Milford Lake has been listed as impaired and designated hypereutrophic because of excessive nutrient loading, specifically biologically available orthophosphate. It is the largest lake by surface area in Kansas and is a reservoir built for purposes including water supply and recreation. In 2015, the Kansas Department of Health and Environment (KDHE) divided the lake into three zones (Zones A, B, and C) for recreational monitoring of harmful algal blooms (HABs). Upstream Zone C has historically been more affected by HABs than Zones B and A, and Zone C has historically had the highest phosphorus concentrations.
The U.S. Geological Survey, in cooperation with the KDHE, completed a study in 2017–18 to assess the spatial and temporal variability of nutrients and algae in the Republican River (the primary inflow to Milford Lake) and Milford Lake using spatially and temporally dense data. During the study period, discrete water-quality samples were collected at 36 lake sites, 21 river sites, and 1 pond. All samples were analyzed for nutrients; some samples were also analyzed for chlorophyll, phycocyanin, microcystin, and (or) phytoplankton community composition and abundance. Results from this study provide perspective for understanding the potential role nutrient and algal conditions have in facilitating the formation of HABs and may inform future actions to prevent and mitigate HABs and their potential effects on human and environmental health.
In 2017, one low-flow floating synoptic on the Republican River into Zone C of Milford Lake and one 24-hour synoptic in Zone C of Milford Lake were completed. Results from the low-flow floating synoptic on July 17, 2017, at 21 river sites, 8 lake sites, and 1 pond site indicated that the Republican River was not contributing dissolved orthophosphate or total phosphorus concentrations higher than those in the main body of Milford Lake.
No patterns in nutrient or total microcystin concentrations were evident from the 24-hour synoptic at two sites on August 24–25, 2017. Total nitrogen was dominated by total Kjeldahl nitrogen (TKN) at both sites. Different oscillation activity in algal biomass and chlorophyll at the two sites demonstrated the variable nature of algal accumulations and their effects on nutrient and dissolved oxygen concentrations. Different patterns in chlorophyll and microcystin concentrations indicate that the relation between algal biomass and cyanotoxin concentrations were different at the two sites, possibly because of differences among algal communities present at each site.
Three whole-lake synoptics through Zones A, B, and C in Milford Lake were completed on July 10, August 9, and October 16–17, 2018, at 30 lake sites. Orthophosphate was consistently at least 77 percent of total phosphorus at all sites except the two most uplake sites. At the two most uplake sites, orthophosphate was between 52 and 72 percent of the total phosphorus present at the site.
Concentrations of TKN were not consistently increasing or decreasing during 2018. Total nitrogen was dominated by TKN in July and August. Very low concentrations of dissolved nitrate plus nitrite indicate that the nutrient was likely tied up in algal biomass. By October, total nitrogen was approximately one-half TKN and one-half dissolved nitrate plus nitrite. Higher concentrations of dissolved orthophosphate and dissolved nitrate plus nitrite in October than in July and August were likely caused by reduced biological activity (less uptake of nutrients) and lower air and water temperatures. Multiple inflow events (streamflow greater than median daily value) between August and October also may have moved nutrients through the lake.
Chlorophyll, phycocyanin, microcystin, and phytoplankton samples were collected at eight sites in 2018. Most sites had their highest chlorophyll concentrations in August. The three most uplake sites had their highest phycocyanin concentrations in July, whereas the other five sites had their highest phycocyanin concentrations in August. Two of 23 samples had detections of total microcystin (0.11 and 0.12 microgram per liter). Phytoplankton community composition mainly consisted of Bacillariophyta, Chlorophyta, Cryptophyta, and Cyanobacteria. Phytoplankton community composition and abundance data described broad seasonal patterns and did not capture the full range of possible conditions at each site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205135","collaboration":"Prepared in cooperation with the Kansas Department of Health and Environment","usgsCitation":"Leiker, B.M., Abel, J.R., Graham, J.L., Foster, G.M., King, L.R., Stiles, T.C., and Buley, R.P., 2021, Spatial and temporal variability of nutrients and algae in the Republican River and Milford Lake, Kansas, June through November 2017 and May through November 2018: U.S. Geological Survey Scientific Investigations Report 2020–5135, 53 p., https://doi.org/10.3133/sir20205135.","productDescription":"Report: viii, 53 p.; 3 Data Releases; Dataset","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-116622","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":383231,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":383228,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XO24L3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Phytoplankton data for Milford Lake, Kansas, June through October 2018"},{"id":383226,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5135/coverthb.jpg"},{"id":383227,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5135/sir20205135.pdf","text":"Report","size":"6.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5135"},{"id":383230,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZA2HE7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Vertical profiles of water-quality data from two sites in Milford Lake, Kansas, August 24–25, 2017"},{"id":383229,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CX2GFI","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Time-lapse photography of Milford Lake, Kansas, June through November 2017 and June through November 2018"}],"country":"United States","state":"Kansas","otherGeospatial":"Republican River, Milford Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.12875366210938,\n 39.03838632847035\n ],\n [\n -96.82937622070312,\n 39.03838632847035\n ],\n [\n -96.82937622070312,\n 39.32367475355144\n ],\n [\n -97.12875366210938,\n 39.32367475355144\n ],\n [\n -97.12875366210938,\n 39.03838632847035\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The Soldier Meadows spring complex provides habitat for the desert dace, an endemic and threatened fish. The spring complex has been altered with the construction of irrigation ditches that remove water from natural stream channels. Irrigation ditches generally provide lower quality habitat for the desert dace. Land and wildlife management agencies are interested in increasing habitat extent and quality by filling in irrigation ditches and restoring streamflow to natural channels. The U.S. Geological Survey measured streamflow, surveyed topography, and combined light detection and ranging data to create a two-dimensional hydraulic model of the study area to understand how restoration would change streamflow extents and hydraulic characteristics. Streamflow measurements indicate that, except for a section of one irrigation ditch at the upstream end of the study area, the total volume of streamflow diverted into the irrigation ditches in the study area was minimal. Hydraulic modeling indicates filling in the irrigation ditch at the upper end of the study area would return streamflow to the natural channel, resulting in an increase in natural channel surface water extent, and a reduction of irrigation ditch surface water flow. The result would be a more heterogenous natural stream channel, ranging from shallow and slow to narrow and fast.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205143","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Morris, C.M., 2021, Evaluation of streamflow extent and hydraulic characteristics of a restored channel at Soldier Meadows, Black Rock Desert–High Rock Canyon Emigrant Trails National Conservation Area, Nevada: U.S. Geological Survey Scientific Investigations Report 2020–5143, 22 p., https://doi.org/10.3133/sir20205143.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-110000","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":383124,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O0GII7","linkHelpText":"Geospatial data and surface-water model archive for evaluation of streamflow extent and hydraulic characteristics of a restored channel at Soldier Meadows, Black Rock Desert–High Rock Canyon Emigrant Trails National Conservation Area, Nevada"},{"id":383123,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5143/images"},{"id":383122,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5143/sir20205143.xml"},{"id":383121,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5143/sir20205143.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383120,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5143/covrthb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Black Rock Desert, High Rock Canyon Emigrant Trails National Conservation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.40765380859375,\n 40.734770989672406\n ],\n [\n -118.35845947265625,\n 40.734770989672406\n ],\n [\n -118.35845947265625,\n 41.45919537950706\n ],\n [\n -119.40765380859375,\n 41.45919537950706\n ],\n [\n -119.40765380859375,\n 40.734770989672406\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
Selenium is a water-quality constituent of concern for aquatic ecosystems in the lower Gunnison River Basin. Selenium is derived from bedrock of the Mancos Shale and is mobilized and transported to groundwater and surface water by application of irrigation water. Although it is recognized that groundwater contributes an appreciable amount of selenium to surface water, few studies have addressed interactions between the two. The U.S. Geological Survey in cooperation with the Colorado Water Conservation Board conducted a study during 2017–19 to characterize the quality and quantity of groundwater discharging to an agricultural drainage near Delta, Colorado, locally known as Sunflower Drain.
Water quality in the study area is characterized by high dissolved solids with elevated concentrations of selenium and nitrate resulting from dissolution of soluble salts in the Mancos Shale. Selenium concentrations have decreased by 50 percent since the early 2000s, possibly in response to irrigation system improvements. Stable water isotopes indicate streamflow is dominated by canal water during the irrigation season (April to October) and, during the nonirrigation season (November to March), is dominated by groundwater that has undergone some degree of evaporation. Pesticide and pharmaceutical compounds were infrequently detected, and results indicate they were derived from sources outside the study area such that they do not appear to be useful as tracers of groundwater sources. Stable isotopes of nitrate indicate that nitrate originates from the Mancos Shale, and the isotopic composition is enriched by denitrification in the groundwater system. Using a mass-balance approach, estimated groundwater discharge rates to Sunflower Drain ranged from 0.15 to 0.27 cubic feet per second per mile with one losing reach identified. Selenium, sulfate, and nitrate concentrations in groundwater estimated by mass-balance calculations were similar to concentrations measured in the Poly 17 observation well, located in a largely irrigated area in east tributary. One tributary reach had higher concentrations of selenium, sulfate, and nitrate likely reflecting localized inputs of more concentrated groundwater, similar to the concentrations in the Poly 7 observation well, which is downgradient from a residential area in the west tributary.
Three pilot studies were conducted, including fiber optic distributed temperature sensing to detect groundwater discharge zones in the stream channel, a passive seismic technique to estimate depth to bedrock, and use of radon-222 as a geochemical tracer of groundwater discharge. All three techniques show promise as additional approaches for investigating groundwater discharge surface-water systems in irrigated drainage areas on Mancos Shale.
The factors that affect groundwater movement mainly include when and where irrigation water is transported and applied, and the distribution of bedrock of the Mancos Shale and overlying alluvial deposits. The average groundwater recharge rate for the study area was estimated at 8.1 inches per year, based on mass balance calculations from synoptic survey data. Along the western tributary of Sunflower Drain, there was evidence that spills from the East Canal may recharge the groundwater aquifer adjacent to the stream channel. Groundwater movement to the stream channel may be controlled by the topography of the alluvial/bedrock interface or focused along human-made features, such as tile drains and ditches constructed around irrigated fields. On larger scales, the top of bedrock was also important, creating a topographic constriction that caused a zone of groundwater discharge. The groundwater system is complex, and further study could better define the system, possibly through application of a groundwater flow model and more extensive studies using some of the exploratory methods evaluated in this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205132","collaboration":"Prepared in cooperation with Colorado Water Conservation Board","usgsCitation":"Mast, M.A., 2021, Characterization of groundwater quality and discharge with emphasis on selenium in an irrigated agricultural drainage near Delta, Colorado, 2017–19: U.S. Geological Survey Scientific Investigations Report 2020–5132, 34 p., https://doi.org/10.3133/sir20205132.","productDescription":"Report: vi, 34 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119514","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":382809,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LKYX9H","text":"USGS data release","linkHelpText":"Near-surface geophysical data collected in the Sunflower Drain study area near Delta, Colorado, March 2018"},{"id":382805,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5132/coverthb.jpg"},{"id":382806,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5132/sir20205132.pdf","text":"Report","size":"5.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5132"}],"country":"United States","state":"Colorado","city":"Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.21945190429688,\n 38.638327308061875\n ],\n [\n -107.97019958496094,\n 38.638327308061875\n ],\n [\n -107.97019958496094,\n 38.82205601494022\n ],\n [\n -108.21945190429688,\n 38.82205601494022\n ],\n [\n -108.21945190429688,\n 38.638327308061875\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
Water resources in Theodore Roosevelt National Park, North Dakota, support wildlife, visitors, and staff, and play a vital role in supporting the native ecology of the park. The U.S. Geological Survey, in cooperation with the National Park Service, completed field work in 2018 for a study to address concerns about water availability and possible sources of groundwater contamination for seeps and springs in Theodore Roosevelt National Park. The objective of the study was to improve hydrologic knowledge and determine the water composition of 11 seeps and springs in the park by collecting water-chemistry data at springs, streams, wells, and rain collectors.
Water samples were collected at 26 sites at springs, streams, wells, and rain collectors in the North and South Units of Theodore Roosevelt National Park. Samples in the North Unit were collected at 5 springs, 1 stream, 2 wells, and 1 rain collector. Samples in the South Unit were collected at 6 springs, 2 streams, 8 wells, and 1 rain collector. Samples from springs, streams, and wells were collected in May, July, and September 2018. Samples from rain collectors were collected when enough daily precipitation accumulated in the collectors. Sampled precipitation events during the study period were in May, June, July, August, and September 2018. Physical properties of sampled water—temperature, pH, and specific conductance—were measured in the field. Water samples were analyzed for stable isotopes of oxygen and hydrogen and for chloride concentration. Recharge rates for aquifers supplying springs were determined using precipitation volume and chloride concentrations for a 12-day period before the sample-collection date. Multivariate statistical analysis methods used on water-chemistry data included principal component analysis, cluster analysis, and end-member mixing analysis.
Water composition was used to determine the spring type and contributing aquifers for 11 springs in the North and South Units of Theodore Roosevelt National Park from analyses of water-chemistry data between May and September 2018. In the North Unit, Achenbach Spring was classified as a filtration spring with water from an unconfined part of the upper Fort Union aquifer and infiltration of precipitation. Hagen Spring, Mandal Spring, and Stevens Spring were classified as contact springs supplied by semiconfined parts of the upper Fort Union aquifer. Overlook Spring at one time may have been a natural spring or seep but now is a developed spring that behaves like a flowing artesian well completed in a confined part of the upper Fort Union aquifer. In the South Unit, six springs were classified into two spring types: filtration and contact springs. Boicourt Spring and Sheep Butte Spring were classified as filtration springs that have water supplied by unconfined parts of the upper Fort Union aquifer and infiltrated precipitation. Big Plateau Spring, Lone Tree Spring, Sheep Pasture Spring, and Southeast Corner Spring were classified as contact springs that receive waters from a semiconfined part of the upper Fort Union aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205121","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Medler, C.J., and Eldridge, W.G., 2021, Spring types and contributing aquifers from water-chemistry and multivariate statistical analyses for seeps and springs in Theodore Roosevelt National Park, North Dakota, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5121, 48 p., https://doi.org/10.3133/sir20205121.","productDescription":"Report: viii, 48 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-115769","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":382693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5121/coverthb.jpg"},{"id":382694,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5121/sir20205121.pdf","text":"Report","size":"4.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"sir2020-5121"},{"id":382695,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"}],"country":"United States","state":"North Dakota","otherGeospatial":"Theodore Roosevelt National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.63334655761719,\n 46.87990702860922\n ],\n [\n -103.29757690429686,\n 46.87990702860922\n ],\n [\n -103.29757690429686,\n 47.02801434856074\n ],\n [\n -103.63334655761719,\n 47.02801434856074\n ],\n [\n -103.63334655761719,\n 46.87990702860922\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.48983764648438,\n 47.52832925298343\n ],\n [\n -103.216552734375,\n 47.52832925298343\n ],\n [\n -103.216552734375,\n 47.65428791076272\n ],\n [\n -103.48983764648438,\n 47.65428791076272\n ],\n [\n -103.48983764648438,\n 47.52832925298343\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.63677978515625,\n 47.22726254715105\n ],\n [\n -103.60965728759764,\n 47.22726254715105\n ],\n [\n -103.60965728759764,\n 47.250106104326235\n ],\n [\n -103.63677978515625,\n 47.250106104326235\n ],\n [\n -103.63677978515625,\n 47.22726254715105\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
This report provides a groundwater-flow conceptualization that integrates geologic, hydrologic, hydraulic-property, and radionuclide data in the Pahute Mesa–Oasis Valley (PMOV) groundwater basin, southern Nevada. Groundwater flow in the PMOV basin is of interest because 82 underground nuclear tests were detonated, most near or below the water table. A potentiometric map and nine sets of hydrostratigraphic and hydrologic cross sections supplement the conceptualization.
Potentiometric contours indicate that groundwater in the PMOV basin generally flows south-southwest and discharges at Oasis Valley. Groundwater encounters an alternating sequence of low- and high-transmissivity rocks, referred to as dams and pools, respectively, as it moves from east to west across eastern Pahute Mesa. Flow from all Pahute Mesa nuclear tests is to Oasis Valley and is well-constrained by water-level data. Flow converges along a corridor of high transmissivity between Pahute Mesa and Oasis Valley.
The location of the lateral PMOV basin boundary is well defined, and this boundary, with a few minor exceptions, represents a no-flow boundary. Some boundary uncertainty exists in the northeastern part of the basin, but potential flow-rate estimates across the northeastern boundary resulting from this uncertainty are small relative to the basin groundwater budget.
Recharge in the PMOV basin is derived from episodic pulses of modern water and the diffuse percolation of old water (greater than 1,000 years). Episodic recharge is a minor recharge component observed as a rise in groundwater levels that occurs 3 months to 1 year following a wet winter. Minor amounts of episodic recharge through an unsaturated zone in excess of 1,000 feet (ft) requires preferential flow through faults and fractures. The dominant recharge component is slow, steady, diffuse percolation of old water through the unsaturated zone. A large component of old water recharging the groundwater system is consistent with observations of isotopically light deuterium and oxygen 18 compositions in water from wells on Pahute Mesa and central Oasis Valley. About half the recharge in the PMOV basin is derived from the eastern Pahute Mesa area. The remaining recharge is derived primarily from other highland areas including Timber Mountain, Belted and Kawich Ranges, and Black Mountain.
The PMOV groundwater system is nearly steady state, where recharge is balanced by the 5,900 acre-feet per year of natural discharge at Oasis Valley. This assumption is reasonable because the basin is dominated by steady-state conditions, where long-term changes in groundwater storage are minimal. Total groundwater withdrawals from 1963 to 2018 have amounted to less than 10 percent of annual groundwater discharge and less than 0.2 percent of the basin’s groundwater storage. Therefore, present-day (2020) conditions are considered representative of predevelopment (pre-1950) conditions in nearly all areas of the basin.
The lower PMOV basin boundary is defined at 4,000 ft below the water table to encompass all underground nuclear tests and tritium plumes. This boundary defines the lower boundary of radionuclide migration. However, nearly all flow and tritium transport occur in the upper 1,600 ft of the saturated zone because a transmissivity-with-depth relation indicates that greater than 90 percent of the transmissivity contributing to groundwater flow occurs within 1,600 ft of the water table. Rocks at deeper depths have low transmissivity because argillic and mineralized alterations plug the fractures.
Volcanic rocks form the primary aquifers and confining units in the PMOV basin. Volcanic hydrogeologic units (HGUs) and hydrostratigraphic units (HSUs) have transmissivity distributions that span up to eight orders of magnitude with considerable overlap between distributions. Despite the large overlap between units, mean transmissivities of aquifers are one-to-two orders of magnitude greater than the confining units. However, all volcanic-rock HGUs and HSUs are composite units, meaning that they can function spatially as either an aquifer or confining unit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205134","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management under Interagency Agreement, DE-EM0004969","usgsCitation":"Jackson, T.R., Fenelon, J.M., and Paylor, R.L., 2021, Groundwater flow conceptualization of the Pahute Mesa–Oasis Valley Groundwater Basin, Nevada—A synthesis of geologic, hydrologic, hydraulic-property, and tritium data: U.S. Geological Survey Scientific Investigations Report 2020–5134, 100 p., https://doi.org/10.3133/sir20205134.","productDescription":"Report: viii, 100 p.; 2 Plates: 26.00 x 42.00 inches and 120.01 x 36.00 inches; 7 Appendixes","onlineOnly":"Y","ipdsId":"IP-095406","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":382683,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix2.xlsx","text":"Appendix 2","size":"78 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 2"},{"id":382684,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix3.xlsm","text":"Appendix 3","size":"530 KB xlsm","description":"SIR 2020-5134 Appendix 3"},{"id":382685,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix4.xlsx","text":"Appendix 4","size":"6.1 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 4"},{"id":382681,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_plate02.pdf","text":"Plate 2","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134 Plate 2"},{"id":382678,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5134/coverthb.jpg"},{"id":382679,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134.pdf","text":"Report","size":"9.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134"},{"id":382680,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_plate01.pdf","text":"Plate 1","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134 Plate 1"},{"id":382682,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix1.xlsx","text":"Appendix 1","size":"2.5 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 1"},{"id":382688,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix7.xlsx","text":"Appendix 7","size":"433 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 7"},{"id":382687,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix6.xlsx","text":"Appendix 6","size":"856 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 6"},{"id":382686,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix5.xlsx","text":"Appendix 5","size":"799 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 5"}],"country":"United States","state":"Nevada","otherGeospatial":"Pahute Mesa–Oasis Valley Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.00,\n 36.65079252503471\n ],\n [\n -116.00,\n 36.65079252503471\n ],\n [\n -116.00,\n 38.00\n ],\n [\n -117.00,\n 38.00\n ],\n [\n -117.00,\n 36.65079252503471\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
This river corridor assessment documents sediment mobility and river response to flood disturbance along a 140-kilometer segment of the main-stem Klamath River below Iron Gate Dam, California. Field and remote sensing methods were used to assess fundamental indicators of active sediment transport and river response to a combination of natural runoff events and reservoir releases during the study period from 2005 to 2019. Discharge measurements at two gaged sites and bed-material samples at two ungaged sites provided direct and indirect evidence of mobile bed conditions, scour and fill, and surface flushing of fine sediment. Available remote-sensing datasets collected in 2005, 2009, 2010, and 2016 were used to determine sediment storage, flood inundation boundaries, and provide indirect evidence of flood-induced scour. These datasets validate channel-maintenance flows defined by Shea and others (2016). During the study period, flows greater than or equal to 6,030 cubic feet per second mobilized the substrate, caused localized scour, and flushed fine sediment from bar surfaces. Flows greater than or equal to 10,400 cubic feet per second stripped vegetation from bars and floodplains and produced deeper scour. Flood disturbance within the study reach is produced by the combined effect of natural flows and reservoir releases, which resulted in mobile bed conditions during the study period. Periodic scour and substrate disturbance are considered by the U.S. Fish and Wildlife Service to be integral for managing disease-induced mortality of juvenile and adult salmonids. Substrate conditions conducive to parasites that host infectious diseases, particularly Ceratonova shasta, occur periodically. Additional studies are required to determine whether disease prevalence can be mitigated by well-timed reservoir releases. Study results are useful for interpreting linkages among physical and biological processes and for evaluating the effectiveness of flow management targeted to improve river bed conditions for endangered salmonid populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201141","collaboration":"Water Availability and Use Science ProgramDirector, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey and the city of Independence, Missouri, Water Pollution Control Department has studied the water quality and ecological condition of urban streams within Independence since 2005. Selected physical properties, nutrients, chloride, fecal indicator bacteria (Escherichia coli and total coliform), total dissolved solids, and suspended-sediment concentration data for base-flow and stormflow samples were used to document temporal trends in concentrations and flow-weighted concentrations; and annual loads were computed and investigated for selected nutrients, chloride, and suspended sediment. The six study sites included in this report are located on five urban streams: Rock Creek, a tributary in the city that drains to the Missouri River; three tributaries of the Little Blue River within the city (East Fork Little Blue River, Adair Creek, and Spring Branch Creek); and two sites on the main stem of the Little Blue River (one upstream from the city and one downstream from the three tributaries).
Many factors such as population, land use, and climate, and combinations of these factors contributed to the significant changes in the concentrations and transport of nutrients, chloride, fecal indicator bacteria, and suspended sediment in the urban streams within Independence. The population of Independence and the amount of developed land in the urban watersheds remained unchanged during the 2005–18 study. Differences were noted in precipitation and in streamflow during the study. Annual precipitation and streamflow were separated into two time periods within the study—period 1 (2006–10), having greater annual streamflow and precipitation, and period 2 (2011–18), having about 30 percent lower annual streamflow and less precipitation. Streamflow was an important factor in the transport of nitrogen, phosphorus, chloride, and suspended sediment from the urban watersheds. Changes in data collection methodology during the study period and improvements to the city stormwater and wastewater infrastructure also could have contributed to some of the trends. Between 2009 and 2015, more than 35 million dollars of improvements were made to stormwater and wastewater infrastructure within the city. These improvements, such as additional sewage overflow holding tanks, removal of septic tanks, and improved and expanded sanitary sewer lines and storm overflows, also could have affected the decreased nutrients and fecal indicator bacteria trends among the urban streams in the study area.
Models were used for analyzing streamflow-related variability in constituent concentrations and loads to determine if the water quality changed significantly during the study period. Trends in concentration data at four sites were analyzed using a statistical package called R–QWTREND and trends in load data were analyzed at six sites using a statistical package called Weighted Regressions on Time, Discharge, and Season-Kalman filter (WRTDS–K); both developed by the U.S. Geological Survey and publicly available for use.
Statistically significant trends in flow-weighted nutrient concentrations and loads generally were downward during the study period. The only nutrient compound with a statistically significant upward trend in flow-weighted concentration was dissolved orthophosphate as phosphorus at the Rock Creek site and the upstream site on the Little Blue River. A statistically significant downward trend in annual dissolved ammonia load was identified at the downstream Little Blue River site. A significant upward linear trend in annual orthophosphate as phosphorus load was identified on Adair Creek.
A statistically significant upward trend in dissolved chloride concentrations was identified at the downstream Little Blue River site. Road salt application near the site during the winter could have resulted in higher concentrated runoff during wet weather conditions. Annual chloride loads significantly decreased in Adair Creek and Spring Branch Creek. The mean annual chloride load transported in the drier (2011–18) period 2 was significantly less than during the wetter (2006–10) period 1, indicating that trends in precipitation runoff are an important factor in trends in annual transport of chloride.
Statistically significant downward trends in flow-weighted fecal indicator bacteria Escherichia coli (E. coli) population densities were noted for Rock Creek and the down-stream site on the Little Blue River. However, no trend was identified in E. coli population density at the upstream Little Blue River site. The downward trend in E. coli population density at the downstream site could be a result of decreased streamflow and precipitation over the study period, storage of fecal indicator bacteria in the Little Blue River streambed within the study area, die-off of fecal indicator bacteria during travel from upstream to downstream, changes in the sample collection methodology, improvements to the city’s storm-water and wastewater infrastructures, or a combination of these factors.
The statistically significant downward trend in suspended-sediment concentration identified at the upstream Little Blue River site could be affected by the decreased streamflow and precipitation during the study period, by changes in sampling methods within the study period, and by the decrease in construction and urban land development upstream from the city.
No statistically significant change was indicated in the annual suspended-sediment load transported from Independence to the Little Blue River during the study period. More than one-half the suspended sediment transported in the Little Blue River originated in the watershed upstream from Independence.
The Little Blue River and many of its tributaries that drain Independence have been designated as recreational waters classified for whole-body contact class B and secondary contact recreation, and some have been listed as impaired for E. coli by the Missouri Department of Natural Resources from urban runoff and storm sewers. Observations were made among the available E. coli population density data for both Little Blue River sites to further understand water-quality conditions over the study period. Both Little Blue River sites had similar medians and geometric means for the recreational season (April through October) and during the full study period, both of which are greater than the regulatory population density for both recreational classes. The Little Blue River drainage area nearly doubles in size from the upstream to downstream site; therefore, the consistent geometric mean and median of E. coli population densities at the upstream and downstream Little Blue River sites could be primarily due to the larger volume of streamflow creating a dilution effect. Other possible factors could be storage of fecal indicator bacteria in stream bed sediments, die-off of fecal indicator bacteria during transport, improvements to the city’s wastewater and stormwater infrastructure, changes to sampling methodology, or a combination of these factors. Specific sources of the E. coli are currently (2019) unknown.
Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Understanding the carbon transport within aquatic environments is crucial to quantifying global and local carbon budgets, yet limited empirical data currently (2021) exist. This report documents methodology and provides data for quantifying the aquatic export of carbon from a cypress swamp within Big Cypress National Preserve and is part of a larger carbon budget study. The U.S. Geological Survey operated two continuous monitoring stations, 022889001 and 022909471, that measured flow volume and water quality within the Big Cypress National Preserve in South Florida from September 2015 to October 2017. Station 022889001 represented the flow into the study area and station 022909471 represented the flow out of the study area. Site-specific regression models were developed by using continuously measured specific conductance and concomitant, discretely collected dissolved organic carbon, dissolved inorganic carbon, and particulate carbon samples to calculate total carbon (TC) concentrations at 15-minute intervals.
Calculated TC concentrations typically increased as flow was decreasing and decreased as flow was increasing. TC loads were calculated by multiplying concentrations and flow volume, and the difference between the load calculations for input/output locations of the swamp flow system was used to determine the aquatic carbon export from the study area.
Calculated monthly TC loads ranged from 0 metric tons in spring 2017 at both stations to 3,145 and 7,821 metric tons in September 2017 at 022889001 and 022909471, respectively. During 2016, the annual loads were 10,479 and 15,243 metric tons at 022889001 and 022909471, respectively. Calculated monthly aquatic TC exports from the study area ranged from −0.7 gram of carbon per square meter in May 2016 to 44.1 grams of carbon per square meter during September 2017. The carbon export from the study area varied monthly, increased as flow increased, and was greatly influenced by Hurricane Irma in September 2017. The aquatic TC export from the Sweetwater Strand study area was 42.0 grams of carbon per square meter per year in 2016, which is substantially (about 15 times) larger than the estimated overall mean riverine carbon export per square meter for the eastern United States; however, it was also less than the monthly export of carbon in September 2017. The monthly aquatic carbon export from the study area in September 2017 alone was greater than the aquatic carbon export from all of 2016, which is largely the result of the substantial increase in flow attributed to Hurricane Irma.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201136","collaboration":"Greater Everglades Priority Ecosystem Science Program","usgsCitation":"Booth, A.C., 2021, Development and application of surrogate models, calculated loads, and aquatic export of carbon based on specific conductance, Big Cypress National Preserve, South Florida, 2015–17: U.S. Geological Survey Open-File Report 2020–1136, 14 p., https://doi.org/10.3133/ofr20201136.","productDescription":"Report: v, 14 p.; Data Release; 2 Appendixes","onlineOnly":"Y","ipdsId":"IP-112929","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":382104,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix2.rtf","text":"Appendix 2","size":"960 kB","description":"OFR 2020-1136 Appendix 2 rtf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022909471: Loop Road Culverts Monroe Station to Florida Trail, Florida (rtf file)"},{"id":382062,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1136/coverthb.jpg"},{"id":382063,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1136/ofr20201136.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1136"},{"id":382064,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EXZLJT","text":"USGS data release","linkHelpText":"Calculated carbon concentrations, loads, and export in Big Cypress National Preserve, South Florida, 2015-2017"},{"id":382101,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix1.pdf","text":"Appendix 1","size":"424 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1136 Appendix 1 pdf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022889001: Tamiami Canal 11 Mile Road to Monroe Station, Florida"},{"id":382102,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix2.pdf","text":"Appendix 2","size":"356 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1136 Appendix 2 pdf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022909471: Loop Road Culverts Monroe Station to Florida Trail, Florida"},{"id":382103,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix1.rtf","text":"Appendix 1","size":"2.91 MB","description":"OFR 2020-1136 Appendix 1 rtf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022889001: Tamiami Canal 11 Mile Road to Monroe Station, Florida (rtf file)"}],"country":"United States","state":"Florida","otherGeospatial":"Big Cypress National Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.22604370117186,\n 25.812254545273433\n ],\n [\n -80.8978271484375,\n 25.812254545273433\n ],\n [\n -80.8978271484375,\n 26.058016587844723\n ],\n [\n -81.22604370117186,\n 26.058016587844723\n ],\n [\n -81.22604370117186,\n 25.812254545273433\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a network of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network (AWQMN). During water year 2019 (October 1, 2018, through September 30, 2019), water-quality data were collected at 73 stations: 71 AWQMN and alternate AWQMN stations, and 2 U.S. Geological Survey National Water Quality Monitoring Program stations. Among the stations in this report, four stations have data presented from additional sampling performed in cooperation with the U.S. Army Corps of Engineers. Summaries of the concentrations of dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticides are presented. Most of the stations have been classified based on the physiographic province or primary land use in the watershed monitored by the station. Some stations have been classified based on the unique hydrologic characteristics of the waterbodies (springs, large rivers) they monitor. A summary of hydrologic conditions including peak streamflows, monthly mean streamflows, and 7-day low flows also are presented for representative streamflow-gaging stations in the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ds1132","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Kay, R.T., 2021, Quality of surface water in Missouri, water year 2019: U.S. Geological Survey Data Series 1132, 26 p., https://doi.org/10.3133/ds1132.","productDescription":"Report: v, 26 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119904","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":381906,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1132/ds1132.pdf","text":"Report","size":"1.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1132"},{"id":381905,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1132/coverthb.jpg"},{"id":382023,"rank":3,"type":{"id":30,"text":"Data 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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
This report documents statistics for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM). The U.S. Geological Survey developed SELDM and the statistics documented in this report in cooperation with the Federal Highway Administration to indicate the risk for stormwater flows, concentrations, and loads to exceed user-selected water-quality goals and the potential effectiveness of mitigation measures to reduce such risks. In SELDM, three treatment variables—hydrograph extension, volume reduction, and water-quality treatment—are simulated by using the trapezoidal distribution and the rank correlation with the associated runoff variables. This report describes methods for calculating the trapezoidal distribution statistics and rank correlation coefficients for these treatment variables and methods for estimating the minimum irreducible concentration (MIC), which is the lowest expected effluent concentration from a BMP site or a category of BMPs. These statistics are different from the statistics commonly used to characterize or compare BMPs; they are designed to provide a stochastic transfer function to approximate the quantity, duration, and quality of BMP effluent given the associated inflow values for a population of storm events.
Analyses for this study were done with data extracted from a modified copy of the December 2019 version of the International Stormwater Best Management Practices Database. Statistics for volume reduction, hydrograph extension, and water-quality treatment were developed with selected data. The medians of the best-fit statistics for selected constituents were used to construct generalized cumulative distribution functions for the three treatment variables. For volume reduction and hydrograph extension, selection of a Spearman’s rank correlation coefficient (rho) value that is the average of the median and maximum values for the BMP category may help generate realistic simulation results in SELDM. The median rho value may be selected to help generate realistic simulation results for water-quality treatment variables.
Water-quality treatment statistics, including trapezoidal ratios and MIC values, were developed for 51 runoff-quality constituents commonly measured in highway and urban runoff studies. Statistics were calculated for water-quality properties, sediment and solids, nutrients, major and trace inorganic elements, organic compounds, and biologic constituents.
Analysis of MIC values provides information to guide professional judgement for selecting values for simulating water quality at sites of interest. The MIC is a lower bound for BMP discharge concentrations and will therefore replace simulated BMP discharge concentrations below the selected value. A new method for estimating MIC values, the lognormal variate of inflow concentrations, was developed in this report and these statistics were calculated for individual constituents and constituent categories. Inflow quality is correlated to MIC values for some constituents, but regional soil concentrations were not strongly correlated to MIC values.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205136","collaboration":"Prepared in cooperation with the Federal Highway Administration","usgsCitation":"Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136.","productDescription":"Report: 41 p.; 4 Tables; Data Release; Software Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119618","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":381933,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.04.txt","text":"Table 1.4","size":"89.4 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of correlations between the geometric mean concentration of inflows and selected minimum irreducible concentration estimates"},{"id":381930,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.01.txt","text":"Table 1.1","size":"91.2 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Median of selected treatment statistics for individual constituents"},{"id":381932,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.03.txt","text":"Table 1.3","size":"89.2 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of the lognormal variate values of selected minimum irreducible concentrations"},{"id":381929,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X3ECTD","text":"USGS data release","linkHelpText":"Statistics for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM)"},{"id":381927,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136.pdf","text":"Report","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5136"},{"id":381928,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9XBPIOB","text":"USGS software release","linkHelpText":"- Best Management Practices Statistical Estimator (BMPSE) Version 1.2.0"},{"id":381931,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.02.txt","text":"Table 1.2","size":"87.5 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of the minimum irreducible concentration"},{"id":381926,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5136/coverthb.jpg"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
In cooperation with the State of Hawai‘i Commission on Water Resource Management and in collaboration with the University of Hawaiʻi Water Resources Research Center, the U.S. Geological Survey developed a water-resource monitoring program—a rainfall, surface-water, and groundwater data-collection program—that is required to meet State needs for water-resource assessment, management, and protection in Hawai‘i. Current and foreseeable issues related to water-resource management and climate-change effects guided the evaluation of data-collection sites within the monitoring program. Data-collection sites currently (2018) being operated in Hawai‘i were evaluated, and additional data-collection sites were selected on the basis of their usefulness for characterizing anthropogenic effects on water resources or representing natural conditions. Data-collection strategies consist of a combination of continuous long-term monitoring to evaluate trends and climate-change effects and occasional and periodic intensive monitoring to enhance spatial understanding of hydrologic conditions and to address current issues in priority areas—areas that currently have water-availability issues or are expected to have the greatest socioeconomic or ecological effects because of climate change.
Priority areas for rainfall monitoring consist of urban and agricultural lands, areas with high rainfall and high-rainfall gradient, and areas within the trade-wind inversion band. Surface-water priority areas consist of streams with major surface-water diversions, with established interim instream-flow standards, in a surface-water management area, that support water leases, and with uncertainties in hydrogeologic characteristics. Priority areas for groundwater monitoring consist of areas with high withdrawal, declining water levels, reduced recharge, limited alternative sources, and uncertainties in hydrogeologic characteristics.
Data-quality objectives for the rainfall, surface-water, and groundwater monitoring programs that describe anticipated uses of the data were established with the goal of producing useful, reliable, and accurate water-resource information of sufficient precision to support decision making. The data-quality objectives also consider quality-assurance and quality-control programs that ensure defensible data. Establishment of common data-quality objectives not only assures comparability of data collected by multiple agencies but also allows data from academic, private, and public organizations to be useful for meeting State monitoring needs, provided the data meet appropriate data-quality objectives and data-accessibility requirements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205115","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management and in collaboration with the University of Hawai‘i Water Resources Research Center","usgsCitation":"Cheng, C.L., Izuka, S.K., Kennedy, J.J., Frazier, A.G., and Giambelluca, T.W., 2021, Water-resource management monitoring needs, State of Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2020-5115, 114 p., https://doi.org/10.3133/sir20205115.","productDescription":"xviii, 114 p.","numberOfPages":"114","onlineOnly":"Y","ipdsId":"IP-106432","costCenters":[{"id":525,"text":"Pacific Islands Water Science 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\"}}]}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Like most streams located in the Ozark Plateaus, the Buffalo River in Arkansas generally has excellent water quality. Water-quality conditions in Big Creek, however, a major tributary of the middle Buffalo River, have been less favorable than that of other Buffalo River tributaries. Concerns regarding the influence of water quality in Big Creek on the Buffalo River magnified in 2013 when a large confined animal feeding operation (CAFO) began operating in the watershed. In response to these concerns, the U.S. Geological Survey compared monthly nutrient concentrations and seasonal periphyton assemblage metrics of a site on Big Creek downstream of the CAFO, two Buffalo River control sites upstream of the confluence with Big Creek, and three Buffalo River test sites downstream of the confluence with Big Creek. In addition to identifying potential nutrient patterns and periphyton responses along a low-level nutrient exposure gradient, the study determined how nutrient contributions from Big Creek (and the CAFO) are affecting ecological conditions and consequent ecosystem services in the Buffalo River. Nutrient and periphyton data exhibited more temporal than spatial variability. Nutrient concentrations were generally highest of all sites at the Big Creek site. Concentrations at the five sites on the Buffalo River were typically low (near laboratory reporting limits), and concentrations at the three test sites rarely exceeded those of the two control sites. An index developed with three ecologically relevant periphyton metrics (oligotrophic taxa and Homoeothrix percent relative abundance and mesotrophic diatoms percent taxa richness) suggested that nutrient uptake at sites downstream of the Big Creek-Buffalo River confluence resulted in subtle shifts in downstream periphyton assemblages. The periphyton index of biological integrity at control sites was slightly and generally more favorable compared to test sites. Even so, when periphyton data were considered in conjunction with both hydrology and water-quality data, the negative consequences of antecedent high flows and associated scouring exceeded the potential positive effects that low-level nutrients had on algal productivity. These findings emphasize the importance of comparing biological and chemical data across extended temporal scales, particularly when working with low-level nutrient gradients.
Coral bleaching is the single largest global threat to coral reefs worldwide. Integrating the diverse body of work on coral bleaching is critical to understanding and combating this global problem. Yet investigating the drivers, patterns, and processes of coral bleaching poses a major challenge. A recent review of published experiments revealed a wide range of experimental variables used across studies. Such a wide range of approaches enhances discovery, but without full transparency in the experimental and analytical methods used, can also make comparisons among studies challenging. To increase comparability but not stifle innovation, we propose a common framework for coral bleaching experiments that includes consideration of coral provenance, experimental conditions, and husbandry. For example, reporting the number of genets used, collection site conditions, the experimental temperature offset(s) from the maximum monthly mean (MMM) of the collection site, experimental light conditions, flow, and the feeding regime will greatly facilitate comparability across studies. Similarly, quantifying common response variables of endosymbiont (Symbiodiniaceae) and holobiont phenotypes (i.e., color, chlorophyll, endosymbiont cell density, mortality, and skeletal growth) could further facilitate cross-study comparisons. While no single bleaching experiment can provide the data necessary to determine global coral responses of all corals to current and future ocean warming, linking studies through a common framework as outlined here, would help increase comparability among experiments, facilitate synthetic insights into the causes and underlying mechanisms of coral bleaching, and reveal unique bleaching responses among genets, species, and regions. Such a collaborative framework that fosters transparency in methods used would strengthen comparisons among studies that can help inform coral reef management and facilitate conservation strategies to mitigate coral bleaching worldwide.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2262","usgsCitation":"Grottoli, A., Toonen, R.J., van Woesik, R., Vega Thurber, R., Warner, M.E., McLachlan, R.H., Price, J., Bahr, K.D., Baums, I., Castillo, K., Coffroth, M.A., Cunning, R., Dobson, K., Donahue, M., Hench, J.L., Iglesias-Prieto, R., Kemp, D.W., Kenkel, C.D., Kline, D.I., Kuffner, I.B., Matthews, J., Mayfield, A., Padilla-Gamino, J., Palumbi, S.R., Voolstra, C., Weis, V.M., and Wu, H.C., 2021, Increasing comparability among coral bleaching experiments: Ecological Applications, v. 31, no. 4, e02262, 17 p., https://doi.org/10.1002/eap.2262.","productDescription":"e02262, 17 p.","ipdsId":"IP-114969","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454223,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2262","text":"Publisher Index Page"},{"id":390962,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Grottoli, Andrea G.","contributorId":267953,"corporation":false,"usgs":false,"family":"Grottoli","given":"Andrea G.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":825698,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Toonen, R. J.","contributorId":267954,"corporation":false,"usgs":false,"family":"Toonen","given":"R.","email":"","middleInitial":"J.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":825699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Woesik, R.","contributorId":40820,"corporation":false,"usgs":false,"family":"van Woesik","given":"R.","email":"","affiliations":[],"preferred":false,"id":825700,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vega Thurber, R.","contributorId":267956,"corporation":false,"usgs":false,"family":"Vega Thurber","given":"R.","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":825701,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Warner, M. E.","contributorId":267959,"corporation":false,"usgs":false,"family":"Warner","given":"M.","email":"","middleInitial":"E.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":825702,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McLachlan, R. H.","contributorId":267962,"corporation":false,"usgs":false,"family":"McLachlan","given":"R.","email":"","middleInitial":"H.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":825703,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Price, James","contributorId":156327,"corporation":false,"usgs":false,"family":"Price","given":"James","affiliations":[{"id":20318,"text":"Bureau of Ocean Energy Management","active":true,"usgs":false}],"preferred":false,"id":825704,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bahr, K. D.","contributorId":267966,"corporation":false,"usgs":false,"family":"Bahr","given":"K.","email":"","middleInitial":"D.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":825705,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Baums, I. B.","contributorId":267968,"corporation":false,"usgs":false,"family":"Baums","given":"I. B.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":825706,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Castillo, K.","contributorId":267971,"corporation":false,"usgs":false,"family":"Castillo","given":"K.","email":"","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":false,"id":825707,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Coffroth, M. A.","contributorId":267973,"corporation":false,"usgs":false,"family":"Coffroth","given":"M.","email":"","middleInitial":"A.","affiliations":[{"id":48981,"text":"State University of New York","active":true,"usgs":false}],"preferred":false,"id":825708,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Cunning, R.","contributorId":267976,"corporation":false,"usgs":false,"family":"Cunning","given":"R.","email":"","affiliations":[{"id":39376,"text":"Shedd Aquarium","active":true,"usgs":false}],"preferred":false,"id":825709,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Dobson, K.","contributorId":267979,"corporation":false,"usgs":false,"family":"Dobson","given":"K.","email":"","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":825710,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Donahue, M.","contributorId":267982,"corporation":false,"usgs":false,"family":"Donahue","given":"M.","email":"","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":825711,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hench, James L.","contributorId":196320,"corporation":false,"usgs":false,"family":"Hench","given":"James","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":825712,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Iglesias-Prieto, R.","contributorId":267986,"corporation":false,"usgs":false,"family":"Iglesias-Prieto","given":"R.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":825713,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Kemp, D. W.","contributorId":267988,"corporation":false,"usgs":false,"family":"Kemp","given":"D.","email":"","middleInitial":"W.","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":825714,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Kenkel, C. D.","contributorId":267991,"corporation":false,"usgs":false,"family":"Kenkel","given":"C.","email":"","middleInitial":"D.","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":825715,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Kline, D. I.","contributorId":267994,"corporation":false,"usgs":false,"family":"Kline","given":"D.","email":"","middleInitial":"I.","affiliations":[{"id":12671,"text":"Smithsonian Tropical Research Institute","active":true,"usgs":false}],"preferred":false,"id":825716,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"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":825717,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Matthews, Jessica","contributorId":198726,"corporation":false,"usgs":false,"family":"Matthews","given":"Jessica","email":"","affiliations":[],"preferred":false,"id":825718,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Mayfield, A.","contributorId":267999,"corporation":false,"usgs":false,"family":"Mayfield","given":"A.","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":825719,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Padilla-Gamino, J.","contributorId":268000,"corporation":false,"usgs":false,"family":"Padilla-Gamino","given":"J.","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":825720,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Palumbi, S. R.","contributorId":268003,"corporation":false,"usgs":false,"family":"Palumbi","given":"S.","email":"","middleInitial":"R.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":825721,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Voolstra, C. R.","contributorId":268006,"corporation":false,"usgs":false,"family":"Voolstra","given":"C. R.","affiliations":[{"id":55536,"text":"University of Konstanz","active":true,"usgs":false}],"preferred":false,"id":825722,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Weis, V. M.","contributorId":268008,"corporation":false,"usgs":false,"family":"Weis","given":"V.","email":"","middleInitial":"M.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":825723,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Wu, H. C.","contributorId":268011,"corporation":false,"usgs":false,"family":"Wu","given":"H.","email":"","middleInitial":"C.","affiliations":[{"id":55538,"text":"Leibniz Centre for Tropical Marine Research","active":true,"usgs":false}],"preferred":false,"id":825724,"contributorType":{"id":1,"text":"Authors"},"rank":27}]}} ,{"id":70222937,"text":"70222937 - 2021 - Select techniques for detecting and quantifying seepage from unlined canals","interactions":[],"lastModifiedDate":"2021-08-10T15:51:00.827832","indexId":"70222937","displayToPublicDate":"2020-09-30T10:39:31","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7504,"text":"Final Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"ST-2020-19144-01","title":"Select techniques for detecting and quantifying seepage from unlined canals","docAbstract":"Canal seepage losses affect the ability of water conveyance structures to maximize efficiency and can be a precursor to canal failure. Identification and quantification of canal seepage out of unlined canals is a complex interaction affected by geology, canal stage, operations, embankment geometry, siltation, animal burrows, structures, and other physical characteristics. Seepage out of unlined canals can be coarsely estimated using a mass balance-type approach (water in minus water out with the difference assumed to be a combination of seepage and evapotranspiration). More sophisticated methods are used in some instances but are typically limited efforts aimed at quantifying seepage in a specific location.
Seepage is generally broken out into two categories: diffuse and concentrated (or focused) seepage. Diffuse seepage is where the seepage discharges relatively constant over a given area, whereas concentrated (point discharge source) seepage discharges along preferentially focused areas. Diffuse seepage typically occurs in homogeneous conditions where the amount of water flowing into the subsurface is controlled by soil permeability and canal stage. Conversely, concentrated seepage occurs in areas of heterogeneous conditions where water flows into bedrock fractures, rodent burrows or other pre-existing discrete flow-paths. Concentrated seepage can also develop in the advent of sudden or excessive increases in hydraulic gradient which can lead to heaving, cracking, and development of backward erosion piping flow-paths. Concentrated and diffuse seepage can lead to seeps, in this case, a surface expression of water fed by irrigation water on canal embankment or at distal regions away from the canal.
This report focuses on work funded by the Research and Development Office from Fiscal Year 2016 through 2021 and the references provided pertain primarily to those efforts. This report also provides a generalized framework for how and when to investigate seepage out of an unlined canal based on the type of seepage, level of understanding about the seepage locations, geology, and knowledge of the subsurface conditions. The various methods used to locate seeps and quantify canal seepage are discussed in further detail, with references provided for the reader.
The following seepage investigation scenarios are discussed within the report:
1. Idealized workflow insensitive to time with highest quality data required
2. General workflow sensitive to time with highest quality data required
3. General workflow insensitive to time with lowest cost items preceding more costly techniques
4. Newly developed concentrated seep(s), concern about consequences (time sensitive)
5. Newly developed or rapidly increasing diffuse seepage, concern about consequences (time sensitive)
6. Existing concentrated seep(s), limited concern about consequences, poor geologic understanding
7. Existing concentrated seep(s), limited concern about consequences, good geologic understanding
8. Existing diffuse seepage, limited concern about consequences, poor geologic understanding
9. Existing diffuse seepage, limited concern about consequences, good geologic understanding
A workflow is given for each scenario which details recommended steps and the order in which those steps should be taken to maximize efficiency and data quality. The various seepage investigation techniques and estimated costs are discussed in more detail later in this report.
The next step is to take the data collected from the various methods and incorporate them into canal operations models to optimize deliveries. This step could also include the development of 3D seepage models to better understand the larger-scale groundwater-surface water interactions and how they are affected by the water delivery system.
","language":"English","publisher":"U.S. Bureau of Reclamation","usgsCitation":"Lindenbach, E.J., Kang, J.B., Rittgers, J.B., and Naranjo, R.C., 2021, Select techniques for detecting and quantifying seepage from unlined canals: Final Report ST-2020-19144-01, viii, 75 p.","productDescription":"viii, 75 p.","ipdsId":"IP-122681","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":387819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387793,"type":{"id":15,"text":"Index Page"},"url":"https://www.usbr.gov/research/projects/download_product.cfm?id=2955"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lindenbach, Evan J.","contributorId":263642,"corporation":false,"usgs":false,"family":"Lindenbach","given":"Evan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":820920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kang, Jong Beom","contributorId":263643,"corporation":false,"usgs":false,"family":"Kang","given":"Jong","email":"","middleInitial":"Beom","affiliations":[],"preferred":false,"id":820921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rittgers, Justin B.","contributorId":263644,"corporation":false,"usgs":false,"family":"Rittgers","given":"Justin","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":820922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Naranjo, Ramon C. 0000-0003-4469-6831 rnaranjo@usgs.gov","orcid":"https://orcid.org/0000-0003-4469-6831","contributorId":3391,"corporation":false,"usgs":true,"family":"Naranjo","given":"Ramon","email":"rnaranjo@usgs.gov","middleInitial":"C.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":820873,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223476,"text":"70223476 - 2021 - Detrital zircon age spectra of middle and upper Eocene outcrop belts, U.S. Gulf Coast region","interactions":[],"lastModifiedDate":"2021-08-27T13:28:26.45083","indexId":"70223476","displayToPublicDate":"2020-05-09T08:23:12","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":972,"text":"Basin Research","active":true,"publicationSubtype":{"id":10}},"title":"Detrital zircon age spectra of middle and upper Eocene outcrop belts, U.S. Gulf Coast region","docAbstract":"Recently reported detrital zircon (DZ) data help to associate the Paleogene strata of the Gulf of Mexico region to various provenance areas. By far, recent work has emphasised upper Paleocene-lower Eocene and upper Oligocene strata that were deposited during the two episodes of the highest sediment supply in the Paleogene. The data reveal a dynamic drainage history, including (1) initial routing of western Cordilleran drainages towards the Gulf of Mexico in the Paleocene, (2) an eastward shift of the western continental divide, from the Jura-Cretaceous cordilleran arc to the eastern edge of the Laramide province after the Paleocene and (3) a southward shift, along the eastern Laramide province, of the headwaters of river systems draining to the Mississippi and Houston embayments at some time between the early Eocene and Oligocene. However, DZ characterisation of most (~20 Myr) of the middle Eocene-lower Oligocene section remains limited. We present 60 DZ age spectra, most of which are from the middle or upper Eocene outcrop belts, with 50–200-km spacing. We define six to eight distinct groups of DZ age spectra for middle and upper Eocene strata. Data from this and other studies resolve at least six substantial temporal changes in age spectra at various positions along the continental margin. The evolving age spectra constrain the middle and upper Eocene drainage patterns of large parts of interior North America. The most well-resolved aspects of these drainage patterns include (1) persistent rivers that flowed from erosional landscapes across the Paleozoic Appalachian orogen either into the low-lying Mississippi embayment or directly into the eastern Gulf; (2) at least during marine regressions, a trunk channel that likely flowed southward along the axial part of Mississippi Embayment and integrated tributaries from the east and west; and (3) rivers that flowed to the Houston embayment in the middle Eocene that likely originated in the Laramide province in central Colorado and southern Wyoming, as Precambrian basement highs in those source areas were being unroofed.
The Dog River and northern part of the Badger Lake 7.5' quadrangles encompasses an area of ~201 km2 (77.6 mi2) of the High Cascades of north-central Oregon, lying across the eastern slopes of Mount Hood volcano (Figure 1-1; Plate 1; referred to herein as Dog River–Badger Lake area). Mount Hood, known as Wy’east to Native Americans, is Oregon’s tallest peak (3,427 m [11,241 ft]). The volcano has erupted episodically for the past 500,000 years, experiencing two major eruptive periods during the last 1,500 years (Scott and others, 1997a; Scott and others, 2003; Scott and Gardner, 2017). Cascade Range volcanism and structural development in the area dates back longer, with eruptive activity dating from latest Miocene to recent time; part of that volcano-tectonic record is detailed by new high-resolution geologic mapping presented here.
The geology of the Dog River–Badger Lake area was mapped by the Oregon Department of Geology and Mineral Industries (DOGAMI) between 2017 and 2020, in collaboration with geoscientists from the U. S. Geological Survey Cascade Volcano Observatory (USGS CVO) and Hamilton College, New York. The primary objective of this investigation is to provide an updated and spatially accurate geologic framework for the Dog River–Badger Lake area as part of a multi-year study of the geology of the larger Middle Columbia Basin (Figure 1-1, Figure 1-2). Additional key objectives of this project are to: 1) determine the geologic history of volcanic rocks in this part of the northern Oregon Cascade Range, including lava flows and volcaniclastic deposits erupted from Middle Pleistocene to Holocene Mount Hood volcano; 2) provide significant new details about the structure and fault history along the northern segment of the High Cascades intra-arc graben (Hood River graben); and 3) better understand geologic hazards in the region, related to earthquakes, volcanoes, and landslides. New detailed geologic data presented here also provides a basis for future geologic, geohydrologic, and geohazard studies in the greater Middle Columbia Basin. Detailed geologic mapping in this part of the Middle Columbia Basin is a high priority of the Oregon Geologic Map Advisory Committee (OGMAC), supported in part by grants from the STATEMAP component of the USGS National Cooperative Geologic Mapping Program (G17AC00210, G19AC00160). Additional funds were provided by the State of Oregon.
The core products of this study are this report, an accompanying geologic map and cross sections (Plate 1), an Esri ArcGIS™ geodatabase, and Microsoft Excel® spreadsheets tabulating point data for geochemistry, geochronology, magnetic polarity, orientation points, and well data. The geodatabase presents the new geologic mapping in a digital format consistent with the USGS National Cooperative Geologic Mapping Program Geologic Map Schema (GeMS) (U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020). This geodatabase contains spatial information, including geologic polygons, contacts, structures, geochemistry, geochronology, magnetic observation, orientation points, and well data, as well as data about each geologic unit such as age, lithology, mineralogy, and structure. Digitization at scales of 1:8,000 or better was accomplished using a combination of high-resolution lidar topography and imagery. Surficial and bedrock geologic units contained in the geodatabase are depicted on the Plate 1 at a scale of 1:24,000. Both the geodatabase and geologic map are supported by this report describing the geology in detail.
","language":"English","publisher":"Oregon Department of Geology and Mineral Industries","usgsCitation":"McClaughry, J.D., Scott, W., Duda, C.J., and Conrey, R.M., 2020, Geologic map of the Dog River and northern part of the Badger Lake 7.5′ quadrangles, Hood River County, Oregon: Geological Map 126, Report: 154 p.; 1 Plate 48 x 52 inches; Database; Metadata.","productDescription":"Report: 154 p.; 1 Plate 48 x 52 inches; Database; Metadata","ipdsId":"IP-126371","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":385093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383245,"type":{"id":15,"text":"Index Page"},"url":"https://www.oregongeology.org/pubs/gms/p-GMS-126.htm"}],"scale":"24000","country":"United States","state":"Oregon","county":"Hood River County","otherGeospatial":"Dog River and Northern Part of the Badger Lake 7.5' Quadrangles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.87683105468749,\n 44.97839955494438\n ],\n [\n -120.5914306640625,\n 44.97839955494438\n ],\n [\n -120.5914306640625,\n 45.73494252455993\n ],\n [\n -121.87683105468749,\n 45.73494252455993\n ],\n [\n -121.87683105468749,\n 44.97839955494438\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McClaughry, Jason D.","contributorId":194544,"corporation":false,"usgs":false,"family":"McClaughry","given":"Jason","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":810242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scott, William E. 0000-0001-8156-979X","orcid":"https://orcid.org/0000-0001-8156-979X","contributorId":250706,"corporation":false,"usgs":true,"family":"Scott","given":"William E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duda, Carlie J. M.","contributorId":250707,"corporation":false,"usgs":false,"family":"Duda","given":"Carlie","email":"","middleInitial":"J. M.","affiliations":[{"id":32397,"text":"Oregon Department of Geology and Mineral Industries","active":true,"usgs":false}],"preferred":false,"id":810244,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conrey, Richard M.","contributorId":194345,"corporation":false,"usgs":false,"family":"Conrey","given":"Richard","email":"","middleInitial":"M.","affiliations":[{"id":13203,"text":"School of the Environment, Washington State University","active":true,"usgs":false}],"preferred":false,"id":810245,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210826,"text":"70210826 - 2020 - Recent planform changes in the Upper Mississippi River","interactions":[],"lastModifiedDate":"2021-11-03T14:42:36.620726","indexId":"70210826","displayToPublicDate":"2020-12-31T09:03:48","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5000,"text":"Long Term Resource Monitoring Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"LTRM-2019GC8","title":"Recent planform changes in the Upper Mississippi River","docAbstract":"Geomorphic changes in the Upper Mississippi River (UMR) have long been a concern of river agencies charged with maintaining and restoring river habitat (GREAT 1980; Jackson et al. 1981; USFWS 1992). Large meandering alluvial rivers like the UMR are expected to constantly change and adjust their fluvial landforms within their riparian corridors as a result of the natural interaction of hydrologic processes, sediment movement, and vegetation over time. However, present geomorphic changes in the UMR reflect altered hydrologic, hydraulic, and sediment conditions caused by regulated flows, constructed agricultural levees and navigation dams, altered land use in the watershed, and climate change. Levees reduce lateral hydrologic and sediment connectivity between channels and floodplains on many tributaries and on the Mississippi River downstream of Pool 13. Between each of the dams are a repeating series of landforms associated with tailwater, intermediate, and impounded conditions. The dams maintain a minimum water level, thus creating many off-channel areas that act as sediment traps. Whereas high-head dams cut off sedimentological connectivity longitudinally through the river corridor (Skalak et al., 2013), low head dams on the UMR only slightly altered transport longitudinally. Deltaic-like sedimentation can be common in the impounded sections of dammed rivers. Erosion of relict land surfaces that remained above the raised impounded water levels has been the dominant change in UMR impounded sections due to increased wind fetch leading to increased wave action. Even though upland sources of sediment from tributaries have decreased over the middle to late 20th century, increased annual precipitation, the interplay of increased variability in flood magnitudes from year to year, and more fall and winter flooding have likely changed erosion and sedimentation patterns in the UMR (Belby, et al., 2019). Paradoxically, monitoring and research indicates that the concentration of some water column constituents like total suspended solids and phosphorous has decreased during the 1991 to 2014 time period (Kreiling and Houser, 2016). In areas prone to increased sedimentation, bed elevations rise and thereby water depths are reduced at a given discharge, resulting in loss of fish habitat. Sediment deposition or erosion further influences water exchange rates between main channel and off-channel areas in the river by increasing resistance in connecting channels or enlarging existing connecting channels. Water depth and water exchange rates are the most prominent features describing habitat quality in the UMR (De Jager et al. 2018), and in some cases, the trajectory of planform change from heightened deposition promises to threaten deep backwater habitats particularly important for overwintering fish.\n\nAlthough information on the rate of vertical change in bed elevation is needed for a complete assessment of geomorphic change associated with the loss of deep backwater habitats, mapping planform changes over time (i.e., lateral changes between the land-water boundary) provide needed information on the location, potential cause, and progressive direction of deposition, especially in the mid sections between dams where deltaic processes are the most pronounced. Several types of planform changes have been observed and identified as concerns. For example, island loss in the large impounded areas of the upper part of the UMR was one of the concerns identified by river managers in the 1980s and 90s, and subsequently island construction became a common form of restoration implemented by the Upper Mississippi River Restoration (UMRR) Program (USACE 2012). Other subtler planform changes, such as channel bank erosion and delta formation in backwaters, are perceived to be important, but have largely gone unquantified. A systemwide reconnaissance of the UMR and IWW conducted in 1998 concluded that 14-percent of the river banks were eroding (Nakato and Anderson 1998). However, stabilization of existing river banks has never been widely pursued as a restoration measure, due to the high cost and uncertain benefits. Delta formation reduces the amount of backwater habitat; however, the deltas maintain and create a mix of riparian and aquatic habitats, and that is generally considered to be beneficial for wildlife and fish. If recent hydrologic trends of more frequent and longer duration flood events continue, a better understanding of planform changes can help in describing past changes, and then be used to forecast potential future trajectories of change. If UMR resource managers determine that past and forecasted conditions are undesirable, then UMRR projects could be identified and prioritized to address those concerns.\n\nVegetative cover associations with landform changes have been used to detect and quantify planform changes in many rivers (Johnson 1985; Hiatt 2015; Volte et al. 2015). Freyer and Jefferson (2013) completed such a study in Pool 6 of the UMR using the landcover data from 12 dates over a 115-yr period, including the 1989, 2000, and 2010/2011 landcover/use (LCU) data from the UMRR Program. Planform change detected over the last 20 years represented by the UMRR Program data best reflect present-day geomorphic patterns, rates and processes. Changes occurring prior to dam construction and changes occurring soon after dam construction are likely not the same as those happening now, 50-70 years after dam construction and creation of the impoundments (McHenry et al., 1984; Bhowmik and Adams, 1986; WEST Consultants, 2000). \n\nThe LCU data from each of the 1989, 2000, and 2010/2011 imagery was developed using similar methods and is available in a Geographical Information System (GIS) for the entire UMR and therefore provides the opportunity for a more comprehensive planform change analysis. This study used GIS overlays of LCU classes to map and quantify changes in planform features over two periods, looking specifically for depositional areas where terrestrial and wetland vegetation expanded at the expense of open water. The land expansion was grouped into four possible process-based types common in large floodplain rivers, some following that used by Lewin et al. (2017). The four types include: crevasse deltas emanating from a breach from a main channel through a natural levee or narrow floodplain into backwaters (crevasse deltas), tributary deltas expanding into backwaters (tributary deltas), deltaic bars at the upstream end of impoundments (impounded deltas), and linear-like bars extending from the downstream ends of narrow levees and remnant floodplains (bar-tail limbs). The methods deployed for change detection addressed possible errors from a variety of sources.","language":"English","publisher":"US Army Corps of Engineers, Upper Mississippi River Restoration (UMRR) Program","usgsCitation":"Rogala, J.T., Fitzpatrick, F., and Hendrickson, J.S., 2020, Recent planform changes in the Upper Mississippi River: Long Term Resource Monitoring Technical Report LTRM-2019GC8, 33 p.","productDescription":"33 p.","ipdsId":"IP-113610","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391325,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391323,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://umesc.usgs.gov/documents/publications/2020/rogala_a_2020.html"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90,\n 38.58252615935333\n ],\n [\n -91.0546875,\n 40.07807142745009\n ],\n [\n -90,\n 41.86956082699455\n ],\n [\n -90.8349609375,\n 43.29320031385282\n ],\n [\n -91.2744140625,\n 44.465151013519616\n ],\n [\n -93.55957031249999,\n 46.01222384063236\n ],\n [\n -93.4716796875,\n 46.619261036171515\n ],\n [\n -95.1416015625,\n 46.46813299215554\n ],\n [\n -94.52636718749999,\n 45.24395342262324\n ],\n [\n -93.251953125,\n 44.55916341529182\n ],\n [\n -91.93359375,\n 43.866218006556394\n ],\n [\n -91.1865234375,\n 42.4234565179383\n ],\n [\n -90.791015625,\n 42.22851735620852\n ],\n [\n -91.14257812499999,\n 41.705728515237524\n ],\n [\n -91.669921875,\n 41.07935114946899\n ],\n [\n -91.97753906249999,\n 39.842286020743394\n ],\n [\n -91.318359375,\n 38.89103282648846\n ],\n [\n -90,\n 38.58252615935333\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rogala, James T. 0000-0002-1954-4097 jrogala@usgs.gov","orcid":"https://orcid.org/0000-0002-1954-4097","contributorId":2651,"corporation":false,"usgs":true,"family":"Rogala","given":"James","email":"jrogala@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":791606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209612,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hendrickson, Jon S.","contributorId":177520,"corporation":false,"usgs":false,"family":"Hendrickson","given":"Jon","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":791608,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229205,"text":"70229205 - 2020 - Reconnaissance map of the Cenozoic geology in the Carlin basin area, Elko and Eureka counties, Nevada","interactions":[],"lastModifiedDate":"2022-03-03T14:38:55.904902","indexId":"70229205","displayToPublicDate":"2020-12-31T08:27:42","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":10147,"text":"Nevada Bureau of Mines and Geology Open File Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"2020-02","title":"Reconnaissance map of the Cenozoic geology in the Carlin basin area, Elko and Eureka counties, Nevada","docAbstract":"The current map publication was supported by the USGS National Cooperative Geologic Mapping Program under STATEMAP award number G19AC00383.
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Stewart B. McKinney National Wildlife Refuge in Connecticut. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of two marsh management units within the refuge and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that would be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that would maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to approximately $1,190,000, but that further expenditures may yield diminishing return on investment. Management actions in optimal portfolios at total costs less than $1,190,000 included controlling avian predators in both management units, managing stormwater on lands adjacent to one marsh management unit, and removing a tide gate and breaching a dike to improve tidal flow in the other marsh management unit. The management benefits were derived from expected increases in the numbers of spiders (as an indicator of trophic health) and tidal marsh obligate birds, and an expected decrease in the use of herbicides to control invasive vegetation. The prototype presented here provides a framework for decision making at the Stewart B. McKinney National Wildlife Refuge that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201139","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Low, L.E. , Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Vagos, K., and Potvin, R., 2020, Optimization of salt marsh management at the Stewart B. McKinney National Wildlife Refuge, Connecticut, through use of structured decision making: U.S. Geological Survey Open-File Report 2020–1139, 28 p., https://doi.org/10.3133/ofr20201139.","productDescription":"vi, 28 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120812","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":381645,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1139/ofr20201139.pdf","text":"Report","size":"2.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1139"},{"id":381644,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1139/coverthb.jpg"}],"country":"United States","state":"Connecticut","otherGeospatial":"Stewart B. McKinney National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.17169189453125,\n 41.15022321163024\n ],\n [\n -73.13186645507812,\n 41.13677892209895\n ],\n [\n -73.10028076171875,\n 41.14867208811923\n ],\n [\n -73.15177917480469,\n 41.18537216794189\n ],\n [\n -73.18113327026366,\n 41.17090135180691\n ],\n [\n -73.17169189453125,\n 41.15022321163024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708–4039
Confined (or buried) aquifers of glacial origin overlain by till confining units provide drinking water to hundreds of thousands of Minnesota residents. The sustainability of these groundwater resources is not well understood because hydraulic properties of till that control vertical groundwater fluxes (leakage) to underlying aquifers are largely unknown. The U.S. Geological Survey, Iowa State University, Minnesota Geological Survey, and Minnesota Department of Health investigated hydraulic properties and groundwater flow through till confining units using field studies and heuristic MODFLOW simulations. Till confining units in the following late-Wisconsinan stratigraphic units (with locations in parentheses) were characterized: Des Moines lobe till of the New Ulm Formation (Litchfield, Minnesota), Superior lobe till of the Cromwell and Aitkin Formations (Cromwell, Minn.), and Wadena lobe till of the Hewitt Formation (hydrogeology field camp [HFC] near Akeley, Minn.). Pre-Illinoian till of the Good Thunder formation (Olivia, Minn.) was also characterized.
Hydraulic and geochemical field data were collected from sediment cores and a series of five piezometer nests. Each nest consisted of five to eight piezometers screened at short vertical intervals in hydrostratigraphic units including (if present) surficial aquifers, till confining units, confined/buried aquifers, and underlying bedrock. Till hydraulic conductivity was estimated from slug tests (horizontal [Kh]) and constant-rate aquifer tests in the confined aquifer (vertical [Kv]). Travel times through the till were evaluated with Darcy’s law and stable isotope concentrations. A series of heuristic MODFLOW simulations were used to evaluate groundwater fluxes through till across the range of till hydraulic properties and pumping rates observed at the field sites.
The field data demonstrated variability in hydraulic properties between and within till stratigraphic units horizontally and vertically. The variability in hydraulic properties within and between sites resulted in substantial differences in groundwater flux through till. A conceptual understanding that emerges from the vertical till profiles is that they are not homogeneous hydrostratigraphic units with uniform properties; rather, each vertical sequence is a heterogeneous mixture of glacial sediment with differing abilities to transmit water.
Till thicknesses varied from 60 to 166 feet, and till textures ranged from a sandy loam (Hewitt Formation, HFC site) to a silt loam/clay loam (Good Thunder formation, Olivia site). Till Kh varied by one to three orders of magnitude within each piezometer nest. Four piezometer nests had downward hydraulic gradients ranging from 0.04 to 0.56, and one nest had a slight upward hydraulic gradient of 0.02. The Cromwell, HFC, and Litchfield 1 sites were examples of “leaky” tills with high Kv (0.001 to 1.1 feet per day [ft/d]) and geometric mean Kh (0.03 to 0.07 ft/d) and extensive vertical hydraulic connectivity between the confined aquifer and the overlying till. Estimated groundwater travel times through these sites ranged from 1 to 81 years, and two of these sites had tritium throughout their till profiles. The tills at the other two sites, Olivia and Litchfield 2, were effective confining units that had low Kv (0.001 to 0.0005 ft/d) and geometric mean Kh (0.0002 to 0.004 ft/d). The till piezometers at these sites had no drawdown response to short-term (up to 10 hours for Olivia and up to 5 days for Litchfield) high-capacity pumping from the confined aquifer. Estimated groundwater travel times through the tills at these sites ranged from 165 to nearly 1,800 years, and tritium was only detected in the upper one-third of these till profiles. Across all sites, the till vertical anisotropy (ratio of Kh to Kv) ranged by four orders of magnitude from 0.05 at the Cromwell nest to 70 at the Litchfield 1 nest. Stable isotopes of oxygen and hydrogen indicate that groundwater throughout all five till profiles is younger than the last glacial advance into Minnesota at about 11,000 years ago.
The heuristic modeling demonstrated that, for understanding sustainability of groundwater pumping from confined aquifers, knowledge of till hydraulic properties is just as important as knowledge of aquifer hydraulic properties. Substantial differences in groundwater fluxes into and through till were observed across hydrogeologic settings representative of the field sites. Over long periods of time (hundreds of years), pumping-induced hydraulic gradients are established in confined aquifer systems and, even in low hydraulic conductivity tills, these pumping-induced hydraulic gradients increase leakage into and through till compared to ambient conditions.
In conclusion, groundwater flowing vertically downward through till confining units (leakage) replenishes water pumped from confined aquifers. Till hydraulic properties, such as those presented in this report, provide important information that can be used to quantify leakage rates through till. Till hydraulic properties are variable over short distances and profoundly affect leakage rates, demonstrating the importance of site-specific till hydraulic data for evaluating the sustainability of groundwater withdrawals from confined aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205127","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources and in collaboration with Iowa State University and the Minnesota Department of Health","usgsCitation":"Trost, J.J., Maher, A., Simpkins, W.W., Witt, A.N., Stark, J.R., Blum, J., and Berg, A.M., 2020, Hydrogeology and groundwater geochemistry of till confining units and confined aquifers in glacial deposits near Litchfield, Cromwell, Akeley, and Olivia, Minnesota, 2014–18: U.S. Geological Survey Scientific Investigations Report 2020–5127, 80 p., https://doi.org/10.3133/sir20205127.","productDescription":"Report: ix, 80 p.; 2 Data Releases; Dataset","numberOfPages":"94","onlineOnly":"Y","ipdsId":"IP-103595","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":381538,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS dataset","linkHelpText":"— USGS water data for the Nation"},{"id":381534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5127/coverthb.jpg"},{"id":381535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5127/sir20205127.pdf","text":"Report","size":"4.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5127"},{"id":381536,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IXC7D3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geochemical data, water-level data, and slug test analysis results from till confining units and confined aquifers in glacial deposits near Akeley, Cromwell, Litchfield, and Olivia, Minnesota, 2015–2018"},{"id":381537,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KOI6T3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Heuristic MODFLOW models used to evaluate the effects of pumping groundwater from confined aquifers overlain by till confining units"}],"country":"United States","state":"Minnesota","city":"Akeley, Cromwell, Litchfield, Olivia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.5758056640625,\n 45.084672408703945\n ],\n [\n -94.48173522949219,\n 45.084672408703945\n ],\n [\n -94.48173522949219,\n 45.16364237397378\n ],\n [\n -94.5758056640625,\n 45.16364237397378\n ],\n [\n -94.5758056640625,\n 45.084672408703945\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.91549682617188,\n 46.65320687122665\n ],\n [\n -92.84614562988281,\n 46.65320687122665\n ],\n [\n -92.84614562988281,\n 46.6965511173143\n ],\n [\n -92.91549682617188,\n 46.6965511173143\n ],\n [\n -92.91549682617188,\n 46.65320687122665\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.74918365478516,\n 46.987044654111116\n ],\n [\n -94.70970153808594,\n 46.987044654111116\n ],\n [\n -94.70970153808594,\n 47.01678007415223\n ],\n [\n -94.74918365478516,\n 47.01678007415223\n ],\n [\n -94.74918365478516,\n 46.987044654111116\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.02418518066406,\n 44.74185630294231\n ],\n [\n -94.95758056640625,\n 44.74185630294231\n ],\n [\n -94.95758056640625,\n 44.79986517347138\n ],\n [\n -95.02418518066406,\n 44.79986517347138\n ],\n [\n -95.02418518066406,\n 44.74185630294231\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Hinkley Valley, in the central to western Mojave Desert of southeastern California, has a long historical record owing to its position as a crossroads for rail and road traffic and its position adjacent to the Mojave River. Subflow in the Mojave River provided groundwater recharge that maintained water consumption and demand by way of shallow wells for local agriculture in the valley. Its crossroads position led to construction of several power-transmission lines, pipeline, and communications cable routes that transect Hinkley Valley. One of these, a natural gas pipeline and its associated compressor station, was the locus of hexavalent chromium, Cr(VI), released into, and consequent contamination of, groundwater. Understanding the movement and fate of the contaminants is a complex hydrologic and geochemical problem. Geologic mapping of the Hinkley Valley area provides framework elements for use in resolving this problem. This report provides new information on surface and subsurface geology to better constrain the origin and geometry of hydrologically important deposits in the Hinkley Valley area and describes youthful faults that may control sediment distribution and groundwater flow. The geologic map (sheet 1) presents substantial new information on surficial geology, including Pliocene deposits, but does not contain significant new work on bedrock. Bedrock investigations were specific to identifying youthful faults and representative outcrops for rocks that were penetrated by boreholes in the valley. Special attention was placed on locating and describing youthful faults. In addition, we analyzed gravity data to (1) map horizontal gradients that we interpret to reflect long-term fault traces and to (2) estimate the depth to bedrock, which is defined as Miocene and older intrusive and metamorphic rocks for the purposes of this report. The subsurface geology of Hinkley Valley was investigated by examining borehole sediment cores and rock encountered at the base of the sediment section. We analyzed the core to determine depositional environments, provenance, and age of the sediment that infilled the valley. Valleys, mountains, and basins in the Hinkley Valley area are topographically complex and incompletely named. The nearly flat floored Hinkley Valley slopes gently northward. It is framed by Mount General and the informally named “Hinkley hills” (southeast of Mount General) on the northeast and by Iron Mountain and Lynx Cat Mountain on the southwest, although breaks in the western mountains allow stream connections between Hinkley Valley and another valley to the west that is herein referred to as Hawes valley. At its south end, Hinkley Valley is traversed by the entrenched Mojave River, which passes east out of the valley past Barstow. North of Hinkley Valley, a few low hills (including Red Hill) separate the valley from a broad west-sloping piedmont that is part of the physiographic Harper Basin (of which the Harper Lake playa is the center). The lower part of this piedmont, however, is referred to as Water Valley, although it is not a distinct valley. The name derives from groundwater sourced from subflow in the Mojave River, which caused shallow water and even artesian flow in Water Valley but not in other parts of the Harper Basin. When water filled the Harper Basin to form Pleistocene Lake Harper it not only submerged Water Valley but also northern Hinkley Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3458","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board and the State Water Resources Control Board","usgsCitation":"Miller, D.M., Langenheim, V.E., and Haddon, E.K., 2020, Geologic map and borehole stratigraphy of Hinkley Valley and vicinity, San Bernardino County, California: U.S. Geological Survey Scientific Investigations Map 3458, pamphlet 23 p., 2 sheets, scale 1:24,000, https://doi.org/10.3133/sim3458.","productDescription":"Pamphlet,: iv, 23 p.; 2 Sheets ; 2 Tables; Database; Data Release; Metadata","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102109","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":381271,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_sheet2.pdf","text":"Sheet 2","size":"32 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381270,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_sheet1.pdf","text":"Sheet 1","size":"40 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381269,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_database.zip","text":"Database","size":"7.5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":381268,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_base.zip","text":"Base","size":"1.25 GB","linkFileType":{"id":6,"text":"zip"}},{"id":381267,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_metadata.txt","size":"10 KB","linkFileType":{"id":2,"text":"txt"}},{"id":381266,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_pamphlet.pdf","text":"Pamphlet","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381451,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FV5LG5","linkHelpText":"Gravity data of the Hinkley area, southern California"},{"id":381273,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_table_7.xlsx","text":"Table 7","size":"60 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":381265,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3458/covrthb.jpg"},{"id":381272,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_table_3.xlsx","text":"Table 3","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"}}],"country":"United States","state":"California","county":"San Bernadino County","otherGeospatial":"Hinkley Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.26257324218749,\n 34.80647431931937\n ],\n [\n -117.06619262695312,\n 34.80647431931937\n ],\n [\n -117.06619262695312,\n 35.060352812431496\n ],\n [\n -117.26257324218749,\n 35.060352812431496\n ],\n [\n -117.26257324218749,\n 34.80647431931937\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
A three-dimensional groundwater-flow model was developed for the aquifer system of Long Island, New York, to evaluate (1) responses of the hydrologic system to changes in natural and anthropogenic hydraulic stresses, (2) the subsurface distribution of groundwater age, and (3) the regional-scale distribution of groundwater travel times and the source of water to fresh surface waters and coastal receiving waters. The model also provides the groundwater flow components used to define model boundaries for possible inset models used for local-scale analyses.
The three-dimensional, groundwater flow model developed for this investigation uses the numerical code MODFLOW–NWT to represent steady-state conditions for average groundwater pumping and aquifer recharge for 2005–15. The particle-tracking algorithm MODPATH, which simulates advective transport in the aquifer, was used to estimate groundwater age, delineate the areas at the water table that contribute recharge to coastal and freshwater bodies, and estimate total travel times of water from the water table to discharge locations.
A three-dimensional, 1-meter (3.3-foot) topobathymetric model was used to determine land-surface altitudes for the island and seabed altitudes for the surrounding coastal waters. The mapped extents and surface altitudes of major geologic units were compiled and used to develop a three-dimensional hydrogeologic framework of the aquifer system, including aquifers and confining units. Lithologic data from deep boreholes and previous aquifer-test results were used to estimate the three-dimensional distribution of hydraulic conductivity in principal aquifers. Natural recharge from precipitation was estimated for 2005–15 using a modified Thornthwaite-Mather methodology as implemented in a soil-water balance model. Components of anthropogenic recharge—wastewater return flow, storm water inflow, and inflow from leaky infrastructure—also were estimated for 2005–15. Groundwater withdrawals for various sources, including public water supply, industrial, remediation, and agricultural, were compiled or estimated for the same period.
These data were incorporated into the model development to represent the aquifer system geometry, boundaries, and initial hydraulic properties of the regional aquifers and confining units within the Long Island aquifer system. Average hydraulic conditions—water levels and streamflows—for 2005–15 were estimated using existing data from the U.S. Geological Survey National Water Information System database. Model inputs were adjusted to best match average hydrologic conditions using inverse methods as implemented in the parameter-estimating software PEST. The calibrated model was used to simulate average hydrologic conditions in the aquifer system for 2005–15.
About 656 cubic feet per second of water was withdrawn on average annually for 2005–15 for water supply and an average of about 349 cubic feet per second of water recharged the aquifer annually from return flow and leaky infrastructure. Parts of New York City have drawdowns exceeding 25 feet, mostly because of urbanization and associated large decreases in recharge rates. Large areas in the western part of the island have drawdowns exceeding 10 feet, mostly from large groundwater withdrawals and sewering, which largely eliminates wastewater return flow. Water-table altitudes in eastern parts of the island increased by more than 2 feet in some areas as a result of wastewater return flow in unsewered areas and changes in land use. Changes in streamflows show a similar pattern as water-table altitudes. Streamflows generally decrease in western parts of the island where there are large drawdowns and increase in eastern parts of the island where water-table altitudes increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205091","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Walter, D.A., Masterson, J.P., Finkelstein, J.S., Monti, J., Jr., Misut, P.E., and Fienen, M.N., 2020, Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15: U.S. Geological Survey Scientific Investigations Report 2020–5091, 75 p., https://doi.org/10.3133/sir20205091.","productDescription":"Report: ix, 75 p.; 3 Data Releases","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112206","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":381521,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5091/images/"},{"id":381195,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5091"},{"id":381194,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5091/coverthb2.jpg"},{"id":381192,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954DLLC","text":"USGS data release","linkHelpText":"Aquifer texture data describing the Long Island aquifer system"},{"id":381191,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KWQSEJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 used to simulate groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15"},{"id":381190,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens Counties, New York, October-November 2017"},{"id":381520,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.XML"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.102783203125,\n 40.55554790286311\n ],\n [\n -73.7017822265625,\n 40.49709237269567\n ],\n [\n -72.8778076171875,\n 40.65147128144057\n ],\n [\n -72.037353515625,\n 40.91351257612758\n ],\n [\n -71.6143798828125,\n 41.16211393939692\n ],\n [\n -71.9384765625,\n 41.178653972331674\n ],\n [\n -72.191162109375,\n 41.236511201246216\n ],\n [\n -72.65808105468749,\n 41.11660732012896\n ],\n [\n -73.4490966796875,\n 40.967455873296714\n ],\n [\n -73.751220703125,\n 40.89275342420696\n ],\n [\n -74.0203857421875,\n 40.718119379753446\n ],\n [\n -74.102783203125,\n 40.55554790286311\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
About one-quarter of the water supply for the Village of Ruidoso, New Mexico, is from groundwater pumped from wells located along North Fork Eagle Creek in the National Forest System lands of the Lincoln National Forest near Alto, New Mexico. Because of concerns regarding the effects of groundwater pumping on surface-water hydrology in the North Fork Eagle Creek Basin and the effects of the 2012 Little Bear Fire, which resulted in substantial loss of vegetation in the basin, the U.S. Department of Agriculture Forest Service, Lincoln National Forest, has required monitoring of a portion of North Fork Eagle Creek for short-term geomorphic change as part of the permitting decision that allows for the continued pumping of the production wells. The objective of this study is to address the geomorphic monitoring requirements of the permitting decision by conducting annual geomorphic surveys of North Fork Eagle Creek along the stream reach between the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station (U.S. Geological Survey [USGS] site 08387550) and the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station (USGS site 08387600). The monitoring of short-term geomorphic change in the stream reach began in June 2017 with surveys of select cross sections and surveys of all woody debris accumulations and pools found in the channel. In June 2018, the monitoring of short-term geomorphic change continued with another geomorphic survey of the stream reach (with some modification to the monitoring methods).
The 2017 and 2018 surveys were conducted by the USGS, in cooperation with the Village of Ruidoso, and were the first two in a planned series of five annual geomorphic surveys. The results of the 2017 geomorphic survey were summarized and interpreted in a previous USGS Open-File Report, and the data were published in the companion data release of that report. In this report, the results of the 2018 geomorphic survey are summarized, interpreted, and compared to the results of the 2017 survey. The data from the 2018 geomorphic survey are published in the companion data release of this report.
The study reach surveyed in June 2018 is 1.89 miles long, beginning about 260 feet upstream from the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station and ending at the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station. Large sections of the study reach are characterized by intermittent streamflow, and where streamflow is normally continuous (including at the upper and lower portions of the study reach, near the streamflow-gaging stations), the streamflow typically remains less than 2 cubic feet per second throughout the year except during seasonal high flows, which most often result from rainfall during the North American monsoon months of July, August, and September or from snowmelt runoff in March, April, and May. Between the 2017 and 2018 surveys, high-flow events resulting from both rainfall (during the North American monsoon season) and snowmelt runoff (during the winter) occurred in the study reach, and those high-flow events appeared to have caused some minor and localized geomorphic changes in the study reach, which were evaluated through comparison of the 2017 and 2018 survey results.
For the 2017 geomorphic survey of North Fork Eagle Creek, cross sections were established and surveyed at 14 locations along the study reach, and in 2018, those same 14 cross sections were resurveyed. Comparisons of the cross-section survey results indicated that minor observable geomorphic changes had occurred in 3 of the 14 cross sections. These minor observable geomorphic changes included aggradation or degradation of surface materials by about 1–2 feet in some parts of the affected cross sections.
To further assess geomorphic changes within the study reach, other features, including woody debris accumulations and pools, were surveyed in both 2017 and 2018. During the 2018 geomorphic survey, 112 distinct accumulations of woody debris and 71 pools were identified in the study reach. Charred wood or burn-marked wood was present in at least 17 of the identified woody debris accumulations (and was present in some of the woody debris accumulations identified during the 2017 survey), indicating that some of the woody debris in the channel may have been sourced from trees or forest litter that had burned during 2012 Little Bear Fire. Only 22 of the 112 woody debris accumulations identified during the 2018 survey were certain to have also been present during the 2017 survey (when 58 woody debris accumulations were identified), indicating that most of the woody debris accumulations surveyed in 2017 were likely transported during the high-flow events between the 2017 and 2018 surveys but also indicating that the flows during those events were not high enough to remove some of the more firmly anchored woody debris accumulations. Most woody debris accumulations identified in 2018 did not appear to have substantially influenced geomorphic change in the locations where they were found. However, the formation of 10 of the 71 pools identified in the study reach in 2018 appeared to have been influenced by the presence of woody debris, indicating that some woody debris accumulations may have driven local geomorphic changes. Notably, pool totals from the 2017 survey could not be accurately compared to the pool totals from the 2018 survey because of differences between the two surveys in the methods used to identify pools.
Because the study began 5 years after the 2012 Little Bear Fire, and because the period and geomorphic scope of the study have so far been limited, it cannot be said that the geomorphic changes observed between the 2017 and 2018 surveys are representative of a pattern of geomorphic change following the 2012 Little Bear Fire. Though, once geomorphic changes between the 2017 and 2018 surveys can be compared with results from geomorphic surveys planned for 2019, 2020, and 2021, it may be possible to develop an understanding of the patterns in geomorphic change following the 2012 Little Bear Fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201121","collaboration":"Prepared in cooperation with the Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., 2020, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2018: U.S. Geological Survey Open-File Report 2020–1121, 37 p., https://doi.org/10.3133/ofr20201121.","productDescription":"Report: v, 37 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-112737","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":381235,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1121/ofr20201121.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1121"},{"id":381236,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94ZQHKU","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data supporting the 2018 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":381234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1121/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5621337890625,\n 32.99023555965106\n ],\n [\n -104.7930908203125,\n 32.99023555965106\n ],\n [\n -104.7930908203125,\n 33.770015152780125\n ],\n [\n -105.5621337890625,\n 33.770015152780125\n ],\n [\n -105.5621337890625,\n 32.99023555965106\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Nitrate, age tracer, and continuous groundwater-level data were interpreted in conjunction with airborne electromagnetic (AEM) survey data to understand the movement of nitrate within the Bazile Groundwater Management Area (BGMA) in northeastern Nebraska. Previously published age tracer data and nitrate data indicated vertical stratification of groundwater quality. Younger groundwater sampled within shallow parts of the aquifer had higher concentrations of nitrate, with 70 percent exceeding the U.S. Environmental Protection Agency maximum contaminant level of 10 milligrams per liter. In contrast, groundwater sampled from deeper parts of the aquifer indicated that nitrate concentrations were less than 2 milligrams per liter and that groundwater likely recharged prior to widespread use of commercial fertilizer.
The hydrostratigraphic interpretation of AEM profiles indicated that shallow and deep monitoring wells were often screened within the same homogenous zone of aquifer material. In contrast, test-hole logs indicated that there often are fine-grained layers within these homogenous zones that separate the shallow and deep monitoring well screens, but these fine-grained layers are not detected by the AEM technique because of decreased resolution of the AEM technique with depth.
The stratification of groundwater ages and nitrate concentrations likely was caused by groundwater-flow paths of different length, location and time of recharge, and denitrification. Within paleochannels interpreted from AEM and test-hole data, pesticides detected in groundwater generally coincide with elevated nitrate concentrations. Continuous groundwater-level data from four monitoring well nests indicated that groundwater pumping can impose or increase downward hydraulic gradients and facilitate the downward movement of nitrate into deeper parts of the High Plains aquifer. Given the density of irrigation wells within the BGMA, this effect on the hydraulic gradient is likely prevalent in other areas of the BGMA. Understanding seasonal water-level changes can allow water managers to better predict and assess the hydraulic gradient and the vulnerability of groundwater in deeper parts of the High Plains aquifer.
Nitrate, age tracer, and continuous groundwater-level data within the BGMA were interpreted in conjunction with AEM data as a case demonstration of the Nebraska Geocloud. The Nebraska Geocloud was initiated to protect taxpayer investments in AEM data collection and realize maximum benefit of these data by creating a publicly available, online digital database for long-term data storage. The Lower Platte North, Lower Platte South, Papio-Missouri River, Nemaha, Lower Loup, Central Platte, Upper Elkhorn, Lower Elkhorn, Lower Niobrara, and Lewis and Clark Natural Resources Districts; the University of Nebraska-Lincoln Conservation and Survey Division, Nebraska Natural Resources Commission, Nebraska Department of Natural Resources; and the U.S. Geological Survey entered a cooperative agreement to begin a program of data management and research aimed at understanding the best use of AEM for groundwater sustainability and management. Resulting case-study interpretations are provided to guide use of the Nebraska Geocloud to assess water-quality conditions and can be used by water managers and staff to address applicable water resource problems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205113","collaboration":"Prepared in cooperation with the Nebraska Natural Resources Commission; Nebraska Department of Natural Resources; and Lower Platte North, Lower Platte South, Papio-Missouri River, Nemaha, Lower Loup, Central Platte, Upper Elkhorn, Lower Elkhorn, Lower Niobrara, and Lewis and Clark Natural Resources Districts","usgsCitation":"Hobza, C.M., and Steele, G.V., 2020, Interpretation of hydrogeologic data to support groundwater management, Bazile Groundwater Management Area, northeast Nebraska, 2019—A case demonstration of the Nebraska Geocloud (ver. 1.1, December 15, 2020): U.S. Geological Survey Scientific Investigations Report 2020–5113, 46 p., https://doi.org/10.3133/sir20205113.","productDescription":"Report: viii, 45 p.; Tables: 4, 5, and 6 (.xlsx and .csv); Data Release; Version History","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112495","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":380917,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table6.xlsx","text":"Table 6","size":"16.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5113 Table 6","linkHelpText":"— Pesticide concentration, nitrate concentration, and calculated apparent groundwater ages for sampled monitoring and irrigation wells with detectable concentrations of pesticides, 1995–2005"},{"id":380915,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table5.xlsx","text":"Table 5","size":"23.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5113 Table 5","linkHelpText":"— Summary of selected water-quality data and groundwater age estimates from wells sampled within the Bazile Groundwater Management Area, 2000–17"},{"id":380914,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table4.csv","text":"Table 4","size":"6.47 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5113 Table 4","linkHelpText":"— Monitoring wells completed in the High Plains aquifer where continuous water-level data were recorded within the Bazile Groundwater Management Area, 2013–18"},{"id":380913,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table4.xlsx","text":"Table 4","size":"17.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5113 Table 4","linkHelpText":"— Monitoring wells completed in the High Plains aquifer where continuous water-level data were recorded within the Bazile Groundwater Management Area, 2013–18"},{"id":380916,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table5.csv","text":"Table 5","size":"12.1 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5113 Table 5","linkHelpText":"— Summary of selected water-quality data and groundwater age estimates from wells sampled within the Bazile Groundwater Management Area, 2000–17"},{"id":380891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5113/coverthb2.jpg"},{"id":380893,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3RVXN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Interpolated groundwater-level surface, spring 2017, Bazile Groundwater Management Area, northeastern Nebraska"},{"id":380918,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table6.csv","text":"Table 6","size":"8.17 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5113 Table 6","linkHelpText":"— Pesticide concentration, nitrate concentration, and calculated apparent groundwater ages for sampled monitoring and irrigation wells with detectable concentrations of pesticides, 1995–2005"},{"id":381479,"rank":9,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113.pdf","text":"Report","size":"4.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5113"},{"id":381480,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5113/versionHist.txt","text":"Version History","size":"585 B","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5113 Version History"}],"country":"United States","state":"Nebraska","otherGeospatial":"Bazile Groundwater Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.35009765625,\n 41.902277040963696\n ],\n [\n -96.74560546875,\n 42.52069952914966\n ],\n [\n -97.1630859375,\n 42.8115217450979\n ],\n [\n -97.62451171875,\n 42.85985981506279\n ],\n [\n -97.91015624999999,\n 42.74701217318067\n ],\n [\n -98.7451171875,\n 42.98857645832184\n ],\n [\n -100.08544921874999,\n 42.956422511073335\n ],\n [\n -99.11865234374999,\n 42.22851735620852\n ],\n [\n -98.6572265625,\n 41.88592102814744\n ],\n [\n -97.470703125,\n 41.60722821271717\n ],\n [\n -96.52587890625,\n 41.541477666790286\n ],\n [\n -96.26220703125,\n 41.623655390686395\n ],\n [\n -96.35009765625,\n 41.902277040963696\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 10, 2020: Version 1.1: December 21, 2020","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512