations, scientists can place the rates and magnitudes of modern Arctic change in the context of those inferred from the geological record. \n\nOver geological time, paleoceanographic reconstructions using, for instance, marine sediment cores indicate that the Arctic has experienced huge sea ice fluctuations. These fluctuations range from nearly completely ice-free to totally ice-covered conditions. The appearance of ice-rafted debris and sea ice-dependent diatoms in Arctic marine sediments indicate that the first Arctic sea ice formed approxi-mately 47 million years ago (St. John 2008; Stickley et al. 2009; Fig. SB5.1), coincident with an interval of declining atmospheric carbon dioxide (CO2) concentration, global climate cooling, and expansion of Earths cryosphere during the middle Eocene. The development of year-round (i.e., perennial) sea ice in the central Arctic Ocean, similar to conditions that exist today, is evident in sediment records as early as 1418 million years ago (Darby 2008). These records suggest that transitions in sea ice cover occur over many millennia and often vary in concert with the waxing and waning of circum-Arctic land ice sheets, ice shelves, and long-term fluctuations in ocean and atmospheric temperature and atmospheric CO2 concentrations (Stein et al. 2012; Jakobsson et al. 2014). Over shorter time scales, shallow sediment records from Arctic Ocean continental shelves allow more detailed, higher-resolution (hundreds of years resolution) reconstructions of sea ice history extending through the Holocene (11 700 years ago to present), the most recent interglacial period.\nA notable feature of these records is an early Holocene sea ice minimum, corresponding to a thermal maximum (warm) period from 11 000 to 5000 years ago, when the Arctic may have been warmer and had less summertime sea ice than today (Kaufman et al. 2004). However, it is not clear that the Arctic was ice-free at any point during the Holocene (Polyak et al. 2010). High-resolution paleosea ice records from the western Arctic in the Chukchi and East Siberian Seas indicate that sea ice concentrations increased through the Holocene in concert with decreasing summer solar insolation (sunlight). Sea ice extent in this region also varied in response to the volume of Pacific water delivered via the Bering Strait into the Arctic Basin (Stein et al. 2017; Polyak et al. 2016). Records from the Fram Strait (Mller et al. 2012), Laptev Sea (Hrner et al. 2016), and Canadian Arctic Archipelago (Vare et al. 2009) also indicate a similar long-term expansion of sea ice and suggest sea ice extent in these regions is modulated by the varying influx of warm Atlantic water into the Arctic Basin (e.g., Werner et al. 2013). Taken together, available records support a circum-Arctic sea ice expansion during the late Holocene. \n\nA notably high-resolution summer sea ice history (<5-year resolution) has been established for the last 1450 years using a network of terrestrial records (tree ring , lake sediment, and ice core records) located around the margins of the Arctic Ocean (Kinnard et al. 2011). Results summarized in Fig. SB5.2 indicate a pronounced decline in summer sea ice extent beginning in the 20th century, with exceptionally low ice extent recorded since the mid-1990s, consistent with the satellite record (see section 5d). While several episodes of reduced and expanded sea ice extent occur in association with climate anomalies such as the Medieval Climate Warm Period (AD 8001300) and the Little Ice Age (AD 14501850), the magnitude and pace of the modern decline in sea ice is outside of the range of natural variability and unprecedented in the 1450-year reconstruction (Kinnard et al. 2011). A radiocarbon-dated driftwood record of the Ellesmere ice shelf in the Canadian High Arctic, the oldest landfast ice in the Northern Hemisphere, also demonstrates a substantial reduction in ice extents over the 20th century (England et al. 2017). A supporting sediment record indicates that inflowing Atlantic water in Fram Strait has warmed by 2C since 1900, driving break up and melt of sea ice (Spielhagen et al. 2011). Complementary mooring and satellite observations show the Atlantification of the eastern Arctic due to enhanced inflow of warm saline water through Fram Strait (Nilsen et al. 2016) and nutrient-rich Pacific water via the Bering has increased by more than 50% (Woodgate et al. 2012), further driving sea ice melt and warming seas. Similar high-resolution proxy records from Arctic regions also indicate that the modern rate of increasing annual surface air temperatures has not been observed over at least the last 2000 years (McKay and Kaufman 2014). Scientists conclude that broad-scale sea ice variations recorded in the paleo record were dominantly driven by changes in basin-scale changes in atmospheric circulation patterns, fluctuations in air temperature, strength of incoming solar radiation, and changes in the inflow of warm water via Pacific and Atlantic inflows (Polyak et al. 2010). \n\nThere is general consensus that ice-free Arctic summers are likely before the end of the 21st century (e.g., Stroeve et al. 2007; Massonnet et al. 2012), while some climate model projections suggest ice-free Arctic summers as early as 2030 (Wang and Overland 2009). Paleoclimate studies and observational time series attribute the decline in sea ice extent and thickness over the last decade to both enhanced greenhouse warming and natural climate variability. While understanding the interplay of these factors is critical for future projections of Arctic sea ice and ecosystems, most observational time series records cover only a few decades. This highlights the need for additional paleoceanographic reconstructions across multiple spatial and temporal domains to better understand the drivers and implications of present and future Arctic Ocean change.","language":"English","doi":"10.1175/2018BAMSStateoftheClimate.1","usgsCitation":"Osborne, E., Cronin, T.M., and Farmer, J., 2018, Paleoclimate Records: Providing context and understanding of current Arctic change: Bulletin American Meteorological Society, v. 99, no. 8, p. s150-s152, https://doi.org/10.1175/2018BAMSStateoftheClimate.1.","productDescription":"3 p.","startPage":"s150","endPage":"s152","ipdsId":"IP-098816","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":468548,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1175/2018bamsstateoftheclimate.1","text":"External Repository"},{"id":396176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Osborne, Emily","contributorId":279642,"corporation":false,"usgs":false,"family":"Osborne","given":"Emily","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":835253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cronin, Thomas M. 0000-0002-2643-0979 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":2579,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","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":835254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Farmer, Jesse","contributorId":279643,"corporation":false,"usgs":false,"family":"Farmer","given":"Jesse","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":835255,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70198036,"text":"ofr20181107 - 2018 - Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2017","interactions":[],"lastModifiedDate":"2023-04-24T21:07:25.110741","indexId":"ofr20181107","displayToPublicDate":"2018-07-19T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1107","title":"Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2017","docAbstract":"Trace-metal concentrations in sediment and in the clam Macoma petalum (formerly reported as Macoma balthica), clam reproductive activity, and benthic macroinvertebrate community structure were investigated in a mudflat 1 kilometer south of the discharge of the Palo Alto Regional Water Quality Control Plant (PARWQCP) in south San Francisco Bay, Calif. This report includes the data collected by U.S. Geological Survey (USGS) scientists for the period January 2017 to December 2017. These append to long-term datasets extending back to 1974. A major focus of the report is an integrated description of the 2017 data within the context of the longer, multi-decadal dataset. This dataset supports the City of Palo Alto’s Near-Field Receiving Water Monitoring Program, initiated in 1994.
Significant reductions in silver and copper concentrations in sediment and M. petalum occurred at the site in the 1980s following the implementation by PARWQCP of advanced wastewater treatment and source control measures. Since the 1990s, concentrations of these elements appear to have stabilized at concentrations somewhat above silver (Ag) or near copper (Cu) regional background concentrations. Data for other metals, including chromium (Cr), mercury (Hg), nickel (Ni), selenium (Se), and zinc (Zn), have been collected since 1994. Over this period, concentrations of these elements have remained relatively constant, aside from seasonal variation that is common to all elements. In 2017, concentrations of silver and copper in M. petalum varied seasonally in response to a combination of site-specific metal exposures and annual growth and reproduction, as reported previously. Seasonal patterns for other elements, including Cr, Ni, Zn, Hg, and Se, were generally similar in timing and magnitude as those for Ag and Cu. This record suggests that legacy contamination and regional-scale factors now largely control sedimentary and bioavailable concentrations of silver and copper, as well as other elements of regulatory interest, at the Palo Alto site.
Analyses of the benthic community structure of a mudflat in south San Francisco Bay over a 40-year period show that changes in the community have occurred concurrent with reduced concentrations of metals in the sediment and in the tissues of the biosentinel clam, M. petalum, from the same area. Analysis of M. petalum shows increases in reproductive activity concurrent with the decline in metal concentrations in the tissues of this organism. Reproductive activity is presently stable (2017), with almost all animals initiating reproduction in the fall and spawning the following spring. The entire infaunal community has shifted from being dominated by several opportunistic species to a community where the species are more similar in abundance, a pattern that indicates a more stable community that is subjected to fewer stressors. In addition, two of the opportunistic species (Ampelisca abdita and Streblospio benedicti) that brood their young and live on the surface of the sediment in tubes have shown a continual decline in dominance coincident with the decline in metals; both species had short-lived rebounds in abundance in 2008, 2009, and 2010 and showed signs of increasing abundance in 2017. Heteromastus filiformis (a subsurface polychaete worm that lives in the sediment, consumes sediment and organic particles residing in the sediment, and reproduces by laying its eggs on or in the sediment) showed a concurrent increase in dominance and, in the last several years before 2008, showed a stable population. H. filiformis abundance increased slightly in 2011–2012 and returned to pre-2011 numbers in 2017. An unidentified disturbance occurred on the mudflat in early 2008 that resulted in the loss of the benthic animals, except for deep-dwelling animals like M. petalum. However, within two months of this event animals returned to the mudflat. The resilience of the community suggested that the disturbance was not due to a persistent toxin or anoxia. The reproductive mode of most species that were present in 2017 is reflective of species that were available either as pelagic larvae or as mobile adults. Although oviparous species were lower in number in this group, the authors hypothesize that these species will return slowly as more species move back into the area. The use of functional ecology was highlighted in the 2017 benthic community data, which showed that the animals that have now returned to the mudflat are those that can respond successfully to a physical, nontoxic disturbance. Today, community data show a mix of species that consume the sediment, or filter feed, have pelagic larvae that must survive landing on the sediment, and those that brood their young. USGS scientists view the 2008 disturbance event as a response by the infaunal community to an episodic natural stressor (possibly sediment accretion or a pulse of freshwater), in contrast to the long-term recovery from metal contamination. We will compare this recovery to the long-term recovery observed after the 1970s when the decline in sediment pollutants was the dominating factor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181107","collaboration":"Prepared in cooperation with the City of Palo Alto, California","usgsCitation":"Cain, D.J., Thompson, J.K., Parchaso, F., Pearson, S., Stewart, R., Turner, M., Barasch, D., Slabic, A., and Luoma, S.N., 2018, Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2017: U.S. Geological Survey Open-File Report 2018–1107, 71 p., https://doi.org/10.3133/ofr20181107.","productDescription":"vi, 71 p.","numberOfPages":"79","onlineOnly":"Y","ipdsId":"IP-098497","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":416196,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231017","text":"Open-File Report 2023-1017","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2020"},{"id":416195,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211079","text":"Open-File Report 2021-1079","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2019"},{"id":416197,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191084","text":"Open-File Report 2019-1084","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2018"},{"id":416194,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20171135","text":"Open-File Report 2017-1135","linkHelpText":"- Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2016"},{"id":416193,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20161118","text":"Open-File Report 2016-1118","linkHelpText":"- Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2015"},{"id":355780,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1107/ofr20181107_.pdf","text":"Report","size":"3.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1107"},{"id":355779,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1107/coverthb.jpg"}],"country":"United States","state":"California","city":"Palo Alto","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.27371215820312,\n 37.315567502511044\n ],\n [\n -121.827392578125,\n 37.315567502511044\n ],\n [\n -121.827392578125,\n 37.655557695625056\n ],\n [\n -122.27371215820312,\n 37.655557695625056\n ],\n [\n -122.27371215820312,\n 37.315567502511044\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Hydro-Eco Interactions Branch
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025
As the population of the Triangle area in central North Carolina increases, the demand for good quality drinking water from streams and lakes within the upper Neuse and upper Cape Fear River Basins also increases. The Triangle area includes Raleigh, Cary, Research Triangle Park, Durham, Chapel Hill, and the surrounding communities. The U.S. Geological Survey examined temporal trends in water quality for 13 stream and 8 reservoir sites in the two basins on the basis of data collected during 1989–2013. Trends were analyzed using a fitted time-series model that accommodated for shifting trends and variations in streamflow at multiple time scales. Seventeen water-quality properties and constituents were evaluated, including specific conductance and major ions, nutrients, and organic carbon. Suspended solids and suspended sediment were examined at stream sites; chlorophyll a and Secchi transparency were examined at lake sites.
The investigation identified considerable changes in population, land cover, streamflow, and selected water-quality characteristics in the study area over the 25-year period. Specific conductance and concentrations of calcium, magnesium, potassium, sodium, and chloride tended to increase throughout the study area. Area-wide increases were also observed for organic nitrogen. Trends for other water-quality constituents varied on a more site-specific basis because of local watershed influences such as changes to wastewater-treatment processes and substantial shifts from rural to urban land use. Water quality is influenced by multiple, often confounding factors, and thus may change in a non-uniform manner over time. Long-term monitoring is critical for tracking these trends and ensuring resiliency of water supplies for the future. Results from this study may promote the understanding of water-quality response to a growing population and land-cover changes and can assist water-resource managers in the Triangle area in tracking progress toward water-quality goals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185077","collaboration":"Prepared in cooperation with the Triangle Area Water Supply Monitoring Project Steering Committee","usgsCitation":"Giorgino, M.J., Cuffney, T.F., Harden, S.L., and Feaster, T.D., 2018, Trends in water quality of selected streams and reservoirs used for water supply in the Triangle area of North Carolina, 1989–2013: U.S. Geological Survey Scientific Investigations Report 2018–5077, 67 p., https://doi.org/10.3133/sir20185077.","productDescription":"Report: viii, 67 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-095343","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":355697,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5077/coverthb.jpg"},{"id":355698,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5077/sir20185077.pdf","text":"Report","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5077"},{"id":355835,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MS3S17","text":"USGS data release","description":"USGS data release","linkHelpText":"Datasets for trends in water quality of selected streams and reservoirs used for water supply in the Triangle area of North Carolina, 1989-2013"}],"country":"United States","state":"North Carolina","otherGeospatial":"Cape Fear River basin, Neuse River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.79919433593749,\n 35.02099970111467\n ],\n [\n -77.84912109375,\n 35.02099970111467\n ],\n [\n -77.84912109375,\n 36.32397712011264\n ],\n [\n -79.79919433593749,\n 36.32397712011264\n ],\n [\n -79.79919433593749,\n 35.02099970111467\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
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Martin et al.'s [1] double-digest, restriction-site-associated DNA sequencing of Death Valley pupfish species (Cyprinodon) and new time-calibrated phylogenetic analysis provide estimated divergence ages for North American pupfish at two scales. On the larger temporal and spatial scale, Martin et al. conclude that the Death Valley pupfish shared common ancestry with: Cyprinodon albivelis Rio Yaqui, Mexico, which drains into the northern Gulf of California, at ca 10 kyr; C. veronicae and C. alvarezi from isolated springs in Nuevo León, Guzmán Basin, northeastern Mexico [2], at ca 17 kyr; and Atlantic coastal pupfish including those from the Yucatan Peninsula, Mexico, and the Bahamas (C. artifrons, C. maya and others) at ca 25 kyr. Martin et al. supported these genetic divergences and temporal estimates in their phylogenetic tree with these statements: ‘these ages are consistent with increased population mixing expected from the formation of large pluvial lakes throughout North America during the most recent glacial period 12–25 thousand years (kya).’ and it ‘is not apparent how low-lying desert populations could have remained isolated within large inland seas … ’ On the smaller scale, Martin et al. also conclude that introgression among pupfish species and subspecies of the 300 km-long Amargosa River of Death Valley occurred in the last 150 years.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rspb.2017.1648","usgsCitation":"Knott, J.R., Phillips, F., Reheis, M.C., Sada, D., Jayko, A.S., and Axen, G., 2018, Geologic and hydrologic concerns about pupfish divergence during the last glacial maximum: Proceedings of the Royal Society B: Biological Sciences, v. 285, no. 1881, 20171648, 3 p., https://doi.org/10.1098/rspb.2017.1648.","productDescription":"20171648, 3 p.","ipdsId":"IP-076573","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":468639,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2017.1648","text":"Publisher Index Page"},{"id":366910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"285","issue":"1881","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Knott, Jeffrey R.","contributorId":81408,"corporation":false,"usgs":true,"family":"Knott","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":769191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Fred","contributorId":218408,"corporation":false,"usgs":false,"family":"Phillips","given":"Fred","affiliations":[{"id":39841,"text":"New Mexico Institute of Mining & Technology","active":true,"usgs":false}],"preferred":false,"id":769192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reheis, Marith C. 0000-0002-8359-323X mreheis@usgs.gov","orcid":"https://orcid.org/0000-0002-8359-323X","contributorId":138571,"corporation":false,"usgs":true,"family":"Reheis","given":"Marith","email":"mreheis@usgs.gov","middleInitial":"C.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":769194,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sada, Donald","contributorId":218409,"corporation":false,"usgs":false,"family":"Sada","given":"Donald","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":769193,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jayko, Angela S. 0000-0002-7378-0330 ajayko@usgs.gov","orcid":"https://orcid.org/0000-0002-7378-0330","contributorId":2531,"corporation":false,"usgs":true,"family":"Jayko","given":"Angela","email":"ajayko@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":769190,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Axen, Gary","contributorId":218410,"corporation":false,"usgs":false,"family":"Axen","given":"Gary","affiliations":[{"id":39841,"text":"New Mexico Institute of Mining & Technology","active":true,"usgs":false}],"preferred":false,"id":769195,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195691,"text":"ofr20181031 - 2018 - Assessment of capacity-building activities for forest measurement, reporting, and verification, 2011–15 ","interactions":[],"lastModifiedDate":"2018-05-31T09:44:13","indexId":"ofr20181031","displayToPublicDate":"2018-05-31T09:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1031","title":"Assessment of capacity-building activities for forest measurement, reporting, and verification, 2011–15 ","docAbstract":"This report was written as a collaborative effort between the U.S. Geological Survey, SilvaCarbon, and Wageningen University with funding provided by the U.S. Agency for International Development and the European Space Agency, respectively, to address a pressing need for enhanced result-based monitoring and evaluation of delivered capacity-building activities. For this report, the capacity-building activities delivered by capacity-building providers (referred to as “providers” hereafter) during 2011–15 (the study period) to support countries in building measurement, reporting, and verification (MRV) systems for reducing emissions from deforestation and forest degradation (REDD+) were assessed and evaluated.
Summarizing capacity-building activities and outcomes across multiple providers was challenging. Many of the providers did not have information readily available, which precluded them from participating in this study despite the usefulness of their information. This issue led to a key proposed future action: Capacity-building providers could establish a central repository within the Global Forestry Observation Initiative (GFOI; http://www.gfoi.org/) where data from past, current, and future activities of all capacity-building providers could be stored. The repository could be maintained in a manner to continually learn from previous lessons.
Although various providers monitored and evaluated the success of their capacity-building activities, such evaluations only assessed the success of immediate outcomes and not the overarching outcomes and impacts of activities implemented by multiple providers. Good monitoring and evaluation should continuously monitor and periodically evaluate all factors affecting the outcomes of a provided capacity-building activity.
The absence of a methodology to produce quantitative evidence of a causal link between multiple capacity-building activities delivered and successful outcomes left only a plausible association. A previous publication argued that plausible association, although not a precise measurement of cause and effect, was a realistic tool. Our review of the available literature on this subject did not find another similar assessment to assess capacity-building activities for supporting the countries in building MRV system for REDD+.
Four countries from the main forested regions of Africa, the Americas, and Asia were chosen as subjects for this report based on the length of time SilvaCarbon and other providers have provided capacity-building activities toward MRV system for REDD+: Colombia (the Americas), the Democratic Republic of the Congo (DRC; Africa), Peru (the Americas), and the Republic of the Philippines (referred to as “the Philippines” hereafter; Asia).
Several providers were contacted for information to include in this report, but, because of various constraints, only SilvaCarbon, the Food and Agriculture Organization of the United Nations (FAO), and the World Wildlife Fund (WWF) participated in this study. These three providers supported various targeted capacity-building activities through-out Africa, the Americas, and Asia, including the following: technical workshops at national and regional levels (referred to as “workshops” hereafter), hands on training, study tours, technical details by experts, technical consultation between providers and recipients, sponsorship for travel, organizing network meetings, developing sampling protocols, assessing deforestation and degradation drivers, estimating carbon stock and flow, designing monitoring systems for multiple uses, promoting public-private partnerships to scale up investments on MRV system for REDD+, and assisting with the design of national forest monitoring systems.
Their activities were planned in coordination with key partners in each country and region and with the support and assistance of other providers. Note that several other organizations and institutions assisted the providers to deliver capacity-building activities, including Boston University, Conservation International, Stanford University, University of Maryland, and Wageningen University & Research.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181031","collaboration":"Prepared in cooperation with Wageningen University, the U.S. Agency for International Development, the U.S. Department of State, and the European Space Agency ","usgsCitation":"Peneva-Reed, E.I., and Romijn, J.E, 2018, Assessment of capacity-building activities for forest measurement, reporting, and verification, 2011–15: U.S. Geological Survey Open-File Report 2018–1031, 35 p., https://doi.org/10.3133/ofr20181031. ","productDescription":"v, 35 p.","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088895","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":354567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1031/ofr20181031.pdf","text":"Report","size":"1.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1031"},{"id":354566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1031/coverthb.jpg"}],"contact":"Director, U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey developed a groundwater-flow model for the uppermost principal aquifer systems in the Williston Basin in parts of Montana, North Dakota, and South Dakota in the United States and parts of Manitoba and Saskatchewan in Canada as part of a detailed assessment of the groundwater availability in the area. The assessment was done because of the potential for increased demands and stresses on groundwater associated with large-scale energy development in the area. As part of this assessment, a three-dimensional groundwater-flow model was developed as a tool that can be used to simulate how the groundwater-flow system responds to changes in hydrologic stresses at a regional scale.
The three-dimensional groundwater-flow model was developed using the U.S. Geological Survey’s numerical finite-difference groundwater model with the Newton-Rhapson solver, MODFLOW–NWT, to represent the glacial, lower Tertiary, and Upper Cretaceous aquifer systems for steady-state (mean) hydrological conditions for 1981‒2005 and for transient (temporally varying) conditions using a combination of a steady-state period for pre-1960 and transient periods for 1961‒2005. The numerical model framework was constructed based on existing and interpreted hydrogeologic and geospatial data and consisted of eight layers. Two layers were used to represent the glacial aquifer system in the model; layer 1 represented the upper one-half and layer 2 represented the lower one-half of the glacial aquifer system. Three layers were used to represent the lower Tertiary aquifer system in the model; layer 3 represented the upper Fort Union aquifer, layer 4 represented the middle Fort Union hydrogeologic unit, and layer 5 represented the lower Fort Union aquifer. Three layers were used to represent the Upper Cretaceous aquifer system in the model; layer 6 represented the upper Hell Creek hydrogeologic unit, layer 7 represented the lower Hell Creek aquifer, and layer 8 represented the Fox Hills aquifer. The numerical model was constructed using a uniform grid with square cells that are about 1 mile (1,600 meters) on each side with a total of about 657,000 active cells.
Model calibration was completed by linking Parameter ESTimation (PEST) software with MODFLOW–NWT. The PEST software uses statistical parameter estimation techniques to identify an optimum set of input parameters by adjusting individual model input parameters and assessing the differences, or residuals, between observed (measured or estimated) data and simulated values. Steady-state model calibration consisted of attempting to match mean simulated values to measured or estimated values of (1) hydraulic head, (2) hydraulic head differences between model layers, (3) stream infiltration, and (4) discharge to streams. Calibration of the transient model consisted of attempting to match simulated and measured temporally distributed values of hydraulic head changes, stream base flow, and groundwater discharge to artesian flowing wells. Hydraulic properties estimated through model calibration included hydraulic conductivity, vertical hydraulic conductivity, aquifer storage, and riverbed hydraulic conductivity in addition to groundwater recharge and well skin.
The ability of the numerical model to accurately simulate groundwater flow in the Williston Basin was assessed primarily by its ability to match calibration targets for hydraulic head, stream base flow, and flowing well discharge. The steady-state model also was used to assess the simulated potentiometric surfaces in the upper Fort Union aquifer, the lower Fort Union aquifer, and the Fox Hills aquifer. Additionally, a previously estimated regional groundwater-flow budget was compared with the simulated steady-state groundwater-flow budget for the Williston Basin. The simulated potentiometric surfaces typically compared well with the estimated potentiometric surfaces based on measured hydraulic head data and indicated localized groundwater-flow gradients that were topographically controlled in outcrop areas and more generalized regional gradients where the aquifers were confined. The differences between the measured and simulated (residuals) hydraulic head values for 11,109 wells were assessed, which indicated that the steady-state model generally underestimated hydraulic head in the model area. This underestimation is indicated by a positive mean residual of 11.2 feet for all model layers. Layer 7, which represents the lower Hell Creek aquifer, is the only layer for which the steady-state model overestimated hydraulic head. Simulated groundwater-level changes for the transient model matched within plus or minus 2.5 feet of the measured values for more than 60 percent of all measurements and to within plus or minus 17.5 feet for 95 percent of all measurements; however, the transient model underestimated groundwater-level changes for all model layers. A comparison between simulated and estimated base flows for the steady-state and transient models indicated that both models overestimated base flow in streams and underestimated annual fluctuations in base flow.
The estimated and simulated groundwater budgets indicate the model area received a substantial amount of recharge from precipitation and stream infiltration. The steady-state model indicated that reservoir seepage was a larger component of recharge in the Williston Basin than was previously estimated. Irrigation recharge and groundwater inflow from outside the Williston Basin accounted for a relatively small part of total groundwater recharge when compared with recharge from precipitation, stream infiltration, and reservoir seepage. Most of the estimated and simulated groundwater discharge in the Williston Basin was to streams and reservoirs. Simulated groundwater withdrawal, discharge to reservoirs, and groundwater outflow in the Williston Basin accounted for a smaller part of total groundwater discharge.
The transient model was used to simulate discharge to 571 flowing artesian wells within the model area. Of the 571 established flowing artesian wells simulated by the model, 271 wells did not flow at any time during the simulation because hydraulic head was always below the land-surface altitude. As hydraulic head declined throughout the simulation, 68 of these wells responded by ceasing to flow by the end of 2005. Total mean simulated discharge for the 571 flowing artesian wells was 55.1 cubic feet per second (ft3/s), and the mean simulated flowing well discharge for individual wells was 0.118 ft3/s. Simulated discharge to individual flowing artesian wells increased from 0.039 to 0.177 ft3/s between 1961 and 1975 and decreased to 0.102 ft3/s by 2005. The mean residual for 34 flowing wells with measured discharge was 0.014 ft3/s, which indicates the transient model overestimated discharge to flowing artesian wells in the model area.
Model limitations arise from aspects of the conceptual model and from simplifications inherent in the construction and calibration of a regional-scale numerical groundwater-flow model. Simplifying assumptions in defining hydraulic parameters in space and hydrologic stresses and time-varying observational data in time can limit the capabilities of this tool to simulate how the groundwater-flow system responds to changes in hydrologic stresses, particularly at the local scale; nevertheless, the steady-state model adequately simulated flow in the uppermost principal aquifer systems in the Williston Basin based on the comparison between the simulated and estimated groundwater-flow budget, the comparison between simulated and estimated potentiometric surfaces, and the results of the calibration process.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175158","collaboration":"Water Availability and Use Science Program","usgsCitation":"Davis, K.W., and Long, A.J., 2018, Construction and calibration of a groundwater-flow model to assess groundwater availability in the uppermost principal aquifer systems of the Williston Basin, United States and Canada: U.S. Geological Survey Scientific Investigations Report 2017–5158, 70 p., https://doi.org/10.3133/sir20175158.","productDescription":"Report: ix, 70; Appendixes 1-2; Data Release","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080007","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":354478,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75B01CZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used to assess groundwater availability in the uppermost principal aquifer systems of the Williston structural basin, United States and Canada"},{"id":354477,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158.pdf","text":"Report","size":"97.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5158"},{"id":354510,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158_appendix_1.xlsx","text":"Appendix Table 1","size":"1.77 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5158 Appendix 1"},{"id":354511,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158_appendix_2.xlsx","text":"Appendix Table 2","size":"25.1 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5158 Appendix 2"},{"id":354476,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5158/coverthb2.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota, Wyoming","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.3359375,\n 42.35854391749705\n ],\n [\n -97.734375,\n 42.35854391749705\n ],\n [\n -97.734375,\n 49.89463439573421\n ],\n [\n -109.3359375,\n 49.89463439573421\n ],\n [\n -109.3359375,\n 42.35854391749705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
South Dakota Office
U.S. Geological Survey
1608 Mountain View Rd.
Rapid City, SD 57702
In this report, precipitation data from 2002 to 2012 from the hourly gridded Next-Generation Radar (NEXRAD)-based Multisensor Precipitation Estimate (MPE) precipitation product are compared to precipitation data from two rain gage networks—an automated tipping bucket network of 25 rain gages operated by the U.S. Geological Survey (USGS) and 51 rain gages from the volunteer-operated Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network—in and near DuPage County, Illinois, at a daily time step to test for long-term differences in space, time, and distribution. The NEXRAD–MPE data that are used are from the fifty 2.5-mile grid cells overlying the rain gages from the other networks. Because of the challenges of measuring of frozen precipitation, the analysis period is separated between days with or without the chance of freezing conditions. The NEXRAD–MPE and tipping-bucket rain gage precipitation data are adjusted to account for undercatch by multiplying by a previously determined factor of 1.14. Under nonfreezing conditions, the three precipitation datasets are broadly similar in cumulative depth and distribution of daily values when the data are combined spatially across the networks. However, the NEXRAD–MPE data indicate a significant trend relative to both rain gage networks as a function of distance from the NEXRAD radar just south of the study area. During freezing conditions, of the USGS network rain gages only the heated gages were considered, and these gages indicate substantial mean undercatch of 50 and 61 percent compared to the NEXRAD–MPE and the CoCoRaHS gages, respectively. The heated USGS rain gages also indicate substantially lower quantile values during freezing conditions, except during the most extreme (highest) events. Because NEXRAD precipitation products are continually evolving, the report concludes with a discussion of recent changes in those products and their potential for improved precipitation estimation. An appendix provides an analysis of spatially combined NEXRAD–MPE precipitation data as a function of temperature at an hourly time scale and indicates, among other results, that most precipitation in the study area occurs at moderate temperatures of 30 to 74 degrees Fahrenheit. However, when precipitation does occur, its intensity increases with temperature to about 86 degrees Fahrenheit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181061","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Spies, R.R., Over, T.M., and Ortel, T.W., 2018, Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12: U.S. Geological Survey Open-File Report 2018–1061, 30 p., https://doi.org/10.3133/ofr20181061. ","productDescription":"v, 30 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057485","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":354281,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1061/coverthb.jpg","text":"Report"},{"id":354282,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1061/ofr20181061.pdf","text":"Report","size":"5.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1061"}],"country":"United States","state":"Illinois","county":"DuPage County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.33,\n 41.5833\n ],\n [\n -87.8333,\n 41.5833\n ],\n [\n -87.8333,\n 42.1667\n ],\n [\n -88.33,\n 42.1667\n ],\n [\n -88.33,\n 41.5833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801