The U.S. Army Corps of Engineers, Jacksonville District, is deepening the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the fifth annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project. The report contains information pertinent to data collection during the 2020 water year, from October 2019 to September 2020. The addition of water-quality data collection at St. Johns River at Buffalo Bluff near Satsuma was the only modification to the previously installed network.
Discharge and salinity varied widely during the data collection period, which included above-average rainfall for 3 of the 5 counties in the study area. Total annual rainfall for all counties ranked third among the annual totals computed for the 5 years considered for this study. Annual mean discharge at Clapboard Creek was highest among the tributaries, followed by Ortega River, Durbin Creek, Pottsburg Creek at U.S. 90, Cedar River, Trout River, Julington Creek, Pottsburg Creek near South Jacksonville, Dunn Creek, and Broward River, whose annual mean was lowest. Annual mean discharge at 8 of the 10 tributary monitoring sites was higher for the 2020 water year than for the 2019 water year, and the computed annual mean flow at Clapboard Creek was the highest over the 5 years considered for this study. The annual mean discharge for each of the main-stem sites was higher for the 2020 water year than for the 2019 water year except for Buffalo Bluff, which remained the same.
Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and was lowest at Durbin Creek, the site farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which was expected. Relative to annual mean salinity calculated for the 2019 water year, annual mean salinity at all monitoring locations was higher for the 2020 water year except at the tributary sites of Trout River, Dunn Creek, and Clapboard Creek, which were lower, and Durbin Creek, which remained the same. The 2020 annual mean salinity on the main-stem of the St. Johns River was the highest since the beginning of the study in 2016 at Dancy Point, Racy Point, Shands Bridge, below Shands Bridge, above Buckman Bridge, and Jacksonville (Acosta Bridge). Among the tributary sites, annual mean salinity rankings for 2020 were highest for Julington Creek and Ortega River, which were the second-highest on record for those sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221024","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2022, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2020: U.S. Geological Survey Open-File Report 2022–1024, 48 p., https://doi.org/10.3133/ofr20221024.","productDescription":"Report: ix, 48 p.; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-133884","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":400657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1024/coverthb.jpg"},{"id":400658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1024/ofr20221024.pdf","text":"Report","size":"3.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1024"},{"id":400659,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1024/ofr20221024.XML"},{"id":400660,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1024/images"},{"id":400661,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":401171,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221024/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":501767,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113057.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","otherGeospatial":"St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.27935791015625,\n 29.14736383122664\n ],\n [\n -80.38970947265625,\n 29.14736383122664\n ],\n [\n -80.38970947265625,\n 30.56226095049944\n ],\n [\n -82.27935791015625,\n 30.56226095049944\n ],\n [\n -82.27935791015625,\n 29.14736383122664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
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A cooperative study between the National Park Service (NPS) and the U.S. Geological Survey (USGS) characterized groundwater quality and hydrogeology in parts of Pinnacles National Park. The water-quality investigation assessed the geochemistry of springs, wells, surface water, and precipitation and analyzed geochemistry of rock formations that affect the water chemistry through water-rock interaction. The hydrogeology investigation used geophysical and groundwater level data to characterize groundwater-flow processes in the alluvial deposits of Bear Valley and the Chalone Creek watershed.
Analysis of aqueous geochemical parameters in water samples from perennial springs, water-supply wells, and surface waters was conducted for samples collected after the dry season (autumnal) and after the wet season (vernal) to assess changes in geochemistry due to changes in groundwater levels or flow resulting from precipitation. The chemistry of bulk precipitation collected during the wet season was also analyzed. Bedrock samples were analyzed for geochemical parameters to help constrain groundwater sources, flow paths, and weathering. The geochemical investigations show a correspondence between the source rock and the spring-water chemistry that can be attributed to the mineralogy of the source rock. The narrow range of strontium isotopes in water samples, sourced in geochemically and mineralogically disparate rocks, indicates that the bedrock groundwater is relatively old and has reached quasi-steady state with respect to weathering of susceptible minerals.
Groundwater-level monitoring indicated that the water table is shallow—from 0 to 10 meters (m) below land surface. In southern Bear Valley and in the Chalone Creek alluvium, water levels rose and declined by several meters over each annual cycle of this study. In northern Bear Valley, water levels rose modestly over two wet seasons but declined during a third wet season. In Bear Valley, groundwater/surface-water interaction occurs along the perennial reach of Sandy Creek. Groundwater discharges to the upstream part of the reach, becomes surface water and is partly consumed by evapotranspiration, and infiltrates farther downstream. In the Chalone Creek alluvium, runoff-generated surface-water flow in intermittent stream reaches is a major component of groundwater recharge. After the onset of significant streamflow, creek water rapidly recharges groundwater until water levels rise to nearly the creek level. Groundwater levels generally remain high throughout the wet season, then gradually decline after the creek becomes dry.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221026","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Scheiderich, K., Tiedeman, C.R., Hsieh, P.A., 2022, Aqueous geochemistry of waters and hydrogeology of alluvial deposits, Pinnacles National Park, California: U.S. Geological Survey Open-File Report 2022-1026, 39 p., https://doi.org/10.3133/ofr20221026.","productDescription":"Report: viii, 39 p.; 3 Data Releases","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-129434","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":400733,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IZXRC0","text":"Streamflow data collected by the wading method, Pinnacles National Park, California, 2018","description":"Tiedeman, C.R., Ingebritsen, S.E., and Hsieh, P.A., 2021, Streamflow data collected by the wading method, Pinnacles National Park, California, 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9IZXRC0."},{"id":400732,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AMDH71","text":"Passive Seismic Data Collected for the Horizontal-to-Vertical Spectral Ratio (HVSR) Method, Pinnacles National Park, California, 2018-2020","description":"Tiedeman, C.R., and Hsieh, P.A., 2021, Passive Seismic Data Collected for the Horizontal-to-Vertical Spectral Ratio (HVSR) Method, Pinnacles National Park, California, 2018-2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9AMDH71."},{"id":400435,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1026/covrthb.jpg"},{"id":400731,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BMM0XG","text":"Geochemistry of rocks, precipitation, and water sources from Pinnacles National Park, California, 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WMA- Laboratory & Analytical Services Division
U.S. Geological Survey
USGS Headquarters
12201 Sunrise Valley Drive
Reston, VA 20192
The alteration of thermal regimes, including increased temperatures and shifts in seasonality, is a key challenge to the health and survival of federally protected cold-water salmonids in streams of the Willamette River basin in northwestern Oregon. To better support threatened fish species, the U.S. Army Corps of Engineers (USACE) and other water managers seek to improve the thermal regime in the Willamette River and key tributaries downstream of USACE dams by utilizing strategically timed flow releases from USACE dams. To inform flow management decisions, regression relations were developed for 12 Willamette River basin locations below USACE dams relating stream temperature with streamflow and air temperature utilizing publicly available datasets spanning 2000–18. The resulting relations provide simple tools to investigate stream temperature responses to changes in streamflow and climatic conditions in the Willamette River system.
Regression relations on the Willamette River and key tributaries show that, at locations sufficiently distant from the direct temperature influence of upstream dam releases, air temperature and streamflow are reasonable proxies to predict the 7-day average of the daily mean (7dADMean) and 7-day average of the daily maximum (7dADMax) water temperature with errors generally ≤1 degrees Celsius (°C). To account for seasonal variations in the relation between air temperature, streamflow, and stream temperature, a transition-smoothed, seasonal regression approach was used. Stream temperature is inversely correlated with streamflow in all seasons except “winter” (January–March), when it is relatively independent. Stream temperature is positively correlated with air temperature in all seasons, but the slope decreases at very low or very high air temperatures. Generally, fit is best for seasonal models “winter” (January–March), “spring” (April–May), “summer” (June–August), and “early autumn” (September–October). Error in “autumn” (November–December) is larger, probably due to variation in the onset timing of winter storms.
Simulated results from a climatological analysis of predicted stream temperature suggest that, excluding extremes and accounting for some seasonal variability, the 7dADMean and 7dADMax stream temperature sensitivity to air temperature and streamflow varies by location on the river. To investigate the potential range of stream temperature variability based on historical air temperature and streamflow conditions, stream temperature predictions were calculated using synthetic time series comprised of daily temperature values representing the 0.10, 0.33, 0.50, 0.67, and 0.90 quantile of air temperature and streamflow from 1954 (the year meaningful streamflow augmentation began) to 2018. Results show that from a “very hot” (0.90 quantile) and “very dry” (0.10 quantile) year to a “very cool” (0.10 quantile) and “very wet” (0.90; all quantiles from 1954 to 2018) year, the stream temperature sensitivity to air temperature and streamflow is about 3 °C at Harrisburg (river mile 161.0) and increases to about 5 °C at Keizer (river mile 82.2). While the number of days exceeding regulatory criteria are fewer in cooler, wetter years than in warmer, dryer years, the models suggest that the Willamette River will likely continue to exceed the State of Oregon maximum water-temperature criterion of 18 °C for sustained periods from late spring to early autumn and that the flow management practices evaluated in this study, while effective at influencing stream temperature, likely cannot prevent many or all such exceedances.
As modeled for 2018, a representative very hot year with normal to below-normal streamflow, stream temperature sensitivity to changes in streamflow of ±100 to ±1000 cubic feet per second produced mean monthly temperature changes from 0.0 to 1.4 °C at Keizer, Albany, and Harrisburg during summer. For a specified change in flow, temperature sensitivity is greater at upstream locations where streamflow is less than that at downstream locations because the change in streamflow is a greater percentage of total streamflow at upstream locations. Similarly, temperature response to a set change in flow is greater in the summer and early autumn low-flow season than in spring when flows are higher. The regression models developed in this study thus indicate that flow management is likely to have a greater effect on stream temperature at upstream locations (such as Harrisburg or Albany) and during the low-flow season than at downstream locations (such as Keizer) or during periods of higher streamflow.
","largerWorkType":{"id":18,"text":"Report"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215022","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Stratton Garvin, L.E., Rounds, S.A., and Buccola, N.L., 2022, Estimating stream temperature in the Willamette River Basin, northwestern Oregon—A regression-based approach: U.S. Geological Survey Scientific Investigations Report 2021–5022, 40 p., https://doi.org/10.3133/sir20215022.","productDescription":"Report: viii, 40 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-119336","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":501948,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113055.htm","linkFileType":{"id":5,"text":"html"}},{"id":400563,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PALKQZ","text":"USGS Data Release","description":"Stratton Garvin, L.E., 2022, Stream temperature predic tions for the Willamette River Basin, northwestern Oregon estimated from regression equations (1954–2018): U.S. Geological Survey data release, https://doi.org/10.5066/P9PALKQZ.","linkHelpText":"Stream temperature predictions for the Willamette River Basin, northwestern Oregon estimated from regression equations (1954–2018)"},{"id":400560,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5022/sir20215022.pdf","text":"Report","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":400559,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5022/covrthb.jpg"},{"id":400561,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5022/sir20215022.xml"},{"id":400562,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5022/images"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.64013671874999,\n 43.54854811091286\n ],\n [\n -122.18994140624999,\n 43.54854811091286\n ],\n [\n -122.18994140624999,\n 45.99696161820381\n ],\n [\n -123.64013671874999,\n 45.99696161820381\n ],\n [\n -123.64013671874999,\n 43.54854811091286\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Frequent and prolonged military training maneuvers are an intensive type of land use that may disturb land cover, compact soils, and have lasting effects on adjacent stream hydrology and ecosystems. To better understand the potential effect of military training on hydrologic and environmental processes, the U.S. Geological Survey in cooperation with the U.S. Army established hydrologic and water-quality data-collection networks at the U.S. Army Garrison Fort Carson (AGFC) in 1978 and at the Piñon Canyon Maneuver Site (PCMS) in 1982. The purpose of this report is to present precipitation, streamflow, suspended-sediment, and water-quality data collected by the U.S. Geological Survey at the AGFC and PCMS for water years (WYs) 2016–18 and to evaluate those data in relation to long-term data from the AGFC and PCMS. In WYs 2016–18, the U.S. Geological Survey monitored 26 sites on the AGFC and 17 sites on the PCMS for precipitation amount, streamflow, suspended sediment, and (or) water quality.
On the AGFC, total annual precipitation in WYs 2016–18 was larger than the long-term mean for all 3 years at Rod and Gun Meteorologic Station at Fort Carson, CO (Rod and Gun). There were statistically significant upward trends in annual precipitation at Rod and Gun and Young Hollow Meteorologic Station at Fort Carson, CO (Young Hollow) with slopes of 1.25 and 0.66 inches per year (in/yr), respectively. The precipitation totals for WY 2017 were either the largest on record or in the top three for both sites and at Sullivan Park Meteorologic Station at Fort Carson, CO. On the PCMS, total annual precipitation was larger than the long-term mean in WYs 2016–18 at Brown Sheep Camp Meteorologic Station near Tyrone, CO; CIG Pipeline South Meteorologic Station near Simpson, CO; Bear Springs Hills Meteorologic Station near Houghton, CO (Bear Springs); and Upper Red Rock Canyon Meteorologic Station near Houghton, CO (Upper Red Rock). There were statistically significant upward trends in precipitation at Bear Springs and Upper Red Rock with slopes of 0.16 and 0.19 in/yr, respectively. The precipitation totals for WY 2017 were the largest on record for all sites except for Upper Bent Canyon Meteorological Station near Delhi, CO.
Streamflow was calculated at 18 sites on the AGFC and 7 sites on the PCMS in at least 1 of WYs 2016–18. At AGFC, mean annual (or seasonal) streamflow in WYs 2016–18 was less than the long-term mean at 7 sites and greater than the long-term mean at 3 sites. There were statistically significant downward trends in mean annual or seasonal streamflow at Womack Ditch from Little Fountain Creek near Fort Carson, CO, and Ripley Ditch from Little Fountain Creek at Fort Carson, CO, with slopes of −0.036 and −0.028 cubic feet per second per year (ft3/s/y), respectively; and a significant upward trend in streamflow at Turkey Creek West Seepage below Teller Reservoir near Stone City, CO, with a slope of less than 0.001 ft3/s/y. Unlike for precipitation, the mean annual or seasonal streamflow for WY 2017 was not in the top 3 for any of the 12 sites with measured flow.
At the PCMS, mean annual (or seasonal) streamflow was less than the long-term mean streamflow in WYs 2016–18 at the Taylor Arroyo below Rock Crossing near Thatcher, CO, and Bent Canyon Creek at Mouth near Timpas, CO, sites; and in WYs 2016 and 2018 at the Purgatoire River near Thatcher, CO (Purgatoire Thatcher), and Purgatoire River at Rock Crossing near Timpas, CO (Purgatoire Rock Crossing). There were no statistically significant trends in mean annual (or seasonal) streamflow at sites on the PCMS, and unlike for precipitation, the mean streamflow for WY 2017 was not in the top three for any sites except Purgatoire Rock Crossing. In WYs 2016–18, streamflow from sites on the AGFC and PCMS represented only a small fraction of streamflow in Fountain Creek or the Purgatoire River, and changes in streamflow that resulted from military maneuvers on the AGFC and PCMS were not likely to be detected in the downstream receiving waters.
Suspended-sediment concentrations, loads, and yields for WYs 2016–18, were analyzed at two sites on the AGFC and five sites on the PCMS. On the AGFC, mean seasonal suspended-sediment concentrations ranged from 3.10 to 155 milligrams per liter (mg/L), mean seasonal suspended-sediment loads ranged from 0.04 to 27.1 tons per day (t/d), and seasonal suspended-sediment yields ranged from 0.28 to 216 tons per season per square mile (t/s/mi2). Suspended-sediment yields at the two AGFC sites in WYs 2016–18 were all less than the long-term means. On the PCMS, mean seasonal suspended-sediment concentrations (at sites with some streamflow during a WY) ranged from 1.12 to 41.8 mg/L, mean suspended-sediment loads ranged from 0.01 to 13.1 t/d, and seasonal suspended-sediment yields ranged from 0.06 to 57.4 t/s/mi2. Suspended-sediment yields at the five PCMS sites in WYs 2016–18 were all less than the long-term means. In WYs 2016–18, mean daily suspended-sediment loads at Little Fountain were 1.3, 2.5, and 7.6 percent, respectively, of the mean daily suspended-sediment load at Fountain Creek at Security, Colorado. Likewise, the total of mean daily suspended-sediment loads from the five tributary sites to the Purgatoire River in WYs 2016–18 were about 0.25, 0.17, and 3.2 percent, respectively, of the historical mean daily suspended-sediment load at Purgatoire Thatcher.
Spearman’s rank correlation coefficient was used to evaluate the strength and form of the relations between daily total precipitation and daily mean streamflow and between daily mean streamflow and suspended-sediment concentration and load for WYs 2016–18. For the sites on the AGFC and PCMS, there were weak or statistically insignificant positive correlations between precipitation and streamflow at nearby streamgauges, but strong statistically significant positive correlations between streamflow and suspended-sediment concentration and load. The ephemeral nature of the streams, quantity and timing of precipitation, air temperature, seasonal soil-moisture deficits, and effective runoff detention in erosion-control ponds could all contribute to inconsistent conversion of precipitation to streamflow.
Water-quality data were analyzed for as many as 43 parameters from 9 samples collected from 3 sites on the AGFC and from 37 samples collected from 4 sites on PCMS during WYs 2016–18. The concentrations of selected water-quality parameters were compared to regulatory standards for aquatic life from the Colorado Department of Public Health and Environment (CDPHE) or aquatic-life criteria from the U.S. Environmental Protection Agency (EPA). There is at least 1 CDPHE standard or EPA criterion for 30 of the 43 water-quality parameters.
For all samples from both the AGFC and the PCMS in WYs 2016–18, the concentrations of most water-quality parameters were compliant with the associated standards or criteria. However, there were some exceedances of standards or criteria: 11 samples exceeded the CDPHE recreational class standard for Escherichia coli concentration, 9 samples exceeded the CDPHE chronic unfiltered phosphorus aquatic-life standard, 36 samples exceeded the CDPHE chronic sulfate aquatic-life standard, 5 samples exceeded the EPA criterion for selenium, 7 samples exceeded the EPA criterion for aluminum, 2 samples exceeded the CDPHE chronic standard for iron, and 15 samples exceeded the CDPHE chronic standard for manganese.
Identifying potential effects of military training on water quality in adjacent streams on the AGFC and PCMS is difficult due to the ephemeral nature of streamflow, limited number of sampling locations and samples, and limited access to the study areas. At the PCMS, pairs of water-quality samples were collected in March and May 2017 before and after an April–May 2017 military training event. At the Purgatoire Rock Crossing site, streamflow at the time of the May sample was approximately 35 times larger than streamflow for the March sample. The absolute percent differences of concentrations for 27 parameters ranged from −71.7 to 183 percent, and 7 parameters had increases in concentration whereas 22 parameters had no change or decreases in concentrations. The absolute percent differences of loads for 24 parameters ranged from 141 to 198 percent. The generally lower concentrations and higher loads were expected given the higher streamflows at the time of collection of the May compared to the March samples.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225018","collaboration":"Prepared in cooperation with the U.S. Department of the Army","usgsCitation":"Battaglin, W.A., and Kisfalusi, Z.D., 2022, Characterization of and relations among precipitation, streamflow, suspended-sediment, and water-quality data at the U.S. Army Garrison Fort Carson and Piñon Canyon Maneuver Site, Colorado, water years 2016–18: U.S. Geological Survey Scientific Investigations Report 2022–5018, 94 p., https://doi.org/10.3133/sir20225018.","productDescription":"Report: ix, 94 p.; Database; Data Release","onlineOnly":"Y","ipdsId":"IP-115539","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":400092,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System—","linkHelpText":"USGS water data for the Nation: U.S. Geological 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U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
A cooperative study led by the U.S. Geological Survey and Wake County Environmental Services was initiated to characterize the fractured-rock aquifer system and assess the sustainability of groundwater resources in and around Wake County. This report contributes to the development of a comprehensive groundwater budget for the study area, thereby helping to enable resource managers to make sound and sustainable water-supply and water-use decisions.
Construction information was used to analyze the well depth, casing depth, and reported well yield of more than 7,500 inventoried wells. The median well depth and casing depth were 265 feet (ft) below land surface (bls) and 68 ft bls, respectively, and the median well yield was 10 gallons per minute. Generally, well yield increased with depth to around 200 ft bls and then began to decrease with depth within the fractured-rock aquifer.
Borehole geophysical logging methods were used to characterize the fractured-rock aquifer by mapping the orientation of geologic structures within the subsurface. Structure measurements were made on resulting log data and mapped to observed general spatial trends within the regional groundwater system and more distinct hydrogeologic units. Many of the fractures observed within the borehole logs are steeply dipping across Wake County, although open fractures with shallow dip angles were also observed in most rock classes. Regional geologic structural trends were observed in proximity to the Jonesboro Fault.
Potential groundwater recharge in the study area was estimated using a Soil-Water-Balance (SWB) model, as well as using base flow hydrograph separation. The SWB model calculated net infiltration below the root zone for 1981 through 2019 for a 5,402-square-mile area that extends to the counties surrounding Wake County. The mean annual net infiltration rate for the 39-year period was about 8.6 inches per year for the study area. The mean annual net infiltration results from the SWB model were comparable to annual base flow estimates using the PART hydrograph-separation method at six U.S. Geological Survey streamgages within the study area. Mean annual base flow for all six drainage basins was near 7.5 inches per year and estimates ranged from 2.9 to 8.9 inches. Comparisons of mean annual potential recharge from the SWB model and base flow estimates were generally within 2 inches, except during high flows for most of the drainage basins in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225041","collaboration":"Prepared in cooperation with Wake County Environmental Services","usgsCitation":"Antolino, D.J., and Gurley, L.N., 2022, Assessment of well yield, dominant fractures, and groundwater recharge in Wake County, North Carolina (ver. 1.1, May 2022) : U.S. Geological Survey Scientific Investigations Report 2022–5041, 35 p., https://doi.org/10.3133/sir20225041.","productDescription":"Report: viii, 35 p.; 3 Data Releases; Database","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-115494","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":399581,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C2J23X","text":"USGS data release","linkHelpText":"Groundwater well yield in Wake County, North Carolina"},{"id":435853,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MO793B","text":"USGS data release","linkHelpText":"Soil-Water-Balance (SWB) model data sets for the Greater Wake County area, North Carolina, 1981 - 2070"},{"id":399596,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225041/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":399578,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5041/images/"},{"id":399575,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5041/coverthb2.jpg"},{"id":400310,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2022/5041/versionHist.txt","size":"508 B","linkFileType":{"id":2,"text":"txt"}},{"id":399582,"rank":8,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":399580,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96HHBIE","text":"USGS data release","linkHelpText":"National Land Cover Database (NLCD) 2016 products (ver. 2.0, July 2020)"},{"id":399579,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95XKK5V","text":"USGS data release","linkHelpText":"Soil-Water-Balance (SWB) model datasets for the Greater Wake County area, North Carolina, 1981–2019"},{"id":399577,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5041/sir20225041.XML"},{"id":399576,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5041/sir20225041.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5041"}],"country":"United States","state":"North Carolina","county":"Wake County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-78.5465,36.0218],[-78.4307,35.9795],[-78.3969,35.9387],[-78.3567,35.9318],[-78.351,35.909],[-78.3385,35.9052],[-78.3347,35.8997],[-78.3302,35.896],[-78.3245,35.896],[-78.3177,35.8963],[-78.3137,35.8976],[-78.3081,35.8935],[-78.2948,35.8797],[-78.292,35.8792],[-78.2893,35.8741],[-78.2859,35.8713],[-78.2831,35.8681],[-78.2782,35.8631],[-78.2749,35.8567],[-78.2756,35.8494],[-78.2707,35.843],[-78.2657,35.8361],[-78.2652,35.8325],[-78.2613,35.8315],[-78.2591,35.826],[-78.2599,35.8183],[-78.3731,35.7523],[-78.4635,35.7072],[-78.4686,35.7087],[-78.4709,35.7078],[-78.4732,35.7046],[-78.4778,35.7011],[-78.5716,35.6255],[-78.708,35.5191],[-78.9196,35.5857],[-78.9956,35.6104],[-78.9796,35.6656],[-78.9439,35.7515],[-78.9421,35.756],[-78.9403,35.7615],[-78.9337,35.7859],[-78.9191,35.8216],[-78.9096,35.8506],[-78.9076,35.8678],[-78.89,35.8676],[-78.8298,35.8689],[-78.8056,35.9281],[-78.7609,35.9176],[-78.751,35.9307],[-78.7372,35.941],[-78.714,35.9729],[-78.7009,36.0068],[-78.6985,36.0131],[-78.7048,36.0091],[-78.7077,36.0087],[-78.7076,36.0132],[-78.7052,36.0223],[-78.7085,36.0287],[-78.7102,36.0287],[-78.713,36.0278],[-78.7164,36.0283],[-78.7232,36.0334],[-78.726,36.0343],[-78.7272,36.0334],[-78.7278,36.0289],[-78.7324,36.0267],[-78.7353,36.0199],[-78.7422,36.0209],[-78.75,36.026],[-78.7551,36.0283],[-78.7545,36.0301],[-78.7511,36.0323],[-78.7499,36.035],[-78.747,36.0395],[-78.7492,36.0427],[-78.7503,36.0468],[-78.7519,36.0491],[-78.7564,36.0532],[-78.7498,36.0718],[-78.7088,36.0768],[-78.6895,36.0752],[-78.5922,36.0378],[-78.5465,36.0218]]]},\"properties\":{\"name\":\"Wake\",\"state\":\"NC\"}}]}","edition":"Version 1.1: May 2022; Version 1.0: April 2022","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive
Norcross, GA 30093
Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
A state-space model (SSM) of infiltration estimates daily groundwater recharge using time-series of groundwater-level altitude and meteorological inputs (liquid precipitation, snowmelt, and evapotranspiration). The model includes diffuse and preferential flow through the unsaturated zone, where preferential flow is a function of liquid precipitation and snowmelt rates and a threshold rate, above which there is direct recharge to the water table. Model parameters are estimated over seasonal periods and the SSM is coupled with the Kalman Filter (KF) to assimilate recent observations (hydraulic head) and meteorological inputs into recharge estimates. The approach can take advantage of real-time hydrologic and meteorological data to deliver real-time recharge estimates. The model is demonstrated on daily observations from two bedrock wells in carbonate aquifers of northwestern New York (USA) between 2013 and 2018. Meteorological inputs for liquid precipitation and snowmelt are compiled from SNODAS (2021). Results for recharge during winter and spring seasons show preferential flow events to the water table from liquid precipitation, snowmelt, or a combination of the two. Recharge estimates summed annually are consistent with previous estimates of recharge reported from groundwater flow and surface-process models. Results from the SSM and KF point to errors in meteorological inputs, such as the snowmelt rate, that are not compatible with hydraulic head observations. Whereas liquid and solid precipitation are measured at discrete stations and extrapolated to 1-km2 grid cells, snowmelt is a meteorological modeled outcome that may not represent conditions in the vicinity of monitoring well locations.
","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwat.13206","usgsCitation":"Shapiro, A.M., Day-Lewis, F., Kappel, W.M., and Williams, J., 2022, Incorporating snowmelt into daily estimates of recharge using a state-space model of infiltration: Groundwater, v. 60, no. 6, p. 721-746, https://doi.org/10.1111/gwat.13206.","productDescription":"26 p.","startPage":"721","endPage":"746","ipdsId":"IP-130903","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":447877,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13206","text":"Publisher Index Page"},{"id":435854,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MRGR88","text":"USGS data release","linkHelpText":"Algorithms for model parameter estimation and state estimation applied to a state-space model for one-dimensional vertical infiltration incorporating snowmelt rate as a system input"},{"id":400497,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":842636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":842637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kappel, William M. 0000-0002-2382-9757 wkappel@usgs.gov","orcid":"https://orcid.org/0000-0002-2382-9757","contributorId":1074,"corporation":false,"usgs":true,"family":"Kappel","given":"William","email":"wkappel@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":842638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, John 0000-0002-6054-6908 jhwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-6054-6908","contributorId":1553,"corporation":false,"usgs":true,"family":"Williams","given":"John","email":"jhwillia@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":842639,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231182,"text":"tm4D3 - 2022 - U.S. Geological Survey Hydrologic Toolbox — A graphical and mapping interface for analysis of hydrologic data","interactions":[],"lastModifiedDate":"2022-05-05T13:50:19.759656","indexId":"tm4D3","displayToPublicDate":"2022-05-04T13:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-D3","displayTitle":"U.S. Geological Survey Hydrologic Toolbox — A Graphical and Mapping Interface for Analysis of Hydrologic Data","title":"U.S. Geological Survey Hydrologic Toolbox — A graphical and mapping interface for analysis of hydrologic data","docAbstract":"The Hydrologic Toolbox is a Windows-based desktop software program that provides a graphical and mapping interface for analysis of hydrologic time-series data with a set of widely used and standardized computational methods. The software combines the analytical and statistical functionality provided in the U.S. Geological Survey Groundwater and Surface-Water Toolboxes and provides several enhancements to these programs. The main analytical methods are the computation of hydrologic-frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10); the computation of design flows, including biologically based flows; the computation of flow-duration curves and duration hydrographs; eight computer-programming methods for hydrograph separation of a streamflow time series, including the Base-Flow Index (BFI), HYSEP, PART, and SWAT Bflow methods and Eckhardt’s two-parameter digital-filtering method; and the RORA recession-curve displacement method and associated RECESS program to estimate groundwater-recharge values from streamflow data. Several of the statistical methods provided in the Hydrologic Toolbox are used primarily for computation of critical low-flow statistics. The Hydrologic Toolbox also facilitates retrieval of streamflow and groundwater-level time-series data from the U.S. Geological Survey National Water Information System and outputs text reports that describe their analyses.
The Hydrologic Toolbox was developed by use of the DotSpatial geographic information system (GIS) programming library, which is part of the MapWindow project. DotSpatial is a nonproprietary, open-source program written for the .NET framework that includes a spatial data viewer and GIS capabilities. Advantages of the DotSpatial system include its pure .NET implementation for both the user interface and the GIS mapping engine, and thus the DotSpatial system simplifies software deployment and installation. In addition to combining the functionality of the separate Groundwater and Surface-Water Toolboxes, the Hydrologic Toolbox also organizes the functionality by theme (Groundwater Tools, Surface-Water Tools, and general Time-Series Tools).
This report provides a description of how to build a Hydrologic Toolbox project and to download and manage hydrologic time-series data. It includes an overview of the analytical and statistical capabilities of the Hydrologic Toolbox and highlights the primary differences between the Hydrologic Toolbox and the Groundwater and Surface-Water Toolboxes. The report supplements information available in an extensive online Help manual and is intended to provide a set of instructions that will allow users to quickly develop skills to use the mapping, data-retrieval, and computational tools of the program.
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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Hawaii stands apart from the rest of the United States, literally and figuratively. The nearest of the eight islands that make up the Hawaiian archipelago is 2,000 miles from the U.S. mainland. Like every bit of land mass within the State, it emerged from the Pacific Ocean after thousands of years of undersea volcanic activity. Kona International Airport, on the “Big Island” of Hawai‘i, was built atop 220-year-old lava flows from the Hualālai volcano.
The volcanic soils and tropical vegetation that stretch across Hawaii’s postcard-perfect peaks and valleys, as well as the trade winds that blow precipitation northeast across the islands and the warm Kona winds that move in from the south, are among the factors that make the study of the State’s landscapes distinct from the rest of the Nation.
Six active volcanoes continue to alter the land surface. Some of them, like the constantly erupting Kīlauea or Mauna Loa, the world’s largest active volcano, can threaten human life and property. Man-made changes to the islands, such as the long-term agricultural production of sugar cane or pineapple, along with the introduction of non-native species, have also affected the health of the State’s ecosystems.
Landsat satellites, with 50 years of repeat Earth observations and decades of data from infrared and thermal instruments capable of tracking lava flows, can offer unique opportunities for the study of Hawaii’s ever-changing landscapes. Here are a few examples of how Landsat benefits the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223024","usgsCitation":"U.S. Geological Survey, 2022, Hawaii and Landsat: U.S. Geological Survey Fact Sheet 2022–3024, 2 p., https://doi.org/10.3133/fs20223024.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-140071","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":400152,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223024/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400123,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3024/images"},{"id":400121,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3024/fs20223024.pdf","text":"Report","size":"3.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
In southeastern New York, the villages of Ellenville, Wurtsboro, Woodridge, the hamlet of Mountain Dale, and surrounding communities in the Neversink River and Rondout Creek drainage basins rely on wells that pump groundwater from valley-fill glacial aquifers for public water supply. Glacial aquifers are vulnerable to contamination because they are highly permeable and have a shallow depth to water table. To protect the quality of these water resources, water managers need accurate information about the areas that contribute recharge to production wells that pump from these aquifers. The New York State Department of Environmental Conservation and the New York State Department of Health designated eight priority wells in this region for which water supply protection is of primary concern.
The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health, began an investigation in 2019 with the general objectives of (1) improving understanding of regional groundwater-flow system, (2) delineating areas contributing recharge to eight priority production wells, and (3) quantifying the uncertainty of these contributing areas in a probabilistic way that can be used to inform decision-making related to priority well source-water protection. To complete these objectives, a MODFLOW 6 groundwater model was created encompassing the eight priority wells and the surrounding flow system, which includes parts of the Neversink River and Rondout Creek Basins in Sullivan County and Ulster County, New York. The model was built using Python tools (such as flopy, modflow-setup, and sfrmaker) that facilitate transparent and repeatable model development using existing datasets. The model parameters were estimated with a stepwise approach using an iterative ensemble smoother implementation of the Parameter ESTimation software PEST++ (version 5.0.0). We evaluated initial “best guess” parameter bounds with a prior Monte Carlo analysis. Results of the first prior Monte Carlo analysis were used to make informed adjustments to model parameter bounds (typically resulting in expanded bounds), and a second prior Monte Carlo analysis was run to identify improved ranges for model parameters during history matching.
The history matching effort produced an ensemble of parameter values for the groundwater-flow model that spans the range of values within prior uncertainty bounds. The ensemble is informed by the historical observation data, within a reasonable range of uncertainty on those observations. This history-matched ensemble was used in a particle tracking Monte Carlo analysis to delineate the areas contributing recharge to priority wells. The groundwater-flow and particle tracking (MODPATH7) models were run once for each ensemble member. Deterministic contributing areas computed for each ensemble member were aggregated to produce maps showing the probability that a location contributes recharge to priority wells. Finally, the particle tracking Monte Carlo analysis was repeated for six pumping scenarios, representing a wide range of possible pumping levels, to incorporate uncertainty in future pumping rates related to population growth or other management decisions. Increasing pumping rates generally led to larger contributing recharge areas and larger areas of high probability that a location contributes recharge to priority wells. These maps show the overall uncertainty of the areas contributing recharge to priority wells in the study area and provide a tool for risk-based decision making for protection of well source water.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215112","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health","usgsCitation":"Corson-Dosch, N.T., Fienen, M.N., Finkelstein, J.S., Leaf, A.T., White, J.T., Woda, J., and Williams, J.H., 2022, Areas contributing recharge to priority wells in valley-fill aquifers in the Neversink River and Rondout Creek drainage basins, New York: U.S. Geological Survey Scientific Investigations Report 2021–5112, 50 p., https://doi.org/10.3133/sir20215112.","productDescription":"Report: ix, 50 p.; 2 Data Releases","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125165","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":399109,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":399101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5112/coverthb.jpg"},{"id":399102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5112/sir20215112.pdf","text":"Report","size":"22.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5112"},{"id":399104,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5112/sir20215112.XML"},{"id":399105,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5112/images/"},{"id":399107,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://ny.water.usgs.gov/maps/neversink/","text":"Neversink-Rondout Source Water Mapper"},{"id":399982,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20215112/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5112"},{"id":399106,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225024","text":"Scientific Investigations Report 2022–5024","linkHelpText":"- Data Sources and Methods for Digital Mapping of Eight Valley-Fill Aquifer Systems in Upstate New York"},{"id":399108,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HWSOHP","text":"USGS data release","linkHelpText":"Groundwater model archive and workflow for Neversink/Rondout Basin, New York, source water delineation"},{"id":502119,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112974.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Neversink River and Rondout Creek Drainage Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.8663330078125,\n 41.40153558289846\n ],\n [\n -74.1961669921875,\n 41.40153558289846\n ],\n [\n -74.1961669921875,\n 41.99216023337633\n ],\n [\n -74.8663330078125,\n 41.99216023337633\n ],\n [\n -74.8663330078125,\n 41.40153558289846\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Digital hydrogeologic maps were developed in eight study areas in upstate New York by the U.S. Geological Survey in cooperation with the New York State Department of Environmental Conservation. The digital maps define the hydrogeologic framework of the valley-fill aquifers and surrounding till-covered uplands in the vicinity of the villages of Ellenville and Wurtsboro and hamlets of Woodbourne and South Fallsburg in Sullivan and Ulster Counties, town of Greene in Chenango County, city of Cortland and town of Cincinnatus in Cortland County, city of Jamestown in Chautauqua County, city of Olean and village of Ellicottville in Cattaraugus County, and villages of Fishkill and Wappinger Falls in Dutchess County. The hydrogeologic framework provided the foundation for groundwater-flow models that were used in the delineation of areas contributing groundwater flow to production wells screened in four of the eight valley-fill aquifers considered in this study. The hydrogeologic framework for the other four study areas was developed for potential future use in groundwater contributing-area studies.
Data used in the creation of all digital surfaces and thicknesses included published surficial geology; aquifer maps and hydrogeologic sections; light detection and ranging (lidar) datasets; the Soil Survey Geographic Database; and lithologic well logs from the National Water Information System, New York State Department of Environmental Conservation, New York State Department of Transportation, and Empire State Organized Geologic Information System databases. Digital maps of the surficial geology; thickness of the surficial sand and gravel aquifers; and tops of the confining lacustrine silt and clay units, confined sand and gravel aquifers, and bedrock surfaces were created by using ArcGIS (a geographic information system). All surfaces and thicknesses were generated by using one of the following ArcGIS interpolation tools: Topo to Raster, Natural Neighbors, Kriging, or Empirical Bayesian Kriging. The datasets developed in this study provide a greater understanding of the underlying hydrogeologic framework in glacial valley-fill aquifers and can be applied in the evaluation of groundwater-supply development and protection.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225024","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Finkelstein, J.S., Woda, J.C., and Williams, J.H., 2022, Data sources and methods for digital mapping of eight valley-fill aquifer systems in upstate New York: U.S. Geological Survey Scientific Investigations Report 2022–5024, 21 p., https://doi.org/10.3133/sir20225024.","productDescription":"Report: v, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122133","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":398708,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":399981,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225024/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5024"},{"id":398710,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20215112","text":"Scientific Investigations Report 2021–5112","linkHelpText":"- Areas Contributing Recharge to Priority Wells in Valley-fill Aquifers in the Neversink River and Rondout Creek Drainage Basins, New York"},{"id":398707,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5024/images/"},{"id":398706,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5024/sir20225024.XML"},{"id":398705,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5024/sir20225024.pdf","text":"Report","size":"4.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5024"},{"id":398704,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5024/coverthb2.jpg"},{"id":398709,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20215083","text":"Scientific Investigations Report 2021–5083","linkHelpText":"- Areas Contributing Recharge to Selected Production Wells in Unconfined and Confined Glacial Valley-Fill Aquifers in Chenango River Basin, New York"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.200439453125,\n 40.59727063442024\n ],\n [\n -73.223876953125,\n 40.59727063442024\n ],\n [\n -73.223876953125,\n 43.48481212891603\n ],\n [\n -79.200439453125,\n 43.48481212891603\n ],\n [\n -79.200439453125,\n 40.59727063442024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
In the Chenango River Basin of central New York, unconfined and confined glacial valley-fill aquifers are an important source of drinking-water supplies. The risk of contaminating water withdrawn by wells that tap these aquifers might be reduced if the areas contributing recharge to the wells are delineated and these areas protected from land uses that might affect the water quality. The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health, began an investigation in 2019 to improve understanding of groundwater flow and delineate areas contributing recharge to 16 production wells clustered in three study areas in the basin as part of an effort to protect the source of water to these wells. Areas contributing recharge were delineated on the basis of numerical steady-state groundwater-flow models representing long-term average hydrologic conditions.
In the Cortland study area, four water suppliers operate 10 production wells that withdraw a total average rate of 2,480 gallons per minute from an unconfined aquifer consisting of well-sorted sand and gravel deposits. Simulated areas contributing recharge to these wells at their average pumping rates covered a total area of 6.93 square miles. Simulated areas contributing recharge extend upgradient from the wells to upland till deposits and to groundwater divides. Some simulated areas contributing recharge include isolated areas remote from the wells. Short simulated groundwater traveltimes from recharging locations to discharging wells indicated that the wells are vulnerable to contamination from land-surface activities; 50 percent of the traveltimes were 10 years or less. Land cover in some of the areas contributing recharge included a substantial amount of urban and agriculture land use.
The groundwater-flow model of the Cortland study area was calibrated to available hydrologic data by inverse modeling using nonlinear regression. The parameter variance-covariance matrix from model calibration was used to create parameter sets that reflect the uncertainty of the parameter estimates and the correlation among parameters to evaluate the uncertainty associated with the single, predicted contributing areas to the wells. This analysis led to contributing areas expressed as a probability distribution. Because of the effects of parameter uncertainty, the size of the probabilistic contributing areas was larger than the size of the single, predicted contributing area for the wells. Thus, some areas not in the single, predicted contributing area might actually be in the contributing area, including additional areas of urban and agriculture land use that have the potential to contaminate groundwater. Additional areas that might be in the contributing area included recharge originating near the pumping wells that have relatively short groundwater-flow paths and traveltimes.
In each of the Greene and Cincinnatus study areas, one water supplier operates three wells that are screened near the top of the bedrock surface in a confined aquifer consisting of poorly to well-sorted sand and gravel deposits. This confined aquifer is overlain by a lacustrine confining unit of very fine sand, silt, and clay, which in turn is overlain by a thin unconfined aquifer of sand and gravel. The groundwater-flow models for these two areas were manually calibrated because of the limited hydrologic data. Simulated areas contributing recharge to the Greene study area wells covered a total area of 0.35 square mile for the average pumping rate of 170 gallons per minute. The contributing areas extended southeastward of the wells to the groundwater divide in the till uplands. The contributing areas also included remote, isolated areas on the opposite side of the Chenango River from the wells primarily in the till uplands. For the Cincinnatus study area wells, which have a low average pumping rate (34 gallons per minute), the simulated contributing areas totaled 0.06 square mile and were on the same side of the river as the wells, but they are isolated areas remote from the wells primarily in the till-covered bedrock uplands. Land cover in these contributing areas for both study areas is primarily agriculture and forested, with the contributing areas to the Greene study area wells also including some urban land uses. Because the Greene and Cincinnatus study area wells are screened relatively deep and some flow paths to the wells partly travel through the confining unit, which impedes the connection with surface sources of recharge, overall groundwater traveltimes are greater than for wells in the Cortland study area. Fifty percent of Cortland study area wells, but only 9 and 44 percent of Greene and Cincinnatus study area wells, respectively, have groundwater traveltimes of 10 years or less.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215083","collaboration":"Prepared in cooperation with New York State Department of Environmental Conservation and New York State Department of Health","usgsCitation":"Friesz, P.J., Williams, J.H., Finkelstein, J.S., and Woda, J.C., 2022, Areas contributing recharge to selected production wells in unconfined and confined glacial valley-fill aquifers in Chenango River Basin, New York (ver. 1.1, 2026): U.S. Geological Survey Scientific Investigations Report 2021–5083, 48 p., https://doi.org/10.3133/sir20215083.","productDescription":"Report: vi, 48 p.; 2 Data Releases; Database","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126791","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":502109,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112975.htm","linkFileType":{"id":5,"text":"html"}},{"id":500551,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5083/versionHist.txt","size":"892 B","linkFileType":{"id":2,"text":"txt"}},{"id":398545,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/sir20225024","text":"Scientific Investigations Report 2022–5024","linkHelpText":"- Data Sources and Methods for Digital Mapping of Eight Valley-Fill Aquifer Systems in Upstate New York"},{"id":398544,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":398543,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":398541,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5083/images/"},{"id":398540,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5083/sir20215083.XML"},{"id":398539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5083/sir20215083.pdf","text":"Report","size":"18.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5083"},{"id":398538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5083/coverthb3.jpg"},{"id":399980,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215083/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5083"},{"id":398542,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HU2G1K","text":"USGS data release","linkHelpText":"MODFLOW -NWT groundwater-flow models used to delineate areas contributing recharge to selected production wells in unconfined and confined glacial valley-fill aquifers in Chenango River Basin, New York"}],"country":"United States","state":"New York","otherGeospatial":"Chenango River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125000001,\n 42.13896840458089\n ],\n [\n -75.16845703125,\n 42.13896840458089\n ],\n [\n -75.16845703125,\n 42.90011265525331\n ],\n [\n -76.11328125000001,\n 42.90011265525331\n ],\n [\n -76.11328125000001,\n 42.13896840458089\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: May 2022; Version 1.1: April 2026","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The U.S. Geological Survey completed gravity transects in 2015, 2018, and 2019 over four features: the Bright Angel Fault, Bright Angel Monocline, Tusayan Graben, and Redlands Ranch Fault Zone in the Coconino Plateau, Coconino County, Arizona, to determine if the existence and width of high porosity (low density) zones could be inferred from the resulting gravity contrasts, which could be used to update groundwater models of the region. Faults and other geological structures in the Coconino Plateau are commonly thought to play a role in the movement of groundwater in the area, but limited data exist to constrain their influence. Some groundwater models of the region have used zones of enhanced permeability and porosity along or near features to model their effect on groundwater flow but have not shown sensitivity to the width of the zones used. Enhanced porosity zones in the subsurface, such as those included along or near features in some groundwater models of the region, could create small mass deficiencies detectable by microgravity methods. However, 3 of the 4 gravity transects, the Bright Angel Fault, Bright Angel Monocline, and Tusayan Graben, showed no negative gravity anomaly over the features that could indicate the presence of a low-density zone. Only the Redlands Ranch Fault Zone that had nearby collapse features showed a negative gravity anomaly that was modeled as a zone of 0.017 increased porosity about 800 meters wide, corresponding to the relative dimension and enhanced porosity used in groundwater models of the area. This study was unable to verify the existence of enhanced porosity zones at the selected locations along the other features. However, faults and other features may affect groundwater flow in different ways at different locations, and this work does not preclude the existence of enhanced porosity zones at other places along these faults.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225031","usgsCitation":"Wildermuth, L.M., 2022, Gravity surveys for estimating possible width of enhanced porosity zones across structures on the Coconino Plateau, Coconino County, north-central Arizona: U.S. Geological Survey Scientific Investigations Report 2022–5031, 22 p., https://doi.org/10.3133/sir20225031.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","ipdsId":"IP-121579","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":399839,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZYHEBB","text":"Data from “Gravity surveys for estimating possible width of enhanced porosity zones across structures on the Coconino Plateau, Coconino County, north-central Arizona”","description":"Wildermuth, L.M., 2021, Data from “Gravity surveys for estimating possible width 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Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The Big River is a tributary to the Meramec River in south-central Missouri. It drains an area that has been historically one of the largest lead producers in the world, and associated mine wastes have contaminated sediments in much of the river corridor. This study investigated hydraulic conditions in four study reaches to evaluate the potential contribution of physical habitat dynamics to mechanical and physiological stress on native mussel populations. We quantified hydraulic conditions and relative bed stability in previously identified and delineated mussel habitats (MHs) and in the surrounding reaches to refine understanding of the reach-scale (about 1 kilometer) hydraulic characteristics that affect the distribution of mussel aggregations in the river. Two-dimensional hydrodynamic models were compiled for discharge scenarios from base flow (90-percent flow exceedance) to the approximate bankfull discharge (2-year mean return interval peak flow) for the reaches. Discharge, velocity, and water-surface elevation data were collected at all four study reaches at various discharges to calibrate the models across a range of discharges. Shields values to predict incipient motion of the substrate were computed for the MHs and surrounding reaches using bed-surface sediment data collected during this study and previous studies.
The distributions of hydraulic values at the range of simulated discharge scenarios were significantly different among the MHs. Depth values in the MHs ranged from 0.03 to 5.7 meters, with parts remaining dry at some lower flow scenarios (for example, 90- and 50-percent flow exceedance). MH velocities and bed shear stresses (shear stresses) reached 3.1 meters per second and 31 newtons per square meter, respectively. Through the range of simulated discharges, velocity and shear stress within the MHs were limited by reach-scale hydraulic behavior.
Our calculations predicted sand mobility within at least 50 percent of the wetted area of all four MHs for discharges from the 50-percent exceedance flow to the approximate bankfull discharge, whereas 50th-percentile (median) particle size fraction mobility was only predicted within a small area of one of the MHs at the 2-year peak discharge. These results indicate that finer size fractions are mobile within the four MHs, but the larger framework grains of the substrate are predominantly stable at the most frequent discharges.
Our results indicate that suitable mussel habitat on the Big River cannot be identified within a narrow range of velocities, depths, and shear stresses. However, the consistent patterns of sediment mobility and the slow increase of hydraulic forces with increasing discharge within all the MHs indicate that flushing flows at low discharges and coarse sediment stability at higher discharges are important for habitat suitability in the Big River. These patterns of sediment mobility are comparable among the robust and depauperate MHs, indicating that the depauperate beds are likely not impaired by bed instability or siltation. Coarse sediment stability up to bankfull discharges further indicates that bed instability is not widespread in these modeled reaches and is likely not related to the spatial distribution of mussels in these locations.
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U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
The Willamette River is home to at least 69 species of fish, 33 of which are native, including Chinook salmon (Oncorhynchus tshawytscha) and steelhead (Oncorhynchus mykiss). These fish need suitable hydraulic conditions, such as water depth and velocity, to fulfill various stages of their life. Hydraulic conditions are driven by interactions between channel morphology and streamflow, which throughout the Willamette River are strongly influenced by the operation of flood-control dams in upstream tributaries. To assess how streamflow management at these dams affects downstream fish habitat, the U.S. Geological Survey has developed high-resolution bathymetric datasets to support the development of two-dimensional hydraulic models. The datasets were created by combining data collected by airborne topo-bathymetric Light Detection and Ranging with boat-based sonar to create a seamless modeling surface over which a computational mesh with a resolution of roughly 5 by 5 meters was overlaid using the U.S. Army Corps of Engineers Hydraulic Engineering Center’s River Analysis System 5.0.7 hydraulic modeling software. Models were developed for about 200 river kilometers, separated into five modeling reaches, and hydraulic conditions were simulated at flows ranging from extremely low values to annual peak flows. Results of the simulations highlight distinct patterns of inundation extents, water depths, and velocities that vary longitudinally along the Willamette River. In the two farthest upstream model reaches, from Eugene to Corvallis, the river is slower, shallower, and inundates more area at similar seasonal flows than in reaches downstream from Corvallis, where the river generally is deeper and faster. These findings align with previous geomorphic analysis of the Willamette River showing the upper reaches of the river to be geomorphically more dynamic compared to the largely single-thread channel farther downstream. Results of simulations made with these hydraulic models can be used to drive fish-habitat models to further inform flow-management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225025","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"White, J.S., and Wallick, J.R., 2022, Development of continuous bathymetry and two-dimensional hydraulic models for the Willamette River, Oregon: U.S. Geological Survey Scientific Investigations Report 2022–5025, 67 p., https://doi.org/10.3133/sir20225025.","productDescription":"viii, 67 p.","onlineOnly":"Y","ipdsId":"IP-112990","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":435874,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NB0KUT","text":"USGS data release","linkHelpText":"Two-dimensional HEC-RAS models and topo-bathymetric datasets for the Willamette River, Oregon"},{"id":435873,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92TTY4R","text":"USGS data release","linkHelpText":"Single-beam Echosounder Bathymetry of the Willamette River, Oregon 2015-2018"},{"id":398795,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5025/images"},{"id":398794,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5025/sir20225025.pdf","text":"Report","size":"20.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5025"},{"id":398793,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5025/coverthb.jpg"},{"id":398796,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5025/sir20225025.XML"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.59619140625001,\n 43.94537239244209\n ],\n [\n -121.904296875,\n 43.94537239244209\n ],\n [\n -121.904296875,\n 45.521743896993634\n ],\n [\n -123.59619140625001,\n 45.521743896993634\n ],\n [\n -123.59619140625001,\n 43.94537239244209\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The conceptual models of groundwater flow outlined herein synthesize what is known and hypothesized about the groundwater-flow systems that discharge to the Grand Canyon of Arizona. These models interpret the hydrogeologic characteristics and hydrologic dynamics of the physical systems into a framework for understanding key aspects of the physical systems as they relate to groundwater flow and contaminant transport. This report describes five individual groundwater-flow systems draining to the Grand Canyon: Kaibab, Uinkaret-Kanab, Marble-Shinumo, Cataract, and Blue Spring. These systems are present in the saturated parts of the lower Paleozoic carbonate section exposed on the walls of the Grand Canyon; specifically, the Mississippian Redwall Limestone down through the Cambrian Muav Limestone of Tonto Group. Together, the systems described in this report compose the regional groundwater-flow system. Local to subregional flow systems in the sedimentary units of the overlying Permian section could provide transport pathways from the land surface to the regional flow system. Despite the potential importance of the local systems, the focus of this report is on the systems present in the lower Paleozoic section because all major springs in the Grand Canyon discharge from those units.
The most important hydrogeologic characteristics include system boundaries imposed by major tectonic structures, and the degree to which karstification influences the magnitude and direction of flow in each system. Important hydrologic dynamics include locations and rates of potential groundwater recharge, vertical pathways to the regional aquifer, and the locations, magnitude, geochemical signature, and hydrostratigraphic setting of groundwater discharge from springs. Unknown properties or conditions that represent the greatest uncertainties in our current understanding of the regional groundwater-flow system are identified for additional consideration.
Groundwater data are sparse owing to geographic remoteness and extreme depth to water throughout much of the study area. This paucity of information was diminished with the development of a structural contour map of the top and bottom surfaces of the regional aquifer, and a Soil-Water-Balance model that produces spatial distributions of rates of potential recharge. Investigation of the five groundwater-flow systems reveals important, though mostly qualitative, characteristics controlling the rates and directions of groundwater flow. Karstification has produced dissolution-enhanced conduit flow pathways to various degrees in each of the systems. Parts of each system exhibit relative structural uplift or downdropping of the hydrostratigraphic units of the regional aquifer, with some uplifted sections dipping inward toward the Grand Canyon and others dipping outward. The Kaibab groundwater system is archetypical of an uplifted, inward-dipping karst system, whereas the Blue Spring groundwater system and most of the Cataract groundwater system are representative instances of a downdropped or basin karst system. The Uinkaret-Kanab groundwater-flow system is structurally similar to the basin karst systems but karstification has not progressed to nearly the same degree. The Marble-Shinumo groundwater system does not fall cleanly into either category and its boundaries are the most uncertain of all the groundwater systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225037","usgsCitation":"Knight, J.E., and Huntoon, P.W., 2022, Conceptual models of groundwater flow in the Grand Canyon region, Arizona: U.S. Geological Survey Scientific Investigation Report 2022–5037, 51 p., https://doi.org/10.3133/sir20225037.","productDescription":"Report: vi, 51 p.; Data Release","numberOfPages":"51","onlineOnly":"Y","ipdsId":"IP-097904","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":398738,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5037/covrthb.jpg"},{"id":398739,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5037/sir20225037.pdf","text":"Report","size":"24 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":398737,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FQ7BSY","text":"Soil-Water-Balance (SWB) model archive used to simulate potential mean annual recharge in the Grand Canyon region, Arizona","description":"Knight, J.E., and Jones, C.J., 2022, Soil-Water-Balance (SWB) model archive used to simulate potential mean annual recharge in the Grand Canyon region, Arizona: U.S. Geological Survey data release, https://doi.org/10.5066/P9FQ7BSY."}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.64257812499999,\n 34.79576153473033\n ],\n [\n -110.58837890625,\n 34.79576153473033\n ],\n [\n -110.58837890625,\n 36.96744946416934\n ],\n [\n -113.64257812499999,\n 36.96744946416934\n ],\n [\n -113.64257812499999,\n 34.79576153473033\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Groundwater-level declines and limited quantitative knowledge of the groundwater-flow system in the Harney Basin prompted a cooperative study between the U.S. Geological Survey and the Oregon Water Resources Department to evaluate the groundwater-flow system and budget. This report provides a hydrologic budget of the Harney Basin groundwater system that includes separate groundwater budgets for upland and lowland areas to avoid double counting water that recharges in the uplands, discharges to streams and springs in the uplands, flows downstream to the lowlands, and recharges the lowland groundwater system. Lowlands generally represent the conterminous valleys within the center of the basin, including floodplains of the major streams and uplands represent all other areas in the basin.
The upland groundwater budget is minimally affected by groundwater development and generally represents the budget of the natural system. In upland areas during 1982–2016, mean-annual recharge totaled 288,000 acre-feet (acre-ft) and mean-annual discharge totaled 239,000 acre-ft, resulting in a net recharge of 49,000 acre-ft. Upland groundwater recharge occurs as infiltration of precipitation and snowmelt and was estimated using the USGS Soil-Water-Balance model calibrated to estimates of runoff, evapotranspiration (ET), base flow, and snow-water equivalent. Groundwater discharge to streams is the predominant discharge mechanism in upland areas and was estimated as 225,000 acre-feet per year (acre-ft/yr) during 1982–2016 using hydrograph separation and summer low-flow estimates in streamgaged watersheds and a linear relation between estimated streamflow and base flow in ungaged watersheds. The remaining upland discharge occurs through springs (14,000 acre-ft/yr) that either emerge downgradient of locations where groundwater discharge to streams was estimated or are routed to irrigated areas. Spring discharge was estimated as a compilation of current and historical measurements. The net upland recharge, which is 17 percent of total upland recharge, ultimately recharges lowland areas as groundwater flow from uplands to lowlands.
The lowland groundwater budget for the Harney Basin represents a combination of natural conditions and human activity as more than 99 percent of groundwater development has occurred either inside or within 2 miles of the lowland boundary. In lowland areas during 1982–2016, mean annual groundwater recharge totaled 173,000 acre-ft and groundwater discharge totaled 283,000 acre-ft, indicating discharge exceeded recharge by more than 60 percent.
Excluding groundwater pumping, the lowland groundwater budget is more in balance with a mean annual recharge of 165,000 acre-ft and a mean annual discharge of 131,000 acre-ft during 1982–2016. The 23-percent difference between non-pumping recharge and discharge mostly represents the cumulative uncertainty in the estimates of the various groundwater budget components but also likely includes a small reduction in natural groundwater discharge captured by pumping. Lowland groundwater is predominantly recharged by infiltration of surface water (116,000 acre-ft/yr) through streams, floodwater, and irrigation, with a lesser amount as groundwater inflow from uplands and minimal recharge beneath Malheur and Harney Lakes. Recharge from streams and floodwater (natural and irrigation) was estimated using a balance of measured and estimated surface-water inflow to and outflow from lowland areas including streamflow, springflow, and ET where a portion of surface-water inflow to lowland areas is comprised of upland discharge to streams and springs. Groundwater ET (119,000 acre-ft/yr) is the predominant natural discharge mechanism in lowland areas and was estimated as the mean from two remote-sensing based approaches incorporating groundwater ET measurements from other similar basins and 23 years (1987–2015) of Landsat imagery. Discharge of lowland groundwater into Malheur and Harney Lakes is about 700 acre-ft/yr and is represented in groundwater ET estimates. The remaining natural groundwater discharge from lowland areas issues from Sodhouse Spring (8,900 acre-ft/yr) and as groundwater flow to the Malheur River Basin through Virginia Valley (3,100 acre-ft/yr). The relatively large amount of groundwater discharged to springs in Warm Springs Valley (25,000 acre-ft/yr) is accounted for in groundwater ET estimates. Natural groundwater discharge in lowland areas of the Harney Basin has remained relatively constant during the last 80 years based on comparisons with estimates north of Malheur Lake and west of Harney Lake published in the 1930s.
Annual net amount of groundwater pumped (pumpage) from the Harney Basin during 2017–18 averaged 144,000 acre-ft. The net value is the difference between pumpage (about 152,000 acre-ft/yr) and reinfiltration of groundwater pumped for irrigation and non-irrigation purposes (about 8,000 acre-ft/yr). Net pumpage was estimated in concurrent studies that compiled groundwater-use data and coupled reported groundwater pumpage data from wells with remote-sensing-based ET estimates from groundwater-irrigated fields. Total pumpage for irrigation has increased from about 54,000 acre-ft/yr during 1991–92 to 145,000 acre-ft/yr during 2017–18. Presently, pumpage is greatest in the lowland region north of Malheur Lake (81,000 acre-ft/yr), with lesser amounts to the north and northwest of Harney Lake (41,000 acre-ft/yr) and to the south and east of Malheur Lake (22,000 acre-ft/yr).
During this study, mean annual lowland groundwater discharge (including pumpage) exceeded mean annual recharge, indicating that the lowland hydrologic budget is out of balance. Net groundwater pumpage during 2017–18 is similar to groundwater discharge from all other sources in the lowlands and is four times the imbalance between non-pumping lowland recharge and discharge (34,000 acre-ft/yr). Declining groundwater levels at depth across many parts of the Harney Basin lowlands indicate that pumpage is depleting aquifer storage and is likely capturing a small amount of natural groundwater discharge to springs and ET in some lowland areas. If pumping continues, aquifer storage depletion will continue until the capture rate of natural discharge to springs and ET is equal to the pumping rate. If groundwater development occurs in upland areas and reduces either the streamflow or groundwater inflow to lowland areas, the deficit in the lowland water budget will increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215128","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Garcia, C.A., Corson-Dosch, N.T., Beamer, J.P., Gingerich, S.B., Grondin, G.H., Overstreet, B.T., Haynes, J.V., and Hoskinson, M.D., 2021, Hydrologic budget of the Harney Basin groundwater system, southeastern Oregon (ver. 1.1, November 2022): U.S. Geological Survey Scientific Investigations Report 2021–5128, 144 p., https://doi.org/10.3133/sir20215128.","productDescription":"Report: xiii, 144 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-119839","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":502128,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112849.htm","linkFileType":{"id":5,"text":"html"}},{"id":398083,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QABFML","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental data–Hydrologic budget of the Harney Basin groundwater system, Oregon"},{"id":398082,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94NH4D8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil- Water-Balance (SWB) model archive used to simulate mean annual upland recharge from infiltration of precipitation and snowmelt in Harney Basin, Oregon, 1982–2016"},{"id":409214,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5128/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2021-5128 Version History"},{"id":398080,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5128/coverthb2.jpg"},{"id":398081,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5128/sir20215128.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5128"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.08056640625,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 42.35854391749705\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: April 2022; Version 1.1: November 2022","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, monitors stations designed for the Ambient Water-Quality Monitoring Network, a collection of stations that monitor streams and springs in Missouri. During water year 2020 (October 1, 2019, through September 30, 2020), the U.S. Geological Survey collected water-quality data at 72 stations: 70 Ambient Water-Quality Monitoring stations and 2 U.S. Geological Survey National Water Quality Network stations. Among the stations in this report, four stations have data from additional sampling completed in cooperation with the U.S. Army Corps of Engineers. Water-quality analyses are provided for 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 pesticide compounds. Monitoring stations have been classified based on the physiographic province or primary land use in the watershed or based on the unique hydrologic characteristics of the waterbodies (springs, large rivers) monitored. A summary of hydrologic conditions including peak streamflows, monthly mean streamflows, and 7-day low flows also are provided for representative streamgages in the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1153","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Buckley, C.E., 2022, Quality of surface water in Missouri, water year 2020: U.S. Geological Survey Data Report 1153, 24 p., https://doi.org/10.3133/dr1153.","productDescription":"Report: vii, 24 p.; Dataset","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-129885","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":398244,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":501204,"rank":6,"type":{"id":36,"text":"NGMDB Index 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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Vance Creek drains a 24 square mile area of the Olympic Mountains in western Washington. The lower 4 miles of the creek often go dry in discontinuous patches during the summer, limiting salmon rearing success. To better understand sediment transport dynamics in the creek and aid in potential restoration design, bedload and suspended-sediment concentration samples were collected for water years 2019–20 at a site about 2 miles upstream from the creek’s confluence with the South Fork Skokomish River.
Fifty bedload samples and 7 suspended-sediment concentration samples were collected over 7 sampling days. These samples were used to develop rating curves relating bedload flux or suspended-sediment concentration to discharge. Mean annual bedload flux was estimated to be 12,200 ± 2,300 tons per year for water years 1930–2020 period of record, based on application of the derived bedload rating curve to an extrapolated daily discharge record. The mean annual suspended-sediment load over the same period was estimated to be 9,000 tons per year with large, but unquantified, uncertainty. Bedload material was predominantly gravel from 0.08 to 2.5 inches (2 to 64 millimeters) in diameter. At the highest sampled discharges, approximately equivalent to a 50 percent annual exceedance probability (2-year peak-flow event), the bedload grain-size distribution was similar to that of the local channel bed. Bedload grain-size distributions generally coarsened as discharge increased. The suspended-sediment load was consistently one-half sand and one-half silt and clay, regardless of discharge. Bedload constituted about 60 percent of the total sediment flux (bedload plus suspended load). This is near the upper limit of values observed in a global compilation of long-term load partitioning data.
Sediment transport at the Vance Creek sampling site was compared with sediment-transport data from five other watersheds in the region. To facilitate comparisons, mean annual loads were divided by mean annual runoff volume to obtain an effective average sediment concentration. This normalization accounts for differences in both drainage area and mean runoff depth between the comparison watersheds. At the three comparison watershed sites with relatively complete sediment-transport data, mean bedload concentrations ranged from 44 to 109 milligrams per liter (mg/L) and mean suspended-sediment concentrations ranged from 139 to 374 mg/L; bedload constituted 21 to 29 percent of the total sediment load. The mean bedload concentration at the Vance Creek sampling site (69 mg/L) fell in the middle of the range observed in comparison watersheds, whereas the mean suspended-sediment concentration (50 mg/L) was markedly lower. Bedload samples at the Vance Creek sampling site also were generally less sand rich (sample-average sand fraction was 13 percent at Vance Creek versus 20 to 37 percent for comparison waters). Bedload transport rates at the Vance Creek sampling site appear relatively average for the region, given the drainage basin area and average runoff. In contrast, the supply and transport of finer material, both in the suspended load and the sand fraction of the bedload, are relatively low.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225019","collaboration":"Prepared in cooperation with the Mason Conservation District","usgsCitation":"Anderson, S.W., 2022, Bedload and suspended-sediment transport in lower Vance Creek, western Washington, water\nyears 2019–20: U.S. Geological Survey Scientific Investigations Report 2022–5019, 25 p., https://doi.org/10.3133/sir20225019.","productDescription":"vii, 25 p.","onlineOnly":"Y","ipdsId":"IP-119859","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":397071,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5019/sir20225019.XML"},{"id":397069,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5019/sir20225019.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5019"},{"id":397068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5019/coverthb.jpg"},{"id":397070,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5019/images"}],"country":"United States","state":"Washington","otherGeospatial":"Vance Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.2889,\n 47.3208\n ],\n [\n -123.2833,\n 47.3208\n ],\n [\n -123.2833,\n 47.325\n ],\n [\n -123.2889,\n 47.325\n ],\n [\n -123.2889,\n 47.3208\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The U.S. Geological Survey (USGS), in cooperation with the Texas Water Development Board, collected streamflow and water-quality data at USGS monitoring stations in the lower Trinity River Basin from January 2016 to December 2019 to characterize streamflow, nutrients, and suspended sediment entering Galveston Bay from the Trinity River. Results from previous studies indicate that water from the main channel of the Trinity River is diverted into surrounding wetlands and water bodies and is stored or discharged directly into Galveston Bay through distributary channels in the delta. This study provides an assessment of the distribution of streamflow in the various channels that form the delta of the Trinity River to evaluate the effects of streamflow diversions on the eventual supply of freshwater, nutrients, and suspended sediment to Galveston Bay.
Instantaneous streamflow data and continuous streamflow records from USGS monitoring stations in the delta of the Trinity River were used to quantify freshwater inflow into Galveston Bay and assess the distribution of streamflow in the lowermost reaches of the Trinity River Basin. In this report, periods in which releases from Lake Livingston caused a rise in streamflow farther downstream at USGS station 08067000 Trinity River at Liberty, Tex. (hereinafter referred to as the “Liberty site”) that did not exceed 20,000 cubic feet per second (ft3/s) are referred to as “low-flow events,” and periods in which streamflow at the Liberty site exceeded 20,000 ft3/s are referred to as “high-flow events.”
During this study, it was estimated that only about 55 percent of the total water volume released from Lake Livingston was accounted for at USGS station 08067252 Trinity River at Wallisville, Tex. (hereinafter referred to as the “Wallisville site”), which is approximately 8 river miles upstream from where the Trinity River enters Galveston Bay. The difference in water volumes between what is released from Lake Livingston and what is measured at the Wallisville site is consistent with findings from previous studies and indicates that a large part of the volume released from Lake Livingston does not reach Galveston Bay through the main channel of the Trinity River.
To assess the distribution of streamflow and estimate the amount of water diverted from the main channel of the Trinity River into distributary channels, instantaneous streamflow measurements were made at USGS station 08067230 Old River Lake near Wallisville, Tex. (hereinafter referred to as the “Old River Lake site”) and the Wallisville site during a range of hydrologic conditions. Results indicate that a large portion of the freshwater inflow was likely delivered to Galveston Bay through pathways other than the main channel of the Trinity River, including Old River Lake. When streamflow at the Liberty site, located upstream from the Wallisville site, exceeded approximately 40,000 ft3/s, Old River Lake and its network of hydrologically connected channels likely became the primary pathway for freshwater inflow entering Galveston Bay.
Water quality was characterized from discrete samples collected during a range of hydrologic conditions at the Old River Lake site and the Wallisville site in order to evaluate the effects of streamflow diversions on the supply of suspended sediment and nutrients into Galveston Bay. Suspended-sediment concentrations were typically higher at the Wallisville site than at the Old River Lake site, likely because of lower water velocities at the Old River Lake site than at the Wallisville site; low water velocities allow suspended sediment to settle, thus reducing concentrations. Suspended-sediment loads were also typically higher at the Wallisville site than at the Old River Lake site during high-flow events. However, when streamflows at the Liberty site exceeded approximately 60,000 ft3/s, suspended-sediment loads were higher at the Old River Lake, which likely became the primary pathway for suspended-sediment delivery into Galveston Bay.
Suspended-sediment concentrations and loads were computed at the Wallisville and Liberty sites for the duration of 11 hydrologic events representing different streamflows by using the regression equations developed for each monitoring station. Overall, approximately 25 percent of the total sediment load measured during events at the Liberty site was measured at the Wallisville site, indicating that only a portion of the suspended-sediment load from the Liberty site reached Galveston Bay through the main channel of the Trinity River during the measured events. Based on data from discrete samples, some of this sediment load was diverted into Old River Lake and associated distributary channels.
Results from analysis of nutrient samples indicate that streamflow conditions affect the nitrogen concentrations in the delta of the Trinity River. At the Old River Lake site, nitrate plus nitrite and total dissolved nitrogen concentrations were typically lower during low-flow conditions than during high-flow events; low-flow conditions represent low-flow events or tidal-flow conditions (during low-flow conditions the streamflow at the Liberty site was less than 20,000 ft3/s). Lower concentrations of nitrate plus nitrite and total dissolved nitrogen at the Old River Lake site may be associated with various physical and biogeochemical processes, including the transformation and biological uptake of nitrate, nitrite, and other species of nitrogen resulting from extended water residence times and relatively small inputs of nitrogen from the upstream reaches of the Trinity River Basin. During high-flow events, the proportions of nitrogen species were similar among sites, indicating that the travel path through wetlands and channels surrounding Old River Lake likely does not affect the relative concentrations of the various nitrogen species present in freshwater inflow to Galveston Bay.
Results from analysis of nutrient samples also indicate that the pathways for nutrient delivery from the Trinity River into Galveston Bay are dependent on event magnitude. When streamflows at the Liberty site were low (approximately 20,000 ft3/s), the main channel of the Trinity River was the primary pathway for nitrogen and phosphorus entering Galveston Bay. Once streamflow at the Liberty site exceeded 20,000 ft3/s, however, the contribution of nutrient loading through Old River Lake to Galveston Bay increased proportionally to the nutrient loading in the main channel, and when streamflow at the Liberty site exceeded approximately 50,000 ft3/s, Old River Lake likely became the primary pathway for nutrient delivery into Galveston Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225015","collaboration":"Prepared in cooperation with the Texas Water Development Board","usgsCitation":"Lucena, Z., and Lee, M.T., 2022, Distribution of streamflow, sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, 2016–19: U.S. Geological Survey Scientific Investigations Report 2022–5015, 55 p., https://doi.org/10.3133/sir20225015.","productDescription":"Report: vi, 55 p.; Dataset","numberOfPages":"66","onlineOnly":"N","ipdsId":"IP-126129","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":397262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5015/coverthb.jpg"},{"id":397263,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5015/sir20225015.pdf","text":"Report","size":"4.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5015"},{"id":397265,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5015/images"},{"id":397264,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5015/sir20225015.XML"},{"id":397266,"rank":5,"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":397341,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225015/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Texas","otherGeospatial":"Galveston Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.86145019531249,\n 29.260044678228486\n ],\n [\n -94.50714111328125,\n 29.537619205973428\n ],\n [\n -94.71038818359375,\n 29.84302629154662\n ],\n [\n -95.03173828125,\n 29.752455480021393\n ],\n [\n -95.0592041015625,\n 29.59017705987947\n ],\n [\n -94.98504638671875,\n 29.489815619374962\n ],\n [\n -94.921875,\n 29.401319510041485\n ],\n [\n -94.8944091796875,\n 29.305561325527698\n ],\n [\n -94.86145019531249,\n 29.260044678228486\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
The Heart River Basin is predominantly an agricultural basin in western North Dakota and is approximately 3,350 square miles. The U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service and the Grant County Soil Conservation District, completed a study to assess spatial and temporal patterns of water quality in the Heart River Basin. The purpose of this report is to describe the methods and results of a study to evaluate salinity and nutrients in the Heart River Basin in western North Dakota. Water-quality and streamflow data used in the study were compiled from 1970 to 2020 using the National Water Quality Monitoring Council Water Quality Portal and National Water Information System.
Changes in streamflow characteristics were investigated at three sites from 1970 to 2020, and changes in water quality were investigated at four sites from 1974 to 2019. Streamflow analysis indicated decreasing streamflow from 1970 until the late 1990s followed by increasing streamflow through 2020, with the largest increase in the 7-day minimum streamflow or base flow. For the historical water-quality trend period (1974–2019), total dissolved solids, sulfate, sodium, chloride, and sodium adsorption ratio concentrations have increased since the mid-1970s through 2019. Potassium concentrations during the historical period remained mostly constant with some small fluctuations. Calcium and magnesium concentrations increased since the mid-1970s at all sites, except for a decrease at one site between 1974 and 1999. During the recent trend period (1999–2019), increasing concentrations in total dissolved solids, sulfate, sodium, chloride, calcium, magnesium, and sodium adsorption ratios were observed across the Heart River Basin. The magnitude of the increases was smaller at tributary sites compared to main-stem sites. During the recent period, potassium was mostly constant, although small (−0.9 milligram per liter or less) decreases on tributaries and minor (1.3 milligrams per liter) increases on the main-stem sites were detected. Unlike dissolved ion concentrations, significant increases in nutrient concentrations were not detected from 1999 to 2019, but nitrate plus nitrite concentrations most likely decreased upstream from Lake Tschida.
Inverse modeling for period 1 (1974–99) in model zone 1 (Heart River reach from site 5 to site 6) had eight reasonable models that indicated the clay mineral-water interactions and dissolution of evaporites control the geochemistry. Results of the inverse modeling for period 2 (1999–2019) in model zone 1 also had eight reasonable models that indicated that the dissolution of evaporites was the major geochemical control. Results of the geochemical modeling for period 1 (1974–99) in model zone 2 (Heart River and Sweetbriar Creek reach from sites 20 and 21 to site 22) produced seven reasonable models, and the geochemical control of the system was the dissolution of sulfate evaporite minerals. Geochemical modeling results for period 2 (1999–2019) in model zone 2 produced 11 reasonable models and was also controlled by the dissolution of sulfate evaporite minerals. Differences between the two model zones indicated that geology controls some of the water-quality changes in the Heart River Basin.
Loads were estimated for total dissolved solids, sulfate, sodium, and chloride and total phosphorus. Annual loads estimated for the Heart River from 2013 through 2020 at the Heart River site upstream from Lake Tschida (site 5) and near Mandan (site 22) were generally greatest in 2014 and least in 2016 for total dissolved solids, sulfate, sodium, and chloride. Most of the annual loads of total dissolved solids, sulfate, sodium, and chloride are delivered in March through July in the Heart River at these sites and are likely from snowmelt and spring and summer rains. The mean annual yields of total dissolved solids and sodium from 2013 to 2020 generally were largest in Big Muddy Creek (site 18), whereas yields of sulfate and chloride were largest at Sweetbriar Creek (site 21) compared to the other selected sites in the Heart River Basin. Larger yields of total dissolved solids, sulfate, sodium, and chloride at sites located on Big Muddy Creek and Sweet Briar Creek in the lower Heart River Basin were likely a result of differences in geology and soils upstream from the selected sites.
A mass balance of total dissolved solids, sulfate, sodium, and chloride was estimated for the lower Heart River Basin, specifically the reach below Lake Tschida to Mandan (site 7 to site 22). Intervening flow was the largest contributor to the dissolved ion loads in the lower Heart River Basin and is an important part of understanding the transport of dissolved ions in the basin. The intervening load can include groundwater discharge, irrigation return flow, local runoff, and input from smaller ephemeral tributaries. Tributaries in the lower Heart River Basin contributed portions of the total dissolved solids, sulfate, sodium, and chloride loads at the Heart River near Mandan (site 22) that generally were proportional to the streamflow contributions.
Annual loads for total phosphorus between 2013 and 2020 at the Heart River site upstream from Lake Tschida (site 5) and near Mandan (site 22) generally were largest in 2019 and smallest in 2016. Most of the total phosphorus loads for main-stem sites 5 and 22 were transported in March, April, and June, likely from snowmelt and early summer rains. The mean annual yields of total phosphorus for 2013–20 were largest on the main-stem site upstream from Lake Tschida (site 5) and Sweetbriar Creek (site 21), whereas the smallest yields were in Big Muddy Creek (site 18). Much of the phosphorus that enters Lake Tschida from the upper basin does not get transported downstream to the lower basin, and much of the phosphorus in the lower basin was attributed to intervening flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225013","collaboration":"Prepared in cooperation with the Department of Agriculture Natural Resources Conservation Service and Grant County Soil Conservation District","usgsCitation":"Tatge, W.S., Nustad, R.A., and Galloway, J.M., 2022, Evaluation of salinity and nutrient conditions in the Heart River Basin, North Dakota, 1970–2020: U.S. Geological Survey Scientific Investigations Report 2022–5013, 76 p., https://doi.org/10.3133/sir20225013.","productDescription":"Report: ix, 76; Data Release; Dataset","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-131163","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":398527,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225013/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397040,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5013/images"},{"id":397039,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5013/sir20225013.XML"},{"id":397038,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5013/sir20225013.pdf","text":"Report","size":"21.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5013"},{"id":397037,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5013/coverthb.jpg"},{"id":397042,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397041,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P987APZ8","text":"USGS data release","linkHelpText":"Data and scripts used in water-quality trend and load analysis in the Heart River Basin, North Dakota, 1970–2020"}],"country":"United States","state":"North Dakota","otherGeospatial":"Heart River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.2769775390625,\n 46.27863122156088\n ],\n [\n -100.8160400390625,\n 46.27863122156088\n ],\n [\n -100.8160400390625,\n 47.25\n ],\n [\n -103.2769775390625,\n 47.25\n ],\n [\n -103.2769775390625,\n 46.27863122156088\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
The Rice Creek Watershed District (RCWD) cooperated with the U.S. Geological Survey to establish a 10-year suspended sediment and bedload monitoring and streamflow modeling study to evaluate the effects of two restored meander sections on middle Rice Creek in Arden Hills, Minnesota. The RCWD goals of this stream restoration were to reduce water quality impairments, improve aquatic habitat, and reduce associated costs of dredging a sedimentation pond. During the study there were several factors that introduced uncertainty in the sampling results; however, the sampling results indicated there was an increase in the post-stream restoration sediment data because of higher streamflows during the post-stream than the pre-stream restoration monitoring period. The negative relation between suspended fines and streamflow was explained by a reduction in the supply of fines with increasing streamflows. The positive relation among suspended sand, bedload, and streamflow was because of those constituents having a functional relation with the hydraulic properties of flow and a consistent supply of sand. Two-dimensional flow modeling simulations indicated the downstream restored section had less shear stress, more pools, and could access the floodplain at a lower streamflow than the original channel. Overall, the uncertainty of the sampling results indicates the complexity of sediment transport in a river and suggests a need for multisite, multifaceted, multiyear data, and tools to simulate those data to effectively evaluate river restorations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225004","collaboration":"Prepared in cooperation with Rice Creek Watershed District","usgsCitation":"Groten, J.T., Livdahl, C.T., DeLong, S.B., Lund, J.W., Nelson, J.M., Coenen, E.N., Ziegeweid, J.R., and Kocian, M.J., 2022, Sediment monitoring and streamflow modeling before and after a stream restoration in Rice Creek, Minnesota, 2010–2019: U.S. Geological Survey Scientific Investigations Report 2022–5004, 40 p., https://doi.org/10.3133/sir20225004.","productDescription":"Report: viii, 40 p.; Data release; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-126710","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":396980,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SJIY32","text":"USGS data release","linkHelpText":"Suspended sediment and bedload data, simple linear regression models, loads, elevation data, and FaSTMECH models for Rice Creek, Minnesota, 2010-2019"},{"id":396978,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5004/images"},{"id":396977,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5004/sir20225004.XML"},{"id":396979,"rank":5,"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":396975,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5004/coverthb.jpg"},{"id":396976,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5004/sir20225004.pdf","text":"Report","size":"19.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5004"}],"country":"United States","state":"Minnesota","otherGeospatial":"Rice Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.21710586547852,\n 45.08236949749694\n ],\n [\n -93.18449020385742,\n 45.08236949749694\n ],\n [\n -93.18449020385742,\n 45.09957848291159\n ],\n [\n -93.21710586547852,\n 45.09957848291159\n ],\n [\n -93.21710586547852,\n 45.08236949749694\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Illinois is home to more than 12 million residents, including those living in Chicago, the third-largest city in the United States. Yet farmland claims about 75 percent of the largely flat terrain in Illinois. Tallgrass prairie once covered “The Prairie State,” and some remnants remain, but corn and soybeans are a far more common sight now. Adding variety to the landscape, beaches line the State’s Lake Michigan shoreline in the northeast, and more than 80,000 miles of rivers and streams flow along and through the State, including the central cities of Springfield and Peoria. Forests fill several million acres, mostly in the west and the rolling hills of the south.
Urban, agricultural, and forested areas each have environmental characteristics that are noticeable to those who live within them and to those who study the Earth’s surface from space. Landsat satellite data can reveal not only the current condition of these areas, but also when and where they have changed. A better knowledge of Illinois’ past helps its residents better prepare for the future.
Here are just a few examples of how Landsat benefits Illinois.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223006","usgsCitation":"U.S. Geological Survey, 2022, Illinois and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2022–3006, 2 p., https://doi.org/10.3133/fs20223006.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-133196","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":411877,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223006/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":411859,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3006/images"},{"id":411858,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3006/fs20223006.XML"},{"id":411857,"rank":3,"type":{"id":25,"text":"Version 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Reliable estimates of the magnitude of peak flows in streams are required for the economical and safe design of transportation and water conveyance structures. In addition, reliable estimates of the magnitude of low flows at defined frequencies and durations are needed for meeting regulatory requirements, quantifying base flows in streams and rivers, and evaluating time of travel and dilution of toxic spills. This report, in cooperation with the Delaware Department of Transportation and the Delaware Geological Survey, presents methods for estimating the magnitude of peak flows and low flows at defined frequencies and durations on nontidal streams in Delaware, at locations both monitored by streamflow-gage sites and ungaged. Methods are presented for estimating (1) the magnitude of peak flows for return periods ranging from 2 to 500 years (50-percent to 0.2-percent annual-exceedance probability), and (2) the magnitude of low flows as applied to 7-, 14-, and 30-consecutive day low-flow periods with recurrence intervals of 2, 10, and 20 years (50-, 10-, and 5-percent annual non-exceedance probabilities). These methods are applicable to watersheds that exhibit a full range of development conditions in Delaware. The report also describes StreamStats, a web application that allows users to easily obtain peak-flow and low-flow magnitude estimates for user-selected locations in Delaware.
Peak-flow and low-flow magnitude estimates for ungaged sites are obtained using statistical regression analysis through a process known as regionalization, where information from a group of streamflow-gage sites within a region forms the basis for estimates for ungaged sites within the same region. Ninety-four streamflow-gage sites in and near Delaware with at least 10 years of nonregulated annual peak-flow data were used for the peak-flow regression analysis, a subset of the 121 sites for which peak-flow estimates were computed. These sites included both continuous-record streamflow-gage sites as well as partial record sites. Forty-five streamflow-gage sites with at least 10 years of nonregulated low-flow data available were used for the low-flow regression analyses, a subset of the 68 sites for which low-flow estimates were computed. Estimates for gaged sites are obtained by combining (1) the station peak-flow statistics (mean, standard deviation, and skew) and peak-flow estimates using the recent Bulletin 17C guidelines that incorporate the Expected Moments Algorithm with (2) regional estimates of peak-flow magnitude derived from regional regression equations and regional skew derived from sites with records greater than or equal to 35 years. Example peak-flow estimate calculations using the methods presented in the report are given for (1) ungaged sites, (2) gaged sites, (3) sites upstream or downstream from a gaged location, and (4) sites between gaged locations. Estimates for low-flow gaged sites are obtained by combining (1) the station low-flow statistics (mean, standard deviation, and skew) and low-flow estimates with (2) regional estimates of low-flow magnitude derived from regional regression equations. Example low-flow estimate calculations using the methods presented in the report are given for (1) ungaged sites, (2) gaged sites, (3) sites upstream or downstream from a gaged location, and (4) sites between gaged locations. A total of 54 sites in the Coastal Plain region were used to develop peak-flow regressions for the region and 40 sites were used for the Piedmont region. Similarly, 24 sites were used for low-flow regression equation development in the Coastal Plain, with 21 in the Piedmont. Peak and low-flow site inclusion in the Coastal Plain tended to be more restricted with tidal influence and ranges of basin characteristics, including drainage area, limiting regression equation development and application.
Regional regression equations for peak flows and low flows, as applicable to ungaged sites in the Piedmont and Coastal Plain Physiographic Provinces in Delaware, are presented. Peak-flow regression equations used variables that quantified drainage area, basin slope, percent area with well-drained soils, percent area with poorly drained soils, impervious area, and percent area of surface water storage in estimating peak-flow estimates, whereas low-flow regression equations used only drainage area and percent poorly drained soils in the estimation of low flows. Average standard errors for peak-flow regressions tended to be lower than those for low- flow regressions, with lower errors in the Piedmont region for both peak- and low-flow regressions. For peak-flow estimates, a sensitivity analysis of Piedmont regression equation estimates to changes in impervious area is also presented.
Additional topics associated with the analyses performed during the study are discussed, including (1) the availability and description of 32 basin and climatic characteristics considered during the development of the regional regression equations; (2) the treatment of increasing trends in the annual peak-flow series identified at 18 gaged sites and inclusion in or exclusion from the regional analysis; (3) regional skew analysis and determination of regression regions; (4) sample adjustments and removal of sites owing to regulation and redundancy; and (5) a brief comparison of peak- and low-flow estimates at gages used in previous studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225005","collaboration":"Prepared in cooperation with the Delaware Department of Transportation and the Delaware Geological Survey","usgsCitation":"Hammond, J.C., Doheny, E.J., Dillow, J.J.A., Nardi, M.R., Steeves, P.A., and Warner, D.L., 2022, Peak-flow and low-flow magnitude estimates at defined frequencies and durations for nontidal streams in Delaware: U.S. Geological Survey Scientific Investigations Report 2022–5005, 46 p., https://doi.org/10.3133/sir20225005.","productDescription":"Report: vi, 46 p.; 4 Data Releases","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127314","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":396171,"rank":9,"type":{"id":39,"text":"HTML 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